The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like microplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. The findings highlight the potential of combining remote sensing and ML for scalable and cost-effective monitoring of EOM applications, supporting precision agriculture and sustainability.

本研究针对农业中外源有机物施用对土壤和作物健康影响的监测空缺，采用光学哨兵-2卫星影像评估消化物施用的效果。通过时间序列分析和机器学习模型，该研究实现了对消化物存在的高准确性检测，表明遥感与机器学习结合在精细农业和可持续发展中的潜在应用与价值。

利用哨兵-2卫星影像监测农业作物上的消化物施用