文献类型： 外文期刊

作者： Hou, Zhenwei ¹ ; Chen, Bangqian ³ ; Liu, Yaqun ⁴ ; Zang, Huadong ¹ ; Manevski, Kiril ⁵ ; Chen, Fangmiao ⁷ ; Yang, Yadong ¹ ; Ge, Junyong ⁸ ; Zeng, Zhaohai ¹ ;

作者机构： 1.China Agr Univ, Coll Agron & Biotechnol, State Key Lab Maize Biobreeding, Beijing 100193, Peoples R China

2.China Agr Univ, Key Lab Farming Syst, Minist Agr & Rural Affairs China, Beijing 100193, Peoples R China

3.Chinese Acad Trop Agr Sci CATAS, Rubber Res Inst RRI, State Key Lab Incubat Base Cultivat & Physiol Trop, Hainan Danzhou Agroecosystem Natl Observat & Res S, Haikou 571101, Peoples R China

4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China

5.Aarhus Univ, Dept Agroecol, Blichers Alle, DK-508830 Tjele, Denmark

6.Univ Chinese Acad Sci, Sino Danish Ctr Educ & Res, Eastern Yanqihu Campus, Beijing 101400, Peoples R China

7.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China

8.Zhangjiakou Acad Agr Sci, Zhangjiakou 075000, Peoples R China

关键词： crop classification; multi-satellite imagery; crop distribution; cropping patterns; farming-pastoral ecotone

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 10 期

页码：

收录情况： SCI

摘要： The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming-pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a multi-sensor remote sensing framework for monitoring crop distribution and analyzing rotation dynamics. After cloud removal and Savitzky-Golay filtering were applied to correct noise, we selected vegetation index features with maximum inter-class separability during the optimal classification window (June 15-August 20) and generated quarterly Sentinel-1 SAR composites. A Random Forest classifier was employed to perform crop classification based on these optimized features, enabling 10 m resolution crop mapping from 2019 to 2023. The proposed method achieved high classification accuracy (overall accuracy and Kappa > 0.90), with strong agreement between mapped and statistical crop areas (R-2: 0.85-0.88; RMSE: 0.42-0.58 x 10(4) ha). Spatial analysis revealed distinct distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in northern Zhangjiakou, while maize dominated southern regions. We observed significant annual variations in crop area proportions and identified specific altitudinal preferences: maize, potato, and sesame were mainly grown at 480-520 m, while oats and other crops at 520-600 m. Slope analysis showed that most crops were cultivated on gentle slopes of 0-5 degrees, with sesame extending to 4-10 degrees slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation was primarily monoculture. Key drivers of rotation change included water scarcity, economic incentives, and continuous cropping constraints. These findings provide critical insights for optimizing crop rotation strategies, enhancing agricultural sustainability, and improving land-use efficiency in ecologically fragile regions.