The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application

Abstract

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R² with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications.

1. Introduction

Atmospheric aerosols refer to various solid and liquid particles with diameters ranging from 10⁻³ to 10² µm, forming a multiphase system that is stably mixed within the atmospheric medium [1,2]. These aerosols are categorized into natural and anthropogenic sources. Aerosols from anthropogenic sources, such as vehicle emissions and fossil fuel combustion, as well as from natural sources like dust storms and volcanic eruptions, can alter the distribution of atmospheric aerosols, leading to changes in aerosol concentration and potentially affecting chemical composition, size, and shape [3]. These changes may influence regional and global climate change and radiation budget [4,5]. Aerosols affect the radiation balance of the Earth–atmosphere system by absorbing and scattering solar short-wave radiation and terrestrial long-wave radiation. Additionally, as cloud condensation nuclei (CCN) or ice nuclei (IN), aerosols participate in cloud formation, influencing precipitation and thereby indirectly affecting climate [6,7,8,9]. In 2014, the Intergovernmental Panel on Climate Change (IPCC) in its Fifth Assessment Report explicitly identified aerosols as one of the most uncertain factors influencing climate change, with changes in aerosol distribution and properties potentially impacting the climate system [10]. An understanding of the physical and optical properties of aerosols is therefore crucial for studying their radiation effects and climate change.

Aerosol optical properties can be obtained through remote sensing. Passive remote sensing satellites can observe the horizontal distribution and transport of aerosols but cannot provide vertical information, which is crucial for studying radiation effects [11,12]. Lidar, an active remote sensing method, can obtain the three-dimensional distribution of aerosols. There are three main types of lidar observations: ground-based, airborne, and spaceborne. Ground-based and airborne observations can provide detailed aerosol optical properties within a specific region, but due to spatial limitations, they cannot conduct large-scale aerosol observations. Spaceborne observations, on the other hand, compensate for these limitations by offering high-precision aerosol optical parameters on a global scale [13]. As a highly representative spaceborne lidar, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), launched in April 2006, is equipped with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which features dual-wavelength polarization channels at 532 nm and 1064 nm. This instrument provides a global, high-temporal and -spatial resolution, and continuous day and night observation of the three-dimensional distribution of aerosol optical properties [14,15,16]. The CALIOP retrieval algorithm has undergone multiple optimizations and upgrades, and the scientific community has extensively compared its aerosol products with those from other instruments to evaluate the accuracy of its aerosol optical parameters. Aravindhavel et al. [17] compared the spatiotemporal matching data between CALIPSO and the MPL site at Kattankulathur from March 2016 to December 2018, finding that the average bias of Level 1 aerosol products below 3.0 km (and above 3–10 km) was −14 ± 29% (18 ± 19%). The mean bias of Level 2 aerosol profiles over the altitude range of 0.3–5 km was found to be 14 ± 28%, 15 ± 44%, −14 ± 25%, and 24 ± 34% during the winter, pre-monsoon, SW monsoon, and NE monsoon seasons, respectively. M. Nikolaos et al. [18] used the EARLINET, primarily based on multi-wavelength Raman lidar systems, to validate the CALIPSO Level 3 product, finding relative errors of 18% and 25% for the aerosol backscatter coefficient and extinction coefficient, respectively, indicating the good retrieval performance of CALIPSO. Kacenelenbogen et al. [19] validated the CALIPSO Level 2 aerosol extinction product through comparisons with multiple data sources, showing correlations of AOD with MODIS, POLDER, ZA airborne HSRL, and AERONET of 0.67, 0.58, 0.52, and 0.48–0.73, respectively. Additionally, CALIPSO data products have been widely used in aerosol science research. Xu et al. [20] utilized CALIPSO and MERRA-2 aerosol data products to analyze dust aerosols’ distribution and transport patterns over the Tibetan Plateau and the Taklamakan Desert. Zhang et al. [21] utilized CALIPSO satellite data, ground-based polarized lidar networks, and AGRI payload data from Fengyun satellites to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Lu et al. [22] utilized the observations from CALIPSO, MODIS, and reanalysis data to analyze the long-term three-dimensional distribution and transport of Saharan dust. CALIPSO data have profound significance for studying the impact of aerosols on global climate and radiation. However, the satellite was decommissioned in August 2023 due to fuel depletion. In April 2022, China launched the Atmospheric Environment Monitoring Satellite (AEMS), equipped with the Aerosol and Carbon Detection Lidar (ACDL). The hyperspectral lidar system included in ACDL has been operational in orbit for over two years, providing continuous and precise global aerosol profile distributions. Preliminary retrieval algorithms have been developed, and comparisons with multiple data sources have demonstrated high accuracy, with applications such as dust transport studies already conducted [23]. Liu et al. [24] compared the ACDL-retrieved observations with ground-based lidar measurements of atmospheric aerosol and cloud over northwest China from May to July 2022 using the Belt and Road lidar network (BR-lidarnet) and the CALIPSO observations to assess the performance of the newly launched satellite lidar.

