Monitoring and Analysis of Water Level Changes in Mekong River from ICESat-2 Spaceborne Laser Altimetry

Abstract

The monitoring and analysis of the water level in the Mekong River is of major importance in water resources security, management, and geostrategic cooperation. This study proposed a new approach for monitoring and analysis of changes in water level of the Mekong River by using ICESat-2 spaceborne laser altimetry. River water levels were extracted from ICESat-2 inland water level data (ATL13). Then, the remote-sensed water level was quantitatively compared with the in situ water level measured by hydrological stations. Finally, the responses of water level to natural and anthropogenic factors were explored. The results showed that the ATL13 products provided river water level data with a high precision (root mean square error (RMSE) = 0.24 m, bias = −0.11 m) and a low measurement uncertainty (median of standard deviations (MSD) = 0.04). The analysis indicated that ATL13 products under different beam intensities and acquisition times can be applied to derive river water level. However, the use of nighttime measurements achieved slightly higher accuracies. The seasonal characteristics of river water level (flooding season from May to October and dry season from November to April of the next year) were because of natural factors, such as upstream flow and rainfall. A comparison of water level changes among different periods showed that hydropower development generally decreased the range of water level in the flooding season and increased water level in the dry season, thereby mitigating the current uneven spatial and temporal distribution characteristics of water resources in Mekong basin.

1. Introduction

River water level is a key indicator of the influence of global environmental change and the status of water resources [1,2]. Increased industrialization and urbanization under globalization and global warming have resulted in more severe and more frequent water deficit crises, thus water crises are the world’s top risk for human in the next 10 years [3]. Among them, disputes over water management and geo-strategic competition of trans-national rivers have attracted a lot of attention from the international community [3,4]. The Mekong River is a key resource shared among multiple countries in Asia, and changes to water resources in Mekong Basin affect the lives of hundreds of millions of inhabitants [5,6,7]. In addition, the mainstem of the Mekong River is highly sensitive to climate change since its headwaters are in the Tibetan Plateau. Therefore, integrated management of Mekong River requires the cooperation of all the countries that share this resource [8,9]. The monitoring and analysis of the water level of Mekong River are not only of fundamental importance for understanding the flow regime of the river [10], but are also of great importance for integrated water resources management (IWRM), protection of the ecological environment, and facilitating water security.

The in situ measurement of river water level by hydrological stations remains the established method [4]. However, installing and maintaining sufficient hydrological stations on the mainstem of Mekong River for monitoring of water level poses a challenge due to the limited accessibility of some sites, the extensive river length, and cost [11]. In addition, the differences in technical standards for environmental observations, statistical criteria for socio-economic data, and resources among countries sharing the Mekong River, as well as data sharing, pose a challenge to unifying the acquisition time and accuracy of water level data for the basin [12,13]. The development of spaceborne altimetry technology has provided a novel and effective means of overcoming these challenges.

There are two main types of altimeters that provide satellite altimetry data: radar altimeters and laser altimeters. Radar altimetry satellites have been in operation since the 1990s and include TOPEX/Poseidon [14], Geosat, ERS-1/2 [15], Jason-1/2/3 [16], Sentinel-3A and B [17,18], SARAL/Altika [19], CryoSat-2 [20], ENVISAT [21], Sentinel6 [22] and HY-2 [23]. These satellites have facilitated the convenient extraction of water level data at large spatial scales, over long periods, and of high precision. The water level of reservoirs, lakes, and rivers at various scales worldwide have been derived from Radar altimetry data extensively. However, the limitations posed by coarse spatial (1–10 km) and temporal resolutions (10–369 d) and extended along-orbit (0.3–7 km) and across-orbit (1–300 km) distances as well as measurement errors resulting from partial terrain and non-uniformity of reflectors have limited the application of radar altimetry data to small rivers monitoring [16,24].

