Assessing multi‐sensor hourly maize evapotranspiration estimation using a one‐source surface energy balance approach

Abstract

AbstractIn this study, the performance of a one‐source surface energy balance (OSEB) remote sensing (RS) of actual crop evapotranspiration (ETa), incorporating data from different spaceborne, airborne and proximal multispectral data, was evaluated. The RS platforms in this study included Landsat‐8 (30 m pixel size), Sentinel‐2 (10 m), Planet CubeSat (3 m), a handheld (proximal) multispectral radiometer (MSR) (1 m) and an unmanned aerial system (UAS) (0.03 m). A 2‐year data set (2020 and 2021) from two maize research sites in Greeley and Fort Collins, Colorado, USA, provided ground‐based data for estimating and evaluating hourly ETa from the OSEB algorithm. The accuracy of OSEB hourly maize ETa estimates was evaluated using calculated hourly maize ETa using high‐frequency data collected with an eddy covariance energy balance system installed at each research site. The results indicated that the Planet CubeSat multispectral sensor (3 m), combined with on‐site surface temperature data, yielded the least errors when predicting maize ETa. The hourly ETa estimation errors for the Planet CubeSat were MBE ± RMSE of −0.02 (−3%) ± 0.07 (13%) mm h⁻1. These results suggest the urgent need for a specific approach to improve RS multispectral and thermal radiometric data (quality) to better support sustainable irrigation water management practices.