Temporal sensitivity of in-season crop classification: An explainable multi-year Sentinel-2 analysis in Western Australia

Authors

• Sneha Sharma, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Harry Eslick, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Rodrigo Pires, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Balwinder Singh, Department of Primary Industries and Regional Development, Western AustraliaFollow
• Hasnein Tareque, Department of Primary Industries and Regional Development, Western AustraliaFollow

Document Type

Article

Publication Date

5-20-2026

Journal Title

Remote Sensing

ISSN

2072-4292

Keywords

Sentinel-2, vegetation indices, time-series analysis, in-season crop mapping, explainable artificial intelligence

Disciplines

Agriculture | Agronomy and Crop Sciences

Abstract

Accurate in-season crop type mapping is critical for agricultural monitoring and yield assessment, yet most operational products remain proprietary, post-seasonal or insufficiently tested across contrasting seasons. This study presents an open and transferable framework that quantifies how in-season crop classification skills evolve through the growing season across the southwest agricultural region of Western Australia (WA) using a multi-temporal (2020–2024) Sentinel-2 derived vegetation indices (VIs) time-series. Six crop classes (i.e., wheat, barley, canola, lupins, pasture, and fallow) were evaluated using extreme gradient boosting (XGBoost) and long short-term memory (LSTM) models under a leave-one-year-out cross-validation (LOYOCV) design. Classification performance increased progressively through the season, with a marked improvement in late winter (late August to early September). In LOYOCV, overall agreement with the reference dataset exceeded 90% once vegetation-index observations through August were included, indicating that reliable in-season mapping was achievable before harvest. Canola was separated consistently from mid-season onwards, whereas reliable discrimination between wheat and barley required later phenological information. Independent field-based testing was used to assess true crop identification accuracy for the three externally observed classes: wheat, barley, and canola. In this test set, precision was highest for canola (0.93), followed by wheat (0.82) and barley (0.71). These field-based results supported the main temporal pattern observed in the LOYOCV analysis, particularly the strong mid-season separability of canola and the persistent confusion between wheat and barley. SHapley Additive exPlanations (SHAP) showed thatVIs centred on late winter contributed most strongly to model predictions, consistent with peak phenological divergence among crop types. These results identify a phenologically meaningful decision window for in-season crop mapping and provide a multi-year benchmark for evaluating temporal transferability in Mediterranean broadacre systems.

Recommended Citation

Sharma, S., Eslick, H., Pires, R., Singh, B., & Tareque, H. (2026). Temporal Sensitivity of In-Season Crop Classification: An Explainable Multi-Year Sentinel-2 Analysis in Western Australia. Remote Sensing, 18(10), 1653. https://doi.org/10.3390/rs18101653