In contrast to radar altimetry, laser altimetry has the advantage of small footprint coverage and high sampling density more suitable for monitoring of small water bodies. Laser altimetry satellite technologies that have been utilized to monitor water levels include ICESat (Ice, Cloud, and Land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System), ICESat-2/ATLAS (Advanced Topographic Laser Altimeter System), and GEDI (Global Ecosystem Dynamics Investigation) [25,26,27,28]. Among these technologies, ICESat was the first laser altimetry satellite. This satellite was launched in 2003 and retired in 2009. It produced a footprint of ~70 m in diameter every ~170 m in the along-track direction at a frequency of 40 Hz. The theoretical accuracy of ICESat water levels can reach 3 cm when applied to a large scale or to areas with a relatively flat water surface [29]. However, the ICESat footprint across the transitions from the land to water in regarding to small rivers, the accuracy of water level measurements is reduced to ~114 cm due to land-water transition sensor inertia [30].

In comparison with ICESat, ICESat-2, which was launched in 2018, provides an improved temporal and spatial resolution (six beams with overlapping footprints of ~17 m in diameter every ~0.7 m in the along-track direction), thereby showing great potential for the monitoring of water level, particularly for narrow rivers with a complex terrain.

Therefore, the aim of the present research was to propose a new approach for water level monitoring over Mekong River from ICESat-2 laser altimetry data. The goals of the present research were: (1) to determine the performance of ICESat-2 data for derive of river water level; (2) to derive and analyze the changes in the timeseries of water level for Mekong River.

2. Materials and Methods

2.1. Study Area

The Mekong River is the longest river in Southeast Asia and runs from China through Myanmar, Lao PRD, Thailand, Cambodia, and Vietnam [31], with a total length of ~4909 km and a width varying from ~100 m upstream to ~2 km downstream (Figure 1). The Mekong River Basin falls within an Asian tropical monsoon climate zone. Consequently, the flooding season of the river extends from May to October, whereas the dry season occurs from November to next April. The Mekong River Basin has a diverse terrain, with altitude decreasing from north to south. While overall Mekong Basin is rich in water resources, the different stream segments show temporal and spatial variability [32]. Global climate change is resulting in increased frequency and severity of droughts and floods in the downstream area [5,33,34]. Water demands in the Mekong River Basin are continuing to grow with an increasing population and socio-economic development. The agricultural economy of the basin is dominated by rain-fed agriculture, which is more vulnerable to drought and floods. Meanwhile, competition for access to water amongst the countries sharing Mekong Basin is becoming increasingly fierce [34].

2.2. ICESat-2 Altimetry Data

ICESat-2 is a laser altimetry satellite launched by the United States National Aeronautics and Space Administration (NASA) in September 2018. This satellite technology has an observation coverage of 88° S–88° N and a repetition period of 91 days. The ATLAS onboard the ICESat-2 transmits three pairs of laser beams (six beams) with wavelengths of 532 nm at a repetitive of 10 kHz, thereby producing overlapping footprints of ~17 m in diameter every ~0.7 m in the along-track direction. The three pairs of beams are 3.3 km apart with a distance of 90 m within each pair [35,36,37,38]. The ICESat-2 generates 21 products (https://nsidc.org/data/icesat-2/products/ (accessed on 9 November 2021).). The present study adopted ATL13 data Version 5.0 (Figure 2) for the period October 2018 to June 2021, which included inland water level data. These data are download from the National Snow and Ice Data Center (NSIDC, https://nsidc.org/data/ATL13/versions/5; accessed on 15 September 2021) for free.

2.3. Remote Sensing Data

• (1). Landsat Images. The present study used Landsat OLI images with 30 m spatial resolution (image set No.: Landsat/lt05/C01/t1_sr) to map the boundary of the Mekong River via the Google Earth Engine (GEE) platform (https://code.earthengine.google.com (accessed on 5 November 2021)). The timeseries of images were selected over the dry season (November 2019 to April 2020) corresponding with the period of the minimum river boundary, thereby ensuring that photons falling on the river surface were picked out to represent river water level.

