Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units DOI Creative Commons
Linjiang Wang,

Bingfang Wu,

Weiwei Zhu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1201 - 1201

Published: March 28, 2025

This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different units are first delineated digital elevation data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, integrated using Sentinel-2 DEM to construct indicating factor. A slope-scale is developed ETWatch (1 km resolution) the factor, yielding reliable 10 m resolution that reflect variations. The validation in Huairou Baotianman shows coefficients of determination 0.9 0.91, respectively, root mean square errors 0.45 mm 0.47 mm. Compared original 1 data, disaggregated results show improved accuracy, R2 values increasing by 1% (Huairou) 2% (Baotianman) RMSE decreasing 21% 13%, respectively. offers a novel approach for estimating forest mountainous areas significant potential water resource management sustainable land–water allocation.

Language: Английский

Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units DOI Creative Commons
Linjiang Wang,

Bingfang Wu,

Weiwei Zhu

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1201 - 1201

Published: March 28, 2025

This study proposes an evapotranspiration (ET) disaggregation model based on slope units. Different units are first delineated digital elevation data with high spatial resolution. Key factors influencing ET variability across topographies, such as radiation, vegetation, and moisture, integrated using Sentinel-2 DEM to construct indicating factor. A slope-scale is developed ETWatch (1 km resolution) the factor, yielding reliable 10 m resolution that reflect variations. The validation in Huairou Baotianman shows coefficients of determination 0.9 0.91, respectively, root mean square errors 0.45 mm 0.47 mm. Compared original 1 data, disaggregated results show improved accuracy, R2 values increasing by 1% (Huairou) 2% (Baotianman) RMSE decreasing 21% 13%, respectively. offers a novel approach for estimating forest mountainous areas significant potential water resource management sustainable land–water allocation.

Language: Английский

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