Improved daily evapotranspiration estimation using remotely sensed data in a data fusion system

Authors: YANG, YUN - Non ARS Employee; Anderson, Martha; Gao, Feng; XUE, J. - Non ARS Employee; Knipper, Kyle; HAIN, C. - Nasa Marshall Space Flight Center

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2022
Publication Date: 4/7/2022
Citation: Yang, Y., Anderson, M.C., Gao, F.N., Xue, J., Knipper, K.R., Hain, C. 2022. Improved daily evapotranspiration estimation using remotely sensed data in a data fusion system. Remote Sensing. 14(8):1772. https://doi.org/10.3390/rs14081772.
DOI: https://doi.org/10.3390/rs14081772

Interpretive Summary: Crop water use (evapotranspiration or ET) can be effectively mapped with satellite imagery and used in irrigation scheduling tools, adjusting water applied based on the amount of water consumed. To make ET maps at the field scales and daily timesteps useful for irrigation management, ET maps from multiple satellites can be fused – combining information from satellites that provide good spatial detail and from others that collect data frequently. This paper describes an improved approach for fusing satellite-based ET images that works better over crops where water use changes rapidly, such as alfalfa crops that are harvested and regrown on a monthly basis. The new method is tested over a variety of crops grown in the Central Valley of California against measurements of water use made in the field, and demonstrated to better represent rapid changes in ET than the standard fusion method. This new fusion method should improve the timeliness and accuracy of water use information for irrigation management.

Technical Abstract: Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of the thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30-m ET retrievals are evaluated with flux tower observations and analyzed based on land use type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.