• (2). DEM data. The Shuttle Radar Topography Mission (SRTM) DEM data with 30 m spatial resolution (data number: USGS/SRTMGL1_003) on the GEE platform were used to remove false water surfaces, such as mountains shadows, and to enhance extraction precision of the water surface. The data were updated in February 2000.

2.4. In Situ Water Level of Hydrological Station

Daily in situ water level records of eight hydrological stations along the mainstem of Mekong River, including Luang Prabang, Chiang Khan, Nongkhai, and Stung Treng hydrological stations, were obtained from the Mekong River Commission [39] website (https://portal.mrcmekong.org/time-series/water-level (accessed on 10 November 2021)). The daily averaged in situ water levels were availed to assess the precision of water level data obtained from the ICESat-2 ATL13 data that were observed at a same time. Since the units of in situ and ICESat-2 water levels were meter above the WGS84 datum and the Earth Gravitational Model (EGM)2008 datum, respectively, conversion of in situ water level into EGM2008 height via the VDatum Tool (https://vdatum.noaa.gov/welcome.html (accessed on 12 December 2021)) was necessary. Although ATL13 data provide the WGS84 ellipsoid heights, since the water level height reference to WGS84 ellipsoid fluctuates with the gravity effect, orthometric height EMG2008 was selected [28].

3. Methods

The process of deriving water level data from the ICESat-2 ATL13 product involved three main steps. First, the minimum boundary of the river was extracted from the Landsat image. The ATL13 data were then processed, including extraction of footprints falling on the river surface with reference to the river boundary, elimination of outliers, and calculation of the average water level within a track at each observation time. Finally, the performance of ICESat-2 water level was verified by comparison with in situ values, and changes of river water level were briefly analyzed.

3.1. Extraction of the River Boundary

The present study used the quality inspection band “pixel_ QA” of Landsat-8 OLI images in the GEE platform to remove clouds in the imagery. The median synthesis method was then applied to the cloud-free images to create the median composite images over the Mekong River from November 2019 to April 2020. The modified normalized difference water index (MNDWI) [40] was utilized to extract the river boundary by highlighting water information by normalizing the different bands of the Landsat images. The expression of MNDWI is as follows:

MNDWI = (Green − SWIR1)/(Green + SWIR1)(1)

In Equation (1), Green and SWIR1 represent green (band 3) band and mid-infrared (band 6) band, respectively, within the Landsat-8 images.

Distinguishing between water and non-aqueous pixels through MNDWI requires the determination of the threshold. The present study used the maximum interclass variance method [41] to calculate the optimal MNDWI threshold adaptively for water classification. However, various misclassifications resulting from cloud cover or mountain shadow remained after application of this method on a large scale and one-time. Therefore, slope (<20) and hillshade (>150) [42] calculated from the DEM were used to mask the pseudo river surface. At the same time, the canny edge detection algorithm was utilized to eliminate the disturbance of small water bodies through connectivity processing. These adaptations to the methods allowed the extraction of the accurate river boundary.

3.2. Extraction of ICESat-2 Water Level

Extraction of ICESat-2 water levels from ATL13 products consisted of three steps:

• (1). Data filtering. The theory behind the ATL13 algorithm [43] indicated that the “qf_bckgrd”, “qf_bias_em”, “qf_bias_fit”, and “stdev_water_surf” quality flags can be directly utilized to filter out erroneous or low-quality observation values. The “qf_bckgrd” indicates the background rate of short segments, the “qf_bias_em” represents errors in calculated elevation due to a shift in slope and wavy surface, “qf_bias_fit” represents the height bias between the centroid elevations of the observed surface water histogram and the fitted integrated histogram, and “stdev_water_surf” represents the standard deviation of the water surface. The following combinations of flags indicates invalid data [28]: (1) “qf_bckgrd” = 6; (2) “qf_bias_em” ≥ 3 or “qf_bias_em” = −3; (3) “qf_bias_fit” ≥ 3 or “qf_bias_fit” = −3; (4) “stdev_water_surf” > 2.

