Unmanned Aerial Geophysical Remote Sensing: A Systematic Review

Abstract

Geophysical surveys, a means of analyzing the Earth and its environments, have traditionally relied on ground-based methodologies. However, up-to-date approaches encompass remote sensing (RS) techniques, employing both spaceborne and airborne platforms. The emergence of Unmanned Aerial Vehicles (UAVs) has notably catalyzed interest in UAV-borne geophysical RS. The objective of this study is to comprehensively review the state-of-the-art UAV-based geophysical methods, encompassing magnetometry, gravimetry, gamma-ray spectrometry/radiometry, electromagnetic (EM) surveys, ground penetrating radar (GPR), traditional UAV RS methods (i.e., photogrammetry and LiDARgrammetry), and integrated approaches. Each method is scrutinized concerning essential aspects such as sensors, platforms, challenges, applications, etc. Drawing upon an extensive systematic review of over 435 scholarly works, our analysis reveals the versatility of these systems, which ranges from geophysical development to applications over various geoscientific domains. Among the UAV platforms, rotary-wing multirotors were the most used (64%), followed by fixed-wing UAVs (27%). Unmanned helicopters and airships comprise the remaining 9%. In terms of sensors and methods, imaging-based methods and magnetometry were the most prevalent, which accounted for 35% and 27% of the research, respectively. Other methods had a more balanced representation (6–11%). From an application perspective, the primary use of UAVs in geoscience included soil mapping (19.6%), landslide/subsidence mapping (17.2%), and near-surface object detection (13.5%). The reviewed studies consistently highlight the advantages of UAV RS in geophysical surveys. UAV geophysical RS effectively balances the benefits of ground-based and traditional RS methods regarding cost, resolution, accuracy, and other factors. Integrating multiple sensors on a single platform and fusion of multi-source data enhance efficiency in geoscientific analysis. However, implementing geophysical methods on UAVs poses challenges, prompting ongoing research and development efforts worldwide to find optimal solutions from both hardware and software perspectives.

1. Introduction

Geoscience, which is also referred to as Earth science, includes a wide array of natural sciences that focus on studying the Earth [1]. These fields include geophysics, geology, geodesy, geography, and others [2,3,4,5]. Within geoscience, geophysics specifically investigates the physical phenomena and characteristics of Earth and its surrounding spatial environment. It combines aspects of geology, physics, and mathematics into a unified approach that uses quantitative methods to analyze these phenomena. Essentially, geophysics provides a means for describing various aspects of the subsurface environment, including composition and structure, thereby providing information on, e.g., objects, geological features, groundwater conditions, or pollution levels through non-invasive techniques [6]. The term “non-invasive” means that these methods do not damage the Earth’s crust through drilling or excavation. Geophysical methods include, among others, magnetic survey (magnetometry) [7], gravity survey (gravimetry) [8], electromagnetic (EM) survey [9,10], gamma-ray spectrometry (GRS)/radiometry [11], ground penetrating radar (GPR) [12], seismic tomography [13], electrical resistivity tomography (ERT) [14], and others.

Geophysical methods can be broadly categorized based on their operational distance from the study target. Traditional ground-based methods involve instruments in direct contact with or close to the Earth’s surface. However, most geophysical methods are inherently remote, with sources and receivers positioned at varying distances from the study target. These remote methods can be further classified into terrestrial (land-based), marine (with distances up to 5–6 km from the ocean surface), and airborne/spaceborne approaches. Airborne methods include manned and unmanned aerial platforms, while spaceborne methods employ satellites [15,16,17,18]. Hereafter, the remote sensing (RS) method refers to the method that is airborne or spaceborne. Notably, geophysical RS is part of a broader category called environmental RS, which also includes urban RS, agricultural RS, marine/oceanographic RS, and atmospheric RS, among others [19].

The increasing popularity of Unmanned Aerial Vehicles (UAVs) in the last decade, along with ongoing efforts by universities and industries to develop UAV-compatible devices and payloads, has profoundly impacted geophysics [20]. UAVs offer several advantages over ground-based geophysical surveys, including cost-effectiveness and the ability to cover larger, harder-to-reach, or non-accessible areas. Furthermore, UAVs have, in general, a lightweight design for easy transport, autonomous flying capability (which eliminates potential risks for onboard pilots in manned aircraft), and low-flying capability. These characteristics make UAV-borne geophysical survey methods a balanced compromise between traditional airborne and ground-based approaches, effectively combining the strengths of both methods simultaneously.

Regarding the comparison of UAV-based and satellite-based geophysical measurements, it is important to note that satellite methods offer extensive coverage and regular repeat measurements, making them particularly suitable for time series analysis. In contrast, low-altitude airborne methods, specifically UAV-borne approaches, operate at much lower altitudes (typically 50–200 m compared to satellite orbits), enabling ultra-high spatial resolution and enhanced signal-to-noise ratios for near-surface investigations. UAVs also offer greater flexibility compared to satellites in survey timing and custom flight paths, which are crucial for time-sensitive or localized studies.

An extensive examination of the literature concerning UAV-based geophysical methods, which draw from both scholarly research and corporate endeavors, reveals that these methods encompass traditional manned airborne techniques and introduce innovative additions such as seismic surveys [21,22]. This evolution has led to the development of UAV-based magnetometry [23], gravimetry [24], EM surveys [10], GPR [25], gamma-ray spectrometry/radiometry [26], and seismic surveys [27]. In addition to these basic geophysical methods, UAV photogrammetry [28] and LiDARgrammetry [29], while not traditionally considered geophysical techniques, are increasingly applied across various geoscientific domains [30,31].

The growing trend of UAV-based geophysical surveys in recent years and the increasing volume of research published annually highlight a need for a comprehensive review of UAV-based geophysical surveys. Existing review articles tend to focus on specific aspects of UAV-based geophysical surveys, as exemplified by the works of [23,25], which review UAV-based magnetometry or GPR. While these studies provide valuable insights into specific methods, no previous work has provided an overview of feasible UAV-based geophysical RS methods or comprehensively analyzed them. To address this gap, we aim to thoroughly review all methods that can feasibly be developed for geophysical (or other geoscientific) surveys using UAVs.

The novelty of our work lies in collecting all standard UAV-based geophysical methods and providing new insights into the methods that may initially not be recognized as geophysical techniques but can be applied effectively in this domain. The key question we aim to answer in this review is how UAVs are transforming geophysical and geological applications. We examine which geophysical survey methods can be effectively deployed using UAV platforms, analyzing their capabilities and limitations. This investigation helps us understand the practical impact of UAV technology on traditional geophysical surveying methods.

2. Research Methodology

A comprehensive search across different databases and sources has been conducted to address our research objectives. Our search comprises two methods: systematic querying in Scopus and a manual search in Google Scholar, ResearchGate, etc. Although it is challenging to determine the definitive superior database for scientometrics, Scopus has been chosen as the foundation for our systematic querying. This decision is based on previous studies that have analyzed bibliometric bases, including [32,33].

Initially, relevant terms were selected in an automated search strategy, and a query was conducted in Scopus using the filtering possibilities available there. The query for keyword search was as follows: TITLE-ABS-KEY (uav AND geophysics) OR TITLE-ABS-KEY (uav AND magnetic AND survey) OR TITLE-ABS-KEY (uav AND gravity AND survey) OR TITLE-ABS-KEY (uav AND ground AND penetrating AND radar) OR TITLE-ABS-KEY (uav AND gamma AND spectrometry) OR TITLE-ABS-KEY (uav AND gamma AND radiometry) OR TITLE-ABS-KEY (uav AND electromagnetic AND survey). Various word forms were examined and compared to ensure no significant articles were overlooked in our collection. For instance, in some cases, along with the commonly used term “UAV”, terms like “unmanned aerial vehicle” or “drone” were also used.

In addition to the automated query, a manual searching approach was also employed in the other resources above to address any gaps in studies that might not have been identified in the automated search. These deficits primarily concern UAV-borne RS methods such as photogrammetry and LiDARgrammetry, which are not typically considered geophysical methods and may not be easily identified through automated searches (We refer to geoscientific studies such as soil mapping, crust deformation mapping/monitoring, and similar applications). Relevant studies in manual searches were finalized by reading the full text of the papers. The combined search returned a total of 587 studies.

Our systematic review employed both qualitative and quantitative approaches to analyze the literature. The qualitative analysis examined the technical aspects, methodological approaches, and applications of UAV-based geophysical methods. We tracked several key variables for quantitative assessment, including publication year, geographic distribution, platform specifications, sensor characteristics, and application domains. Papers were selected based on four main criteria: primary focus on UAV-based geophysical or relevant RS methods, peer-reviewed publication status, sufficient technical detail, and English language. For each study, we systematically extracted information about platform specifications (UAV type, payload capacity, endurance), sensor characteristics (type, resolution, accuracy), and operational parameters (survey height, speed, coverage).

An overview of the gathered publications is presented in Figure 1. This emerging topic, as shown by an increase in publications (Figure 1a), is mainly developing through articles in journals and presentations at conferences, with no dedicated books yet available (Figure 1b). The impact of this topic extends across various disciplines, including computer science and environmental science (Figure 1c). Figure 1d illustrates that both developed and developing countries have begun to delve into the domain of UAV-borne geophysical RS, indicating its widespread relevance. Notably, universities and research centers, predominantly from developed countries, are leading in this field (Figure 1e). Furthermore, Figure 1f reveals that research is published in various journals, ranging from geosciences to RS technology, underscoring the interdisciplinary nature of this topic. Our review follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews (Supplementary Materials). Following the PRISMA flowchart [34] in Figure 2, the gathered documents were refined (containing three steps: identification, screening, and eligibility). Ultimately, 435 papers were considered eligible for review.

3. Review Results: UAV-Borne Geophysical Survey Methods

Geophysical methods are classified into two primary categories based on sensor operation: those employing passive sensors and those using active sensors. For detailed information on passive and active sensors and their operation, refer to [35]. Additionally, a third category considers methods that integrate sensors and fuse data. Figure 3 illustrates this categorization. Note that “active/passive methods” indicate whether the method utilizes an active or passive sensor.

3.1. UAV-Borne Geophysical Survey: Passive Methods

3.1.1. Unmanned Aerial Magnetometry

The introduction of UAVs in geophysics, especially in airborne magnetometry, has driven notable global academic and technological progress [36]. UAV-borne magnetometry offers safety, cost-efficiency [23], and prolonged flight endurance, facilitating low-altitude flights and high-resolution magnetic data collection [37,38,39], compared to traditional ground-based or airborne surveys using a low-flying airplane. There is a noticeable trend towards the adoption of UAV-borne magnetometry, with universities and companies actively engaging in pioneering research [40].

Reliable surveying systems on lightweight UAV platforms address the limitations of traditional terrestrial and aerial magnetometry [23,41]. These systems efficiently collect high-quality magnetic data, enhancing spatial resolution at low altitudes [42]. They extend operations to previously inaccessible areas, reducing costs and offering flexibility [43,44]. UAV-based surveys bridge the gap between terrestrial and airborne methods, enhancing detectability [45]. Challenges include ensuring data quality comparable to manned aerial systems and developing lightweight magnetometers for small UAV platforms. Figure 4 depicts a UAV-based magnetometer system, with subsystems detailed in [23].

Current UAV-compatible magnetometers utilize potassium, cesium, or rubidium vapor technologies, with the Gemsystems GSMP-35U/25U offering the highest sensitivity (0.0002 nT) but greater weight (1 kg), while newer systems like MFAM and QTFM provide reduced sensitivity but significantly lighter packages (0.15–0.23 kg). These systems generally operate in temperature ranges of −30 °C to +60 °C with sampling rates from 1–5000 Hz, suitable for diverse aerial survey requirements [23,42]. Detailed specifications are provided in Table 1.

UAV-borne magnetometry systems have been implemented across various platforms such as multi-rotors [36,41,42,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66], fixed-wings [23,37,48,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83], and helicopters [40,57,84,85,86,87,88,89,90,91,92,93]. While DJI-based multi-rotor platforms are most common due to accessibility and stability, each platform type offers distinct advantages. Sensor integration primarily utilizes fluxgate, cesium vapor, and potassium magnetometers, with payload capacities from 2 kg to over 40 kg. System endurance varies significantly: multi-rotors can fly for 15–30 min, helicopters from 1.5 to 4 h, and fixed-wings range up to 8–15 h. Most systems operate at speeds of 7–20 m/s and altitudes of 50–200 m, incorporating GPS, IMU, and laser altimeters for enhanced accuracy. This diversity enables tailored solutions for both detailed local surveys and extensive regional mapping. Detailed specifications are provided in Table 2.

In the domain of UAV magnetometry, one of the critical issues is EM interference. Mounting magnetic sensors on aerial vehicles poses challenges due to magnetic interference from propulsion and flight control systems. This interference originates from both the environment and onboard systems, diminishing sensor sensitivity and detection ranges [40,81,87,95]. Environmental factors include anything in the UAV’s surroundings that impacts the magnetic survey, while onboard factors pertain to various UAV components with magnetic characteristics. Due to these magnetic components, UAVs may compromise the accuracy of total magnetic field measurements [59]. Advances in magnetometer technology enable increased use in small to medium UAVs, but miniature UAVs face challenges due to their compact size and shorter distances between interference sources and sensors [23,44,50,81]. Addressing magnetic interference is a paramount challenge in aerial magnetometry development [40,50,95,96,97]. This section explores solutions to counter magnetic interference, considering both active and passive approaches.

• Active Solution: Post-compensation addresses UAV magnetic interference [23,40,97], utilizing calibration flights to gather high-altitude data and calculate compensation coefficients using a model (Equation (1)) [98].

  (1)$\begin{array}{c}{H}_T=C_{1}Cos\alpha +C_{2}Cos\beta +C_{3}Cos\gamma +H_G\{C_4{Cos}^2\alpha +C_{5}Cos\alpha Cos\beta +C_{6}Cos\alpha Cos\gamma +\hfill \\ C_7{Cos}^2\beta +C_{8}Cos\beta Cos\gamma +C_9{Cos}^2\gamma \}+H_G\{C_{10}Cos\alpha {\left(Cos\alpha \right)}^{\prime }+C_{11}Cos\alpha {\left(Cos\beta \right)}^{\prime }+\hfill \\ C_{12}Cos\alpha {\left(Cos\gamma \right)}^{\prime }+C_{13}Cos\beta {\left(Cos\alpha \right)}^{\prime }+C_{14}Cos\beta {\left(Cos\beta \right)}^{\prime }+C_{15}Cos\beta {\left(Cos\gamma \right)}^{\prime }+\hfill \\ C_{16}Cos\gamma {\left(Cos\alpha \right)}^{\prime }+C_{17}Cos\gamma {\left(Cos\beta \right)}^{\prime }+C_{18}Cos\gamma {\left(Cos\gamma \right)}^{\prime }\}=\sum _{i=1}^{18}{C}_i{A}_i\hfill \end{array}$

  where $H_T$ is the total interference field intensity, and $H_G$ represents the geomagnetic field. Cosα, cosβ, and cosγ are the directional cosines of the geomagnetic field vector concerning the UAV’s axes. Cj (1 ≤ j ≤ 18) are compensation coefficients aimed at mitigating magnetic interference effects.

  Compensation coefficients (Cj) are estimated using magnetometer data (α, β, γ) through the least squares method according to Equation (2). These coefficients, along with the model, mitigate aircraft interferences during magnetic surveys.

  (2)$C={\left(A^{T}A\right)}^{-1}{A}^T{H}_{int}$

  where $H_{int}$ and C are column vectors representing $H_T$ and Cj, respectively, and A is the design matrix [23,40].

  To evaluate compensation, the “improvement ratio” and the “fourth-difference” metrics are used [23,99]. Calibration flights correlate UAV maneuvers with magnetic field changes for compensation. Flight of maneuver data should mirror UAV behavior, ideally collected at high altitudes. Post-compensation is not suitable for low-altitude flights, especially for multi-rotor UAVs, due to instability. Yet, during high-altitude operations, post-compensation can be applied with magnetometers placed away from the UAV’s platform [23,40].

• Passive Solution: Post-compensation may not suffice for UAV magnetometry, necessitating an alternative approach by placing the magnetic sensor away from the aerial platform. Methods include suspending it beneath the UAV with a semi-rigid tether or affixing it to the UAV frame with a rigid bar. Various sensor attachment configurations are illustrated in Figure 5, accommodating different UAV types [42,100,101,102]. Placing the magnetometer away from the aerial platform can lead to sensor errors and fluctuations due to vibrations. Firmly affixing it to the airframe or using an extended boom may compromise flight stability, especially for fixed-wing UAVs [23,44,59,97,99]. Comparative studies suggest optimal sensor-platform distances of 3 to 5 m to minimize interference [23,44,50,55,65,81,97,103]. For VTOL fixed-wing systems, mounting sensors at the winglets or nose-tip via a fixed-boom configuration is effective [80].

UAV-borne magnetometry has found successful applications across diverse domains such as mineral exploration and deposit characterization [36,40,48,50,52,55,59,60,66,67,73,94,104,105,106,107,108], archaeological surveys [65,109], near-surface object detection and identification, specifically mines and unexploded ordnance (UXO) [41,49,57,68,90,110,111], geological structure and fault mapping [112], volcanology [64,84,85,113], and oil/gas infrastructure identification [53,61,114,115,116]. Several studies have been conducted for general purposes rather than specific applications [37,51,54,87,91,92,93,102,117,118,119,120]. The applications extend beyond terrestrial environments to marine domains, including offshore geophysical surveying, beach-shallow sea transitions, and anti-submarine warfare system detection [81,86,121]. These systems have proven particularly valuable in remote or hazardous environments where traditional ground surveys are impractical or highly challenging [71,72]. Detailed specifications for each application are provided in Table 3.

3.1.2. Unmanned Aerial Gravimetry

Integrating UAV-borne gravity surveys with ground-based methods aids subsurface resource exploration, enhancing gravity field determination for diverse applications [125]. The UAV-based gravimetry system, depicted in Figure 6, includes a ground base station and an airborne gravimetry system. The ground base station comprises components like a ground control/command station (GCS), ground data transmission (GDT) station, and ground support equipment (GSE). The GCS facilitates real-time transmission of gravimeter data via satellite links. The airborne gravimetry system consists of an unmanned vehicle, UAV-compatible gravimeter, data-transferring computer, GNSS signal recorder, and uninterruptible power supply (UPS). The UAV, central to any unmanned aerial system (UAS), can take various forms, including airships, helicopters, fixed-wing, and multirotors, with no usage restrictions [24].

Two main operational modes of UAV-based gravimetry exist: continuous-flight and grasshopper mode (see Figure 7). The continuous-flight mode entails the drone collecting data while traversing an assigned area and is ideal for large surveys such as those in the petrochemical industry. Challenges include distinguishing between platform and gravitational accelerations, often addressed using isolation platforms and gimbals. The grasshopper mode involves the UAV landing at specific points for data collection before taking off again. This mode, suitable for rotary-wing drones, collects data during stationary periods. Challenges in this mode include aligning the gravimeter’s axis and sensitivity to angular errors. The grasshopper mode typically requires more time for gravimetry due to additional flight time to return to fixed places and an increased number of landings [128]. Further details and comparisons are available in the cited reference. The successful integration of UAV-compatible gravimeters onto unmanned platforms for continuous-flight and grasshopper mode operations requires several auxiliary systems. These include isolation (self-leveling) platforms, gimbals, and differential GPS capabilities [128,129].

Various corrections must be applied during gravity data collection, particularly in airborne gravimetry operations, including UAV-borne methods. These corrections include latitude correction, free-air (elevation) correction, Bouguer correction, topography (terrain) correction, Earth tides, Eötvös correction, and low-frequency translation correction, among others. For brevity, readers seeking more detailed insights into these corrections are referred to [24,130,131,132].

This section provides a concise overview of gravity sensors compatible with UAVs. Miniature gravity sensors are designed for UAV integration, considering the payload capacity of UAVs. These sensors fall into two main categories: strapdown systems and Micro-Electro-Mechanical-Systems (MEMS) accelerometers. In the strapdown configuration, a triad of accelerometers is directly affixed to the airborne vehicle. MEMS accelerometers offer a lightweight solution for UAV-based gravimetry. Triaxial MEMS devices have been successfully deployed in various UAV-based gravimetry operations.

Notable implementations of strapdown systems include the lightweight iCorus [133,134,135] and iMAR iNAV-RQH [136]. The MEMS-based solutions are represented by systems such as Wee-g [128,137,138,139], Imperial College’s devices [140], HUST’s MEMS [141], Silicon Micro Gravity [142,143], FG5-L [144,145], and Scintrex RG1 (despite being primarily designed for underwater operations, they can also be used in UAV gravimetry) [128,146].

The development of UAV-borne gravimetry systems has been led by various organizations, including military institutions (e.g., the Portuguese Ministry of Defence [146]), universities (such as the University of Glasgow [138,147], Technical University of Denmark [135], National University of Defense Technology [24]), and government agencies (like the National Oceanic and Atmospheric Administration (NOAA) [148], and Geological Survey of Japan [149]). Additionally, several self-developed systems have been successfully implemented for regional studies [136,150,151,152,153,154]. While both rotary-wing [136,149,150,152,155,156] and fixed-wing [24,135,151,153,157,158,159,160,161] platforms have been employed, long-endurance fixed-wing drones dominate national and international projects. Strapdown gravimeters are the most commonly used sensors [24,151,154,157], with some systems incorporating additional geophysical sensors for integrated surveys [159,160,161]. Table 4 presents detailed specifications of these systems.

UAV-borne gravimetry applications primarily span three domains: system research and development (R&D), geological studies, and exploration [24,128,135,136,138,147,149,152,153,155,156,157]. Most research focuses on system development, including INS/GNSS integration [136,157] and miniature system refinement [138,149]. Applied studies demonstrate successful implementation in earthquake research [150], Arctic exploration [155,156], mineral prospecting [128], and datum definition [148]. International projects like DroneSOM [159,160,161] showcase the technology’s potential for integrated geophysical surveys. Table 5 provides more details of these applications.

3.1.3. Unmanned Aerial Gamma-Ray Spectrometry and Radiometry

Gamma-Ray Spectometry (GRS) geophysical technologies are challenging to integrate with drones due to their bulkiness and traditional design [162,163]. New UAV-compatible spectrometer systems, designed within drone weight constraints and improved data processing methods, e.g., [164,165], show promise for enhanced survey efficiency and accessibility. This shift requires innovative equipment design and data analysis methods to maximize potential.

UAV-borne GRS feasibility relies on precise gamma-ray radiation measurement in low-altitude environments with limited air attenuation [166,167]. UAVs use gamma-ray spectrometers to map soil properties, texture, and contamination [168], advancing from proof-of-concept to common practice [169,170]. Radiation mapper UAVs carry payloads suitable for efficiently mapping radionuclide activities [171]. UAV GRS combines ground-based and traditional airborne methods, using UAVs for gamma radiation investigations at lower altitudes, minimizing risks and costs, especially in smaller survey areas [170,172,173]. Radiometric surveys by UAVs are more common than spectrometric surveys due to lightweight UAV constraints, limiting traditional GRS methods’ effectiveness [172]. Ground-based GRS requires stops, while airborne GRS uses large-volume scintillation detectors for high-quality surveys [174]. Semiconductor detectors are expensive and less adopted compared to classical scintillation detectors, preferred for spectrometric measurements during movement [175,176,177]. Unmanned systems utilize heavy detection units with multiple crystals [172,175,176], impacting UAV flight time and range. Increasing UAV size is not cost-effective. Compact scintillation detectors offer economical and reliable data acquisition for geological mapping, ensuring high-quality outcomes in gamma-ray surveys [172,176,178].

Let us examine how a UAV GRS system operates. In a UAV GRS system (Figure 8), the calibrated gamma-ray spectrometer is mounted beneath the UAV to measure radionuclide concentrations [168]. An onboard rover logs GNSS data for georeferencing, with corrections from a ground-based receiver improving accuracy. The spectrometer captures radiation spectra at each position, synchronized with location data, and processed onboard [169,179]. Spectrometer data are transmitted to the GCS via RF systems like the TETRA network, UMTS, and others [179,180,181,182]. Post-flight processing includes spectrum analysis for radiation heatmap generation [181]. The GCS software integrates radiation levels, coordinates, and altitude for visualization [182]. Measured radionuclide concentrations obtained from the spectrometer are used in application models, correlating them with soil properties or contaminants. Proper sensor calibration is crucial for accurate results, often achieved through laboratory analyses known as ground truthing [168,181]. During field operations, soil samples are collected. These samples undergo air-drying, milling, and sieving for clay and sand content analysis [170,183]. Lab testing includes radionuclide content, grain-size distribution, and clay fraction analysis, which are essential for application model development. Models can be developed based on as few as 14–20 soil samples, generating soil maps for various applications [168]. Expert analysis can further enrich the final map.

In this section, spectral data processing is explored, specifically the methods for calculating radionuclide concentrations. Fundamental processing steps involve consolidating data from different detectors and filtering out points during UAV stationarity or survey line transitions [184]. Spectrum analysis techniques, such as the Windows method and Full Spectrum Analysis (FSA) procedure, are used to derive radionuclide concentrations from recorded spectra [162,171,185]. The Windows method extracts ⁴⁰K, ²³⁸U, and ²³²Th concentrations from specific energy windows [162,170]. In contrast, the FSA method, introduced in [186,187], utilizes nearly all spectral data, enhancing precision [162,185]. FSA fits “standard spectra” to acquired spectra, reflecting the detector’s response to pure radionuclide sources (⁴⁰K, ²³⁸U, or ²³²Th) [170,185]. Monte Carlo simulations and modeling generate standard spectra, which are now a standardized practice detailed in references such as [164,170,188]. FSA reduces uncertainties by half compared to the Windows method, improving data quality [185]. It enables the use of smaller detectors while maintaining quality and provides a richer count and spectrum structure [162].

In UAV-borne GRS, elevation impacts signal reception and footprint size. Higher altitudes reduce detected signal quantity due to increased air attenuation [164]. Elevating the spectrometer broadens the footprint, known as the “table lamp effect”, expanding the effective measured area [170,183]. Gamma-ray spectra conversion to radionuclide concentrations requires altitude correction for signal attenuation and detector field-of-view variations. Traditional corrections for airborne ranges may not suit UAV altitudes. Previous studies proposed corrections, but wide adoption was lacking. Some surveys (e.g., [176,189,190,191]) used International Atomic Energy Agency (IAEA) corrections even at lower altitudes. In [170], the corrections were refined specifically for UAV GRS operational ranges (0–40 m) using experimental and computational methods, as detailed in the cited study. For more detailed insights into the commonly used footprint and height corrections applied in UAV-borne spectrometry and radiometry and some innovative methods in this domain type, refer to [170,181,192,193].

Several ready-to-use gamma-ray spectrometers have been tested and validated for UAV deployment. These include the Medusa MS Spectrometer Series [164,170,183], Georadis D230A Spectrometer [176,184], CeBr3 (and Twin NaI-CeBr3) Scintillation Detector [181], CsI(Tl) detector [172,194,195], Cadmium Zinc Telluride (CdZnTe or CZT) Semiconductor Detector, GR1/-A Kromek Spectrometer [169,173,195,196,197], Cs2LiYCl6:Ce3+ (CLYC) Elpasolite scintillation sensor [173,198,199], and Geiger–Müller Tube Particle Counter [200]. Among these, the Medusa MS Spectrometer series has gained widespread commercial adoption, while other sensors are primarily research-oriented developments with limited commercial availability. Table 6 provides detailed specifications of these UAV-compatible radiation sensors.

A review of developed UAV-borne GRS systems (Table 7) [167,169,170,172,173,176,182,183,184,195,199,201,202,203,204,205,206,207,208] reveals that their primary objective is RS of radionuclide contamination in radioactive incidents and nuclear emergency monitoring. Some systems integrate additional geophysical capabilities, such as magnetic and electromagnetic surveys, to enhance survey efficiency and reliability [202]. Helicopters and multi-rotors demonstrate superior applicability for these systems (e.g., RotorRAD [206] and Radiation Monitoring System (RMS) [195]) due to their ability to hover and maintain precise control near radiation sources, although fixed-wing platforms have also been employed (e.g., Autonomous Airborne Radiation Mapping (AARM) system [195]).

UAV-borne GRS has diverse applications, including soil (texture) mapping [162,163,164,168,170], environmental contamination monitoring at critical sites (including mine tailings [163], industrial plant surveillance [197], lost radioactive source location [206]), characterization of Uranium Legacy Sites (ULSs) [181,195,209], accurate mapping of radiation sources and polluting gases [207], and radiometric measurements for mining applications [184]. Table 8 provides in-depth information on these applications.

3.1.4. Unmanned Aerial Imaging Geophysics

A UAV photogrammetry system comprises both aerial and ground sections, with the aerial section featuring the UAV and various imaging sensors such as visible-light (RGB), multispectral (MS), hyperspectral (HS), or thermal-infrared (TIR) cameras [210,211]. The system operates through fieldwork, including ground operations and aerial surveys, as well as office tasks involving survey planning and data processing. Key steps involve acquiring overlapped images, identifying key points, and performing Structure-from-Motion (SfM) and Multi-View Stereo (MVS) [212] to generate dense point clouds, orthomosaics, and different other geospatial products. While this overview does not delve deeply into UAV photogrammetry, interested readers can refer to relevant references (e.g., [213]) for more details. UAV photogrammetry extends beyond civil applications [214], offering valuable geometrical, structural, spatial, and spectral data about the natural Earth. Consequently, it is a versatile UAV RS method applicable to various geoscientific endeavors.

This section provides an overview of UAV-compatible optical and TIR imaging sensors. In the UAV visible photogrammetry category, two types of visible cameras are commonly used on UAV platforms for imaging and photogrammetry [215]. These include UAV-integrated cameras, exemplified by the 20-MP 4/3-inch image sensor (e.g., used in DJI Mavic 3 drone) and the 20-MP 1-inch image sensor (e.g., used in AUTEL’s EVO II Pro V3 drone), and Digital Single Lens Reflex (DSLR) cameras like the Sony A7RIII. In UAV MS photogrammetry and RS, state-of-the-art sensors include the Parrot Sequoia+, MicaSense Altum-PT, MicaSense RedEdge-MX/P, Sentera 6X, and DJI P4 Multi [215,216]. State-of-the-art TIR sensors include the WIRIS Pro/Pro Sc, Zenmuse XT 2 (dual-light TIR imager), Uncooled FPA 6404, FLIR SC305, and TELEDYNE FLIR VUE Pro. In the realm of UAV HS photogrammetry, cutting-edge sensors include OCI™-UAV-1000/2000, OCI™-F series, and GoldenEye™ (by BaySpec); CHAI S/V-640, S 185 FIREFLEYE SE, S 485 FIREFLEYE XL, and Q 285 FIREFLEYE QE (by Cubert); Nano/Micro-HyperSpec, VNIR-1024, Mjolnir V-1240, and SWIR-384 (by Headwall Photonics HySpex); Rikola, vis-NIR microHSI, Alpha-vis microHSI, SWIR 640 microHSI, and Alpha-SWIR microHSI by MosaicMill NovaSol; MV1-D2048x1088-HS05-96-G2 (by PhotonFocus); Hyperea 660 C1, Pika L, Pika XC2, Pika NIR, and Pika NUV (by Quest Innovations Resonon); VIS-VNIR Snapshot by SENOP; SPECIM FX10/17 (by SPECIM); SOC710-GX (by Surface Optics); and MQ022HG-IM-LS100-NIR/IM-LS150-VISNIR (by XIMEA) [210]. For the sake of brevity, no further details about the sensors or their specifications are provided here. Readers are referred to the cited references for more information.

In UAV photogrammetry, spatial resolution—denoted by ground sampling distance (GSD)—along with spectral resolution (referring primarily to the number of bands the sensor can capture) and radiometric resolution are key factors [217,218]. Common corrections in UAV photogrammetry include radiometric and geometric calibration [219,220]. Radiometric calibration aims to derive absolute reflectance measurements from the digital number (DN) [219]. Geometric calibration encompasses band-to-band registration (primarily for multi-lens sensors) and true orthorectification, which allows objects in the orthomosaic to be viewed from a top–down perspective [220]. Additionally, point cloud filtering is another type of correction applied not to images but to point clouds, eliminating non-ground points to generate a bare-land point cloud and digital terrain model (DTM) from the initial point cloud [221].

In reviewing the geoscientific applications of UAV photogrammetry, fine-scale digital terrain modeling for geomorphological studies emerges as a significant area of implementation. A crucial step in this methodology involves filtering the original point cloud data to isolate the natural terrain points [221]. The subsequent generation of topographical maps, based on orthophoto-mosaics with contour lines, enables comprehensive geomorphological analysis and various practical applications. The second major application category focuses on landslide mapping and monitoring. A review of the literature (Table A1, Appendix A) demonstrates that this application requires multi-temporal surveying and analysis of orthophoto-mosaics and DTMs from pre- and post-landslide events to map horizontal and vertical displacements, respectively [222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237]. The number, distribution, and accuracy of ground control points (GCPs) are critical in this methodology. UAV photogrammetry serves as a complementary approach to ground-based methods, including geodetic surveys, terrestrial laser scanning (TLS), and GNSS-based solutions. However, the co-registration of multi-temporal photogrammetric products remains a significant technical challenge.

The third major category of applications focuses on land subsidence and ground failure mapping, where vertical displacement measurements are paramount and precise multi-temporal DTMs play a crucial role (Table A2, Appendix A) [238,239,240,241,242,243,244,245,246,247,248,249]. Within this category, subsidence mapping in mining areas has received particular attention in the literature. The methodological considerations previously discussed for landslide monitoring are equally applicable in this context. Research has demonstrated that integrating UAV photogrammetry with other ground-based and remote sensing methods, such as GNSS, LiDAR, and Differential Interferometry Synthetic Aperture Radar (DInSAR), enhances the accuracy and reliability of results [241,242,244,245,248]. In both landslide and subsidence mapping applications, where terrain geometry is the primary concern rather than spectral information, visible-light imaging sensors typically provide sufficient data. A consideration in these applications is the sensor’s capacity to deliver fine ground sampling distance (GSD), which serves as an indicator of spatial resolution.

The fourth major category of applications addresses geothermal exploration [250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265]. These studies focus on remote measurement of ground-level surface and subsurface temperatures and the detection of geothermal activity in both terrestrial and aquatic environments. Thermal-infrared imaging sensors and video capture systems serve as the primary data collection tools, yielding thermal orthophotos (with visible orthomosaics as supplementary data), 3D thermal models, and DTMs. In the reviewed literature, multirotor drones have played a predominant role in these applications. For a comprehensive overview of research conducted in this field, refer to Table A3, Appendix A.

The fifth category encompasses mineralogy, mining, and soil mapping applications [266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294], spanning a broad range of activities from mineral detection and soil type classification to mine topographic mapping, volume estimation, and geological structure mapping (detailed information provided in Table A4, Appendix A). Within this research domain, soil moisture mapping, specifically the remote detection of subsurface water content, has garnered significant attention [263,295,296,297,298,299,300,301] (Table A5, Appendix A). MS and HS imaging sensors have proven particularly effective in these applications. Due to the requirement for precise spectral analysis, robust radiometric and geometric corrections are crucial [268]. Structure from Motion (SfM) and Multi-View Stereo (MVS) processing techniques have been widely employed to generate MS or HS orthomosaics, while artificial neural networks (ANNs) and machine learning (ML) approaches have played significant roles in extracting valuable higher-level spectral features [263,278,279,295,298].