On 4 August 2022, the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) was successfully launched from the Taiyuan Satellite Launch Center using the Long March 4B vehicle. The satellite’s primary payload, the Carbon Sinks and Aerosol LIDAR (CASAL), is designed to detect aerosol distribution [25]. To support refined atmospheric aerosol and environmental monitoring research, this study uniquely utilized the Level 2 (L2) on-orbit data from CASAL and applied the Fernald forward integration method to retrieve aerosol optical parameters for the first time. The accuracy of the retrieval results was verified through comparative analysis with multi-source data. Additionally, the retrieved aerosol optical parameters were used to analyze a typical dust transport event, marking the inaugural scientific application of these data. The results indicate that TECIS aerosol optical parameters can provide reliable research data in global aerosol and atmospheric environmental monitoring.

2. Materials and Methods

2.1. TECIS Retrieval Algorithm

CASAL, as a key payload of the TECIS, consists of two components: vegetation measurement and aerosol detection. The vegetation measurement component utilizes a five-beam 1064 nm laser, which can be used to retrieve vegetation biomass and carbon sinks. The aerosol detection part is equipped with a single-beam 532 nm and 1064 nm laser, capable of detecting aerosol vertical profiles and retrieving parameters such as the aerosol backscatter coefficient, extinction coefficient, and depolarization ratio, thereby obtaining information on aerosol vertical distribution and particle shape [26,27].

Table 1 summarizes the key technical specifications of CASAL for aerosol detection. The system is composed of two parts: emission and reception. The lidar aerosol detection subsystem operates in an atmospheric aerosol detection mode for the long-term monitoring of global land areas [28].

Figure 1 illustrates the main flowchart of the retrieval algorithm. The ERA5 and TECIS L2A datasets serve as the initial data for the algorithm. ERA5 temperature and pressure data are spatially and temporally matched with the TECIS trajectory and combined with the S6 molecular model to calculate the molecular backscattering coefficient and extinction coefficient [29]. The system-corrected attenuated backscatter L2A data undergo several preprocessing steps, including SNR control, pulse averaging, and moving average. First, the signal-to-noise ratio (SNR) is calculated as the ratio of the signal below 30 km to the average value of the signal above 30 km. This is because the atmosphere above 30 km is clean, and the signal at this altitude is dominated by system noise, so this portion of the echo signal is considered as system noise. An SNR threshold is set to filter out invalid or weak echo signals below the clouds. To improve the SNR of the raw data, 55 sets of pulse averaging are performed to achieve a horizontal resolution of 20 km, and the averaging of four sampling points within a single profile is conducted to achieve a vertical resolution of 60 m. The reference height is determined using the scattering ratio, and the Fernald algorithm with forward integration calculates the aerosol backscatter coefficient below the reference height. After obtaining the backscatter coefficient, the extinction coefficient is computed using the lidar ratios for different aerosol types, and the AOD is derived by integrating the extinction coefficient. The aerosol depolarization ratio is calculated using the data from both the parallel and perpendicular depolarization channels.

The atmospheric observation data products obtained by the CASAL system belong to L2 data products. These products provide the attenuated backscatter coefficient (unit: km⁻¹sr⁻¹) after system calibration and range correction. The corrected data are solely related to atmospheric properties, and the starting point of the retrieval algorithm is the attenuated backscatter coefficient, expressed as follows:

(1)$B\left(r\right)={\displaystyle \frac{2P(r)r^2}{P_0\eta cA}}=\left[{\beta }_m\left(r\right)+{\beta }_a(r)\right]exp\left\{-2{\int }_0^r\left[{\alpha }_m\left(r\right)+{\alpha }_a(r)\right]dr\right\}$

where $P(r)$ shows the raw signal received by the system, $r$ is the distance between the observation target and the lidar, $P_0$ is the energy emitted by the system, $\eta$ represents the total optical efficiency of the receiving system, $c$ is the speed of light, and A is the telescope receiving area. ${\beta }_m$ and ${\beta }_a$ represent the backscatter coefficients of atmospheric molecules and aerosols, respectively. ${\alpha }_m$ and ${\alpha }_a$ represent the extinction coefficients of atmospheric molecules and aerosols, respectively. The S6 molecular model is used to calculate the backscatter and extinction coefficients of atmospheric molecules. This model uses ERA5 data, spatially and temporally matched, along with temperature and pressure as inputs for direct computation [29].

This study employs the forward integration Fernald algorithm [30] to calculate the aerosol backscatter coefficient ${\beta }_a$ by Equation (2).

