OPTRAM-ET: A novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations

Abstract

Satellite remote sensing technology provides a promising means for near real-time monitoring of crop water status and requirements in agricultural and hydrological applications. Estimation of actual evapotranspiration (ETa) often requires thermal information; however, not every satellite is equipped with a thermal sensor, which limits the estimation of ETa. To address this limitation, here we propose a satellite-based ETa estimation model, OPTRAM-ET, based on the optical trapezoid model (OPTRAM) estimates of soil moisture and a vegetation index (VI). We applied the OPTRAM-ET model to Sentinel-2 and Landsat-8 satellite data and evaluated the model for ETa estimates using 16 eddy covariance flux towers in the United States and Germany with different landcover types, including agriculture, orchard, permanent wetland, and foothill forests. Next, OPTRAM-ET was compared with the conventional land surface temperature (LST)-VI model. The proposed OPTRAM-ET model showed promising performance over all the studied landcover types. In addition, OPTRAM-ET showed comparable performance to the conventional LST-VI model. However, since the OPTRAM-ET model does not need thermal data, it benefits from higher spatial and temporal resolution data provided by ever-increasing drone- and satellite-based optical sensors to predict crop water status and demand. Unlike the LST-VI model, which needs to be calibrated for each satellite image, a temporally-invariant region-specific calibration is possible in the OPTRAM-ET model. Therefore, OPTRAM-ET is substantially less computationally demanding than the LST-VI model.

Yasamin Afrasiabian

PhD student

My research interests include remote sensing, particularly hyperspectral UAV and satellite imaging, and machine-learning methods for ecosystem-biodiversity characterisation, precision agriculture, and hydrological analysis.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.