Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Remote Sensing

Daniel Kpienbaareh¹  Xiaoxuan Sun¹  Isaac Luginaah¹  Jinfei Wang¹  Rachel Bezner Kerr²  Laifolo Dakishoni³  Esther Lupafya³ 

[1] Department of Geography and Environment, Social Science Centre, Western University, London, ON N6A 5C2, Canada;Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA;Soils, Food and Healthy Communities (SFHC), P.O. Box 36 Ekwendeni, Malawi;

关键词: crop classification;    data fusion;    food security;    random forest classification;    PlanetScope;    Sentinel-1;   

DOI  :  10.3390/rs13040700

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown