Abstract
Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.
Highlights
IntroductionMapping agricultural landscapes to identify crop types, analyze the spatial distribution of crops and cropping systems, and document land cover types in countries in the Global South is critical for guiding agricultural and environmental planning decisionmaking, especially in areas experiencing rapid climate change and chronic food insecurity
The fusion of Sentinel-1 C2 matrix, Sentinel2, and PlanetScope optical data with the Sentinel-1 H/α polarimetric decomposition outperformed all other combinations of images
Fusing the images created high temporal resolution data, with Sentinel-1 contributing the greatest number of images due to the ability of synthetic aperture radar (SAR) to penetrate cloud cover
Summary
Mapping agricultural landscapes to identify crop types, analyze the spatial distribution of crops and cropping systems, and document land cover types in countries in the Global South is critical for guiding agricultural and environmental planning decisionmaking, especially in areas experiencing rapid climate change and chronic food insecurity. The type of crops and land cover on the landscape contribute to preventing soil degradation and maintaining soil health [1,2,3]. The diversity of crops on the landscape contributes to weed control on farmlands and yield improvements by reducing the ability. A diverse landscape that comprises different crop cultivars and varying plant species supports ecosystem services, including pollination and water quality [7].
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