A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery
نویسندگان
چکیده
Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation “small farmers a big country”. However, mapping smallholder agriculture systems remains challenging due to diversity types, complex cropping practices, fragmented cropland. This research established sub-seasonal identification framework based on time series Sentinel-2 imagery. The designed separate models different growth seasons crops reduce interclass similarity caused by same certain growing season. Features were selected separately according characteristics, finally explored rotations between them generate map. was evaluated study area Shandong Province, China, mix single-cropping double-cropping area. accuracy assessment showed that two maps achieved an overall 0.93 0.85 with Kappa coefficient 0.86 0.80, respectively. results practice mainly occurred plains Shandong, predominant pattern wheat maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation (SAVI), Green Chlorophyll (GCVI), red-edge, other spectral bands during peak season enabled better performance mapping. demonstrated capability identify patterns potential multi-temporal under system.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14246280