Yield Prediction Model Based on Multitemporal Satellite Data and Open Public Data: Case Study for Bulgaria

The motivation behind this research is the fact that agricultural production in Bulgaria is a key contributor to the national economy.The current study presents a robust methodology for predicting crop yields using multitemporal Sentinel-2 satellite imagery, public agricultural statistics, and climate data in Bulgaria.Focusing on the municipalities of Medkovets, Yakimovo, and Knezha-regions with over 90% arable land-we conducted time-series analyses of the NDVI, EVI, and GCI for the 2023 and 2024 growing seasons.These indices were used to derive statistical features, which were then combined with ERA5-based climate variables and public yield records from the State Agricultural Fund.A Random Forest regression model was trained on 2023 and 2024 data and used to simulate predictions for 2025.The model achieved an R 2 of 0.78 and an RMSE of 1.24 t/ha, indicating good agreement between predicted and observed yields despite the relatively small dataset.The preliminary results reveal the importance of the EVI and NDVI as indicators of crop productivity and demonstrate variations in vegetation development between years.The findings highlight the potential of remote sensing and open data integration for regional yield forecasting while also identifying areas for future improvement, including dataset expansion and the use of ground-truth yields.