Mapping Crop Types in Smallholder Mono- and Intercropping Systems with Multi-Sensor Data in Regions with Multiple Growing Cycles

Complex smallholder agriculture, characterized 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 crop 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. Fieldbased crop inventories were incorporated to identify eight farming system classes in the southern 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 to mapping similar crop types across multiple growing cycles accounted for inter-growing cycle crop dynamics and demonstrated how integrating local cropping practices and crop calendars with satellite data can advance the remote sensing of smallholder agriculture.