Double cropping detection in the moderate continental climate region of Serbia using machine learning and Sentinel-2 data - Miljana Marković; Predrag Lugonja; Sanja Brdar; Ioannis Athanasiadis; Mirjana Radulović; Branislav Pejak; Minučer Mesaroš; Vladimir Crnojević (European Journal of Remote Sensing, 2025)
Global food security is challenged by population growth and limited agricultural land. Intensifying existing cropland use is crucial for increasing agricultural production. Mapping cropping intensity, defined by annual crop cycles and commonly classified as single, double, or triple cropping, is vital for food production modeling. While most studies focus on regions with higher cropping intenisity, this study targets Vojvodina in northern Serbia, where double cropping is less prevalent. We investigated the impact of different vegetation indices (VIs) on detecting double cropping using machine learning (ML) and Sentinel-2 imagery with collected ground truth data over two years with contrasting weather conditions: one dry and one with above-average rainfall. Our approach improves existing methods by integrating VIs not previously examined in related studies. Alongside NDVI, indices such as CVI, VARI, and ExG significantly improved model performance. In the dry year (2022), overall accuracy reached 95.80%, with an F1-score of 91.19% for classifying double cropping, while in the wet year (2023), the accuracy was 93.56% and the F1-score 84.96%. This approach simplifies data requirements while maintaining high accuracy, making it applicable to regions with similar geographical characteristics. Additionally, this study fills a key knowledge gap on the extent and distribution of this practice in Serbia.