The resulting RS data have been successfully integrated into Geographic Information Systems (GIS) for further analysis [275,299]. Research has demonstrated that combining these imaging results with other ground-based and UAV geophysical data (e.g., magnetic surveys) enables a more comprehensive geological analysis of subsurface conditions [270,272,277]. The final category of geoscientific applications focuses on volcanic research [302,303,304] (Table A6, Appendix A). Although this category comprises a smaller body of literature compared to other applications, these studies primarily concentrate on visual inspection and modeling of active volcanic landforms, conducted through both single-time observations and temporal monitoring campaigns.

3.2. UAV-Borne Geophysical Survey: Active Methods

3.2.1. Unmanned Aerial EM Survey

The EM method, utilizing induced currents to detect conductive underground structures, aids in subsurface geology understanding. It is divided into time-domain EM (TDEM) and frequency-domain EM (FDEM) survey methods. Aerial EM (AEM) schematics depict induced and measured magnetic fields (see Figure 9). Measurements involve primary EM fields from the transmitter and secondary EM fields correlated with geological features [305,306,307,308]. AEM methods are categorized by excitation modes (AFEM and ATEM), platform types (fixed-wing TEM, fixed-wing FEM, helicopter-borne TDEM or simply HTEM, and helicopter-borne FDEM or simply HFEM), and EM field transmission types (active, passive, and semi-passive/airborne systems) [305,309,310,311,312,313]. Different ATEM and AFEM systems have been developed based on various types of manned aerial platforms [310,312,314,315,316,317,318,319,320,321,322,323,324,325,326]. Despite this variety, UAV-borne EM survey systems are a relatively new topic compared to traditional methods.

AEM has found various geoscientific applications, including finding Quick Clay [327,328,329], identifying hazardous substances [330], mapping the fresh–saltwater interface [331], a freshwater potential investigation [332], deep groundwater mapping in Antarctica [333,334,335], hydrologic mapping and environmental assessments [336], detection and mapping impermeable aquifer boundaries [309], groundwater and soil investigations [337], exploring the relationship between groundwater and surface water [338], 3D geological modeling of complex buried valleys [339], observations of a collapse-prone volcano [340], mineral exploration [326], gold exploration [341], mapping sub-Phanerozoic basement features [342], UXO detection [62], magnetite ore tonnage estimation [343], and application in the fields of uranium exploration [344,345].

Developing EM-sounding systems for lightweight UAVs faces challenges due to weight and bulkiness issues with conventional techniques, such as helicopter-based systems. Aerial exploration presents difficulties like precise loop positioning and adapting methods to UAVs, requiring bulky generator setups [346]. Integrating EM sensors with UAVs introduces stability, interference, and control challenges addressed with solutions like sensor suspension, tail fins, noise avoidance, and real-time monitoring [347,348]. These innovations aim to enhance data quality and address engineering challenges in UAV-based EM surveys. The scheme of Figure 9 is also valid for the UAV EM method.

There are two configurations in UAV-borne EM surveys: single-drone and dual-drone configurations. To illustrate these configurations, we focus on the Louhi geophysical EM survey system. Developed by Radai Ltd. under the NEXT project, this system facilitates practical drone-based operations for FDEM methods [308]. It features a single drone equipped with a transmitter, offering flexibility in surveying approaches (see Figure 10). Additionally, a two-drone configuration is utilized to maintain a consistent separation distance between the receiver and transmitter drones, allowing for deeper exploration and rapid deployment in challenging terrains [349]. The fixed loop transmitter system enhances the source moment and signal-to-noise ratio (SNR), which are crucial for subsurface exploration. Accurate position and orientation measurements enable conversion to global 3D coordinates, although variations in loop spacing and orientation can introduce noise. Synchronization between the receiver and transmitter units is achieved using GNSS time, allowing for precise data recording and analysis. Payload constraints and synchronization between dual-drone autopilots pose challenges, necessitating ongoing research and development efforts [308].

In the realm of UAV-based EM RS, the concept of the “Semi-UAV-borne EM (SUEM) System” is emerging as a promising innovation. The Semi-Airborne EM (SAEM) setup presents a promising avenue for UAV-based EM surveys [350]. In this configuration, the transmitter remains stationary on the ground while the receiver is deployed on a UAV (similar to Figure 10b), offering unique advantages over conventional AEM [351,352]. However, the payload limitations of current UAVs restrict the maximum speed of the SAEM system. Addressing challenges related to turbulence-induced shaking and motion-related noise in the dataset necessitates innovative solutions [310]. Advancements in drone technology have enabled the adaptation of SAEM systems to UAVs, reducing application and maintenance costs and facilitating multi-component surveys. Notably, MGT’s SAEM system, developed in collaboration with the geophysical industry and research organizations in Germany, exemplifies the successful integration of an EM system onto an unmanned multicopter, enabling data collection with enhanced depth penetration and accuracy. These developments mark significant progress in the evolution of SUEM systems, holding considerable potential for future UAV EM RS applications [310,351,352,353].

Very-low Frequency (VLF) EM methods are frequently mentioned in the literature. UAV integration with the VLF method evolved since Kipfinger’s lightweight system in 1996–97 [352,354]. Recent developments led to VLF systems on rotary-wing UAVs with a 12 kg payload capacity [355]. VLF induces primary horizontal magnetic fields and captures secondary fields in conductive subsurface formations with a receiver equipped with two coils [356,357,358]. Equation (3) calculates the vertical magnetic field component using transfer functions or “tippers” [359,360].

(3)$H_z\left(\omega \right)=A\left(\omega \right)\times H_x\left(\omega \right)+B\left(\omega \right)\times H_y\left(\omega \right)$

Modern VLF instruments capture time series data for all three magnetic field components, enabling analysis in both time and frequency domains. Automated spectral analysis, like the method proposed in [361], identifies VLF transmitters. These advances highlight the potential of UAV-based VLF EM systems for large-scale geophysical surveys [355,362].

Let us explore the data collection modes in UAV-borne EM surveys. UAV-borne EM surveys use fixed-point and continuous data collection modes [363]. In fixed-point mode, the UAV hovers at each measurement point, ensuring high-quality data but limiting data points due to energy-intensive hovering. Continuous mode collects data seamlessly during low-level flight, offering substantial data volumes similar to grasshopper mode [128]. Post-processing ensures data quality comparable to fixed-point mode [364].

This section discusses three key aspects of UAV-based EM surveys: compatible EM sensors for UAVs, UAV platforms used in EM surveys, and the development of UAV-based EM systems.

• EM Sensors: Various EM instruments have been utilized in UAV-borne surveys, including GEM-3D, MPV, MPV-II, Pedemis, High-Frequency EMI, Dualem-1S, EM38, Profiler 400-EMP, CMD MiniExplorer, US Army’s drone-mounted EM induction sensor, CAS & Jilin University single-component sensor and others [310,348,365,366]. Among these, the GEM-2UAV is prominent, weighing 3 kg and operating at ten frequencies (25 Hz to 96 kHz). It requires a GNSS antenna and WinGEM software and consumes 20 W during surveys, thus offering configurable operational modes and data logging initiated through a control unit [347,348,367].

• UAV Platforms: UAV platforms for carrying EM instruments are categorized into four types: multi-rotor [346,348,351], fixed-wing [73,368], helicopters [355], and airships/balloons [369]. The choice of platform depends on factors like survey objectives and the size of the EM instrument.

• UAV-borne EM Survey Systems: Building on the previously discussed platforms and sensors, 26 distinct UAV-borne EM systems that have been developed [10,73,159,161,202,308,310,346,347,348,350,351,352,355,364,365,368,369,370,371,372,373,374,375,376,377,378,379,380], with a thorough overview provided in Table 9. Rotary-wing platforms dominate system development compared to unmanned helicopters or fixed-wing drones, with the GEM-2 emerging as the most widely implemented EM instrument.

Table 9

Review of state-of-the-art UAV-borne EM survey systems.

Sys.	Platform Type	UAV Name/Model	EM Instruments	References
1	Multi-rotor	MTOW octocopter	Miniaturized induction coil triple	[351,352]
2	Multi-rotor	X825 octocopter	Metronix SHFT-02e induction coil triple	[350]
3	Multi-rotor	SibGIS hexacopter	A measuring system with an inductive sensor	[202,346,370,371]
4	Multi-rotor	SibGIS hexacopter	A grounded transmitter line spanning 2.2 km serves as the origin of the current pulses, coupled with an airborne PDI-50 receiver loop on the UAV.	[372]
5	Helicopter	Aeroscout Scout B1-100	The Super High-Frequency Induction Coil Triple sensor, in conjunction with the ADU07 data logging module, both developed by MGT.	[355]
6	Multi-rotor	DJI Matrice 600 Pro	GEM-2UAV CSEM sensor	[348]
7	Fixed-wing	VTOL Mother-Goose	Louhi portable EM transmitter and three-component receiver	[308]
8	Unmanned VTOL airship	Quaddirigible (filled with helium)	A VLF-type EM survey system	[369]
9	Multi-rotor	ZION CH940 and LAB6106 multicopters	GEM-2	[347]
10	Multi-rotor	Hexacopter	A coil wound with enameled copper wire, comprising 25 turns and a diameter of 25 cm, designed for the generation of a magnetic field.	[373]
11	Hexacopter and fixed-wing	SGU’s fixed-wing VLF	Three orthogonally mounted induction coil sensors and a data acquisition system with up to 1 MHz continuous data sampling of the EM components.	[368]
12	Multi-rotor	Hexacopter	The drone-borne TEM system utilized a central loop device.	[364]
13	Not specified	Not specified	D-GREATEM system	[374]
14	Fixed-wing	Silver Fox UAV	A sensing coil towed behind the UAV	[73]
15	Multi-rotor	Hexacopter	A measurement setup using an inductive sensor (receiving loop) is tethered by a UAV, while a galvanically grounded power transmitter is positioned on the ground and linked to a pulse generator.	[375]
16	Unmanned helicopter	Tianxiang V-750	A SAEM system, designed by the CAS, encompasses a single-component sensor, transmitter, and receiver, all equipped with vibration isolation.	[310]
17	Multi-rotor	Hexacopter	A SAEM system, engineered by Jilin University, consists of a robust ground-based transmitter generating high-power signals and a single-component sensor.	[310]
18	Multi-rotor	DJI Matrice 600	The Geophex multi-coil, CMD MiniExplorer EM instruments, and GEM-2	[365]
19	Multi-rotor	DJI Wind4 quadcopter	Geophex GEM-2UAV	[376]
20	Fixed-wing	A long-range drone	UAV-borne gravity and EM sensors	[159,161]
21	Octocopter	System name: MGT-GEO Radio EM	The sensor system weighs 6.5 g, operates in a frequency range of 1–524 kHz, encompasses channels for Hx, Hy, and Hz, boasts a sample rate of up to 524 kHz, achieves synchronization through GPS, and utilizes compact flash disk storage media.	[365]
22	Rotary-wing	DroneSAM	A low-frequency hybrid geophysical system integrating a ground active source transmitter system with a drone for slow-flying, low-level data acquisition of TEM and magnetometric resistivity data.	[377]
23	Rotary-wing	Multi-rotor (DronEM)	Drone for EM field Measurements (DronEM) is outfitted with a Selective Electric Triaxial Probe and is capable of scanning the EM spectrum ranging from 10 MHz to 3 GHz at altitudes up to 200 m.	[378]
24	Rotary-wing	Not specified	UAV VLF EM System: two VLF UAV sensor coils with cables accompanied by other instruments.	[379]
25	Rotary-wing	Octo/helicopter	3-component EM sensor (induction coil DEEP) and fluxgate magnetometer.	[380]
26	Rotary-wing	Hexacopter	Time-domain EM system suspended beneath the UAV	[10]

In this part, an overview is provided on the data integration, processing and inversion principles, and methods applied. The UAV-based EM data processing, following methodologies by [365,381], involves several key steps (Figure 11). Initially, raw datasets from various sources, including drone, EM instrument data, and additional sources (e.g., LiDAR, if available), are integrated, synchronized, and preprocessed for interpretation [382]. Noise filtering enhances data quality by removing high-frequency noise using a low-pass filter. Data segmentation categorizes raw EM sensor data into non-informative, grid/profile, and vertical sounding segments, facilitating anomaly analysis [365]. Inversion iteratively updates resistivity models by comparing field data with synthetic data, employing methods like Layered Constrained Inversion and Spatially Constrained Inversion [383,384,385]. Validation involves addressing noise sources and correlating EM data with geological maps and boreholes to discern soil characteristics and validate results. Additional geophysical data and hand drilling may be utilized for validation when alternative datasets are unavailable.

UAV-borne EM applications span several key domains, including structural discordance and tectonics mapping [346], uranium deposits analysis [372], detection/investigation of subsurface targets (infrastructures) such as buried wires/power cables and pipelines [73,355,386], fence crossings [365], landmine/UXO [364,373], and even buried vehicles [347], underground tunnel investigation [73,387], fresh–saline water mapping [355,365], soil/sub-soil mapping (soil resistivity mapping [347], sand–clay lithology mapping [365]), and deep slope subsurface resistivity structure/distribution investigation [347,374,388]. Table 10 provides detailed information about these applications.

3.2.2. Unmanned Aerial GPR

Over the past decade, UAV-based radar imaging has seen significant advancement, with various radar technologies, unmanned platforms, and payload configurations showcased [389,390,391,392,393,394]. Early research by [395,396] laid the foundation, leading to tests with high-frequency radars at P, X, and C bands [397,398,399] despite limited penetration capabilities. In [400], multi-frequency GPR for rotary-wing UAVs was explored, sparking a surge in contactless GPR research with UAV platforms. The convergence of UAVs and radar technology offers all-weather data recording and buried object detection, driving interest across scientific and industrial sectors [401,402,403,404]. Applications range from landmine detection and criminal investigation to environmental monitoring, highlighting UAV-based GPR’s promising future in geophysical surveys and underground exploration [405,406,407,408]. UAV-based GPR systems are categorized as prototypes explicitly designed for UAVs or conventional GPR devices adapted for UAVs, with systems further classified based on whether antennas contact the ground, leading to ground-coupled and air-launched GPR systems [409,410,411].

Two approaches exist for assembling GPR payloads on UAV systems: independent and integrated designs (see Figure 12). In the independent approach, the UAV and payload are separate subsystems, while in the integrated method, they are developed collaboratively. The independent setup allows for compatibility with various UAV platforms but requires a separate interface for integration. Conversely, the integrated architecture simplifies subsystem synchronization and enables high-accuracy geo-referencing for navigation without redundant sensors. This approach is preferred for UAV systems with advanced sensors or specialized flight modes [25]. Visual examples include [412] for independent architecture and [391] for integrated architecture.

In this part, the observation modes in UAV-borne GPR are reviewed. The scanning strategies employed in UAV-borne GPR are of significant importance. These systems are categorized into three main types based on observation modes and antenna orientation relative to the Earth’s surface (see Figure 13) [391,414]. Down-looking GPR (DLGPR) configurations position antennas perpendicular to the surface, offering advantages in detecting deeper targets while potentially obscuring shallow ones due to reflections at the air–soil interface [25,409,415,416,417]. Forward-looking GPR (FLGPR) orientations minimize clutter by reducing reflections from the air-ground interface through oblique antenna incidence [25,416,418,419]. Side-looking GPR (SLGPR) configurations utilize tilted antennas to mitigate specular reflection from the air–soil interface, often moving laterally or following circular paths for data capture [391,420,421,422,423].

The scanning architecture choice depends on the target depth and scenario. FLGPR or SLGPR configurations suit shallow targets, minimizing clutter and enhancing detection. DLGPR systems excel for deeper targets despite increased clutter, offering extended dynamic range. DLGPR and FLGPR imaging capabilities are compared, with FLGPR optimizing transverse magnetic wave penetration, while DLGPR provides enhanced resolution but increased clutter [414,415].

UAV-based GPR methods are divided into fully airborne and semi-airborne setups. In fully airborne configurations, both antennas are on one UAV or split between two UAVs for transmission and reception. Semi-airborne setups involve a ground-based vehicle with the transmission antenna and a UAV with the reception antenna, operating in down-looking mode (see Figure 14).

Data processing in UAV-borne GPR, inspired by air-launched systems (e.g., [416]), involves two categories: Standard methods and Advanced algorithms (see Figure 15). Standard methods filter noise and correct motion errors, while advanced algorithms aim for high-resolution imaging [394].

In standard processing, data undergoes positioning management and preprocessing, including background removal [393], dewow [425], and clutter elimination. Techniques such as time-gating [427], average subtraction [428,429], and SVD-based filtering [430] are employed for clutter removal. Ground profile retrieval and height correction are also essential for data accuracy [426].

Focusing methods aim to enhance image resolution and interpretability by transforming diffraction hyperbolas into distinct bright spots [431,432]. Figure 16 depicts a UAV-mounted DL-GPR surveying a region of interest, capturing backscattered radar signals along its flight trajectory (Γ) across the angular frequency range $\Omega =[{\omega }_{min},{\omega }_{max}]$. Each measured point (m) in the volumetric subsurface domain (D) is defined by the position vector ${\overrightarrow{r}}_m=x_m\widehat{i}+y_m\widehat{j}+z_m\widehat{k}$, representing various buried targets. The radar imaging process employs a simplified linear scattering model [433]. Equation (4) defines the scattered field at each point, with $E_s$ representing the scattered field, $E_i$ denoting the incident field within domain D, χ(r) characterizing the unknown contrast function at any point $r$ in D, G corresponding to Green’s function, and k being the propagation constant.

