
IEEE Access, Год журнала: 2024, Номер 12, С. 183394 - 183409
Опубликована: Янв. 1, 2024
Язык: Английский
IEEE Access, Год журнала: 2024, Номер 12, С. 183394 - 183409
Опубликована: Янв. 1, 2024
Язык: Английский
Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106334 - 106334
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1Water, Год журнала: 2025, Номер 17(7), С. 1098 - 1098
Опубликована: Апрель 6, 2025
This study evaluates the performance of ICESat-2 ATL13 altimetry product for estimating water levels in 182 Canadian lakes by integrating satellite-derived observations with situ gauge measurements and applying spatial filtering using HydroLAKES dataset. The analysis compares ATL13-derived lake surface elevations hydrometric data from national monitoring stations, providing a robust framework assessing measurement accuracy. Statistical metrics—including root mean square error (RMSE), absolute (MAE), bias (MBE)—are employed to quantify discrepancies between datasets. Importantly, application HydroLAKES-based reduces RMSE 1.53 m 1.40 m, further exclusion high-error lowers it 0.96 m. Larger deeper exhibit lower margins, while smaller complex shorelines show greater variability. Regression confirms excellent agreement satellite (R2 = 0.9999; Pearson’s r 0.9999, n lakes, p < 0.0001). Temporal trends reveal declining 134 increasing 48 2018 2024, potentially reflecting climatic variability human influence. These findings highlight potential utility large-scale inland when combined techniques such as HydroLAKES.
Язык: Английский
Процитировано
0Signal Image and Video Processing, Год журнала: 2025, Номер 19(7)
Опубликована: Май 8, 2025
Язык: Английский
Процитировано
0International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)
Опубликована: Сен. 23, 2024
Язык: Английский
Процитировано
1International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)
Опубликована: Дек. 16, 2024
Water depth, a fundamental characteristic of lake, is important for understanding climatic, ecological, and hydrological processes. However, lake water depth data are still scarce due to the high cost in-situ measurements limitations remote sensing observations. In this study, novel method was developed estimate time series pixel-wise depths lakes that have ever exposed their bottom by Lake were calculated as difference between elevations dynamic surface historical lakebed using optical images DEM data. The applied in Sahel-Sudano-Guinean region Africa where complex climatic conditions rare measurements. Experiments showed proposed could get consistent compared with HydroLAKES data, i.e. MAE 0.86 m RMSE 1.69 m, similar estimates derived from ICESat/ICESat-2 3.79 5.92 m. can provide information on at temporal frequency, expected an efficient solution gather essential lakes.
Язык: Английский
Процитировано
1IEEE Access, Год журнала: 2024, Номер 12, С. 183394 - 183409
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0