Coupling optical and SAR imagery for automatic garlic mapping - Chen, Youkuo; Guo, Yan; Qiao, Longxin; Xia, Haoming (Frontiers in Sustainable Food Systems, 2022)
Accurate garlic identification and mapping are vital for precise crop management and optimization of yield models. However, the previous understanding of garlic identification was limited, and a single sensor was mostly used. Here, an automatic mapping framework for garlic was proposed using optical and SAR images on Google Earth Engine. The algorithm extracts the phenology of winter crops based on Sentinel-2 time-series images, and identifies winter crops accordingly. The identification of garlic is based on the winter crops map, and experiments are carried out using Sentinel-1 and Sentinel-2 time-series images respectively. Additionally, validation data from multiple sources were used to evaluate the results. Coupled optical and SAR images (OA 95.34% and kappa 0.91) outperformed in garlic mapping compared to using only optical images (OA 74.78% and kappa 0.50). The algorithm explored the potential of multi-source remote sensing data to identify target crops in mixed and fragmented planting regions.