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Bathymetry of Lake Poopó ( a ) inclined view ( b ) viewed from top. 

Bathymetry of Lake Poopó ( a ) inclined view ( b ) viewed from top. 

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Located within the Altiplano at 3,686 m above sea level, Lake Poopo is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopo-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake Poopo's surface and its regional water supplies. Ou...

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... MODIS surfaces ( Figure 5) are linked to bathymetry through the Landsat/MODIS calibration curve ( Figure 6) and a correlation coefficient of R 2 = 0.97 was obtained. A top and inclined view of the final bathymetry is presented in Figure 7a,b. Table 2 shows relations between isolines vertical elevation, the elevation standard deviation (SD) and the number of profiles used for the calculation. ...

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... Several studies have focused on determining bathymetry in shallow water from ICESat and ICESat-2, for which vari-ous methods have been used. Arsen et al. (2014) determined bathymetry in a lake from ICESAT. Parrish et al. (2019) showed the importance of applying a refraction correction and the capability of ICESat-2 to determine bathymetry at depths of 40 m. ...
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The bathymetry of coastal bay environments, such as Moreton Bay near Brisbane in eastern Australia, is constantly reworked because of changes in energy dispersal and related sediment transport pathways. Updated and accurate bathymetric models are a crucial component for scientific, environmental, and ship safety studies. NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with a laser detecting system (green light) that penetrates the air-water interface. Under optimal conditions, it can provide shallow water bathymetry (depths <40 m). We attempted to use ICESat-2 measurements to study bathymetry and possible bathymetry changes from repeated tracks across Moreton Bay. We found that the water turbidity in Moreton Bay varies with time. More than half of the water area is affected by suspended sediment, which makes ICESat-2 difficult to obtain bathymetric measurements. In other areas, repeated ICESat-2 tracks performed consistently on the 1-meter level. This means that ICESat-2 can be used to update existing bathymetry in the region. We also devised a method to determine bathymetry in the shallower parts of the zone affected by mud.
... • Imagery from various satellites (and spatial and spectral resolutions) such as Landsat [4,5], Sentinel [6,7], hyperspectral imagery [8,9], and others [10,11]; ...
... • Imagery from various satellites (and spatial and spectral resolutions) such as Landsat [4,5], Sentinel [6,7], hyperspectral imagery [8,9], and others [10,11]; • Different modelling techniques including physics-based [12,13], empirical [14,15], machine learning [16,17], and others [18,19]. ...
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Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.
... With the development of Earth observation information technology, e.g., satellite remote sensing instruments, quantitative estimation and dynamic monitoring of water quantity using remote sensing techniques has become an important area of hydrological research (Mashala et al., 2023). Since remote sensing technology has the advantages of being able to cover large areas at a high spatial and temporal resolution, at relatively low cost (compared to traditional field-based data collection methods), remote sensing technology has a unique advantage in calculation and monitoring the dynamic reservoir capacity (Arsen et al., 2014;Armon et al.,2020). The current remote sensing-based reservoir capacity calculation models belong to two main categories: statistical empirical models and physical measurement models (Yang et al., 2022). ...
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... Given the frequent lack of bathymetric data to characterize the bottom surface of reservoirs, remote sensing techniques and spatial interpolation rise as potentially suitable alternatives to unfeasible vessel-based surveys (Arsen et al. 2013;Brando et al. 2009;Misra et al. 2018;Tseng et al. 2016). These bathymetric estimation alternatives become increasingly viable when local conditions prevent fieldwork; assessments are advancing through early stages in which highly accurate data are not indispensable; or equipment, resources, and technical capabilities are limited. ...
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... Relative water volume variation modeling based on the area-water level relationship is one of the most used methods for estimating relative water volume variations with multi-source remote sensing data (Gao et al., 2012;Duan and Bastiaanssen, 2013;Arsen et al., 2014;Tong et al., 2016). During modeling, lake water volume (V) was divided into constant water volume (V o ) and variable water volume (ΔV). ...
... Also in central Sahel, Fowe et al. (2015) studied the water balance of a small reservoir in southern Burkina Faso, highlighting the variations caused by anthropogenic water withdrawal. Other studies assessed lake topography through bathymetry (Arsen et al., 2013) or a digital elevation model (DEM; Avisse et al., 2017) to retrieve lake storage. The variability in the reservoirs at the global scale has been addressed by some recent works. ...
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... Many methods have been developed for WA delineations from satellite images, of which using spectral indices such as the Normalized Difference Water Index, NDWI (McFeeters, 1996), the Normalized Difference Lake Index, NDLI (Morriss et al., 2003;Hereher, 2015) or the Automated Water Extraction Index, AWEI; (Feyisa et al., 2014) is often preferred because of its rapid, simple and accurate procedure. The vertical dimension of water volume (WL and WD) can also be measured using the laser (Chipman et al., 2007;Wang et al., 2011;Zhang et al., 2011;Arsen et al., 2013) or radar altimetry (Swenson & Wahr, 2009;Birkett, 2000;Crétaux & Birkelt, 2006;Pham-Duc et al., 2019;2022). In some cases, lake depths are also estimated from optical images such as Landsat-8 and Sentinel-2 (Pope et al., 2016;Duan et al., 2022). ...
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Water stored by reservoirs is critical for irrigation, electricity generation, drinking water supply, recreation, fisheries, and flood control. Therefore, the reservoir's water storage volume (SW) must be measured and monitored frequently for better watershed management. Since SW data is often not publicly available, finding a method to quantify SW objectively and accurately but to facilitate local water management is necessary. This study proposes a method for monitoring water surface area and storage volume using multi-sensor satellite remote sensing data through the Tuyen Quang Reservoir case study in Northern Vietnam. Accordingly, the water surface area was first delineated from multi-temporal optical satellite images, such as Landsat series and Sentinel-2 images, using the Modified Normalized Difference Water Index and resampled into 30-m pixel resolution data. Using the Shuttle Radar Topography Mission Digital Elevation Model data, the water depth at each pixel was then calculated by the difference between its elevation and the reservoir shoreline's mean elevation. The results showed that the reservoir's water surface area increased rapidly during 2003-2007 (from 579 ha to 5,516 ha), fluctuated insignificantly in 2008-2020, and reached 7,196 ha in 2021. Consequently, SW was raised from 11.8 million m 3 in 2003 to 1.68 billion m 3 in 2021. Our estimations agree with the depth and SW of Tuyen Quang Reservoir published in 2019. Our proposed method could be an effective water resource management tool in developing countries where the number of impounding reservoirs increases dramatically yearly without the financial afford to build gauging stations.
... To overcome the disadvantages of existing bathymetry derivation techniques, several studies either integrate waterlines obtained from a series of images with a spatial interpolation of geocoded heights [150]- [152] or combine multisatellite observations for deriving bathymetry (e.g., optical imagery, radar, and laser altimetry) [153]- [155]. For example, Abileah and Vignudelli [156] proposed the combination of radar altimetry and multitemporal Landsat data to calculate bathymetry in Lake Nasser. ...
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... Combined with Landsat series and Sentinel-2 satellite images, the functional relationship between the water level and water storage capacity is constructed, and Ngoring Lake has been continuously monitored for nearly 30 years, including the changes in water level, lake area, and water storage capacity. Compared with previous studies [16][17][18], the data and methods used in this paper are novel and have a longer observation time. It is of great benefit to the follow-up study of lakes. ...
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Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.