The European Union (EU) member states are expected to develop new procedures, based on automatic earth observation data analysis, for the management and control of direct aids to the farmers, as part of the Common Agricultural Policy (CAP) reform of 2020. Here, we propose an operational monitoring system based on Sentinel-2 surface reflectance (SR) data and machine learning (ML) algorithms, consisting of a hierarchical approach triggering 3 color-coded warning alerts to distinguish among compliant (green), non-compliant (red), and inconclusive (yellow) parcels comparatively to the farmer’s declaration. A Random Forest (RF) model is applied to a 5-day interpolated SR time series to generate a preliminary crop map, where all the parcels whose predicted and declared crop type match are flagged as compliant. Next, a refinement procedure is adopted to improve the discrimination between temporary and permanent crops. At this stage, VI temporal metrics and texture are used as input to a Support Vector Machine (SVM) classifier trained using only the previous compliant parcels. Through a set of decision rules, SVM crop class predictions are flagged as compliant, non-compliant and inconclusive. The system was tested for a significant area in mainland Portugal, using the 2019 Land Parcel Information System (LPIS) data. The system returned 96.5%, 2.5% and 1% of the parcels as compliant, inconclusive, and non-compliant, respectively. Comparison with field inspections for the subsidy control of 2019 revealed that only 1.1% of the correct declarations were classified by the system as non-compliant (5% as admissible value), while less than 5% of the real non-compliant declarations passed through the system (10–20%).

作为 2020 年共同农业政策 (CAP) 改革的一部分，预计欧盟 (EU) 成员国将开发基于自动地球观测数据分析的新程序，用于管理和控制对农民的直接援助。 ，我们提出了一种基于 Sentinel-2 表面反射率 (SR) 数据和机器学习 (ML) 算法的操作监控系统，该系统由触发 3 个颜色编码警告警报的分层方法组成，以区分合规（绿色）、不合规（红色）和不确定（黄色）包裹与农民的声明相比。随机森林 (RF) 模型应用于 5 天内插 SR 时间序列以生成初步作物图，其中预测和声明的作物类型匹配的所有地块都被标记为合规。下一个，采用了一种改进程序来改善对临时作物和永久作物的区分。在这个阶段，VI 时间度量和纹理被用作支持向量机 (SVM) 分类器的输入，该分类器仅使用先前的兼容地块进行训练。通过一组决策规则，SVM 作物类别预测被标记为符合、不符合和不确定。该系统使用 2019 年土地包裹信息系统 (LPIS) 数据在葡萄牙大陆的一个重要区域进行了测试。系统分别返回 96.5%、2.5% 和 1% 的包裹为合规、不确定和不合规。与 2019 年补贴控制的现场检查相比，只有 1.1% 的正确申报被系统归类为不合规（5% 为可接受值），