(4)$E_s\left(r_m,\omega \right)=k^2{\iiint }_{D}G\left(r_m,r,\omega \right)E_i(r,\omega )\chi (r)dr$

Various algorithms were proposed to address the UAV-GPR imaging challenge, which were discussed as follows:

• Migration Techniques: Migration techniques like Kirchhoff’s wave-equation and phase-shift migration (PSM) algorithms, commonly used in GPR, are applied in UAV-based GPR data processing for efficient analysis [435,436,437,438,439]. However, PSM requires data interpolation to a regular grid, which may pose challenges in irregular survey trajectories. Newer approaches like Piecewise SAR (P-SAR) address these limitations by considering the reflection and transmission coefficients of EM waves through diverse subsurface layers [440].

• Back-projection (BP) or Delay-and-Sum (DAS) Method: UAV-based GPR often employs beam-forming or SAR-like Back-projection (BP) or Delay-and-Sum (DAS) methods due to non-rectilinear measurement trajectories [413]. They integrate radar echoes from the flight path to generate a reflectivity map using Equation (5).

  (5)$\chi \left(r\right)={\int }_{\mathsf{\Gamma }}{\int }_{\mathsf{\Omega }}{E}_s\left(r_m,\omega \right)\left(G\left(r_m,r,\omega \right)E_i(r,\omega )\right)\ast d{r}_{m}d\omega$

  where * represents the conjugation operator.

  DAS involves the summation of measurements at focal points across the target area [441]. Reflectivity $\mathsf{\rho }\left(\mathrm{r}\right)$ is determined using scattered field data from N acquisition points and M discrete frequencies (Equation (6)), considering phase shifts from wave propagation (Equation (7)).

  (6)$\mathsf{\rho }\left(\mathrm{r}\right)={\sum }_{n=1}^N{\sum }_{m=1}^M{E}_s(r_n,f_m)e^{+j2({\varphi }_0+{\varphi }_1)}$

  (7)${\varphi }_0+{\varphi }_1=k_{0,m}{‖r^{\prime }-r_n‖}_2$

  where $r_n$ represents the position where the n-th measurement was acquired, $f_m$ stands for the m-th discrete frequency (it is related to angular frequency), and ${\varphi }_0$ and ${\varphi }_1$ correspond to the phase shifts resulting from wave propagation. $k_{0,m}$ represents the free-space wavenumber for the m-th discrete frequency, while $r_{i,n}$ signifies the refraction point on the air-ground interface. The refraction point position ($r_{i,n}$) is determined using Snell’s law [441].

  DAS, suitable for irregular flight trajectories, is comparable to PSM techniques in performance for UAV-based GPR processing. They find application in various UAV-based GPR studies [391,393,396,405,413,428,429,442].

• Microwave Tomography (MT): MT focusing algorithms, using inverse filtering techniques, aim to solve the EM inverse scattering problem [432,443]. Unlike SAR-like methods, MT directly inverts the linear integral equation [25,394], addressing the imaging problem through a solution to the linear inverse problem (Equation (8)).

  (8)$E_s=L_{\chi }$

  $L: {\mathcal{L}}^2\left(D\right)\to {\mathcal{L}}^2(\Gamma \times \Omega )$ maps unknown to data space, both being square-integrable function spaces. Regularization, often through truncated SVD, stabilizes the ill-posed inverse problem [444]. MT’s resilience to noise surpasses SAR-like methods, promoting its widespread adoption in UAV-based GPR imaging across various data collection scenarios and environments [25,394,425].

• Full-waveform Inversion (FWI) or Integral Equation (IE)-based Methods: FWI or IE GPR processing links radar backscattered fields with soil EM properties [403]. It estimates soil conductivity and permittivity by minimizing a cost function by comparing the model and observed data [25]. In UAV-based GPR, FWI estimates soil permittivity, facilitating SWC mapping [394,405,434].

The mentioned methods reconstruct 2D images or 3D models of the subsurface and potential buried targets. These results can be refined using automatic target detection and recognition techniques, from traditional CFAR detectors [445] to advanced DL-based methods [446,447].

In this part, the state-of-the-art UAV-compatible GPR antennas are reviewed. UAV-borne GPR systems rely on antennas to convert guided waves [424,448]. Modern UAV-compatible antennas include Vivaldi-like antennas, Archimedean spiral antennas, and helix antennas, as reported in [25,392,413,414,422], detailed in Table 11. Antennas in UAV-based GPR systems prioritize weight, dimensions, and radiation performance, often favoring horn-like or planar designs. These antennas are commonly featured in UAV-based GPR systems developed by [401,405,414,422,426,449,450].

Approximately 40 cutting-edge UAV-based radar and GPR systems have been identified in the literature [389,390,391,392,394,395,396,397,398,399,400,401,402,403,404,405,406,407,412,413,414,415,417,421,422,423,425,426,429,439,442,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466]. Multi-rotors are predominantly used, which is consistent with GPR survey requirements as they allow effective scattering and backscattering reception. The systems operate across diverse frequency ranges (100 MHz to 9.8 GHz, with many systems operating in the 0.5–5 GHz range), utilizing various antenna designs (linear arrays, horn, helix, and Vivaldi). Systems employ Pulsed, FMCW, and SFCW technologies, with penetration depths ranging from centimeters to several meters depending on the application. Flight heights range from 0.5 to 7 m for most applications, though some systems operate at heights up to 10 m, with systems implementing different radar architectures (DLGPR, FLGPR, SLGPR) and measurement configurations (monostatic, bistatic). Table 12 provides detailed specifications of these systems.

Review and categorization of UAV-borne GPR applications reveal several key domains. These are, among others, buried threats object (landmines, IDEs, and UXOs) detection [391,400,401,402,405,413,414,415,421,422,426,442,449,450,451,453,455,458,464], snow and ice studies [392,454,456,457], archaeological mapping [394,465], agricultural applications [467], soil moisture mapping [403], bathymetry [460], and search and rescue (SaR) operations [423,468], with buried threat detection emerging as the predominant application area. Beyond geoscience applications, the technology has demonstrated utility in other disciplines such as agriculture and SaR. Table 13 provides a comprehensive review of these applications.

3.2.3. Unmanned Aerial LiDARgrammetry

A UAV-based LiDAR system consists of an aerial section with a UAV platform and sensors, including compact LiDAR, GNSS receiver, and IMU, while the ground section comprises a control terminal, flight control system, and ground control points [469,470,471]. LiDAR emits laser pulses to the Earth’s surface, measuring distances based on pulse travel time. Integration of timing, LiDAR orientation, and location data determines point cloud accuracy [469]. For further details, interested readers are referred to [472,473].

When discussing UAV-compatible LiDAR sensors, it is important to distinguish between their mechanisms: Mechanical Scanning-based Sensors, Solid-state Technique-based Sensors, and Solid-state Hybrid Sensors [215,470,474,475]. The cutting-edge survey-grade UAV LiDAR sensors include Riegl VUX-1 UAV, Riegl mini VUX-1 UAV, Riegl mini VUX-1DL, Riegl VUX-240, Velodyne Puck LITE VLP-16, Quanergy M8, and Livox Mid-40. For more information, readers are referred to the relevant references (e.g., [470]).

To provide an overview of data processing and corrections in UAV-borne LiDARgrammetry, a comprehensive workflow comprising three main parts was proposed in [470]: point cloud production, point cloud refinement, and DEM generation. The initial phase involves calibration [476] and a combination of laser data and trajectory for initial point cloud generation [477]. Point cloud refinement includes the removal of LiDAR point noises and outliers [478], as well as ground point classification [479]. Finally, DEM generation encompasses ground point filtering, followed by visualization and analysis. For further details, interested readers are referred to the provided reference.

Alongside the applications of UAV-borne LiDARgrammetry in urban mapping [29], this RS method is increasingly used in geoscientific studies for mapping inaccessible terrain and generating 3D models of geological features like cliffs, coasts, and volcanoes. This section explores UAV-borne LiDARgrammetry applications in geoscience, emphasizing its distinct utility from airborne LiDARgrammetry despite shared feasibility. The applications mainly focus on capturing the surface geometry of the Earth’s exterior. These applications include fine-detailed digital terrain modeling [480,481,482,483,484,485], fault zone mapping [486], landslide mapping and monitoring [487,488,489,490,491,492,493,494,495], subsidence and fissure mapping [31,495,496,497,498,499,500], geological mapping (geological structure measurement [501,502,503,504,505], geological catalog production [506], characterization of ice morphology evolution [507]), glaciological investigations [507], groundwater level mapping [508], topographic mapping for precision land leveling [509], volcanological studies [510], and soil mapping (e.g., estimation of soil organic carbon (SOC)) [511,512]. In addition to the spatial and structural information of LiDAR, some sources of imaging information are sometimes combined for enhanced analysis ability [511,512]. Table 14 provides a detailed explanation of these applications.

3.3. UAV-Borne Geophysical Survey: Integrated Approach

Sensor integration and data fusion are hot topics in traditional geophysical methods, encompassing ground-based methods [513,514,515,516,517,518,519,520,521,522,523,524], as well as spaceborne and manned airborne methods [525,526,527,528,529,530]. Even fusion scenarios involving ground-based geophysical data and spaceborne imageries are explored [531]. Extending into UAV-based geophysical surveys, sensor integration, and data fusion continue to be of interest. Table 15 offers a comprehensive review of studies in this domain.

The sensor integration and data fusion solutions in UAV-borne geophysical survey that have been applied include photogrammetry-magnetometry [48,532,533], photogrammetry-GPR [407,534], magnetometry-GPR [535], photogrammetry-magnetometry-GPR [536], magnetometry-gravimetry [24], magnetometry-radiometry/spectrometry [172], radiometry/spectrometry-EM [346], magnetometry-EM [535,536], photogrammetry-LiDAR [507,537,538,539], photogrammetry-GPR [540], and integrated aerial photogrammetry and spaceborne RS [244,245,248,541,542,543,544]. Most of these sensor integration and data fusion approaches involve UAVs exclusively, though some cases integrate ground-based, aerial, and spaceborne data, resulting in complex multi-platform and multi-sensor data integration systems for geoscientific applications.

Table 15

Sensor integration and data fusion in UAV-borne geophysical survey.

Integration/Fusion Method	Descriptions	References
Fusion of UAV Images and Magnetic Data	Integrating magnetic data with RGB, MS, and HS images enhances mineral exploration efficiency. This fusion combines RGB photogrammetry for surface analysis, HS imaging for mineral signatures, and magnetometers for detecting magnetic minerals. Likewise, integrating MS photogrammetry with magnetometry and radiometry enables detailed geological mapping and mineralization modeling. This integration produces a realistic model of magnetic mineralization within its geological context.	[48,532,533]
Fusion of UAV Images and GPR Data	Integrating photogrammetry with GPR enhances quarry characterization and archaeological prospection. This fusion approach enables comprehensive subsurface investigation, aiding in identifying optimal areas for railway ballast production in quarries. Moreover, combining MS imagery and GPR survey facilitates precise archaeological anomaly detection and enables detailed 3D reconstruction, supporting interpretation in archaeological investigations.	[407,534]
Integration of UAV Magnetic and GPR Data	A UAV-based system combining GPR and magnetometer (MAG) for landmine detection was developed. Advanced methods like finite-difference time-domain simulations, SVD, Kirchhoff migration, and matched filtering were used for GPR signal identification and focusing. Magnetic dipole models with de-trending and spatial median filtering methods were employed for MAGs. Integration of the UAV GPR and MAG systems enabled experimental validation, crucial for parameter acquisition in landmine detection systems.	[545]
Integration of UAV Images with Magnetic and GPR Data	UAV images, magnetic, and GPR data were simultaneously surveyed at the Grumentum archaeological site. The integrated approach fused VNIR MS and infrared thermography with GPR and geomagnetic data, revealing Roman-era urban blocks and late antique/early medieval church features. The study underscores the potential and limitations of image fusion in enhancing archaeological insights, urging further experimentation across diverse case studies.	[546]
Integrated UAV Magnetic and Gravity Survey System	Integration of gravimetry with other methods is rare, but a system was developed involving the modification of a CH-4 medium-range drone. This work involved integrating a strapdown airborne gravimeter with a UAV-compatible aeromagnetic recorder, marking significant progress in this field.	[24]
Integration of UAV-borne Magnetic, Gamma Radiometric, and Spectrometric Surveys	The SibGIS UAS is a notable example of an integrated geophysical survey system, incorporating gamma radiometric, spectrometric, and magnetic surveys through integrated spectrometry-magnetometry systems. Experimental surveys demonstrate the feasibility of integrating gamma surveys with other geophysical surveys on a single UAV, offering rich information for geological and geophysical mapping.	[172]
Integration of UAV-borne Gamma and EM Survey Methods	TDEM offers promising capabilities to complement gamma surveys on UAVs. Lightweight TDEM systems can integrate seamlessly with gamma survey systems, enhancing geological information without a significant impact on productivity or costs.	[346]
Integration of UAV-borne Magnetic and EM Survey Methods	In the Smart Exploration initiative, SGU and Uppsala University developed two UAV-based systems to jointly measure the total magnetic field and EM signals. Tests showed high-quality data collection with a strong signal-to-noise ratio. SGU applies the systems in projects like the FUTURE project, mapping and modeling mineral resources.	[535]
Integration of UAV-borne Magnetometry System and Ground-based TDEM System	A joint detection system was introduced, integrating UAVMAG and TDEM-Cart for UXO detection. The approach fuses magnetic field and EM data, yielding accurate positioning and enhanced UXO detection. Successful detection of various targets was demonstrated in field tests, with improved efficiency in cued survey mode and positioning accuracy of <10 cm achieved in joint interpretation.	[536]
Fusion of UAV photogrammetry and TLS Data for Geophysical Applications	UAV photogrammetry and laser scanning data fusion enhance geological mapping precision. It addresses the limitations of laser scanners by merging UAV photogrammetry point clouds and filling blind spots. Researchers employ algorithms like ICP to merge and retain laser scanning precision. This method offers an approach for precise geological hazard assessment, yielding high-resolution DEMs for geomorphological studies.	[537,538]
Integration of UAV LiDARgrammetry and Photogrammetry for the Characterization of Ice Morphology Evolution	During consecutive Chinese Antarctic expeditions in 2017 and 2018, specialized UAV systems were used for glaciological investigations. The UAV-LiDAR system, named Polar Elf, characterized the spatiotemporal evolution of an ice doline using multi-temporal and multi-modal UAV RS, employing an analysis of DTM of Differences.	[507]
Fusion of UAV HS-LiDAR, UAV MS-photogrammetry, and Ground-based LiDAR-digital Photography for Soil Mapping	UAV RS accurately maps soil nutrients, detecting changes in rangelands. Combining multispectral imagery and photogrammetry achieved 95% accuracy in bare soil cover classification. Fusion with LiDAR improved classification to 87%, revealing carbon and nitrogen loss post-fire. Insights into post-fire plant-soil-nutrient interactions were gained, favoring grasses in shrub-affected rangelands, illuminating soil surface carbon and nutrient dynamics.	[539]
Integration of UAV Imaging (MS and Thermal) and GPR for SWC Estimation	UAV-based data enhanced SWC predictions using thousands of GPR-derived SWC measurements pre and post-precipitation events. The RF method predicted SWC in a central US vineyard employing MS and thermal UAV data. Combining thermal data with MS data notably improved SWC estimation accuracy, while reflectance data showed comparable significance to VIs.	[540]
Integration of UAV RGB, TIR, and MS Imageries for Biocrust Ecology Mapping	In Spain’s dryland environment, UAV imagery mapped biocrust distribution. RGB and MS imagery delineated terrain attributes and ecosystem components. Thermal infrared data correlated with soil moisture levels. Analysis linked biocrusts to terrain attributes, highlighting apparent thermal inertia, elevation, and potential solar incoming radiation as influencers. Integrated UAV RS enhances dryland ecosystem understanding.	[541]
Integration of UAV Magnetometry and LiDARgrammetry	Eagle Geosciences applied UAV surveys with magnetic and LiDAR technologies for geological and structural mapping in the Miakadow project. Integrated data identified structures and favorable contexts for lithium-bearing pegmatite formations, enhancing insights alongside magnetic survey results.	[483]
Fusion of UAV and Satellite Imageries for Geoscientific Applications	Integrated satellite and UAV data enhance understanding of natural Earth processes. Researchers combine diverse data sources, such as historic aerial photographs and modern satellite imagery, to study archaeological sites and historical land use patterns. Additionally, studies use integrated approaches like D-InSAR and UAV photogrammetry to map surface subsidence in mining areas, providing insights into deformation patterns and land subsidence.	[244,245,248,542]

4. Discussion

While our study initially focused on geophysical survey methods, we also observed a significant rise in the use of UAV-based RS methods for various geoscientific applications, including geological mapping. The number of research conducted in these areas has increased dramatically, leading to the emergence of numerous UAV-based geophysical RS systems worldwide, with their numbers continuing to grow. This growing interest can be attributed to the cost-efficiency and effectiveness of UAVs, sensors, and related devices, which strike a balance between traditional RS-based (satellite-based and manned aerial) geophysical methods and ground-based methods. Additionally, the unmanned nature of UAVs reduces risks and challenges associated with traditional surveying methods, making them a groundbreaking tool in geoscience and related disciplines, which usually deal with rough terrains.

Upon reviewing the literature, it became evident that UAV-based magnetometry and GPR survey methods are the most commonly utilized among standard geophysical survey techniques. In contrast, UAV-based gravimetry receives less attention due to the challenges associated with deploying gravimetric instruments on lightweight drones. Our observations also revealed a variety of options for UAV platform types suitable for unmanned aerial geophysical RS. Rotary-wing multi-rotor drones, known for their high maneuverability, are the most commonly used platform type, with unmanned helicopters showing similar applicability. Fixed-wing UAVs are better suited for surveying larger areas, and the addition of VTOL capability enhances their applicability in scenarios where traditional runways are unavailable. However, unmanned airships are not as favored as the other three drone types. Regarding sensors, magnetometry benefits from a wide variety of sensor types among standard geophysical methods, while photogrammetry, as a non-standard method, exhibits the most variability in this regard.

