Assessing Key Factors Affecting Water Use in Winter Wheat in Slovakia Using Earth Observation Data and Random Forest-Based Model

Identifying the primary soil parameters, weather variables and crop management practices that influence spatial variations in crop water use is essential for strategically defining optimal agricultural management practices. In this study, soil physico-chemical, weather and crop management variables were used through random forest (RF)-based modeling to evaluate the determinants of actual evapotranspiration (ETa) in winter wheat across Slovakia. ETa was estimated using Landsat imagery and the Python implementation of the Surface Energy Balance Algorithm for Land (PySEBAL), along with information from the Land Use/Cover Area frame Survey (LUCAS) over four cropping seasons. Overall, good agreements were found between PySEBAL-derived ETa and measured values, with RMSE and R² values of 0.93 mm and 0.87, respectively. Seasonal ETa values ranged from 434.87 mm to 506.12 mm, with the highest and lowest average values found in the 2011/2012 and 2017/2018 cropping seasons, respectively. The RF model showed good performance in predicting seasonal ETa, with an RMSE of 21 mm/season for the training data and 32 mm/season for the validation data, and R² values of 0.90 and 0.72, respectively. Our analysis indicated that ETa was primarily influenced by relative humidity, wind speed, solar radiation, altitude, and pH. The study further indicated that wheat production was unsuitable above 600 m elevation, while optimal crop water use occurred below 200 m. Addressing issues such as soil erosion and acidification could improve wheat crop water use efficiency across Slovakia. This modeling approach can serve as a basis to develop a crop water use forecasting system for sustainable wheat production in the region.