(2)$\begin{array}{c}{\beta }_a\left(r\right)\hfill \\ =-{\beta }_m\left(r\right)+{\displaystyle \frac{B(r)exp\left\{-2(S_a-S_m){\int }_{r_c}^r{\beta }_m(r)dr\right\}}{\frac{B(r_c)}{{\beta }_m\left(r_c\right)+{\beta }_a(r_c)}-2S_a{\int }_{r_c}^{r}B(r)exp\left\{-2(S_a-S_m){\int }_{r_c}^r{\beta }_m(r)dr\right\}}}\hfill \end{array}$

where $S_a$ and $S_m$ represent the extinction-to-backscatter ratios for aerosols and atmospheric molecules, respectively. The extinction-to-backscatter ratio for molecules is $8\mathsf{\pi }/3$. $r_c$ denotes the reference altitude, which should be chosen at a height where the atmosphere is the cleanest and the aerosol concentration is minimal [31]. To obtain an ideal reference altitude, the scattering ratio $R\left(r\right)$ is introduced as shown in Equation (3). Before retrieving each profile, the scattering ratio is calculated, and the height corresponding to the minimum scattering ratio is selected as the reference altitude to ensure that the atmosphere at the reference height is relatively clean.

(3)$R\left(r\right)={\displaystyle \frac{B(r)}{{\beta }_m(r)}}$

The extinction coefficient of atmospheric aerosols is expressed in Equation (4). The extinction coefficient is calculated based on the lidar ratios specific to different types of aerosols.

(4)${\alpha }_a\left(r\right)=-S_a{\beta }_a(r)$

The aerosol optical depth (AOD) is defined as follows:

(5)$\tau \left(r_0\right)={\int }_0^{r_0}{\alpha }_a(r)dr$

The ratio of perpendicular polarization to parallel polarization is the total atmospheric depolarization ratio $\delta \left(r\right)$, and the depolarization ratio of molecules ${\delta }_m(r)$ is calculated theoretically [32]. The expression for the aerosol depolarization ratio is expressed as follows [33]:

(6)${\delta }_p={\displaystyle \frac{{\beta }_m\left[\delta \left(r\right)-{\delta }_m(r)\right]+{\beta }_a(r)\delta (r)\left[1+{\delta }_m(r)\right]}{{\beta }_m\left(r\right)\left[{\delta }_m\left(r\right)-\delta \left(r\right)\right]+{\beta }_a(r)\left[1+{\delta }_m\left(r\right)\right]}}$

2.2. Other Data

2.2.1. CALIPSO

CALIPSO operates continuously day and night in a sun-synchronous polar orbit at an altitude of 705 km, with a repeat cycle of 16 days. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite is a dual-wavelength (532 nm and 1064 nm) polarization-sensitive lidar, capable of obtaining global vertical profiles of clouds and aerosols [34,35,36]. The comparison data used in this study include CALIPSO version V4.51 Level 1 532 nm attenuated backscatter coefficient and Level 2 aerosol profile products, including aerosol backscatter coefficient, extinction coefficient, and depolarization ratio data. The Level 1 product has a temporal resolution of 0.05 s and a vertical and horizontal resolution of 30 meters (0–8.2 km) and 333 meters, respectively. The Level 2 aerosol profile resolution has a temporal resolution of 5.92 s and a vertical and horizontal resolution of 60 m and 5 km, respectively.

2.2.2. AERONET

The global automatic observation network AERONET (AErosol RObotic NETwork) is a ground-based aerosol remote sensing network jointly established by NASA and LOA-PHOTONS (CNRS). The CIMEL CE318 sun photometer is the standardized instrument used across the network to measure aerosol characteristics. With over 500 sites in operation worldwide, AERONET provides AOD measurements at spectral channels of 340 nm, 380 nm, 440 nm, 500 nm, 675 nm, 870 nm, and 1020 nm [37]. AERONET’s measurement data are considered highly accurate, closely approximating true values, and widely used globally for satellite retrieval validation and bias correction [38,39]. The common uncertainty of its AOD is approximately 0.01 to 0.02 [40]. This study uses cloud-screened and quality-assured Level 2 AOD data.

2.2.3. MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS), a widely utilized multispectral sensor, is mounted on the Terra and Aqua satellites, launched on 18 December 1999 and 4 May 2002, respectively [41]. Due to its numerous spectral channels (36 wavelengths ranging from 0.41 to 14 µm) and high resolution, MODIS is capable of detecting AOD [42]. The latest Level-2 MODIS aerosol product collections, C5.1 and C6.1, employ both the Deep Blue (DB) and Dark Target (DT) algorithms [43]. The C6.1 data include aerosol products with 10 km and 3 km resolutions. Most studies have demonstrated that the 10 km resolution retrieval data exhibit reliable and good agreement with ground-based measurements [44,45]. Therefore, in this study, the 10 km resolution MOD04 C6.1 DT&DB AOD was selected to evaluate and validate the AOD retrieved by the TECIS.

