Crop Evapotranspiration Estimates for Sugarcane Based on Remote Sensing and Land Surface Model in Thailand

Abstract

Sugarcane is a high biomass crop that requires large quantities of water for maximum yield. The study aims to estimate daily actual water consumption or crop evapotranspiration ( $ET_{c}$ ) at field scales for practical applications with real data, remote sensing (RS) observation using novel methodologies (ML: machine learning and LSM: land surface model). Northeast Thailand is the study area chosen and three Sugarcane growing seasons (2016 to 2019) is the duration of the study. Similarities between the crop coefficient ( $K_{c}$ ) curve and a satellite-derived leaf area index (LAI) showed potential for estimation of $K_{c}$ maps. A regression model has been developed to establish LAI vs $K_{c}$ relation and used to derive daily $K_{c}$ maps. With a view to computing daily reference evapotranspiration ( $ET_{o}$ ) driven by weather, RS data, soil texture, land charac-teric etc, a high resolution LSM has been customized. The results shows $ET_{c}$ distribution low at initial and early development stages, while $ET_{c}$ tends to be high during grand growth and yield formation stages. Significant spatiotemporal variation has been observed across fields. Analysis of 19 fields for complete three seasons has been undergone with regard to yield response to water consumption and outcomes confirm standard yield reduction due to water stress. The daily $ET_{c}$ maps aided to demonstrate the variability of crop water use during growing season at field scales. Further, using $ET_{c}$ maps at the field scale in near real time, growers can supply optimal water to maximize yield, leading to water conservation in scale.