ABSTRACT Graphical overview of the methodology and results for assessing agricultural water demand in semi-arid regions of Iran using remote sensing–based evapotranspiration (ET) and soil moisture models. The upper-left panel shows the location of the study area within Iran and a land use/land cover map of the agricultural region. The upper-right panel presents the methodological workflow implemented in Google Earth Engine, including Landsat imagery, ET modeling, and soil moisture data processing. The lower panels display statistical validation results, including scatter plots comparing observed and modeled values, bar charts evaluating model performance metrics, and a temporal trend of ET derived from Landsat-8 imagery. Spatial distribution maps of daily evapotranspiration (mm/day) are also presented, illustrating variations in agricultural water demand across the study area. The figure summarizes the integrated remote sensing framework used to estimate agricultural water demand in semi-arid environments. In recent years, remote sensing technologies have gained increasing attention as effective tools for estimating evapotranspiration (ET) over large areas with high spatial and temporal accuracy. This study focuses on the Qazvin Plain, located in northwestern Iran, an important agricultural region with a semi-arid climate and limited water resources. The performance of two conventional algorithms, PySEBAL and METRIC, implemented in the Python environment, was evaluated for ET estimation in this area over the period 2018–2022. Additionally, the novel OPTRAM-ET model in the Google Earth Engine (GEE) environment was introduced, which assesses water demand without relying on thermal bands. The algorithms were tested using satellite imagery and relevant data under various land conditions, particularly in irrigated and rain-fed fields. Results indicate that the OPTRAM-ET model achieved high correlations with the PySEBAL algorithm across different land types, with values of 0. 91 and 0. 73 for Land Surface Temperature-Vegetation Index (LST-VI) and OPTRAM models in general, 0. 75 and 0. 72 in irrigated lands, and 0. 53 for OPTRAM in fallow lands. These findings demonstrate that OPTRAM-ET is a reliable tool for estimating ET, supporting improved water resource management and optimized irrigation planning in Qazvin Plain and other semi-arid agricultural regions.
Fakhar et al. (2026) studied this question.