Discussions on applications revealed that mineral exploration, detection of near-surface ferrous objects (primarily in magnetometry and GPR), soil contamination mapping (mainly using gamma survey), and landslide/subsidence mapping (mainly in photogrammetry and LiDARgrammetry) were the most prevalent applications. While some methods, such as photogrammetry and LiDARgrammetry, were primarily used for spatial and geometric analysis of the Earth’s crust (e.g., deformation mapping), others, like MS imaging and HS spectroscopy, found various applications related to soil mapping and analysis. Interestingly, certain applications, such as mine or UXO detection, were shared between different geophysical methods, illustrating the versatility of UAV-based geophysical techniques.

Although UAV-based RS is unmanned, it does not eliminate the need for fieldwork entirely. While certain survey methods like GPR require less fieldwork, others such as spectrometry/radiometry (for collecting ground-truth samples for laboratory analysis) and photogrammetry (for establishing ground control points) necessitate more extensive fieldwork. Thus, despite advancements, fieldwork remains essential in many UAV-borne survey methods.

Given these ongoing requirements for fieldwork and other technical challenges, improving UAV-based RS analysis in geophysics and related fields requires attention to four key aspects: (1) Platform development—enhancing drone endurance and payload capacity; (2) Sensor advancement—developing lightweight, energy-efficient sensors with improved resolution; (3) Software optimization—updating processing capabilities to handle increasing data volumes efficiently; (4) Field validation—implementing more effective ground-truth data collection methods to ensure comprehensive coverage of surveyed areas.

The reviewed literature highlights significant attention to sensor integration in UAV-based geophysical RS. This integration involves deploying multiple geophysical sensors on a single or multiple UAV platforms. The benefits are evident, as it reduces costs, time, and human resources by enabling the collection of diverse data modalities in a single sortie. Notably, while individual sensors offer limited insights, combining data from multiple sensors can uncover novel perspectives not achievable with a single sensor. For instance, while an optical camera captures surface information, magnetometers delve into subsurface details, enhancing our understanding of the study area. However, sensor integration and data fusion pose several challenges, including differences in data modalities, acquisition time, misregistration, varying viewpoints, and spatial resolutions. These challenges become more pronounced when integrating UAV-based data with data from different sources such as space-borne, airborne, and ground-based platforms, as observed in our review. Consequently, careful consideration is essential when fusing different types of geophysical data to ensure accurate and meaningful results.

5. Conclusions

This study presents a comprehensive review of the cutting-edge geophysical survey techniques that use UAVs. To the best of our knowledge, this is the first review to systematically compile and evaluate these methods. The reviewed methods encompass traditional geophysical approaches such as magnetometry, gravimetry, EM survey, GPR, gamma spectrometry, and radiometry, as well as non-geophysical methods like photogrammetry and LiDARgrammetry. The collected papers were categorized based on sensor type (active or passive), sensor integration, and data fusion concepts in UAV-based geophysical surveys.

The reviewed studies demonstrate that UAV-borne geophysical RS methods deliver results comparable to traditional ground-based and aerial methods. UAVs, with their cost-effectiveness and unmanned operation, have revolutionized geoscience by bridging the gap between satellite-based, aerial, and ground-based geophysical techniques. The integration of UAV-based methods combines the strengths of both ground-based and traditional airborne or spaceborne RS approaches, efficiently meeting project requirements in terms of accuracy, speed, and cost-effectiveness. These methods provide a flexible and reliable solution for a wide range of geophysical surveys.

Despite significant progress, ongoing efforts are essential for further advancement in sensors, platforms, and methods. In terms of sensors, there is a need for more options capable of deployment on lightweight UAVs, like the variety available for traditional methods. Platforms are also evolving to become lighter and more endurance-focused, benefiting not only UAV-borne geophysical RS but also all domains of unmanned aerial RS. Methodologies require tailored development to suit UAV-specific requirements such as customized processing methods.

Looking ahead, we note that research should focus on several areas: developing robust methods for multi-source and multi-modal data fusion from different UAV-based sensors, implementing advanced artificial intelligence (AI) and DL algorithms for automated data analysis, and exploring Internet of Things (IoT) integration for real-time data collection and processing. These technological advances would further enhance the integration capabilities of UAV-based geophysical surveys.

The contribution of this review study extends to researchers across geoscience disciplines, providing a comprehensive and systematic overview of the possibilities offered by UAV RS in geophysical surveying. For instance, it aids researchers in selecting suitable methods, sensors, and UAV platforms for their desired applications. Overall, this review acts as a valuable resource for the geoscience community.

Author Contributions

Conceptualization, F.S., F.D.J. and A.T.; methodology, F.S., F.D.J. and A.T.; software, A.T.; investigation, A.T.; data curation, A.T.; writing—original draft preparation, A.T.; writing—review and editing, F.S., F.D.J. and M.v.d.M.; visualization, A.T.; supervision, F.S. and F.D.J.; project administration, F.S.; funding acquisition, F.D.J. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. A review of the collected publications by the following categories: (a) Year; (b) Type; (c) Subject area; (d) Country (first author); (e) Affiliation (first author); (f) Source (Source: Scopus).

Figure 2. Refinement of studies based on the PRISMA workflow.

Figure 3. Categorization of geophysical methods applicable to UAVs.

Figure 4. UAV-borne magnetometry system (A ground-based magnetometer is typically used in extended aerial operations where the diurnal variations of the Earth’s magnetic field are significant. The data from this base station is essential for modeling these variations and correcting the data captured by the aerial method).

Figure 5. Different arrangements for mounting magnetometers on UAVs: (a-i–a-iv) fixed-boom design for rotary-wings, fixed-wings, helicopters, and airships; (b-i–b-iv) towed sensor design for the mentioned UAV types; (c-i–c-iv) towed bird design for the mentioned UAV types; (d) fixed wing-tip design for fixed-wing UAV.

Figure 6. UAV-based gravimetry system (the scheme was depicted based on [24,126,127]).

Figure 7. Operational modes in UAV-borne gravimetry (R: flight rans, P: sampling points).

Figure 8. The manner in which a UAV GRS system operates (depicted based on the outputs of [162]).

Figure 9. AEM survey—induced vs. measured magnetic fields (depicted based on [310,312]).

Figure 10. Single-drone and dual-drone configurations in UAV-borne EM: The EM primary field is produced using one of the following: (a) A compact mobile current loop transported by a UAV; (b) A large loop placed on the ground. In both scenarios, the EM response is detected with a receiver transported by another UAV (depicted based on [308]).

Figure 11. The processing chain of UAV-borne EM data (depicted based on [365]).

Figure 12. Payload assembly architectures in UAV-GPR systems: (a) Independent payload; (b) Integrated payload architectures. The principles were borrowed from [25,413], with the flowcharts being reconfigured.

Figure 13. Observation modes in UAV-borne GPR survey (reconfigured based on [25]).

Figure 14. Fully/semi-airborne UAV-GPR: (a,b) Fully airborne GPR using Tx and Rx onboard a single UAV operating in DLGPR and/or FLGPR modes; (c) Fully airborne GPR using double UAVs; (d) Semi-airborne scheme combining ground-based FLGPR and UAV-borne DLGPR (subfigure ‘d’ conceptualized from [415,424]).

Figure 15. UAV-GPR data processing workflow (reconfigured based on [25,425,426]).

Figure 16. UAV-borne GPR imaging problem (reconfigured based on [417,434]).

Table 1

Review of the cutting-edge UAV-compatible magnetometers (building on [23]).

Spc./Mag.	GSMP-35U/25U	MFAM	QTFM	MG01	G823A
Type	Potassium vapor	Cesium vapor	Rubidium vapor	3-axis solid state	Cesium vapor
Manufacturer	Gemsystems (GEM)	Geometrics	QuSpin Ltd.	UAV Navigation	Geometrics
Sensitivity	0.0002/0.022 nT @ 1 Hz	7 nT @ 1 Hz	1 nT @ 1 Hz	-	0.0004 nT @ 1 Hz
Resolution/Accuracy	0.0001 nT/±0.1 nT	0.0001 nT	-	27 μG/±1 G	-
Heading Error	±0.05 nT	±7.5 nT	±1.5 nT	-	±0.15 nT
Dynamic Range	15,000 to 120,000 nT	20,000 to 100,000 nT	1000 to 100,000 nT	±2 G	-
Gradient Tolerance	50,000 nT/m	-	10,000 nT/m	-	500 nT/in
Sampling Intervals	1, 2, 5, 10, 20 Hz	1000 Hz	400 Hz	5000 Hz	20 Hz
Temperature Operating Range	−40 to +55 °C	−35 to +50 °C	−30 to +60 °C	−40 to +85 °C	−35 to +50 °C
Power Consumption	12 W	1–2 W	2 W	0.5 W	24–32 W
Head/Control box dimension	16.1 × 6.4/23.6 × 5.6 × 3.9 cm	3.3 × 2.5 × 3.2/12 × 5.2 × 2.2 cm	1.9 × 1.9 × 4.7/1.9 × 3.5 × 8.9 cm	-	6 × 14.6/51 × 51 × 53 cm
Weight	1 kg	0.23 kg	0.15 kg	0.15 kg	14 kg

Table 2

Review of UAV-borne magnetometry systems.

Sys.	Manufacturer	Platform Type	Magnetometer	Specifications	References
AirBird/GradBird	Geosystems (GEM)	Suitable for rotary-wing drones	Single/double sensor GSMP-35U/25U	Weight: 3.5 kg; Speed: >10 m/s; Endurance: 1.5 h; Tow cable length: 10 m; Sensor shell: fiberglass; Components: GPS, IMU, laser altimeter, data acquisition module, etc.	[46]
MagArrow	Geometrics	Any enterprise UAVs	Two MFAM sensors	Weight: >2 kg; Speed: 10 m/s; Endurance: 2 h; Positioning: from UAV’s GNSS; Sensor shell: carbon fiber; Components: GPS, IMU, etc.; Sampling rate: every 1 cm.	[47]
MagDrone	Geometrics	Theolog Tho-R-PX8-12 octocopter	MFAM and fluxgate	Weight: 10 kg; Speed: 40 km/h; Endurance: 25 min; Positioning: onboard GPS; Sensor shell: fiberglass; Payload: 4.5 kg.	[42,48]
MG-1P	DJI, Lab of EM Radiation, and Sensing Tech.	Rotary-wing (octocopter) MG-1P	Cesium OPM (CAS-18-VL)	Total/take-off weights: 9.8/13.7 kg; Endurance: 20 min; Speed: 7 m/s; Payload: 10 kg; Outline dimension: 1.4 × 1.4 × 0.5 m.	[49]
CMAGTRES-S100	DJI	DJI M210 and Wind-4 rotary wing drones	Optically pumped scalar magnetometer	Weight: 6–7 kg; Survey speed: ~14 m/s; Sensor type: scalar total-field; Sampling rate: 10–20 Hz.	[42]
Geoscan-401	Geo Matching	Rotary-wing (quadcopter)	Quantum magnetometer	Speed: 50 km/h; Endurance: 40 min with a 2.5 kg load.	[50]
Tholeg tho-R-PX-12	Tholeg	Rotary-wing (octocopter)	Fluxgate	Endurance: 25 min with a 4.5 kg load; Max speed: 40 km/h.	[48]
3DR X8+	Not specified	Rotary-wing (octocopter)	Fluxgate	Weight: 2.56 kg; Endurance: 15 min with a 1 kg load; Max speed: 90 km/h.	[51]
S1000	DJI	Rotary-wing (multi-rotor)	Overhauser	Endurance: 20 min with a 2 kg load; Max speed: 64.8 km/h.	[52]
Matrice 600	DJI	Rotary-wing (multi-rotor)	MFAM	Endurance: 18 min with a 5.5 kg load; Max speed: 64.8 km/h.	[53]
Heavyweight	Not specified	Rotary-wing (hexacopter)	MMPOS-1 Quantum magnetometer	Take-off weight: 15 kg; Speed: 7–10 m/s.	[54]
Skylance 6200	Stratus Aeronautics	Rotary-wing (octocopter)	Cesium vapor magnetometer	Endurance: 30 min with a 5 kg load; Cruise speed: 37 km/h.	[67,94]
UAV-Mag	Pioneer Exploration	Rotary-wing (quadcopter)	GEM GSMP35-A	Sensitivity: 0.3 pT@1 Hz; Resolution: 0.0001 nT; Accuracy: ±0.1 nT; Sampling rate: 20 Hz.	[55]
IT180-120	Sterna	Mini multirotor	Not specified	Engine type: gasoline power	[56]
MD4-1000	Microdrones, Germany	Rotary-wing (quadcopter)	Fluxgate	Length: 1.03 m; Payload capacity: 1.2 kg; Endurance: 1 h with a 1 kg load.	[57]
Single/Dual Mag	Mobile Geophysical Technology (MGT)	Multi-rotor (hexacopter)	Fluxgate magnetometers	Endurance: 20 (15) min with Single (Dual) Mag Payloads; Speed: 15 m/s; Resolution: 10 pT; Sampling rate: 1, 10, 50, 100 Hz.	[58]
Wind 4 and Spreading Wings S900	DJI	Multi-rotor (hexacopter)	Potassium vapormagnetometer	Total weight: 3.3 kg; Endurance: 5–7 min with a 2 kg load; Speed: 57.6 m/s.	[59]
MG-1P	DJI	Multi-rotor (octacopter)	CAS-L3 Cesium OPM	Endurance: 20 min; Speed: ~43 km/h; Sensitivity: 0.6 pTrmsp Hz0.5@1 Hz.	[41]
MAG-DN20G4	Zhejiang Danian Tech. Co.	Multi-rotor	Fluxgate	Endurance: 25 min with a 7 kg load; Speed: 28.8 km/h.	[60]
UMT Cicada	Not specified	Multi-rotor (hexacopter)	Geometrics MFAM	Endurance: 1 h with a 2.5 kg load; Engine type: hybrid gas-electric.	[61,62]
Matrice M210	DJI	Multi-rotor (quadcopter)	Fluxgate	Endurance: 27 min; Platform weight: 2.3 kg; Payload weight: 0.484 kg; Speed: 61.2 km/h.	[63]
FY680	Tarot company	Multi-rotor (hexacopter)	Magneto-inductivemagnetometer	Platform weight: 0.6 kg; Endurance: 30 min; Speed: ~47 km/h; Type: carbon-fiber.	[36]
Mavic Pro2	DJI	Mini multi-rotor (quadcopter)	Geometrics QTFM	Endurance: 31 min; Speed: 72 km/h.	[64]
Phantom 4	DJI	Quadcopter	Fluxgate	Endurance: 28 min; Speed: 72 km/h.	[65]
GJI S900	Queen’s University	Multi-rotor	GSMP-35U	Endurance: 18 min; Payload: 2.2 kg.	[66]
GeoRanger	Fugro/CGG, the Netherland	Fixed-wing (GeoRangerTM)	Cesium vapor magnetometer	Endurance: 15 h; Cruise speed: 75 km/h; Max payload: 5.4 kg.	[23,67,68]
AeroVision	Abitibi Geophysics and GEM Systems	Fixed-wing (AeroVision)	Cesium vapor magnetometer	Endurance: 10 h; Cruise speed: 120 km/h; Max payload: 8.2 kg.	[67,69]
Venturer	Stratus Aeronautics Inc.	Fixed-wing (Venturer)	Fluxgate and two Geometrics G-823A cesium vapor magnetometers	Endurance: 9–10 h; Speed: 95 km/h; Sensor shell: carbon fiber and fiberglass; Engine: gasoline-powered; Components: IMU, DGPS, altimeter, autopilot, etc.; Payload: 8.2 kg.	[67]
ScanEagle	Insitu & Boeing	Fixed-wing drone	OP magnetometer	Weight: 12 kg; Wingspan: 3.1 m; Length: 1.2; Engine: fuel-based; Endurance: ~22 h; Components: GPS, gyros, accelerometers, magnetometer, etc.	[70]
Ant-Plane series	Not specified	Ant-Plane 1, 2, 3, 3-2, 3-4, 4-1, 5, 6-3 fixed wings	Magneto-resistantmagnetometer	Endurance: 1.5–10 h; Cruise speed: 70–150 km/h; Payloads: 0.8–2 kg.	[71,72]
Prion	Magsurvey, UK	Fixed wing	G822 cesium vapor	Cruise speed: 90 km/h; Payload: 9 kg.	[73]
GeoSurv II	Sander Geophysics & Carleton University	Fixed-wing	Cesium G822A and fluxgate magnetometers	Endurance: 8 h; Cruise speed: 111 km/h; Payload: 9.1 kg.	[37]
SIERRA	NASA	Fixed-wing	Cesium vapor sensor	Endurance: 8 h; Speed: 117 km/h; Payload: ~28 kg.	[74]
Cai Hong-3 (CH-3)	IGGE & CAAA *	Fixed-wing	CS-VL cesium vapor sensor	Endurance: 10 h; Cruise speed: 180 km/h; Payload: 145 kg.	[75]
Albatros VT2	Radai Oy, Finland	Radai Albatros VT fixed wing	Fluxgate	Take-off weight: 5 kg; Endurance: 3 h; Speed: 50–110 km/h; Resolution: 0.5 nT; Sampling range: ±${10}^5$ nT; Engine: electric-1000 W; Payload: 2 kg.	[48,76,77]
Cai Hong-4 (CH-4)	Chinese CAAA	Fixed-wing	Cesium fluxgate sensor	Endurance: 12 h; Cruise speed: 150 km/h; Payload: 345 kg.	[78]
MONARCH	GEM Systems	CTOL/VTOL fixed-wing	GSMP-35U/25U potassium magneto/gradio-meters	Endurance: 1.5-h range with 70 km/h cruise speed; Cruise speed: 70 km/h; Payloads: 4 kg.	[79]
Skywalker X8	Skywalker	VTOL fixed-wing	3-axis Fluxgate (FGM3D100 sensor)	Weight of magnetometer: 0.18 kg; Endurance: 25 min; Air speed: 65–70 km/h; Flying altitude: 200 m; Sampling rate: 10–20 Hz.	[80]
Brican TD100	Brican	VTOL fixed-wing	MAD-XR sensor unit (a 3-axis vector and three scalar magnetometers)	Engine type: electric motor; Max payload: 8.2 kg; Max flight altitude: 91 m.	[81,82]
Nebula N1	Nebula UAV Systems	VTOL fixed-wing	Not specified	Cruise Speed: 50 km/h; Engine type: electric motor.	[81]
JOUAV CW-25E	JOUAV UAS	VTOL fixed-wing	Rubidium and Cesium OP magnetometers	Endurance: 4 h (240 km); Engine: electric motor; Cruise speed: 20 m/s; Payload: 4 kg.	[83]
RMAX-G1	Japanese Yamaha-Motor Co.	Unmanned helicopter	Cesium OPM	Endurance: 1.5 h; Max speed: 20 m/s; Max payload: 10 kg; Platform’s total weight: 1.2 kg; Towing cable length: 4.5 m.	[84,85]
V750	Weifang Freesky Aviation Ind. Co.	Unmanned helicopter	Helium OPM and fluxgate magnetometers	Endurance: >4 h; Payload: 80 kg; Overall length: 6.6 m.	[86]
Z3	Nanjing Research Inst. on Simulation Technique	Unmanned helicopter	Helium OPM and fluxgate magnetometers	Payload weight: 25 kg; On-load endurance: ≥1.5 h; Overall length: 2.7 m.	[86]
Scout B1-100	Aeroscout, Switzerland	Unmanned helicopter	Fluxgate	Endurance: 1.5 h (with 10 L of fuel); Max speed: 110 km/h; Payload: 18 kg.	[57,87]
GEM Hawk	GEM Systems	Unmanned helicopter	Potassium magnetometers (GEM Airbird)	Takeoff weight: 16.4 kg; Endurance: 50 min; Speed: 50 km/h; Payload: 4 kg; Resolution: 0.0001 nT; Sensitivity: 0.0003 nT@ 1 Hz.	[88]
AutoCopter (XL), Bergen, & RaptorCam	INEEL	Unmanned helicopter	G823A magnetometer	Endurances: 35, 30, and 20 min; Payloads: 6.8, 4.5, and 0.9 kg; Engine types: 120 cc Gas, 28 cc Gas, and 8 cc Nitro.	[89]
Maxi-Joker	DJI	Unmanned helicopter	G823A magnetometer	Endurance: 15 min; Payload: 4 kg.	[90]
SICX-12 Mongoose	Not specified	Unmanned helicopter	G823A magnetometer	No information was released.	[90]
WH-110A	China	Unmanned helicopter	CS-VL cesium and fluxgate magnetometers	Endurance: 3 h; Speed: 60 km/h; Payload: 35 kg.	[91,92]
Unmanned flying object (UFO)-H	China	Unmanned helicopter	Cesium fluxgate magnetometer	Endurance: 180 min; Speed: 43 km/h; Payload: 35 kg.	[93]
SU-H2M	China	Unmanned helicopter	Potassium (GSMPc35U) and fluxgate (TFM100-G2) magnetometers	Endurance and battery life: 3 h; Speed: 60 km/h; Payload: 45 kg; Engine type: oil-powered.	[40]

