Mapping 10-m monoculture and intercropped maize of Kenya with phenology knowledge and Sentinel-2 data

In East Africa, lack of agriculture inputs and unstable climates lead to 50% yield gaps, making intercropping—the planting of more than one crop in the same parcel of land—a common agricultural management practice among smallholder farmers to improve land-use efficiency and reduce risks. In Kenya, where maize is the staple food, maize is often intercropped with beans, legumes, and potatoes. Despite its widespread, agricultural statistics on intercropping are currently sparse, and remote sensing approaches for large-scale crop monocultures are often unsuitable for intercropping monitoring. Mapping intercropping at national scale is extremely challenging because of heterogeneous landscapes, lack of cloud-free satellite imagery, and the scarcity of high-quality ground-based situ data in these regions. This study addressed these challenges using a phenology-assisted automated mapping framework on Google Earth Engine (GEE) to create 10m-resolution maps of monoculture and intercropped maize across Kenya for the long and short rainy seasons of 2023.
First, we computed 10-day median composites of Sentinel-2 optical reflectance data for each pixel in the region to build monoculture/intercropped/non-maize Random Forest (RF) classifiers. Several thousand crop ground labels were collected during field surveys in 2023, including monoculture maize (mono-maize), intercropped maize (in-maize), and other crops (e.g., wheat, rice, coffee, tea, sugarcane, potatoes, beans, etc.). To address the limited availability of intercropped maize samples, a novel phenology-based approach was implemented. Maize was first differentiated from other crops by analyzing TCARI and OSAVI during the vegetative phase and ARI during maturity. Additionally, lower greenness and moisture levels in intercropped systems, which have larger planting width and more short-term crops, were detected using the SWIR1/NDVI ratio, effectively distinguishing mono-maize from in-maize. Automatically derived monoculture/intercropped maize samples and 40% of ground samples were used for training, while the remaining ground data were used for accuracy assessment. 
For the long rainy season, the overall accuracy (OA) was 0.88, with an F1-score of 0.87 for mono-maize and 0.78 for in-maize. For the short rainy season, OA dropped to 0.85, with F1-scores of 0.82 for mono-maize and 0.72 for in-maize. Misclassification primarily arose from phenological similarities between mono-maize and in-maize and increased planting of other crops with similar patterns during the short rainy season. Results revealed that 854,432 hectares of mono-maize were concentrated in the Western region and Rift Valley plateau during the long rainy season, while 1,061,701 hectares of in-maize were widely distributed across the region, particularly near Mount Kenya and the Eastern region. In the short rainy season, reduced and erratic precipitation led to decreased maize planting, with more farmers opting for intercropped systems and short-term crops to reduce risks of crop failure. 
We are convinced that this study is a crucial first step to demonstrate the potential of Sentinel-2 data and phenology-based automated mapping for large-scale monitoring of intercropping, providing critical insights for agricultural monitoring in sub-Saharan Africa. It serves as a foundation for developing a regional archive of monoculture and intercropped crop systems and addressing key agricultural challenges across the region.