用卫星影像+机器学习监测农田有机肥施用，助力精准农业
Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery
- 结合哨兵-2影像与多时相指数分析土壤光谱变化
- 机器学习模型检测准确率最高达F1=0.85
- 适合农业监管与环境可持续性研究者参考
农业中广泛使用外源有机物（EOM）需监测其对土壤和作物健康的影响。本研究评估光学哨兵-2卫星影像在检测有机肥（消化液）施用方面的潜力，该做法虽提升土壤肥力，但存在微塑料污染和氮素流失等环境风险。首先，利用哨兵-2时间序列（SITS）分析特定指数（EOMI、NDVI、EVI），表征希腊色萨利地区四种作物类型土壤施用后的光谱特征。其次，采用随机森林、k-NN、梯度提升及前馈神经网络等机器学习模型，实现消化液存在检测，最高获F1分数0.85。结果表明，遥感与机器学习结合可实现大范围、低成本的EOM应用监测，支持精准农业与可持续发展。
原文摘要 · Abstract (English)
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.
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