2.2.4. Air Pollution Data

Ground-based hourly PM10 and PM2.5 monitoring data were obtained from the China National Environmental Monitoring Center. The PM10 and PM2.5 values for each station are hourly averaged data from all monitoring stations in the respective city. This paper uses the ground monitoring data of PM10 and PM2.5 to corroborate dust transport.

2.2.5. ERA5 Data

ERA5 data consist of the fifth-generation global atmospheric reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) for the Copernicus Climate Change Service (C3S) of the European Union. The data assimilate remote sensing data from various regions and sources worldwide, as well as conventional meteorological data from the surface and upper atmosphere. Covering the historical period from 1950 to the present, the data are updated in near real time with a data delay of about one month. ERA5 has been significantly upgraded from its predecessor, ERA-Interim, particularly in terms of improved spatial and temporal resolution. It offers hourly estimates of atmospheric variables from the surface to 0.01 hPa, using 137 model levels [46,47]. In this study, temperature and pressure data from 1000 hPa to 1 hPa, with a horizontal spatial resolution of 0.25°, were used to retrieve aerosol optical parameters.

2.3. Backward Trajectories

The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, developed by the National Oceanic and Atmospheric Administration (NOAA), is a widely used specialized model for calculating and analyzing the transport and dispersion trajectories of atmospheric pollutants [48]. It has been extensively applied in fields such as long-range air pollution transport, atmospheric research, and radiation assessment. This paper primarily utilizes the HYSPLIT model to simulate the 144 h backward trajectories of the dust transportation application in central China, analyzing the sources of polluted air masses at the altitudes of 1 k, 2 k, and 3 k in the central region.

3. Validation Results

3.1. Validation with CALIPSO

The Sahara Desert, one of the world’s largest deserts, is a major source of dust aerosols globally. Each year, dust and sandstorms in the Sahara generate a significant amount of aerosol particles. Research indicates that Saharan dust aerosols exhibit substantial spatial variation, with peak dust activity occurring in summer and spring. Dust is primarily transported towards Europe at high altitudes of 2 to 6 km [49].

This study compares data from CALIPSO and the TECIS for 7 April 2023, focusing on their near-coincident orbital paths, as shown in Figure 2. The orbits of the two satellites range from southern Algeria to Niger and Nigeria, with a spatial separation of 150 km. CALIPSO passed over this region at 03:00 UTC, while the TECIS passed over at 10:00 UTC, resulting in a time difference of approximately 7 h.

To verify the consistency of the raw data, Figure 3a,b show the total attenuated backscatter coefficients from the TECIS and CALIPSO. Extensive dust aerosol plumes are observed in the African region from 18°N to 4°N, primarily within altitudes of up to 4 km, with some areas reaching 5 km. The attenuated backscatter coefficients range from 10⁻³ to 10^−2.5 km⁻¹sr⁻¹. The TECIS detected cirrus clouds at approximately 15 km altitude, with some parts of the laser echo signal not penetrating the cloud layers. Due to temporal and spatial differences, CALIPSO observed cloud heights below 15 km. North of 20°N, both satellites observed cirrus clouds between 5 km and 10 km, where the laser echo signals did not penetrate the clouds. These differences in cloud observations are attributed to the temporal and spatial variations between the two satellite overpasses. Local variations in the high-altitude atmosphere are minimal, allowing for an easier distinction of system noise. High-altitude echo signals are utilized as noise references, with the SNR defined as the ratio between echo signals and noise. The SNR of the attenuated backscatter signals was evaluated to assess signal quality (Figure 4a,b). The TECIS has a vertical resolution of 60 m, whereas CALIPSO has a vertical resolution of 30 m below 8 km and 60 m between 8 km and 20 km. In the region of aerosol distribution, the SNR for both systems ranges between 30 and 60. Figure 5 is the mean profile of the total attenuated backscatter coefficient in latitudes between 13°N and 14°N for a further analysis of the raw signal differences. TECIS values are slightly lower than those from CALIPSO, but the distribution trends are highly consistent. Aerosols are primarily distributed below 4 km, with cloud layers observed above 10 km. The TECIS observed higher cloud heights compared to CALIPSO. While CALIPSO shows a larger standard deviation at lower altitudes, the standard deviations converge at higher altitudes for both instruments. In summary, the two satellites provide relatively consistent raw data results for their near-coincident orbits.