* IGGE: Institute of Geophysical and Geochemical Exploration; CAAA: Chinese Academy of Aerospace Aerodynamics.

Table 3

Review of UAV-borne magnetometry applications.

Application/Aim of Study	Platform Type	UAV Name/Model	Magnetic Sensor(s)	References
Offshore geophysical surveying	Fixed-wing	GeoRanger	Cesium vapor	[121]
Beach-shallow sea transition area magnetic surveying	Unmanned helicopter	Z3 and V750	Helium OPM and fluxgate	[86]
UAV magnetometry feasibility study	Unmanned helicopter	RMAX, AutoCopter, Bergen R/C, and RaptorCam	Geometrics G823A	[89]
Geomagnetic field variations mapping	Fixed-wing	GeoRanger	Cesium vapor	[122]
Volcanology using UAV magnetometry	Unmanned helicopter	RMAX-G1 (in [84,85])	Cesium OPM (in [84,85])	[84,85,113]
Volcanology (assessing geohazards associated with volcanic activity)	Multi-rotor	DJI Mavic 2	QTFM	[64]
UAV magnetometry for antarctic studies	Fixed-wing	The Ant-Plane generation (e.g., Ant-Plane 6-3)	Magneto-resistant and fluxgate	[71,72]
Geophysical fault mapping	Unmanned helicopter	Bell 206B3 helicopter	Cesium OPM	[112]
Geophysical exploration	Fixed-wing	SIERRA	Cesium vapor	[74]
Geophysical/archeological exploration and UXO/pipeline detection	Fixed-wing and multi-rotor	Single/Dual Mag	Fluxgate	MGT
Anti-submarine warfare system	Unmanned helicopter	MQ-8 Fire Scout and Brican TD100	Not specified	[81]
Integrated geophysical survey	Fixed-wing	CH-3	Cesium vapor	[75]
UAV magnetometry for general purpose	Fixed-wing	Venturer	Cesium vapor	[102]
UAV magnetometry for general purpose	Multi-rotor	3DR X8+	fluxgate	[51]
UAV magnetometry for general purpose	Unmanned helicopter	Scout B1-100	Fluxgate	[87]
UAV magnetometry for general purpose	Fixed-wing	GeoSurv II	Cesium vapor and fluxgate	[37,117,118]
UAV magnetometry for general purpose	Multi-rotor	Hexacopter	Fluxgate	[119]
Investigate mineral prospects, delineate UXOs, and survey archaeological sites	Fixed-wing UAV	The MONARCH	Potassium vapor	GEM Systems
Low-altitude geophysical magnetic prospecting	Multi-rotor	Heavyweight	Quantum Overhauser	[54]
Geophysical exploration	Unmanned helicopter	WH-110A and UFO-H	Cesium OPM and Fluxgate	[91,92,93]
Aeromagnetic survey and assessing the magnetization of a dipole	Multi-rotor	STERNA and IT180-120	Not specified	[120]
Gas and oil infrastructure mapping	Multi-rotor	Octocopter	Cesium and Rubidium vapor	[114]
Orphaned gas and oil wells locating	Multi-rotor	UMT Cicada and DJI Matrice 600	MFAM	[53,61,115,116]
Subsurface geophysical exploration	Multi-rotor	DJI Matrice 600 Pro	Fluxgate	[123]
Aeromagnetic mapping of regional scale	Multi-rotor	DJI M210	Fluxgate	[63]
Mapping geological and geophysical features of surface outcrops	Fixed-wing	Albatros VT2	Fluxgate	[48]
Flight safety test and data acquisition	Fixed-wing	CH-4	Fluxgate and Cesium vapor	[78]
Planetary exploration	Multi-rotor	DJI Matrice 600 Pro	Vector magnetometer	[124]
Archeological survey	Multi-rotor	DJI Phantom 4 and S1000+	Fluxgate and Cesium vapor	[65,109]
Mineral exploration/mining applications	Multi-rotor	DJI S1000, S900, and Matrice 600 Pro	Overhauser and Potassium vapor (e.g., GSMP-35U)	[52,59,66,108]
Mineral exploration	Multi-rotor and fixed-wing	SkyLance, Venturer, and The Prion	Cesium vapor	[67,73,94]
Mineral exploration	Multi-rotor	Geoscan 401	Quantum magnetometer	[50]
Mineral exploration	Multi-rotor	Tholeg and MAG-DN20G4	Fluxgate	[48,60]
Mineral exploration	Unmanned helicopter	SU-H2M	Potassium OPM (GSMPc35U) and fluxgate (TFM100-G2)	[40]
Mineral exploration	Multi-rotor	FY680	Magneto-inductive	[36]
Mineral exploration	Multi-rotor	DJI M210	Scalar magnetometer	[104]
Mineral exploration	VTOL fixed-wing	Not specified	GSMP-35U Potassium, GSM-19 Overhauser	[105]
Mineral (Chromite) exploration	Multi-rotor	Pioneer UAV-MAG	Potassium vapor	[55,106]
Mineral (Gold) exploration	Multi-rotor	SkyLance 6200	Cesium vapor	[107]
Target detection and identification	Fixed-wing	GeoRanger	Not specified	[68]
Near-surface target detection	Multi-rotor	DJI MG-1P octocopter	Cesium OPM (CAS-18-VL)	[49]
Near-surface ferrous objects (e.g., ordnance) detection	Unmanned helicopter and multi-rotor	Scout B1-100 and MD4-1000	Fluxgate	[57]
UXO detection	Unmanned helicopter	Maxi-Jocker and Mongoose	Geometrics G823A	[90]
UXO detection	Multi-rotor	DJI MG-1P	Cesium OPMs	[41]
UXO detection	Multi-rotor	An Octocopter	Fluxgate	[110]
UXO detection	Multi-rotor	DJI Wind 4 quadcopter	QTFM	[111]

Table 4

Review of state-of-the-art UAV-borne gravimetry systems.

Sys.	Manufacturer/Funder	Platform	Gravity Sensor	Specifications	Reference
1	Portuguese Ministry of Defence	CASA C212, Litton LN-200, and Crossbow AHRS440	Strapdown gravimeter	UAV power consumption: <3 W, Gravimetry system: Strapdown, Aim: developed for the PITVANT project.	[157]
2	Self-developed	Autonomous cruise-type unmanned helicopter	Not specified	Not specified	[150]
3	Geological Survey of Japan	Unmanned helicopter	Not specified	Not specified	[149]
4	Self-developed	Penguin-B miniature drone	Strapdown gravimeter	Engine: combustion, Wingspan: 3.3 m, Payload: 10 kg, Flight altitude: 4500 m, Endurance: 20 h, Cruise speed: 120 m/s, Max range: 1400 km.	[151]
5	University of Glasgow	A type of UAV	Miniaturized chip-based gravimeter	Not specified	[147]
6	Self-developed	VTOL unmanned helicopter	IMU iNAV RQH/RQT for navigation, coupled with GNSS receiver.	Resolution: 0.5 km, Accuracy: 4–11 mGal, Navigation modes: DGPS and PPP, Syetem name: INS/DGNSS UAV gravimeter	[136,152]
7	National Oceanic and Atmospheric Administration (NOAA)	Aurora Centaur OPA fixed-wing UAV	Micro-g LaCoste TAGS-7 gravimeter	Control type: optionally piloted aircraft, endurance: 16 h at 25,000 ft.	[148]
8	Self-developed	Long-endurance Boreal drone	Gravimeter and GNSS antenna	Weight: 20 kg, Endurance: 10 h, Stabilization: robust to flight turbulent conditions	[153]
9	UK-funded project	Fixed-wing Prion Mk3	Not specified	Endurance: ~2 h, Payload: 15 kg, Cruising speed for surveying: 80 km/h, Note: BP proof of principle demonstrated its feasibility.	[158]
10	University of Glasgow	A drone with an isolation platform and active stabilization	Wee-g MEMs gravimeter	Not specified	[138]
11	National University of Defense Technology (NUDT), China	CH-4 medium-range fixed-wing UAV	SGA-WZ04 strapdown gravimeter	Endurance: 21 h, Range: 2712 km, Gravimeter weight: <50 kg, Max. takeoff weight: 1330 kg, Gravimetry accuracy: >0.6 mGal	[24]
12	Project team: UAVE, DTU, and iMAR’s joint UAV gravimetry systemSponsor: bp and New Resolution Geophysics (NRG)	The long-endurance Prion Mk3 fixed-wing UAV	iMAR’s iCORUS SISG	Gravimeter weight: 6.8 kg, Endurance: 2.5 h, UAV Dimension (length×wingspan): 4 × 3 m	[135]
13	Self-developed	A type of UAV	Strapdown gravimeter	Max. accuracy: 0.47 mGal, Configuration: strapdown	[154]
14	The Russian Helicopters holding (the Rostec State Corporation)	The unmanned helicopter-type BAS-200	A modern 31 kg UAV-borne gravimeter	Payload: 50 kg, Endurance: 4 h, Flight altitude: 3900 m, Dim.: 3.9 × 1.2 m, Range: 100 km.	[155,156]
15	EIT Raw Materials, Geological Survey of Finland, RADAI Oy, Technical University of Denmark, and Beak Consultants GmbH	Long-range fixed-wing drone	UAV-borne gravity and EM sensors	The system was used for Drone Geophysics and Self-Organizing Maps (DroneSOM) project	[159,160,161]

Table 5

Review of UAV-borne gravimetry applications.

Aim of the Study/Application	Platform	Gravity Sensor	Reference
System R&D: Assessment of affordable IMUs for UAV-based gravimetry to estimate gravity disturbances	UAVs developed within PITVANT and different regular aircraft (CASA C212, Litton LN-200, and Crossbow AHRS440)	Strapdown gravimeter	[157]
System R&D: Development of UAV gravimetry system	Unmanned helicopter	A type of drone-deployed gravimeter	[149]
System R&D: Developing a drone-borne gravimeter for geophysics surveying purposes	Not specified (any kind of drone can be utilized).	Miniaturized chip-based gravimeter	[147]
System R&D: Analyzing the performance of the UAV-based vector gravimetry system by surveying the gravity disturbance vector	Unmanned helicopter	A navigation grade IMU iNAV-RQH/RQT and a GNSS receiver	[152]
System R&D: Developing an INS/GNSS UAV-based vector gravimetry system	Unmanned helicopter	iMAR iNAV-RQH and NovAtel GNSS receiver	[136]
System R&D	Long-endurance Boreal drone	Gravimeter and GNSS antenna	[153]
System R&D: Developing a miniature UAV-borne gravimetry system	A drone with an isolation platform and active stabilization	Wee-g MEMs gravimeter	[138]
System R&D: Developing a UAV-borne gravimetry system	CH-4 medium-range fixed-wing UAV	SGA-WZ04 stap-down gravimeter	[24]
Datum definition: NOAA’s Centaur program for conducting gravimetry across the US and redefining the American vertical datum (GRAV-D)	Aurora Centaur OPA fixed-wing UAV	Micro-g LaCoste TAGS-7 gravimeter	[148]
Proposal for 100 km line survey of gravimeter/gradiometer on drone-based platforms	Fixed-wing Prion Mk3	A type of UAV-compatible gravimeter	[158]
Earthquake study: Quick survey of gravity and magnetic data for earthquake ground motion prediction	Autonomous cruise-type unmanned helicopter	A type of UAV-compatible gravimeter	[150]
Mineral exploration	Any possible type of UAV in continuous flight and grasshopper modes	Any possible type of UAV-borne gravimeter	[128]
System R&D: Flying a SISG device on a fixed-wing UAV with suitable endurance, less cost, and less carbon for commercial gravity data surveys	The long-endurance Prion Mk3 fixed-wing UAV	iMAR’s iCORUS strapdown inertial scalar gravimeter (SISG)	[135]
Error compensation based on the undulating flight in UAV gravimetry	A type of UAV	Strapdown gravimeter	[154]
Arctic research: Explorations of geophysics in the Arctic region, encompassing mineral, oil, and gas investigations.	The Russian unmanned helicopter-type BAS-200	A modern 31 kg UAV-borne gravimeter	[155,156]
DroneSOM: Using commercially available drones for the acquisition of gravity and EM data, followed by data interpretation using integrated modeling software.	Long-range fixed-wing drone	UAV-borne gravity and EM sensors	[159,160,161]

Table 6

Review of the most widely available UAV-compatible gamma-ray spectrometers.

Radiation Sensors	Specifications	References
Medusa MS Spectrometer Series	Medusa Radiometrics provides UAV-ready gamma spectrometers, like the MS-1000 for real-time analysis, MS-2000-CsI-MTS for vehicle mounting, MS-4000 for airborne mapping, and MS-700 series for on-foot or drone-based applications. The MS-350 ultralight detector serves for small-scale UAV surveys and handheld use.	[164,170,183]
Georadis D230A Spectrometer	This spectrometer, suitable for drone-based applications, serves multiple fields, including security, environmental monitoring, health protection, and exploration.	[176,184]
CeBr3 (and Twin NaI-CeBr3) Scintillation Detector	Medusa offers the CeBr3 scintillation detector for UAV applications, featuring a 3 × 6-inch crystal and 2048 spectral channels. They also provide a twin NaI-CeBr3 scintillation detector, with a NaI detector boasting a 3 × 3-inch crystal and a CeBr3 detector featuring a 2 × 2-inch crystal.	[181]
CsI(Tl) detector	The Hamamatsu C12137-01 CsI(Tl) scintillator and CsI 6.5/100 cm3 device are designed for drone-mounted radiometric and spectrometric surveys.	[172,194,195]
Cadmium Zinc Telluride (CdZnTe or CZT) Semiconductor Detector and GR1/-A Kromek Spectrometer	The CZT semiconductor radiation detector integrates seamlessly with UAVs, offering lightweight and low-power operation. The GR1-A CZT module by Kromek, designed for UAVs like multicopters, features a compact 1 cm3 CZT crystal, providing discrete gamma spectra data. It operates with low power consumption (~250 mW) and covers an energy range of 30–3000 KeV, ensuring versatile performance.	[169,173,195,196,197]
Cs2LiYCl6:Ce3+ (CLYC) Elpasolite scintillation sensor	A cylindrical CLYC sensor, measuring 2.54 cm × 2.54 cm, facilitated gamma-neutron sensing on a UAV platform. Emitting scintillation light in the 275–450 nm range, peaking at 370 nm, boasted a 95% 6Li isotope enrichment and operated sans cooling. The setup comprised a customized housing, super bialkali photomultiplier tube, compact digitizer, and high-voltage generator.	[173,198,199]
Geiger–Müller Tube Particle Counter	Geiger–Müller tube detectors are commonly used for drone-mounted radiation detection due to their simplicity and compatibility with digital systems. Although they lack energy measurement capabilities and may miss higher-level radiation events, they offer a solution for basic radiation detection tasks.	[200]

Table 7

Review of the state-of-the-art UAV-borne GRS systems.

