Integrating Landsat and ECOSTRESS to explore the value of additional thermal infrared satellite observations for field-scale evapotranspiration mapping

Landsat has provided an indispensable foundation for field-scale evapotranspiration (ET) mapping but is constrained by its overpass temporal frequency. Although integrating additional thermal data offers a pathway to bridge this gap, the magnitude of improvement varies across observation characteristics and surface context and remains less understood. Here, ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is used as an additional thermal source on top of the Landsat baseline to evaluate the value of more frequent thermal observations for improving field-scale ET time series. We implemented a Google Earth Engine -based Disaggregated Atmosphere-Land Exchange Inverse + Data Mining Sharpener framework to generate a Landsat-based ET baseline and enhanced it with ECOSTRESS observations. The resulting ET data was validated across 25 flux-tower sites spanning diverse land-cover types and climate zones across the contiguous United States. When integrated with Landsat, the addition of ECOSTRESS observations reduced interpolation errors and improved temporal consistency across most sites. However, improvements in ET time series agreement depended strongly on (1) the extent to which ECOSTRESS reduced effective Landsat sampling gaps and (2) the accuracy of overpass-day ECOSTRESS ET retrievals. Improvements were largest when ECOSTRESS shortened Landsat observation gaps by ~50% and maintained high scene quality. ECOSTRESS overpass time also influenced performance, with acquisitions between 10:00–14:00 producing most reliable ET estimates. These results support developing methods with multi-satellite observations to strengthen field-scale ET and help address the key knowledge gap regarding the value of increased thermal observations.