• (2). Extraction of water level from ATL13 data. After data filtering, the river boundary was used to select the photons falling on the river surface. The orthometric heights EMG2008 and significant wave heights were then derived. The instantaneous water level was calculated by the orthometric height EMG2008 minus the significant wave height.

• (3). Outlier removal. Outliers in the river surface remained evident after data filtering. Therefore, 2-sigma criteria were adopted to remove the outliers of each ground track for each observation time. Finally, the remaining observations were used to calculate the average water levels for each observation time.

3.3. Uncertainty Estimation and Accuracy Validation

• (1). Estimation of uncertainty in ICESat-2 water level

Several observations of along-track water level were available for each observation date. The uncertainty in water level at each observation date was described by calculating the standard deviation (SD) of the filtered observations. Moreover, since there were many observation times for each station, the median of the timeseries of standard deviation (MSD) was quantified as the final uncertainty in water level for each station [44].

(2)${\mathrm{SD}}_n=\sqrt{\frac{{\sum }_{m=1}^p{\left(x_m-\overline{x}\right)}^2}{p}}$

(3)${\mathrm{MSD}}_k=median\left({\mathrm{SD}}_1,{\mathrm{SD}}_2,\dots ,{\mathrm{SD}}_q\right)$

In Equations (2) and (3), m and n represent the m-th observation value at the n-th time, respectively, k is the current station number, and p, q, and $\overline{x}$ represent the number of along-track observation values, observation times, and the average of along-track observation values, respectively.

• (2). Validation of measurement precision of ICESat-2 water level

Through comparing in situ water level data and the timeseries of ICESat-2 values, three metrics could be calculated to assess the measurement precision of ICESat-2 water level data over Mekong River, namely correlation coefficient, bias, and root-mean-square error (RMSE).

4. Results and Discussion

4.1. Performance of ICESat-2 ATL13 Data

According to the availability of satellite orbit, ICESat-2 remote-sensed water level data within 1 km from each hydrological station were collected. A comparison of ICESat-2 water level data with corresponding in situ water level allowed an assessment of the performance of ICESat-2 ATL13 data (Figure 3 and Table 1). The MSD values were pretty stable among the different stations which confirmed that water level derived from ICESat-2 were highly accurate with a low measurement uncertainty at a station scale. The RMSE values of ICESat-2 water level ranged from −0.31 m to 0.23 m, consistent with the bias (from 0.05 m to 0.39 m). Three possible reasons for this variation were analyzed: (1) ICESat-2 has a short ground track on a river surface and the ICESat-2 water level were associated with the distance from ground track to hydrological station; (2) the temporal discrepancy between ICESat-2 and in situ data, representing instantaneous and daily average values, respectively; (3) the presence of observation errors in some ICESat-2 water levels due to the influence of topographic slope and water waves.

In Table 1, Num refers to the number of average observation values along-track at the different observation times within 1 km from hydrological station were collected.

The present study also analyzed the impact of acquisition time and beam intensity on measurement accuracy. Figure 4 shows the measurement accuracy of ICESat-2 ATL13 data under different acquisition condition stratified by daytime/nighttime measurements and strong/weak beam. The RMSEs of strong beam and weak beam measurements were 0.18 m and 0.19 m, respectively. There was not a clear difference between strong/weak beams (0.01 m difference). A comparison of water level errors between daytime and nighttime measurements showed that the latter was slightly more accurate, with RMSE value of 0.19 m and 0.16 m, respectively. This result could possibly be attributed to the ICESat-2 measurements being more sensitive to background noise, especially during the daytime.

4.2. Analysis of Changes in Water Level

The acquisition time of ICESat-2 altimetry limited the analysis of temporal changes in water level to three stations along the mainstem of Mekong River, which can mirror the change features of water level during the past period. The ICESat-2 and in situ water level were combined for analysis (Figure 5). Observations at each station covered at least 4 months over both the dry and flooding seasons.

