Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?

Abstract

The European legislative guidelines on the Common Agricultural Policy (CAP) lead to an obligation of area-wide information about cross-compliant Greening measures in EU countries. But by on-site controlling, agricultural authorities can monitor only a small portion of all registered parcels. With the Copernicus Programme, the freely available Sentinel-1 and Sentinel-2 imagery increasingly supports remote sensing-based agricultural monitoring on a large-scale. However, most prototypes lack the topic of winter catch cropping. Therefore, we developed a new machine learning method for catch crop monitoring and detection at the parcel-level. To gain training and test data for supervised machine learning, we collected winter catch crop parcel data from different years (2016–2019) and four federal states in Germany. Normalized Difference Vegetation Index (NDVI) time series were calculated for each parcel from Sentinel-2 data within the typical winter catch crop cultivation season (July-April). We revealed distinctive temporal patterns of catch cropping and developed nineteen descriptive features for automatized prediction. Then, we trained fifteen Random Forest classifiers comprising different regions and years and conducted a multi-level validation to identify the model with the highest robustness on new data. The Random Forest classifier trained with the input data from all federal states and years outperformed the other models. It reached a mean prediction accuracy of 84% for both classes (catch crop and non-catch crop) across eleven different spatio-temporal domains. Under optimal annual weather conditions it reached accuracies close to 90%. Anomalies caused by heat waves and early frost events were found to have a high influence on the phenology of catch crops and thus lead to reduced prediction accuracies. From the set of predictors, those features with the highest importance measured the correlation between the observed time series and a simulated NDVI phenological profile. We concluded that catch cropping parcels are automatically separable from parcels with other winter cultivations (e.g., winter cereals, grasslands, fallow) with Sentinel-2 NDVI time series data. Different catch crop subgroups (i.e., seed mixes) could not be differentiated by our approach due to very similar phenological profiles. Nonetheless, the used approach allows for large-scale winter catch crop monitoring and supports authorities in the selection of parcels with high demand for on-site controlling. By merging the training datasets from different federal states and years, we could overcome the typical spatial and temporal overfitting problem in machine learning. Therefore, the study’s final classifier can be reliably transferred to new datasets in Germany and other regions with similar bio-geographical conditions.