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With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth's surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period. [ABSTRACT FROM AUTHOR]

Currently, in-situ data and the time duration of altimeter data are limitations in calculating the water level and water volume of lakes and reservoirs from remotely sensed data. A novel method is proposed to estimate the temporal change in water levels and water volumes for lakes with only remotely sensed data. First, the surface profiles, including the ground and the underwater bottom, were extracted from the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar raw data via a new surface detecting algorithm. Second, the lake boundaries between land and water in different years were identified using a thresholding method based on the annual median Landsat composite. Third, water levels were calculated by matching the lidar surface profiles with the lake boundaries based on the nearby georeferenced coordinates. Finally, the water volumes in different years were estimated via the contours (i.e., lake boundaries) with different elevations. Lake Mead was selected as the study area, which is the largest reservoir in the United States in terms of water capacity. With only one day measuring lidar points in February 2012 and over 20 years of Landsat images (from 1987 to 2007), the water levels and water volumes in different years were estimated and compared with the in-situ data. Our results performed well in accordance with the in-situ measurements; the R-square of the water levels and water volumes were both over 0.99; the RMSE of the interannual variations of water levels and water volumes were 0.96 m and 0.31 km3, respectively. The MABEL was used as a technology demonstrator for the satellite photon-counting laser altimeter and had similar data to the ICESat-2 dataset. Future ICESat-2 datasets will broaden this method to estimate water volumes for remote lakes from the 1980s, where no in-situ data are available (such as the Tibetan Plateau and polar regions with thousands of remote and wild lakes), which could not be achieved in previous studies. • A new method to estimate water volumes for lakes with only remotely sensed data • Mapped water extents and lake boundaries using annual Landsat composite • Extracted profile of over and underwater surface from photon-counting lidar data • Obtained water levels by matching lidar profiles with the lake boundaries • Estimated water volumes of Lake Mead over 1987–2007 and verified by in-situ data [ABSTRACT FROM AUTHOR]