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- Title
Mapping annual 10-m soybean cropland with spatiotemporal sample migration.
- Authors
Zhang, Hongchi; Lou, Zihang; Peng, Dailiang; Zhang, Bing; Luo, Wang; Huang, Jianxi; Zhang, Xiaoyang; Yu, Le; Wang, Fumin; Huang, Linsheng; Liu, Guohua; Gao, Shuang; Hu, Jinkang; Yang, Songlin; Cheng, Enhui
- Abstract
China, as the world's biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices' temporal characteristics. This method uses a feature space of six integrals from the crops' phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.
- Subjects
CHINA; RANDOM forest algorithms; FARMS; SUSTAINABLE agriculture; FOOD supply; SOYBEAN; GROWING season
- Publication
Scientific Data, 2024, Vol 11, Issue 1, p1
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-024-03273-5