Zhong et al. (2026) High-Spatiotemporal-Resolution Remote Sensing Retrieval of Evapotranspiration with Sentinel-2 Data by Sharpening MODIS Land Surface Temperature

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-09-05
• Authors: Xiaochun Zhang, Liangsheng Shi, Tianyu Shi
• DOI: 10.3390/rs18173039

Research Groups

• Institute of Remote Sensing Science, Beijing Normal University
• Key Laboratory of Land Surface Processes and Climate Change, Chinese Academy of Sciences

Short Summary

This study proposes a remote sensing evapotranspiration (ET) retrieval method based on land surface temperature (LST) sharpening, achieving high-spatiotemporal-resolution ET estimation with improved accuracy. The method combines Sentinel-2 multispectral data with auxiliary variables to enhance the spatial reality of LST and temporal details of ET.

Objective

• Investigate the feasibility of using a remote sensing ET retrieval method based on LST sharpening for precision irrigation management and water resource regulation

Study Configuration

• Spatial Scale: Local scale (Luancheng District, Hebei Province)
• Temporal Scale: Daily to seasonal scale (winter wheat growing season)

Methodology and Data

• Models used:
  • DMS algorithm for LST sharpening
  • Cubist regression tree for integrating auxiliary variables
  • Surface energy balance model for ET estimation
  • Unmixing–weight ET image fusion model (UWET) for fusing high-resolution and low-resolution ET datasets
• Data sources:
  • Sentinel-2 multispectral data
  • MODIS LST data
  • Auxiliary variables (DEM, albedo, NDVI, land cover)
  • Eddy covariance flux measurements

Main Results

• The proposed method achieved a high correlation coefficient (R = 0.921) and low root mean square error (RMSE = 0.779 mm/day) in ET estimation during the 2019–2020 growing season.
• The results demonstrated that auxiliary variables significantly enhanced the spatial reality of LST, while LST sharpening effectively improved the spatial heterogeneity of ET.

Contributions

This study contributes to the development of a reliable high-spatiotemporal-resolution ET dataset for refined farmland irrigation management and water resources regulation. The proposed method combines the strengths of Sentinel-2 multispectral data with auxiliary variables to compensate for the temporal deficiency of Landsat, thereby greatly promoting the accuracy of spatiotemporal fusion.

Funding

• National Natural Science Foundation of China (Grant No. 41801330)
• Key Research and Development Program of Hebei Province (Grant No. 19226621D)

Citation

@article{Zhong2026HighSpatiotemporalResolution,
  author = {Zhong, Liao and Zhang, Xiaochun and Shi, Liangsheng and Shi, Tianyu},
  title = {High-Spatiotemporal-Resolution Remote Sensing Retrieval of Evapotranspiration with Sentinel-2 Data by Sharpening MODIS Land Surface Temperature},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18173039},
  url = {https://doi.org/10.3390/rs18173039}
}