High-resolution mapping of evapotranspiration over heterogeneous cropland affected by soil salinity

Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, particularly in arid areas such as those of the Hetao Irrigation District (HID) in Inner Mongolia, China, where water scarcity and salinity variability pose significant challenges for irrigated agriculture and ecological monitoring. Although advances in remote sensing have enhanced ET estimates, high spatial (30 m) and temporal resolution (daily) datasets, which are critical for detailed monitoring remain scarce. This study fills this gap by focusing on the ET variations influenced by salinity, with the aim of assessing the water consumption of saline cropland more accurately. Extensive datasets, including meteorological parameters, normalized difference vegetation index (NDVI), land surface temperature (LST), reflectance and salinity measurements were extracted across the region in 2017. Using a spatiotemporal image fusion algorithm implemented on the Google Earth Engine (GEE) platform, daily NDVI and LST datasets with a 30 m resolution were generated by fusing Landsat 8 and MODIS satellite data. Subsequently, the high-resolution datasets were fed into the revised Priestley Taylor Jet Propulsion Laboratory (PT-JPL) model, which incorporates salinity effects, to generate the final ET estimates. The extended NDVI and LST datasets showed high spatiotemporal accuracy (NDVI: r ≥ 0.78, RMSE=0.029–0.083; LST: r ≥ 0.99, RMSE=0.47∼1.82°C), and effectively captured significant ET changes influenced by salinity variations. Compared to the remote sensing products including PML_V2 and ETMonitor (both with resolutions below 1 km), the modeling results in this study provided greater detail in highly heterogeneous cropland. It was found that crop ET during the growing season accounted for nearly 88 % of the annual ET in HID. Among the components of crop ET, canopy transpiration contributed 75.4 %, soil evaporation 16.5 %, and canopy interception 8.1 %. By comparing PT-JPL model simulations without considering salt stress, the contribution of salt stress to the inhibition of crop ET reached 10 %. These findings enable more accurate ET modeling and prediction in arid regions and provide valuable insights for water resource management, particularly irrigation scheduling, in areas affected by salt-induced stress. By incorporating salinity factor, this research lays the groundwork for more efficient water use and land management strategies in arid and heterogeneous landscapes. • Daily high-resolution NDVI and LST datasets were generated by fusing Landsat 8 and MODIS data. • Modeling tool enhancements accounted for soil salinity effects on crop ET, improving ET estimates. • The new ET product outperformed existing products by capturing salinity-induced ET changes. • Accurate ET estimates provide key insights for water resource management in saline landscapes.