Figure 5 shows large annual fluctuations in water level at each station varying from ~5 m to ~8 m. The results also showed similar significant seasonal variations among each station, with a lower range of water level from January to April, and higher levels starting from May, corresponding with the increase in upstream flow and rainfall, peaking in October, and then a gradual decline. The seasonal variation in water level was consistent with the climatic characteristics of the basin.

Aside from natural factors, anthropogenic activities also affect water level. These activities include hydropower development and agricultural irrigation. Therefore, the present study selected two periods before and after hydropower development (1986–1989 and 2018–2021, respectively) for the comparative analysis of changes in water level. Figure 5 illustrates very consistent intra-year patterns of water level. While the flooding season extends from May to October and the dry season extends from December to April of the next year, the range of water levels over the flooding season during 1986 to 1989 generally exceeded that during 2018 to 2021. This was particularly evident at the Chiang Khan station. This reduction in range of water level effectively reduced pressure on flood control during the flooding season, although the peaks during the two periods remained basically the same. However, the dry season water level during 2018–2021 mostly exceeded those during 1986–1986. The rise in dry season water level during the latter period was conducive to meeting water demand along the river. The analysis of changes in water level showed that the development of hydropower acted to mitigate the uneven spatial and temporal distribution characteristics of water resources in the Mekong basin.

4.3. Comparison of Precision of Water Level Measurements with Those of Previous Studies

The precision of ICESat-2 water level measurements in this study (RMSE = 0.19 m, bias = −0.05 m) was consistent with that of Xiang et al. [27]. The study of Xiang et al. [27] on water level of the lower Mississippi River, United States, determined that ICESat-2 water level were similar to in situ water level, with a bias and RMSE of −0.08 m and 0.12 m, respectively. The slight difference in accuracy between the present study and that of Xiang et al. [27] may be attributed to differences in data preprocessing or the different topographies between the study basins. The lower Mississippi River has a flat terrain with differences in elevation of less than 30 m, whereas the terrain of the Mekong River is predominantly mountains and hills with steep slopes. The turbulence resulting from water flow can increase observation errors along the ground track in a river. Secondly, there are differences in accuracies of in situ water levels provided by Laos, Thailand, and Cambodia. Finally, Xiang et al. [27] concluded that the interpolation of ICESat-2 water level may increase their accuracy when compared with in situ water levels.

Prior to ICESat-2, ICESat altimetry data and radar altimetry data have been utilized to monitoring river water level. As an example, RMSE and bias of ICESat water level data for the lower Mississippi River were 0.25 m and −0.18 ± 0.16 m, respectively [27]. Urban et al. [45] determined that measurements precision of ICESat water level data varies from 0.03 m to 0.25 m in a study of Tapajos River. Hall et al. [46] identified a mean absolute error (MAE) of 0.19 m between ICESat and in situ water levels in Amazon River. A comparison between ICESat and in situ water levels for the Ob and Pur rivers obtained RMSEs of 0.63 m and 1.1 m, respectively [47]. Verification of ICESat water levels for Mekong River by comparison with in situ data obtained an RMSE ranging from 0.3 m to 0.6 m [48]. The above analysis showed that ICESat-2 altimetry has great potential for the monitoring of river water level.

5. Conclusions

The launch of the ICESat-2 satellite in 2018 provided the possibility of observing water levels over large spatial scales and at a fine spatial resolution. A new approach for the monitoring and analysis of water level over Mekong River based on the ICESat-2 spaceborne laser altimetry inland water level data (ATL13) was proposed in this study. The ICESat-2 remote-sensed water level data approach presented in the present study can overcome the limitations related to in situ water level measurements availability; these data also can improve water security and facilitate international cooperation within the IWRM of Mekong Basin. The main conclusions of this study were as follows:

• (1). Remote-sensed water level data extracted from ICESat-2 laser altimetry data showed a high accuracy and a low measurement uncertainty when compared to in situ data, with an MSD, bias, and RMSE of 0.04, −0.05 m, and 0.19 m, respectively. Therefore, these remote-sensed water level data can be applied to supplement in situ water level for areas lacking observation records.

