EO-Based Crop Classification for Rotation Monitoring – Evaluating Temporal Consistency of Operational Models for Sustainable Agricultural Management

Accurate crop rotation monitoring is essential for sustainable agricultural management, supporting policy compliance, soil health assessment, and climate-resilient farming practices. Earth observation-based crop classification has become operational across Germany, with models producing annual outputs since Sentinel-2's launch in 2017. While these systems report high single-year accuracies, their reliability for multi-temporal applications—particularly rotation pattern detection remains insufficiently evaluated. ­

This study assesses the performance of two operational German crop classification models from Thünen Institute and the German Aerospace Center (DLR) for rotation analysis in Saxony-Anhalt, testing their applicability beyond original training regions. We processed multi-year classification outputs (2017–2024) using CropRotViz, an open-source R package specifically designed for handling temporal intersection, change detection and rotation pattern visualization. Model outputs were validated against Land Parcel Identification System (LPIS) reference data, evaluating both spatial accuracy and temporal consistency—the latter being critical for reliable rotation monitoring. The rotation Sequences of 3, 4 and 5 years were analyzed.

Preliminary results revealed a significant performance gap between single-year classification accuracy and multi-year rotation detection reliability. The DLR and Thünen models achieve annual accuracies of 0.81–0.90, with variability across years and crop types. However, when comparing overlapping areas with LPIS data across multi-year sequences (3-, 4-, and 5-year rotations), accuracies dropped substantially to 0.36–0.57. These errors compound over time, limiting model utility for applications requiring temporal stability, such as crop diversification monitoring, compliance verification for sustainable farming schemes, or assessing rotation impacts on soil health and carbon sequestration.

Our findings highlight a critical challenge for operational EO-based agricultural monitoring: current validation frameworks emphasizing annual accuracy may inadequately assess suitability for sustainability-relevant applications requiring temporal field level consistency. To transition from observation to actionable agricultural management support, classification systems must explicitly optimize for temporal robustness. We recommend incorporating rotation-specific validation metrics and developing approaches that leverage temporal context during classification to enhance consistency.

This work contributes to improving large-scale agroecosystem monitoring capabilities by identifying limitations in current operational systems and providing methodological tools (CropRotViz) for temporal analysis. Enhanced rotation monitoring supports evidence-based sustainable management, from precision agriculture to policy evaluation for climate-resilient farming transitions.