A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles.

The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ETc and ETa on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (Ks) based on its relationship with the crop water stress index (CWSI). The crop coefficient (Kc) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ETa showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency. [ABSTRACT FROM AUTHOR]