Sys.	Specifications	Objectives	Reference
1	Platform: APID One unmanned helicopter; Engine type: petrol-powered; Rotor diameter: 3.3 m, Weight: 130 kg; Max take-off weight: 210 kg; Payload: 25 kg; Endurance: 4 h.Payload and sensors: A suite of three gamma spectrometers, crafted by Medusa Radiometrics, comprises the MS-2000 Agri detector, MS-1000 UAV-borne detector, and MS-350 ultralight UAV detector, securely housed in a dedicated container. These detectors feature scintillation crystals of varying sizes—2000 mL CsI(Na), 1000 mL CsI(Tl), and 350 mL CsI(Tl), respectively. The survey system seamlessly integrates GPS, LiDAR, and a barometer within its navigation modules to ensure precise 3D positioning of the measurements.	System development: Enhancing the efficiency of UAV-borne gamma spectrometers for geophysical applications.	[170]
2	Platform: RMAX G1 unmanned helicopter; Weight: 94 kg; Payload: 10 kg; Max speed: 72 km/hPayload and sensors: The setup includes three 38.1 × 38.1 mm LaBr3:Ce scintillation detectors, forming the Aerial Radiation Measurement System (ARMS), weighing about 6.5 kg. It features a DGPS module and a Multi-Channel Analyzer (MCA) to process pulse signals within the 0 to 3000 keV range across 1024 channels.	Nuclear emergency monitoring (the FDNPP case study).	[201]
3	Platform: SibGIS hexacopter; Flight speed during the survey: 5 m/sPayload and sensors: three types of payloads were used, including a gamma spectrometer with a CsI(Tl) detector, a superior crystal, and a PMT providing an energy resolution of 6% for 137Cs mapping. The spectrometer has 8096 ADC channels. The recording frequency of the spectra is 0.3 Hz.	Developing a triad of UAV-borne GRS-TDEM-Magnetic prospecting systems for geological mapping (blind ore deposits prospecting).	[202]
4	Platform: SibGIS UAS.Payload and sensors: Including three payloads: (1) A radiometer equipped with a CsI(Tl) crystal measuring 6.5 cm3 and a silicon photomultiplier (SiPM); (2) A spectrometer featuring a CsI(Na) crystal measuring 30 × 150 mm and a Hamamatsu 6095 photomultiplier; (3) A spectrometer equipped with a CsI(Tl) crystal measuring 40 × 80 mm and a SiPM, featuring a detector volume of approximately 100 cm3.	Comparative analysis of gamma spectrometry and radiometry using compact detectors at various altitudes and ground levels.	[172]
5	Platform: DJI-S1000 octocopterPayload and sensors: CZT GR1-A Kromek and Cs2LiYCl6:Ce3+ radiation sensor for gamma and neutron detection, gas sensors, thermal imaging camera, LiDAR system, RTK GPS module, SONAR sensor, manipulator, sampling equipment, and radio transceiver.	Creating an integrated sensor for UASs dedicated to remote monitoring of gamma and neutron radiation.	[173,199]
6	Platform: A hexacopter (the Kingfisher model from Robodrone Industries).Payload and Sensors: Equipped with a 1024-channel Georadis D230A gamma spectrometer, this setup uses two Bismuth Germanium Oxygen scintillation detectors with a volume of 103 cm3.	Evaluation of the D230A for the detection and localization of uranium anomaly.	[176]
7	Platform: DJI Spreading Wings S1000+.Payload and Sensors: Compact Compton camera with a wide field of view fisheye lens. The camera includes scatterer and absorber layers, both featuring Ce:Gd3(Al,Ga)5O12 (Ce:GAGG) scintillator arrays optically coupled to a multi-pixel photon counter array.	Identification of nuclear disaster-related contamination in residential areas, exemplified by the Fukushima Daiichi NPP case.	[203]
8	System’s name: Radai’s UAV-based radiometric measurement system.Platform: A custom-designed quad-copter drone (Terrain Scout 3.2)Payload and sensors: Georadis D230A digital spectrometer equipped with two sets of 1024 channels each for gamma radiation intensity measurement. Detectors include BGO and NAI/TI.	Using UAVs for radiometric surveys over the tailings of the deserted Rautuvaara iron mine to assess the feasibility of radiometric data collection.	[184]
9	Platform: A customized hexa-rotor aerial vehicle (Hexa XL, Mikrokopter)Payload and sensors: GR1 Kromek spectrometer, AR2500 LiDAR, and GPS module.	Creating a UAV-based system for rapid high-resolution evaluation of radionuclide contamination in radioactive incidents.	[169]
10	Platforms: Electric-powered multirotors such as Hexacopter V680, Quadcopter V650, Octocopter V1000, and Heavy Lift Quadcopter V690. Payload and sensors: The system incorporates detectors for beta radiation (electrons), gamma radiation (photons), and X-rays. Additionally, it features an air/gas sensor for collecting air quality data. Supplementary sensors include HD Video/DSLR/thermal cameras.System description: A wireless radiation monitoring system named Aretas Aerial-Live Actionable Data has been developed with the capability to remotely detect beta (electrons), gamma (photons), and X-ray radiations.	Monitoring NPP events/disasters using drones for radiation source detection and injured personnel location	[204]
11	System’s name: Radiation Monitoring System (RMS)Platform: DJI MATRICE–600Payload and sensors: The system comprises various modular components, such as the RMS-000 communication and control module, the RMS-WASP communication software, and three distinct sensor modules: RMS-001, RMS-002, and RMS-003. RMS-001 is dedicated to online measurements of the effective dose rate of gamma and beta radiation, RMS-002 serves as the air sampler module, and RMS-003 functions as the GPS tracker.	Monitoring radiations in the proximity of an NPP or any area where ionizing radiation sources may exist.	[205]
12	Platform: A hexapod-type dronePayload and sensors: CdZnTe semiconductor detector and a video cameraSystem description: The gamma monitoring system integrates a drone module and supplementary components attached to the drone. These modules incorporate lightweight radiation detection and position monitoring elements crafted to gather data on radiation levels along with the respective coordinates of the locations.	Environmental radionuclide surveillance	[182]
13	Platform: DJI M600 Pro UAVPayload and sensors: The instruments comprise the Medusa MS-1000 sensor, housing a 1-L NaI scintillation crystal and equipped with a GPS module.	Soil nuclide concentrations mapping	[183]
14	System’s name: RotorRADPayload and sensors: Specifications and technical attributes include a system mass of less than 15 kg, a dose rate range spanning 0.1 μSv/h to 100 mSv/h, gamma rays’ energy coverage from 20 keV to 3 MeV, energy resolution below 7% at 662 keV, a flight endurance of approximately 30 min, a maximum transmission distance of 5 km, and an operational temperature range from −20 to 40 °C.	Swiftly locating lost radioactive sources	[206]
15	The team of developers installed gamma radiation and gas sensors on a custom-built robotic fixed-wing unmanned aircraft named Chelidon and on multirotors known as Inspire drones.	Detection of gamma radiation and airborne pollutants in three dimensions.	[207]
16	System’s name: AARM—stands for “autonomous airborne radiation mapping”Platform: WingtraOne Gen One COTS fixed-wing VTOL drone.Payload and sensors: The instrumentation comprises a Hamamatsu C12137-01 CsI(Tl) scintillator and a Kromek GR-1 CZT semiconductor detector with volumes of 1 cm3 and 36.1 cm3, respectively, alongside an SF11/C LiDAR and a GNSS receiver.	Incorporating gamma spectrometry capability into the drone to map legacy uranium mine sites.	[195]
17	Platform: The Penguin C fixed-wing UAV.System description: Communication between UAV and GCS is established through long-range data transferring using a tracking antenna, providing an impressive range of 100 km and a data transfer rate of 12 Mbps. The UAV operates within an altitude range of 120–5000 m, maintaining a cruise speed between 19 and 22 m/s and reaching a maximum speed of 32 m/s.	Radiological monitoring—to identify and quantify releases or contamination in scenarios involving gamma-emitting nuclides.	[167]
18	System description: The Patria mini-UAV stands as a versatile modular multi-mission airborne RS system, proficient in executing a spectrum of tasks ranging from reconnaissance to the surveillance of radiological, biological, chemical, and nuclear elements.Unmanned systems configuration: The configuration comprises one to three UAVs, each equipped with a range of payload options. Additionally, the system includes a communication suite, a GCS featuring a laptop PC, a telescopic antenna mast, launching equipment, and a dedicated sampling unit.Payload and sensors: Using a handheld radiation detection device featuring a cylindrical CsI probe with a volume of 5 cm3, diameter of 13 mm, and length of 38 mm, complemented by a photodiode.	UAV-based remote radiation surveillance	[208]

Table 8

Review of UAV-borne GRS applications.

Applications	Descriptions	References
Precise soil mapping (for precision farming and related topics)	Agricultural field properties, like clay content and grain size, were mapped using drone-borne GRS with MS-1000 spectrometers mounted on a DJI M600 PRO drone. Results closely matched ground measurements, demonstrating UAV GRS’ effectiveness in predicting soil properties.	[162,163,164,170]
Soil texture and environmental contamination mapping	A DJI M600 multi-rotor drone with an MS-1000 mapping system assessed sediment contamination along Spittelwasser Creek floodplains. Results (Dioxin concentrations maps) informed basin-scale remediation decisions.	[162,168]
Contamination mapping and monitoring at critical sites—mapping mine tailings	A drone-mounted MS-1000 system mapped an inaccessible mine tailing area, replacing expensive helicopter surveys. Flying at 15 m, it identified a significant 238U hotspot above the tailings, challenging to detect with ground-based or higher-elevation helicopter surveys.	[163]
Contamination mapping and monitoring at critical sites— monitoring radioactive substances in industrial plants	UAV-borne GRS conducted at the Novellara landfill in Italy used a CdZnTe gamma detector to detect nuclear waste materials. Altitude trials confirmed no nuclear waste detection, with garbage shielding reducing background gamma radiation. The prototype’s effectiveness in localizing dispersed nuclear materials was validated through laboratory and operational tests involving an intense 192Ir nuclear source and the landfill scenario.	[197]
Contamination mapping and monitoring at critical sites— locating lost radioactive sources	A method for rapidly localizing lost radioactive sources was proposed using RotorRAD, a UAV-based radiation mapping/monitoring system. Upon detecting a radiation anomaly, the UAV surveys a selected square area for precise localization, calculating the actual source location in real time after completing the final hover.	[206]
Characterization and surveillance (exploration and monitoring) of Uranium Legacy Sites (ULSs)	In the DUB-GEM project, a UAV-borne GRS system with CeBr3 and NaI gamma spectrometers was integrated into a multi-rotor drone for prolonged surveillance of ULSs. Test flights over ULSs in Kyrgyzstan and Kazakhstan demonstrated satisfactory lateral resolution for risk assessments. UAV-borne GRS holds promise for nuclear emergency response and historical uranium mine exploration and monitoring.	[181,195,209]
Accurate mapping of radiation sources (gamma rays) and polluting gases	Practical systems were developed, using gamma detectors for localizing low radiation doses and generating gamma radiation maps. Gas sensors were utilized for visualizing pollutant distribution, finding primary applications in field scenarios for detecting low-activity gamma emitters, and analyzing emissions from industrial facilities.	[207]
Radiometric measurements for mining applications	The Rautuvaara mine near Hannukainen village, Finland, was subjected to a UAV-based radiation survey. The survey employed a quadcopter system, with measurements taken at heights of 2, 5, and 10 m AGL, employing a 50 m line spacing covering approximately 14.4 km in total.	[184]

Table 10

Review of UAV-borne EM applications.

Applications	Descriptions	References
Mapping structural discordance and tectonics	UAV-TEM mapped Eastern Siberia’s uranium region, overcoming terrain challenges. Surveying at 7.5 m/s and 40 m altitude, it covered 20 km in four hours, excluding transmitter setup. Control measurements followed opposite and orthogonal routes.	[346]
A drone-borne TEM survey over Lake Baikal and Uranium deposits	UAV-based TEM systems identified uranium ore-bearing strata in Bolshoe Goloustnoye, Lake Baikal. High-resistivity layers over the lake and deposit area indicated sediment deposits. Productive uranium ore deposits were reliably detected at depths of 120–170 m.	[372]
Detection of buried power cables and pipelines in Neuchatel, Switzerland	UAV-based VLF surveys identified a buried pipeline and power cable spaced 90 m apart. Anomalies were successfully detected using frequencies of 18.3 kHz and 23.4 kHz, which showed good agreement with results from the RMT approach.	[355,386]
A survey over a Transition Zone from Freshwater to Saltwater in Cuxhaven, Germany	UAS-VLF effectively mapped the freshwater-saltwater transition zone, showing conductivity shifts via transfer functions. Alignment with RMT data confirmed its efficacy.	[355]
Mapping soil resistivity and investigating buried vehicles	A drone system for EM mapping utilizes GPS, Wi-Fi, and ultrasonic sensors to control height, detect buried objects (e.g., vehicles), and map soil resistivity. It focuses on shallow subsurface resistivity surveys across large areas.	[347]
Landmine detection	A hexacopter-mounted EM sensor introduces a method for landmine detection, enhancing safety and efficiency in clearance operations by effectively locating landmines in mined areas.	[373]
UXO detection	A drone-borne TEM system was developed for UXO and ground fissure detection. It used compact coils for ATEM data collection, offering efficiency and safety in challenging terrains. The system proved effective in detecting near-surface UXO.	[364]
Investigation of slope subsurface resistivity structure	The D-GREATEM drone system mapped a steep slope, revealing shallow, intermediate, and deep resistivity layers. This validated the effectiveness of drone-borne EM surveys in mapping slope resistivity structures.	[374]
Detection of underground tunnels and buried wires	In a lecture note on UAV applications in resource exploration, a drone-mounted EM system was studied for detecting underground tunnels and buried wires. The setup included a sensing coil towed by a hexacopter.	[73]
Fresh–saline water mapping	A Netherlands site near Gouda was surveyed for brackish groundwater using a UAV equipped with a CMD MiniExplorer on a DJI Matrice 600. Within four hours, it generated a 3D resistivity model, shedding light on fresh–saline water interactions.	[365]
Sand–clay lithology mapping	A UAV-EM system was used to map diverse lithology along the southern levee of the Lek riverside in Vianen, Netherlands, outpacing ground-based FDEM mapping by 2–4 times and successfully identifying distinct lithological units.	[365]
Cable, pipeline, and fence crossings	A UAV-EM system, using GEM-2 with DJI Matrice 600, validated in Vianen, Netherlands, revealed line objects with clarity through multiple profiles and a single grid survey.	[365]
Deep resistivity distribution mapping	The “grounded electrical-source airborne transient EM system (GREATEM)” was introduced for resistivity distribution assessments at deep levels. It uses a grounded wire as a transmitter on the ground and a receiver coil suspended from a drone.	[347,388]
Tunnel investigation	A novel semi-airborne method for tunnel exploration was introduced, utilizing a UAV-based SATEM system with a grounded-wire source and an induction coil carried by a UAV. Its efficacy was validated at the Damo Tunnel in Guangxi, China.	[387]
Subsurface target detection	A hexacopter-based TDEM survey, combined with YOLOv8, was utilized to identify anomalous regions for subsurface target detection.	[10]

Table 11

Review of cutting-edge UAV-compatible GPR antennas.

Antenna	Specifications	References
Vivaldi Antennas	Vivaldi antennas, known for wide bandwidth and directional radiation, are popular in UAV applications due to their compact design and high performance. They come in two types: horn and planar. While horn antennas offer excellent radiofrequency characteristics, planar Vivaldi antennas are smaller and more suitable for UAV integration. Common models include IS-AV-0106G, TSA-600, and TC930-83 (dual-polarized Vivaldi), which provide versatility for different UAV-GPR applications.	[25,405,414,448,451]
Helix Antenna	Traditional cavity-backed antennas, like sinuous and helix types, offer high directivity and bandwidth but are limited by their weight, often >1 kg, making them less suitable for airborne GPR systems. Recent studies have addressed this issue by employing miniature helix antennas mounted on lightweight rotary-wing drones for UAV-based GPR surveys.	[25,413,451]
Spiral Antenna	A miniature spiral antenna has been successfully employed for GPR surveys in snow and ice. Archimedean spiral antennas, used in UAV systems with absorbing material, offer consistent gain and nearly frequency-independent input impedance. They may distort wideband signals, necessitating dechirping during post-processing to correct antenna group delay fluctuations across the frequency band.	[25,392]

Table 12

Review of the cutting-edge UAV-Radar/GPR systems.

