Abstract
The extraction of sugarcane planting distribution is the basis of crop monitoring. The extraction of timely and accurate sugarcane planting distribution is of great significance for monitoring sugarcane crops and making adjustments to the planting structure. Based on Sentinel-1 and Sentinel-2 data, in this paper, we adopted the active and passive remote sensing collaborative method and decision tree classification method to carry out research on the extraction method of sugarcane planting distribution in Guangxi. Then we verified the data by referring to multi-year survey ground sample data. On this basis, we produced dataset of the sugarcane planting distribution with the spatial resolution of 10 m in Guangxi from 2018 to 2020. The remote sensing extraction-based method yielded an overall accuracy of 92% for sugarcane planting distribution in Guangxi in 2018, with a Kappa coefficient of 0.8. The overall accuracy of sugarcane planting distribution in Guangxi in 2019 is 94%, with a Kappa coefficient 0.88; the overall accuracy of sugarcane planting distribution in Guangxi in 2020 is 94%, with a Kappa coefficient of 0.88. This dataset can be used as the basic data for the analysis of temporal and spatial changes of sugarcane in Guangxi. And it can also provide basic data support for the optimization and adjustment of sugarcane production management and planting structure in Guangxi.
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