• (2). Although all the ICESat-2 water level data could be applied for water level monitoring, the measurement precision of data under different acquisition conditions showed that there was not a clear difference between measurement precision of strong beam data and weak beam data (difference in RMSE of 0.01 m), whereas nighttime measurements were more accurate than daytime measurements (RMSEs of 0.16 m and 0.19 m, respectively).

• (3). The variation in water level among different stations along the Mekong River from 2018 to 2021 showed that variations in water level due to natural factors were similar between the upstream and downstream of Mekong River, although there were also intra-annual and inter-annual changes. An analysis of changes in water levels between two periods representative of before and after hydropower development showed that hydropower development generally decreased the range of water level during the flooding season and increased the water level during the dry season. These changes were conducive to the regulation of water resources.

In addition to the measurement accuracy, the temporal and spatial resolution of laser altimetry was also very important for water level monitoring. The present study did not conduct a detailed analysis of changes in water level due to limitations in the continuity and density of observations. Future studies can integrate multiple sources altimetry satellites to raise temporal resolution and span of water level monitoring, such as various radar altimetry satellites and GEDI. This would allow the analysis of both short and extended periods of river water level.

Author Contributions

Conceptualization, C.W. and S.N.; Data curation, J.L.; Formal analysis, J.L., C.W. and X.X.; Funding acquisition, S.N.; Methodology, J.L. and S.N.; Project administration, X.X.; Supervision, C.W.; Writing—original draft, J.L.; Writing—review and editing, J.L., C.W., S.N., X.X. and J.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We acknowledge the NSIDC for supplying the ICESat-2 ATL13 products. We also appreciate the MRC for supplying the in situ water lever data of hydrological stations over Mekong River. We are also grateful to editors and reviewers for their comments on our paper.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Table

Figure 1. Map of the Mekong Basin showing elevation, mainstem of Mekong River and distribution of main hydrological stations. The Digital Elevation Model (DEM) was obtained from Google Earth Engine (GEE) platform and the shape files for the Mekong River and basin boundary were download from https://www.hydrosheds.org/ (accessed on 2 November 2021).

Figure 2. Ground track and six beams of ATL13 data covering the Mekong River. The image was derived from https://openaltimetry.org/data/icesat2/website (accessed on 17 December 2021).

Figure 3. Comparison between ICESat-2 water level data (blue dot) and daily in situ water level data (gray straight line) in the Mekong River at different hydrological stations. The time span was from October 2018 to June 2021 ((a) Luang Prabang, (b) Chiang Khan, (c) Nongkhai, (d) Savannakhet, (e) Khong Chiam, (f) Pakse, (g) Stung Treng, (h) Kompong Cham).

Figure 4. Box plot of RMSE between ICESat-2 water level data and in situ water level data for the Mekong River under different data acquisition conditions. The white crosses express the average of RMSE. The red lines in the box express the median of RMSE. The red crosses express outlier.

Figure 5. Timeseries of water level data in the Mekong River at different hydrological stations. ((a) Chiang Khan, (b) Khong Chiam, (c) Kompong Cham).

Table 1

Accuracy indicators of ICESat-2 water level data for the Mekong River.

Location	Stations	River Width (m)	Num	Mean (m)	MSD (m)	Bias (m)	RMSE (m)
Upper Mekong	Luang Prabang	450	9	276.04	0.06	−0.28	0.30
	Chiang Khan	600	23	201.14	0.03	−0.02	0.05
Central Mekong	Nongkhai	620	12	155.69	0.04	0.04	0.07
	Savannakhet	760	11	127.03	0.03	0.23	0.25
	Khong Chiam	450	28	93.61	0.02	−0.31	0.39
	Pakse	1400	16	88.69	0.03	−0.12	0.18
Lower Mekong	Stung Treng	1085	20	39.90	0.04	0.11	0.12
	Kompong Cham	900	17	4.67	0.05	−0.04	0.13
Mean of river					0.04	−0.05	0.19

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