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A multi-class; multi-temporal crop and Land cover mapping framework for Morocco using Sentinel-1/2 monthly composites and advanced machine learning ensembles
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DOI：10.3389/frsen.2026.1827393.png)
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Abstract
En 中文
Timely and accurate crop-type mapping is fundamental for sustainable agricultural management and food security in semi-arid regions; where climate variability and fragmented landscapes present persistent challenges. This study develops and validates an operational; multi-temporal framework for classifying five key agricultural classes (i.e.; soft wheat; durum wheat; barley; trees; and other crops) across diverse Moroccan agroecosystems. By integrating monthly Sentinel-1 Synthetic Aperture Radar and Sentinel-2 optical time series spanning six growing seasons (2018–2025); we extracted 156 features comprising 13 spectral indices across 12 monthly composites. Ground truth data from the national Al Moutmir database; strategically balanced to address natural class imbalances; supported comprehensive training and validation of six machine learning models (i.e.; Random Forest; Extra Trees; XGBoost; LightGBM; Voting Ensemble; and Stacking Ensemble). Our findings showed that the LightGBM and Stacking Ensemble achieved the highest performance with 88.04% overall accuracy; followed closely by XGBoost (87.93%). Feature importance analysis revealed that monthly temporal resolution significantly outperformed traditional phenological-stage approaches; with March and April indices (particularly Normalized Difference Vegetation Index and Normalized Difference Red Edge) contributing most to class discrimination. Notably; early-season radar features (Vertical-Vertical polarization in September) provided valuable complementary information when optical data were limited. The framework demonstrated robust generalization through 10-fold cross-validation while explicitly quantifying a 12.56% overfitting gap (train-CV difference); acknowledging a non-negligible overfitting risk. Offering transparent performance assessment. Error analysis identified persistent confusion between spectrally similar cereals; particularly durum and soft wheat; highlighting priority areas for future sensor integration. This scalable; cloud-based pipeline directly supports Morocco’s Green Generation strategy by providing a reproducible; high-accuracy solution for annual crop inventories; with transferable applications across similar Mediterranean and semi-arid agricultural systems.
Keywords:
deep learning
remote sensing
time series analysis
crop type classification
Sentinel-1 and Sentinel-2
Journal
F
IF:
3.7
Papers:
560
Citations:
993
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Cited Papers