Figure 6 presents a time series comparison of the aerosol retrieval results between the two satellites, specifically for the depolarization ratio, aerosol backscatter coefficient, and aerosol extinction coefficient. Figure 6a,b show the latitudinal distribution of aerosol depolarization ratios. Within the 4 km altitude range, the depolarization ratios for both satellites are concentrated between 0.2 and 0.4, consistent with the depolarization characteristics of Saharan dust aerosols. Since the TECIS passed over this area closer to noon, the influence of solar background radiation caused some low signal-to-noise ratio polarization signals to be filtered out, leading to less continuous depolarization ratio results in some areas. Figure 6c–f depict the latitudinal distribution of the aerosol backscatter and extinction coefficients for both satellites. The aerosol backscatter coefficients primarily range between 10⁻³ and 10^−2.5 km⁻¹sr⁻¹, while the extinction coefficients are mainly around 10⁻¹ km⁻¹. Aside from variations caused by temporal and spatial differences, both satellites’ trends and distribution values are quite similar. Figure 7 compares the mean profiles of aerosol optical parameters between the two satellites at latitudes from 13°N to 14°N. In Figure 7a, the depolarization ratio trends are consistent at different altitudes, with values between 0.2 and 0.3 below 2.7 km and between 0.3 and 0.4 within the range of 2.7 km to 4 km. This indicates that high-altitude dust aerosols are more irregular in shape than those near the surface. The mean profile results for the backscatter coefficient (Figure 7b) and extinction coefficient (Figure 7c) show a high degree of consistency between the two satellites. The shaded area represents the standard deviation, and except for the depolarization ratio, the TECIS has a relatively larger standard deviation.

Figure 8 presents the relative error statistics of the optical parameters between the two satellites within the mean profile range (latitude 13°N to 14°N). Within the height range below 4 km, where dust is primarily distributed, the relative errors for the aerosol depolarization ratio, backscatter coefficient, and extinction coefficient between the two satellites are 13.7 ± 9.2%, 14.2 ± 12.9%, and 12.4 ± 9%, respectively. Considering that the relative error of CALIPSO’s ground-based comparison is within 40% [17,18] and acknowledging the temporal and spatial differences in this particular comparison, the results indicate a high degree of consistency between the TECIS and CALIPSO.

3.2. AOD Validation

For the comparison between TECIS and AERONET AOD, a spatial radius of 100 km and a temporal interval of ±2 h were selected as the spatiotemporal matching criteria. The TECIS data used were the average AOD retrieved within a 100 km radius centered on the AERONET site, while the AERONET site data corresponded to the average AOD within 15 min of the closest point of the TECIS nadir track. Additionally, matches where the altitude difference between the site and the nadir point exceeded 200 m were excluded to reduce errors. The AOD values obtained by the AERONET CIMEL sun photometer at wavelengths of 400 nm, 440 nm, 675 nm, 870 nm, and 1020 nm are inconsistent with the 532 nm AOD of TECIS. To ensure consistency, the spectral AOD values from AERONET were linearly fitted in the log (AOD) versus log (wavelength) space. This method was used to interpolate the AOD measured by AERONET to the corresponding 532 nm wavelength for comparison with the TECIS.

Figure 9 presents a scatterplot comparison between the AOD retrieved by the TECIS and the AERONET L2.0 AOD products. To ensure the reliability of the results, the TECIS AOD data where cloud signals were excluded using the backscatter ratio were compared with the AERONET AOD data. Data points were excluded from the optical depth calculation when the backscatter ratio exceeded 10, as they were considered to be cloud-contaminated [50]. The linear fit between the two datasets shows an R² of 0.71 and an RMSE of 0.13. Considering the spatiotemporal differences, the satellite and ground-based observations show good agreement. Additionally, TECIS AOD values at certain individual sites are significantly lower than those from AERONET. The primary reason for this discrepancy is that the TECIS’s capability to detect near-surface aerosols during the day is limited by a low SNR, mainly due to strong solar background radiation. When the TECIS fails to detect certain thin layers during daytime observations due to a low SNR, the AOD values are underestimated. Considering that the correlation coefficient between CALIPSO and AERONET ranges from 0.48 to 0.73 [19], this supports the good correlation between the AOD retrieved by the TECIS and the AERONET AOD.

The wavelengths used for AOD measurements by the TECIS and MODIS are 532 nm and 550 nm, respectively. This wavelength difference is considered negligible in this study due to its relatively minor impact compared to the uncertainties associated with AOD measurements from both CALIOP and MODIS [51,52]. The horizontal resolution of the aerosol AOD retrieved by the TECIS is 20 km, while the MODIS AOD data are provided in a grid of 10 × 10 km². For comparison, the MODIS AOD products within a 50 km radius centered on the TECIS nadir point were selected, using a time window of ±1 h for matching. The matched MODIS grid AOD values were averaged and then compared with the corresponding AOD values from the TECIS nadir point.

