TY - JOUR
T1 - A high-resolution bathymetry dataset for global reservoirs using multi-source satellite imagery and altimetry
AU - Li, Yao
AU - Gao, Huilin
AU - Zhao, Gang
AU - Tseng, Kuo Hsin
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/7
Y1 - 2020/7
N2 - Although accurate 3-D reservoir bathymetry is a key input for multiple applications (such as global hydrological models, local water resources related studies, and others), such information is only available for very few locations. To fill this knowledge gap, we generated a 30 m resolution bathymetry dataset for 347 global reservoirs, representing a total volume of 3123 km3 (50% of the global reservoir capacity). First, Area–Elevation (A–E) relationships for the identified reservoirs were derived by combining altimetry data from multiple satellites with Landsat imagery data. Next, the resulting A–E relationships were applied to the Surface Water Occurrence (SWO) data from the JRC Global Surface Water (GSW) dataset to obtain bathymetry values for the dynamic areas of the reservoirs. Lastly, an extrapolation method was adopted to help achieve the full bathymetry dataset. The remotely sensed bathymetry results were primarily validated against the following: (1) They were validated against Area–Elevation (A–E) and Elevation–Volume (E-V) relationships derived from the in situ elevations and volumes for 16 reservoirs, with root-mean-square error (RMSE) values of elevation from 0.06 m to 1.99 m, and normalized RMSE values of storage from 0.56% to 4.40%. (2) They were also validated against survey bathymetric maps for four reservoirs, with R2 values from 0.82 to 0.99 and RMSE values from 0.13 m to 2.31 m. The projected portions have relatively large errors and uncertainties (compared to the remotely sensed portions) because the extrapolated elevations cannot fully capture the underwater topography. Overall, this approach performs better for reservoirs with a large dynamic area fractions. It can also be applied to small reservoirs (e.g., reservoirs with surface areas of a few square kilometers or less), where ICESat observations are available, and to large natural lakes. With the contribution of ICESat-2, this dataset has the potential to be expanded to thousands of reservoirs and lakes in the future.
AB - Although accurate 3-D reservoir bathymetry is a key input for multiple applications (such as global hydrological models, local water resources related studies, and others), such information is only available for very few locations. To fill this knowledge gap, we generated a 30 m resolution bathymetry dataset for 347 global reservoirs, representing a total volume of 3123 km3 (50% of the global reservoir capacity). First, Area–Elevation (A–E) relationships for the identified reservoirs were derived by combining altimetry data from multiple satellites with Landsat imagery data. Next, the resulting A–E relationships were applied to the Surface Water Occurrence (SWO) data from the JRC Global Surface Water (GSW) dataset to obtain bathymetry values for the dynamic areas of the reservoirs. Lastly, an extrapolation method was adopted to help achieve the full bathymetry dataset. The remotely sensed bathymetry results were primarily validated against the following: (1) They were validated against Area–Elevation (A–E) and Elevation–Volume (E-V) relationships derived from the in situ elevations and volumes for 16 reservoirs, with root-mean-square error (RMSE) values of elevation from 0.06 m to 1.99 m, and normalized RMSE values of storage from 0.56% to 4.40%. (2) They were also validated against survey bathymetric maps for four reservoirs, with R2 values from 0.82 to 0.99 and RMSE values from 0.13 m to 2.31 m. The projected portions have relatively large errors and uncertainties (compared to the remotely sensed portions) because the extrapolated elevations cannot fully capture the underwater topography. Overall, this approach performs better for reservoirs with a large dynamic area fractions. It can also be applied to small reservoirs (e.g., reservoirs with surface areas of a few square kilometers or less), where ICESat observations are available, and to large natural lakes. With the contribution of ICESat-2, this dataset has the potential to be expanded to thousands of reservoirs and lakes in the future.
KW - Bathymetry
KW - DEM
KW - Global reservoirs
KW - ICESat-2/ATLAS
KW - ICESat/GLAS
KW - Landsat
KW - Radar altimetry
UR - http://www.scopus.com/inward/record.url?scp=85084188490&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2020.111831
DO - 10.1016/j.rse.2020.111831
M3 - 期刊論文
AN - SCOPUS:85084188490
SN - 0034-4257
VL - 244
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111831
ER -