Complex smallholder agriculture, characterised by overlapping sowing windows and crop mixtures, poses a challenge to crop type mapping using remote sensing. While previous studies have addressed smallholder crop type classification, few have examined intercropping, necessitating a nuanced understanding of the temporal characteristics of cropping practices (e.g., crop combinations and sequencing). We integrated Sentinel-1 and Sentinel-2 for mapping mono- and intercropping systems across multiple growing cycles, which has been overlooked by studies treating the rainy season with multiple growing cycles as a single temporal block. Field-based crop inventories were incorporated to identify eight farming system classes in the Guinea Savannah of southwest Nigeria (SGS). These include early maize, late maize, early cassava, late cassava, yam, rice, maize-cassava intercropping, and others, comprising sweet potato, cocoyam, and cowpea, as well as other minority crops. Random Forest models were trained using monthly and bimonthly composites in seven experiments which were validated through 30-fold cross-validation. Models with only Sentinel-1 had low overall accuracy (0.50). Accuracy improved to over 0.75 for all classes in the best-performing model combining monthly Sentinel-1 and bimonthly Sentinel-2 data. Class-wise accuracy for rice was highest (UA=0.90, PA=0.81), whereas maize-cassava intercropping had PA=0.85, UA=0.79. Early maize was higher (UA=0.81, PA=0.89) than late maize (UA=0.74, PA=0.58). Regional distribution across the SGS reveals that yam concentrates in the north, while early cassava and early maize are mainly found in the central areas, and intercropping dominates fragmented southern landscapes. The scalable approach accounted for inter-growing cycle crop dynamics and demonstrates how integrating local cropping practices and crop calendars can advance remote sensing of smallholder agriculture. • Local cropping practices were integrated in the remote sensing workflow • Identified eight farming system classes in mono- and intercropping systems • S1 monthly + S2 bimonthly model class-wise accuracy exceeded 0.75 for all classes • Early maize class-wise accuracy is higher than that of late maize • Feature importance showed VV, VH, Blue, and SWIR1 bands to be most discriminative
Akinyemi et al. (Sun,) studied this question.