Figure 10 shows the comparison between AOD retrieved by the TECIS and MODIS. The solid line represents the linear fit of the data using the least-squares method. The figure displays the two-dimensional frequency distribution of TECIS and MODIS AOD, with the color scale indicating the fraction of the total data. It is evident that the main distribution ranges of AOD differ across regions. In North Africa and the Middle East, AOD is densely distributed within the range of 0.1 to 0.3, while in North America, the dense AOD region is below 0.1, and in Central Asia, it is below 0.2. The higher AOD in North Africa is likely due to the presence of the Sahara Desert and the frequent occurrence of dust storms in spring. Overall, the correlation coefficients for the four regions are all greater than 0.6, indicating a good correlation between TECIS AOD values and MODIS AOD. There are differences in the observation methods between MODIS and TECIS, with MODIS using a passive method and the TECIS using an active method. Additionally, performance parameters such as orbital paths, revisit periods, and spatial resolution are not the same. These differences result in temporal and spatial inconsistencies during the validation process, which is one of the main reasons for the discrepancies in the validation results. Given that the correlation coefficient between CALIPSO and MODIS is 0.67 [19], the results showed that the AOD obtained from the TECIS and MODIS exhibit good correlation with each other.

4. Application of TECIS Data

Dust storms frequently occur in the desert regions of Asia, notably in the Taklamakan Desert of northwestern China and the Gobi Desert of southern Mongolia [53,54]. Eastward-moving cyclones and northwesterly winds often carry large amounts of dust particles to eastern China, the Korean Peninsula, and other regions [55].

Based on TECIS observations over China from 2 to 7 April 2023, and combined with backward trajectory analysis, Figure 11 illustrates the temporal evolution and transport process of a typical dust event. The NOAA HYSPLIT model was used to perform a six-day backward trajectory analysis originated from Henan on 7 April (with blue, red, and black lines representing backward trajectories at altitudes of 3 km, 2 km, and 1 km, respectively), starting from central China and extending to the Taklamakan Desert. To minimize cloud interference and optimize visualization, a height of 5 km was selected for the plot, encompassing the majority of the aerosol distribution. The vertical cross-section superimposed on the map shows TECIS observations of 1064 nm attenuated backscatter, clearly identifying aerosol distribution along the dust transport path. Aerosols are represented in blue, green, or yellow, while clouds are shown in orange or red. The upstream regions along the dust transport path exhibit higher values, shown in yellow-green or bright blue, while the midstream and downstream regions generally display lower values, appearing in blue. TECIS data combined with backward trajectory analysis in Figure 11 indicate that at an altitude of 1 km, the dust aerosols primarily originated from Mongolia, whereas at 2 km and 3 km altitudes, the dust mainly originated from the Taklamakan Desert, passing through Qinghai, Gansu, Inner Mongolia, and Shaanxi, moving towards Henan and Anhui. This suggests that the dust primarily originated from the Taklamakan Desert and was transported along the red and blue paths to central China. Dust events are frequent in the Taklamakan region, where floating dust is predominant. The westerlies primarily control the higher altitude paths, and dust can also be lifted to higher altitudes and transported over long distances by westerly winds, affecting air quality in the regions it passes through. At the same time, the lower-altitude trajectories are influenced by topography and regional weather systems [56]. Ground station PM data were used to validate the TECIS observation results and verify the reliability of satellite observations. The red stars on the map indicate ground-based air quality monitoring stations located near the satellite trajectory along the dust transport path. The selected stations include Bayingolin Mongol Autonomous Prefecture in Xinjiang on 2 and 3 April and Jiuquan in Gansu, Zhongwei in Ningxia, Xi’an in Shaanxi, and Zhengzhou in Henan from 4 to 7 April.

Figure 12 shows the hourly observation data from various ground air quality monitoring stations. On 2 April, at the Bayingolin station in Xinjiang, PM10 and PM2.5 levels reached up to 800 µg/m³, indicating severe dust and fine particle pollution. During intense dust storms, PM10 dust concentrations can increase by at least twice compared to normal atmospheric conditions [57]. By 3 April, the PM10 and PM2.5 levels at the Bayingolin station had decreased to around 200 µg/m³. On April 4, at the Jiuquan station in Gansu, PM10 levels rose to 600–1000 µg/m³, while PM2.5 remained around 200 µg/m³, indicating that the pollution was primarily due to coarse particles. On 5 April, at the Zhongwei station in Ningxia, PM10 increased from 200 µg/m³ to 600 µg/m³, and PM2.5 gradually rose above 100 µg/m³ over time, suggesting mixed pollution dominated by coarse particles. On 6 April, satellite observations detected widespread dust aerosols in Shaanxi, with PM10 levels in Xi’an exceeding 400 µg/m³ and PM2.5 levels rising above 100 µg/m³. By 7 April, PM10 levels had increased over 150 µg/m³ at the Zhengzhou station, with PM2.5 ranging between 80 and 100 µg/m³. The results indicate that the pollution was primarily composed of coarse particles along the dust transport path, with a mix of fine particulate pollutants. The downstream areas of the dust transport showed lower PM10 and PM2.5 levels compared to upstream and midstream regions. High PM10 values were observed at ground stations corresponding to the regions where the satellite trajectory passed, and the ground station observations during the corresponding times aligned well with TECIS satellite observations.

