A Spatial and Temporal Correlation between Remotely Sensing Evapotranspiration with Land Use and Land Cover

In recent years, remote sensing technology has enabled researchers to fill the existing statistics and research gaps on evapotranspiration in different land use classes. Thus, a remotely sensed-based approach was employed investigate how rates changed use/cover classes across Lake Urmia Basin from 2016 2020. This accomplished by applying Surface Energy Balance System (SEBS) maximum likelihood algorithm. Results showed that 2020, grassland, savanna, wetland decreased 1%, 0.58%, respectively, whereas an increase of 0.4%, 2.5%, 1.2% occurred cropland, urban, shrubland, water bodies, respectively. Based model’s results, over 98, 63, 90, 93, 91% studied area, experienced value between 0–6, 3–8, 0–4, 0–6 mm It also found these values are more closely related bodies wetlands, followed urban areas, non-vegetated, shrubland. A strong correlation with R2 > 70% observed SEBS ground-measured values, while this is lower than 50% for MODIS Global Evapotranspiration Project (MOD16A2). The findings suggest can be extracted large-scale using satellite images; thus, their maps presented accurate manner.