Sys.	Platform	Specifications and Parameters	Purpose/Application	References
1	A drone non-specified type	Model/antenna: Linear array; Technology: Pulsed; Frequency: 100 MHz; Penetration ability: None	Environment monitoring	[395]
2	Small fixed-wing unmanned airplane (ARTINO)	Model/antenna: Linear array; Technology: FMCW; Frequency: Ka band; Measurement configuration: MIMO; Penetration ability: none	Environment monitoring	[396]
3	The NASA SIERRA UAS	Model/antenna: Patch array; Technology: FMCW (LFM-CW SAR system); Frequency: 80–200 MHz; Penetration depth: A few meters	Sea ice experiments/monitoring	[397]
4	Fixed-wing drone	Model/antenna: Log periodic; Technology: Pulsed; Frequency: 250–350 and 9400–9800 MHz; System type: InSAR; Penetration ability: None	Forest mapping and environmental monitoring	[398]
5	Fixed-wing drone	Model/antenna: Patch array; Technology: FMCW; Sensor: CW/FM SAR; Frequency: 5.3–9.65 GHz; Penetration ability: None	Environment monitoring	[399]
6	Quadcopter	Model/antenna: Horn and helix antennas; Technology: SFCW and Pulsed; Frequency: 350 MHz at 5 GHz; Penetration depth: A few cm for landmine and UXO detection task	Landmine and UXO detection; security and Earth observation	[400]
7	A mini multi-rotor UAV	Antenna: Two Logarithmic-periodic dipole antennas (LPDA) and one Raspberry Pi; Technology: Portable FMCW Radar; Frequency: 745 MHz with a bandwidth of 510 MHz; Penetration depth: <20 m; Processing technique: SAR; Flight height: 1.5 m	Archeological and geological applications	[452]
8	Rotary-wing hexacopter drone	Antanna: Vivaldi antipodal; Technology: Pulsed; Frequency: 1.5–6 GHz; Measurement config.: Bistatic config. with a 45° inclination; Penetration depth: <0.2 m; Radar technology: Bistatic SDR; Flight height: ∼0.5 m	Landmine and UXO detection	[401,449]
9	Self-assembled DJI F550 hexacopter	Antenna: LPDA (two log-periodic PCB antennas named Ramsey LPY26); Technology: Pulsed Pulson P440; Frequency: 3.1–4.8 GHz; Measurement config.: Quasi monostatic in DL mode; Penetration depth: Not specified	Archaeology and infrastructure monitoring	[390]
10	DJI Matrice 600 Pro hexacopter	Antenna: Horn (1 Tx and 2 Rx orthogonal arranged antennas); Technology: FMCW; Architecture: SLGPR; Frequency: 1–4 GHz; Measurement configuration: Bistatic or quasi-monostatic; Flight height: 3–4 m; Radius: 7.5 m; Penetration depth: objects buried at 5 cm depth; SAR processing: polarimetric CSAR; GPR payload: Independent	Several: Infrastructure inspection, archaeological surveys, geological surveys, landmine and UXO detection	[404,421,422,442]
11	Octocopter (Kraken)	Antenna: One Spiral (Tx) and two Vivaldi (Rx) antennas with orthogonal arrangement and DL mode; Technology: Pulsed; Frequency: 0.95–6 GHz (M-Sequence UWB Radar); Penetration depth: A few meters (up to 1.7 m)	Snow and ice monitoring (retrieval of snowpack properties)	[392]
12	Multicopter: X8 model, made of 8 motors and 4 arms	Antenna: Hybrid horn-dipole antenna in DL mode; Technology: SFCW Planar R60 VNA; Frequency: 0.25–2.8 and 0.5–0.7 GHz; Measurement config.: Monostatic SFCW; Penetration depth: operation from 10–20 cm depth in bare agricultural fields; Flight height: 1–5 m	Soil moisture measurement (mapping)	[403]
13	Rotary-wing drone	Antenna: Ultrahigh frequency-UWB Radar; Technology: FMCW; Frequency: 0.5–3 GHz; Penetration depth: Not specified	Buried IEDs (e.g., landmine) detection	[453]
14	DJI SpreadingWings S1000+ octocopter	Antenna: Helix with DL mode; Tech.: Pulsed (Pulson P410); Frequency: 3.1–4.8 GHz; Measurement config.: Quasi-monostatic; Penetration depth: <1.5 m; SAR processing: able (DAS); Flight height: ∼1.5 m	Landmine and UXO detection	[413]
15	DJI Matrice (M) 600 Pro	Frequency: 1.5 GHz; Survey velocity: 1.2 m/s; Flight height: ~1 m.	Snow hydrology	[454]
16	DJI Matrice 600 Pro hexacopter	Antenna: Hybrid Vivaldi-Horn antennas with DL mode; Technology: SFCW; Frequency: 0.55–2.7 GHz; Measurement config.: Bistatic or quasi-monostatic; Penetration depth: <0.5 m (objects in 0.2 m deep); Flight height: ≤0.5 m	Landmine detection	[405]
17	Octocopter	Antenna: UWB Vivaldi; Technology: SFCW; Frequency: 150–309 MHz; Penetration depth: <3 m	Buried object detection	[455]
18	Rotary-wing mini-UAV	Antenna: 1 Tx antenna and 3 Rx with DL mode; Technology: SFCW; Frequency: 0.5–2 GHz; SAR processing: available; Flight height: ∼1.5 m; Penetration depth: detection of objects 5–15 cm deep	Detection of buried objects (mines, explosive objects, and concealed targets)	[450]
19	Hexacopter	Antenn: Vivaldi patch antennas; Technology: FMCW; Frequency: 0.5–3 GHz; Architecture of Radar technology: SLGPR; Measurement config.: Bistatic or quasi-monostatic; SAR processing: available	Landmine detection	[391]
20	DJI Matrice 600 Pro hexacopter	Antenna: horn; Technology: FMCW; Frequency: 1–4 GHz; SAR processing: SLGPR-CSAR; Flight height and Radius: 2.5–5 m and 7.7 m; Penetration depth: <1 m	Landmine detection	[422]
21	Hexacopter	Frequency: 3.1–4.8 GHz; Observation mode: DLGPR; Technology: Pulsed; SAR processing: MT; Flight height: 7.6–10.5 m	Archaeologicalsurveys	[394,425]
22	Quadcopter (Cryocopter FOX)	Antenna: Dual Vivaldi; Configuration: DLGPR pseudo-random radar (1 Tx and 2 Rx); Frequency: 0.7–4.5 GHz; SAR processing: frequency-wavenumber for velocity estimation; Penetration depth: snow depth from 1.5 m to 5.5 m.	Snow and ice studies (snow water equivalent content measurement and snowpack properties retrieval)	[456,457]
23	Ground vehicles and UAVs	Configuration: semi-airborne (an FL transmitter mounted on a ground vehicle and a drone-borne DL receiver); Frequency: 3.5–5.5 GHz; Survey schemes: Multimonostatic, multistatic, and multi-bistatic	Landmine and IED detection	[415]
24	DJI S1000 octocopter	Sensor: UWB SDRadar; Tx/Rx antennas: UWB Vivaldi; Frequency: 0.6–6 GHz; Flight height: ~2 m	Landmine detection	[458]
25	DJI M600 hexacopter	System name: IGPR-30; Central frequency: 0.4 GHz; Penetration depth: able to detect ice thickness of 6 m; Flight endurance: 30 min	Revealing morphology dynamics of ice cover	[459]
26	Hexacopter	Antenna: Gekko-80; Central frequency: 80 MHz; Data processing unit: RTS1600; Flight height: ~1 m	Mapping inland water bathymetry	[460]
27	DJI Matrix 600 Pro hexacopter	Antenna: COBRA plug-in SE-150 monostatic antenna; Frequency: 0.5–260 MHz; Technology: DLGPR pulsed radar; Measurement config.: Monostatic; Flight height: 6 m; Penetration depth: <40 m; Vertical resolution: 0.27 m	Excavation area characterization	[407]
28	Quadcopter	Antenna: Horn; Technology: FMCW; Frequency: 5.4–6 MHz; System type: SAR; Penetration ability: None	A wide variety of applications	[461]
29	Unmanned helicopter	System name: SIR-3000 (GSSI); Antennas frequency: 400 MHz; Positioning devices: Onboard DGPS and Garmin handheld receiver	Feasibility test of UAV-based geophysical (EM and GPR) measurements	[462]
30	Hexacopter	Technology: SFCW (SDR-USRP); Frequency: 0.55–2.7 GHz (UWB principle); SAR processing: available	Anti-tank landmine detection	[463]
31	Hexacopter	System config.: array-based GPR SAR; Radar subsystem composition: UWB module with 1 Tx and 2 Rx, Frequency: 0.6–6 GHz	Enhanced buried threats (IEDs and landmines) detection	[464]
32	Hexacopter	Technology and architecture: DLGPR impulsed radar; SAR processing: available (PSM); Flight height: ∼1.5 m; Frequency: C-band (3.1–5.1 GHz); Range resolution: 7.5 cm	Non-destructive identification of buried objects, such as landmines	[402,439]
33	Hexacopter	Antenna: UWB Vivaldi; Config.: DL pseudo-random radar (1 Tx and 2 Rx); Frequency: 0.6–6 GHz; SAR processing: available; Flight height: 1.2–2.3 m; Penetration depth: 0.25–1.5 m	Landmine and IED detection	[414,426,451]
34	Hexacopter	A GPR drone (GPRD) system with independent design: drone + GPR module	Search and rescue (SaR)	[412]
35	DJI M600 hexacopter	Antenna: Drone it GmbH cylindrical-shape radar antenna; Central frequency: 80 MHz; Survey endurance: 15 min	Archaeological prospection	[465]
36	Hexacopter	Technology: SLGPR FMCW; SAR processing: CSAR; Frequency: 1–4 GHz; Flight height: 2–4 m in 40 cm steps; Radius: 7.5 m; Penetration depth: <0.4 m	Detection of snow avalanche victims	[423]
37	DJI Spreading Wings S1000+	Radar technology: M-sequence UWB radar; Frequency: 0.1–6 GHz; Antenna: 2 UWB Vivaldi or two log-periodic antennas; Measurement configuration: Quasi-monostatic	Landmine and IED detection	[429]
38	Venture VFF-H01	Radar technology: Pulsed K2 IDS; Carrier frequency:900 MHz; Antenna: Not specified; Measurement config.: Monostatic	Snow cover mapping	[406]
39	DJI Matrice 600/Pro	Radar technology: Pulsed Cobra Plug In GPR Cobra CBD Zond-12e; Frequency: 0.5–1000 MHz; Antenna: COBRA Plug-in SE-70 COBRA Plug-in SE-150 Cobra CBD 200/400/800; Measurement config.: Monostatic	A variety of potential applications	[417,466]
40	DJI Phantom 2	Radar technology: Pulsed PulsON P410; Frequency: 3.1–5.3 GHz; Antenna: Helix; Measurement configuration: Bistatic or quasi-monostatic in DL mode; Penetration ability: None	Radar imaging of the environment	[389]

Table 13

Review of UAV-borne GPR applications.

Application	Descriptions	References
Buried Threats Object (Landmines, IDEs, and UXOs) Detection	UAV technology advancements have revolutionized buried threat object detection, particularly in landmine detection systems, where safety is paramount. UAVs offer faster scanning, access to remote areas, and increased safety by avoiding ground contact. This progress has made UAV-GPR surveys a primary tool for detecting buried threat objects.	[391,400,401,402,405,413,414,415,421,422,426,442,449,450,451,453,455,458,464]
Snow and Ice Studies	In snow regions, UAV-GPR surveys prove valuable. Researchers in Quebec, Canada, used UWB radar-equipped UAVs for snowpack data collection during 2020–2021, enhancing safety and coverage. They achieved precise estimation of Snow Water Equivalent by integrating airborne snow density and depth measurements with UAV-mounted UWB pseudo-noise radar. Additionally, a UAV-GPR system demonstrated promising results in snow depth measurement quality, resolution, and accuracy.	[392,454,456,457]
Archaeological Mapping	UAV-GPR is widely used in archaeology for non-invasive surveys. Researchers employ drone-borne surveys, showcasing GPR’s detailed prospection capabilities. Despite shallow penetration depth, they achieve high resolution and develop imaging strategies using Mini-UAV sounders for robust 3D representations of investigated volumes.	[394,465]
Agricultural Applications	In precision farming, drones give detailed information about crops and soil but are more expensive than satellites. Research on GPR in farming and studies related to AI prepare the ground for combining GPR with drones in farming, showing great potential.	[467]
Soil Moisture Mapping	In Belgium’s loess belt, UAV GPR mapped soil moisture across three fields, employing full-wave inverse modeling. This generated high-resolution soil moisture maps aligned with topography and aerial observations, showcasing UAV GPR’s efficiency in rapid, precise soil moisture mapping for agriculture and environmental monitoring.	[403]
Bathymetry	UAV-GPR holds potential for inland water bathymetry, rivaling water-coupled GPR accuracy in Danish research. Despite constraints like minimum depth prerequisites (80–110 cm) and antenna height (~ 50 cm) above water, UAV-GPR surpassed sonar measurements in specific water body analyses.	[460]
SaR (e.g., victim detection)	A groundbreaking approach utilizes UAV-GPR for avalanche victim detection, eliminating the need for avalanche beacons. Operating as a Synthetic Aperture Radar (SAR) with FMCW modulation, the system was empirically validated in detecting buried mannequin torsos across varied snow conditions.	[423,468]

Table 14

Review of UAV-LiDAR geoscientific applications.

Application	Descriptions	References
Fine-detailed digital terrain modeling	UAV-LiDAR is crucial for generating detailed DEMs essential for landform research. Eagle Geosciences used UAV surveys integrating magnetic and LiDAR technologies in the Miakadow project, aiding geological and structural mapping. Despite challenges like noise filtering, UAVs offer cost-effective and detailed DEM generation, which is foundational for various geophysical applications, including morphometric analysis and geomorphological mapping.	[480,481,482,483,484,485]
Fault zone mapping	Near Burwash Landing, YT, UAV-LiDAR was used to map fault zones and assess geothermal potential adjacent to the Eastern Denali fault (EDF). The system generated 30 cm resolution bare-earth DTMs of EDF segments, surpassing the resolution and canopy penetration of photogrammetric DSMs and DTMs. An analysis revealed dextral offsets along the fault, with the geothermal drill site strategically positioned at a minor releasing bend.	[486]
Landslide mapping and monitoring	UAV-LiDAR plays a crucial role in landslide mapping and monitoring, particularly in hazardous or inaccessible terrains like Ystalyfera, Wales. In this project, the technology penetrated dense vegetation, enabling the creation of high-resolution DTMs for detailed analysis. Regular surveys facilitated the understanding of landslide dynamics, with results integrated into risk maps for informed decision-making by the local community.	[487,488,489,490,491,492,493,494,495]
Land subsidence and fissure mapping	UAV-LiDAR has become instrumental in monitoring subsidence. Studies have validated its accuracy and compared its performance against traditional methods. Techniques like Digital Subsidence Models (DSuMs) and algorithms such as Local Flat Point Extraction (LFPE) have improved subsidence monitoring in mining areas. UAV-LiDAR has also been used to map road subsidence, highlighting its versatility beyond mining contexts. These findings underscore its importance in environmental management and risk mitigation efforts.	[31,495,496,497,498,499,500]
Geological mapping—geological structure measurement	LiDAR enables precise measurement of geological structures, which is crucial for assessing hazards like rockfalls and pre-earthquake indicators. These structures, including folds and fault planes, influence slope stability and rock mass behavior. Traditionally, studying rock discontinuities required manual methods, limiting assessments in hazardous areas. Integration of UAV-LiDAR allows remote 3D investigation of slopes, facilitating detailed structural measurements. Recent studies highlight its efficacy in geological structure analysis.	[501,502,503,504,505]
Geological mapping—geological catalog production	Geological cataloging is vital in geological applications, including mapping, prospecting, and sampling. Laser scanners, especially when integrated with UAVs, are invaluable for creating detailed geological maps efficiently. They replace traditional surveying methods, reducing workload and enabling comprehensive database creation for mining areas.	[506]
Geological mapping—structural plane measurement	In geological surveys, assessing structural planes, especially extended faults, is challenging due to variations and topographical factors. Moreover, 3D laser scanning has emerged as a valuable solution. The associated software features a fitting plane tool that determines structural plane occurrences, overcoming limitations of single-point measurements with geological compasses. This approach yields satisfactory results in determining geological structures.	[506]
Glaciological investigations: characterization of ice morphology evolution	UAV-based technologies revolutionize the study of ice dolines, which are unique formations in remote ice streams. Researchers used specialized UAV systems to analyze the spatiotemporal evolution of an ice doline during Antarctic expeditions. They found that a collapse event in 2017 was induced by surface melting, with the doline growing in area and volume by early 2018. Photogrammetry proved cost-effective for large-scale surveys, while LiDAR excelled in detailing intricate ice features. They recommend an integrated approach for optimal performance.	[507]
Groundwater level mapping (hydrogeological studies)	In a geoscientific RS project, a UAV-LiDAR efficiently acquired piezometric information from traditional large-diameter wells. Tested in a coastal aquifer, it provided high vertical accuracies (RMSE of 5 cm), surpassing official DTMs in Spain. This method eliminated the need for laborious leveling work and proved effective for monitoring extensive or inaccessible areas, filling gaps in hydrogeological databases.	[508]
Topographic mapping for precision land leveling	An innovative method using a low-altitude UAV with LiDAR and PPK-GNSS technology mapped elevation variations on farmland in Henan Province, China. PPK-GNSS data ensured accurate ground survey point elevations, factoring installation height and nadir distance. Over 2300 sets of mapping data per field were interpolated, yielding precise topographic maps for precision land leveling.	[509]
Volcanological studies	UAV-LiDAR is revolutionizing volcano mapping by providing precise topographic data collection and overcoming obstacles like vegetation, gas emissions, or water bodies. This technology enhances RS capabilities, enabling comprehensive volcano studies.	[510]
Soil mapping—estimation of SOC	Integrated UAV LiDAR/HS enhances soil mapping in forests. Using 40 HS visible and 101 LiDAR-derived variables, the study selected robust variables with the RRelieff algorithm to estimate forest SOC. Effective vegetation indices (VIs) included carotenoid reflectance index 2, non-linear index, and carotenoid reflectance index 1, while optimal LiDAR features were the canopy height model and DEM. Combining VI and LiDAR variables significantly improved estimation accuracy, with LiDAR features outperforming VIs.	[511,512]

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs17010110/s1, The supplementary material is the PRISMA checklist.

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