Figure 13 illustrates the retrieved results of aerosol optical parameters along the TECIS orbital path during the dust transport. Since the data were collected around 2 to 3 PM, the influence of solar background radiation introduced noise, leading to some discontinuities in the retrieved results after filtering out low SNR signals, particularly in the depolarization ratio. On 2–3 April, aerosols up to 5 km high were observed in the western and eastern regions of the Taklamakan Desert, with the backscatter coefficient ranging from 10^−2.8 to 10^−1.5 km⁻¹sr⁻¹. On 3 April, the depolarization ratio near the surface in southern Xinjiang exceeded 0.4, consistent with Zheng et al.‘s study using CALIPSO satellite data, which found that aerosols in southern Xinjiang were concentrated at altitudes of 2.5 to 3 km, with a depolarization ratio between 0.3 and 0.5 [58]. The results indicate that this large-scale dust event in central China originated from the Taklamakan Desert in Xinjiang, with large particle sizes. Due to the non-spherical nature and relatively large size of these particles, the depolarization ratio of dust aerosols is higher compared to other aerosol types [59]. On 4 April, the satellite passed over western Gansu and northeastern Qinghai, where the terrain is elevated around 3 km due to the Qilian Mountains. Aerosols were observed within 3 km above the mountain altitude, with backscatter coefficients ranging from 10^−2.5 to 10^−1.5 km⁻¹sr⁻¹ and depolarization ratios mainly between 0.3 and 0.4, consistent with dust aerosol characteristics. On 5 April, the satellite’s trajectory passed through the southern part of Inner Mongolia and the border region between eastern Gansu and Ningxia, where abundant aerosols were observed within this area, primarily within 5 km altitude, with backscatter coefficients around 10^−2.5 km⁻¹sr⁻¹. The intensity decreased compared to the dust source area, and depolarization ratios were between 0.2 and 0.4. On 6 April, the TECIS-retrieved aerosol optical parameters revealed widespread aerosols in Shaanxi and Hubei, mostly within 3 km altitude, with backscatter coefficients around 10^−2.5 km⁻¹sr⁻¹ and depolarization ratios between 0.15 and 0.4, consistent with mixed dust aerosol characteristics. In some areas, near-surface depolarization ratios exceeded 0.4, indicating irregularly shaped, large dust particles that had settled into the boundary layer due to gravity, with pollution concentrated near the surface. On 7 April, the observed aerosol range was reduced compared to 6 April, with the height lowering to within 3 km and values primarily around 10^−2.8 km⁻¹sr⁻¹, while depolarization ratios decreased to between 0.05 and 0.2. This suggests a reduction in the influx of large dust particles and indicates mixing with local pollution. Pure dust particles from the desert were contaminated by gaseous and particulate pollutants along the transport path, mixing with local pollutants upon reaching central China.

The results retrieved from the TECIS reveal the spatial distribution and changes in the optical properties of dust aerosols during the transport. Ground station data for PM2.5 and PM10 validate the reliability of the satellite observations. Throughout the dust transport process, large dust particles gradually settled due to gravitational effects, leading to a decrease in the distribution height of the dust, with the aerosol backscatter coefficient and depolarization ratio gradually diminishing along the transport path.

5. Conclusions

This study retrieved aerosol optical parameters using the Fernald forward integration method. Data were sourced from the multi-beam lidar aboard the Terrestrial Ecosystem Carbon Inventory Satellite, launched in August 2022. The retrieved parameters were validated through comparison with multiple data sources and were used to monitor a typical dust transport event. Comparisons between the aerosol optical parameters retrieved by TECIS and CALIPSO aerosol products showed a relative error within 30%. The correlation coefficients (R²) with AERONET and MODIS AOD were greater than 0.7 and 0.6, respectively, indicating good consistency. These comparisons validate the accuracy of the retrieved aerosol optical parameters, demonstrating their applicability to aerosol science research. An application study was conducted using the retrieved data to analyze a dust transport event from the Taklamakan Desert in Xinjiang, China. The results indicated that in the upstream region of the transport path, dust was distributed up to 5 km with a high depolarization ratio. In contrast, dust was primarily concentrated within 3 km in altitude in the downstream regions, and the depolarization ratio decreased, suggesting that the frequency of larger dust particles diminished during the transport process. Additionally, the backscatter coefficient decreased throughout the transport process, and ground-based PM10 and PM2.5 data confirmed the reliability of the TECIS observations of the dust transport process. This research represents the inaugural demonstration of the TECIS’s capabilities in the realm of global aerosol monitoring. Future work will focus on further optimizing the retrieval algorithm and validating it with additional data sources, allowing the retrieval data to be applied to broader aerosol science research and atmospheric environmental monitoring.

