首页|Mapping upland crop-rice cropping systems for targeted sustainable intensification in South China
Mapping upland crop-rice cropping systems for targeted sustainable intensification in South China
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Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural intensification.Accurate UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural revital-ization.However,datasets describing cropping systems are limited in spatial coverage and crop types.Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large regions.We describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)data.One indicator,the VV backscatter range,was proposed to discriminate UCR and another two indi-cators were designed by coupling greenness and pigment indices to further discriminate tobacco or oil-seed UCR.The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions.This study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)region.The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92％.There were 7348 km2 areas of UCR,roughly one-half of them located in plains.The UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69％and 15％of UCR area.UCR patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice cropping.Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.
Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education,Academy of Digital China(Fujian),Fuzhou University,Fuzhou 350116,Fujian,China
Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs,Beijing 100081,China
Department of Land Surveying and Geo-Informatics,The Hong Kong Polytechnic University,Kowloon 999077,Hong Kong,China
国家自然科学基金国家自然科学基金National Key Research and-Development Program of ChinaScience Bureau of Fujian ProvinceFinance Department and the Digital Economy Alliance of Fujian Province
NETL
NSTL
万方数据
