Using sentinel-2 data to quantify the impacts of drought on crop yields at local and regional scales in Sweden

• Developed a framework using Sentinel-2 data to map drought yield losses in fields. • PPI and TPROD enabled accurate crop yield monitoring. • In 2018, spring crops lost more yield; winter barley showed the highest resilience. • Clay and wilting point affected yield loss, but their influence was minor. • Approach aids in crop selection and irrigation planning. A causal inference framework was developed to investigate crop responses to agricultural drought by integrating meteorological data, Sentinel-2-derived data, and soil property maps. To account for crop rotation, soil, and topographical variables, propensity score matching was employed to estimate drought-induced yield losses at the field level for selected periods. The Plant Phenology Index (PPI) and the derived Total Productivity (TPROD) parameter enabled monitoring of crop development and productivity. TPROD showed high regional accuracy (R² = 0.93) and field-level accuracy for estimating crop yields (R² = 0.42–0.73, varying by crop type). The monitoring of common production crops in Sweden during the 2018 drought revealed that all crops had a shortened growing season, with spring-sown crops experiencing greater yield losses. The influence of soil texture variables, which act as indicators of water holding capacity, on the variability of drought-induced yield losses was assessed, and seasonal dynamics were examined, thereby improving the comprehension of the interactions among soil-plant-atmosphere dynamics at a local scale. We conclude that applying propensity score matching combined with satellite remote sensing can provide site-specific information on crop selection and timing and facilitate economically efficient irrigation planning. Nevertheless, further improvements are recommended, such as incorporating more detailed field-level data on yields and management practices, to enhance the approach's robustness and applicability for drought preparedness and adaptive agricultural management.