Author Contributions

Conceptualization, Y.R. and B.C.; methodology, Y.R.; validation, L.B., B.C. and G.H.; writing—original draft, Y.R.; writing—review and editing, B.C., P.L. and J.F.; supervision, L.B. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The TECIS L2A data were not publicly available at the time of submission; we obtained access to the data by participating as members of the TECIS scientific team. The CALIPSO dataset was downloaded from the website https://search.earthdata.nasa.gov/search/ (accessed on 25 August 2024). The AERONET dataset was downloaded from the website https://aeronet.gsfc.nasa.gov/ (accessed on 20 July 2024). The MODIS dataset was downloaded from the website https://ladsweb.modaps.eosdis.nasa.gov/search/ (accessed on 30 July 2024). The PM dataset was downloaded from the website https://www.cnemc.cn/ (accessed on 30 August 2024). The Hysplit model can be accessed at https://www.arl.noaa.gov/hysplit/ (accessed on 30 July 2024).

Acknowledgments

The authors kindly acknowledge the teams of CALIPSO, AERONET, MODIS, and China National Environmental Monitoring Center for their effort in making the data available.

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 Table

Figure 1. A flowchart of the TECIS retrieval algorithm.

Figure 2. Trajectory of CALIPSO and TECIS.

Figure 3. Total attenuated backscatter coefficient obtained from TECIS and CALIPSO. (a) TECIS, (b) CALIPSO.

Figure 4. SNR of total attenuated backscatter coefficient obtained from TECIS and CALIPSO. (a) TECIS, (b) CALIPSO.

Figure 5. A comparison of total attenuation backscatter coefficient mean profiles between the TECIS and CALIPSO at 13° to 14°N; the shaded area represents the standard deviation of the two satellites. The blue solid line represents the TECIS result, and the red solid line represents the CALIPSO result.

Figure 6. (a) Profile of aerosol depolarization ratio of TECIS, (b) profile of aerosol depolarization ratio of CALIPSO, (c) profile of aerosol backscatter coefficient of TECIS, (d) profile of aerosol backscatter coefficient of CALIPSO, (e) profile of aerosol extinction coefficient of TECIS, (f) profile of aerosol extinction coefficient of CALIPSO.

Figure 7. A comparison of aerosol optical parameter mean profiles between the TECIS and CALIPSO at 13° to 14°N, where the blue solid line represents the TECIS result, the red solid line represents the CALIPSO result, and the shaded area represents the standard deviation within the average range of the two satellites. (a) Aerosol depolarization ratio, (b) aerosol backscatter coefficient, (c) aerosol extinction coefficient.

Figure 8. Relative error of retrieval results between TECIS and CALIPSO.

Figure 9. TECIS 532 nm AOD retrievals against AERONET AOD during April to June 2023; the dashed line is the linear fit described by the regression equation; the black line is the 1:1 line.

Figure 10. A scatterplot comparison of TECIS AOD data against MODIS AOD data during April to June 2023; the color scale represents the fraction of the total data. (a) North Africa, (b) the Middle East, (c) North America, (d) Central Asia.

Figure 11. TECIS 1064 nm total attenuation backscattering coefficient and HYSPLIT backward tracking from 2 to 7 April 2023 (blue, red, and black represent backward tracking at heights of 3 km, 2 km, and 1 km, respectively).

Figure 12. Variations in PM10 and PM2.5 concentrations from 2 to 7 April 2023. (a) PM10, (b) PM2.5.

Figure 13. Optical parameters obtained by TECIS inversion from 2 to 7 April 2023. (a,c,e,g,i,k) show backscattering coefficient; (b,d,f,h,j,l) show depolarization ratio.

Figure 13. Optical parameters obtained by TECIS inversion from 2 to 7 April 2023. (a,c,e,g,i,k) show backscattering coefficient; (b,d,f,h,j,l) show depolarization ratio.

Table 1

Main technical indicators of CASAL (aerosol detection).

System Parameter	Indicator
Beams	1
Pulse repetition	20 Hz
Wavelength	1064 nm, 532 nm
Polarization detection	Parallel and perpendicular @532 nm
Divergence angle	≤200 μrad (100 m)
Sampling rate	10 MHz

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