High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine
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 develo...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1707 |
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| author | Zhenwei Hou Bangqian Chen Yaqun Liu Huadong Zang Kiril Manevski Fangmiao Chen Yadong Yang Junyong Ge Zhaohai Zeng |
| author_facet | Zhenwei Hou Bangqian Chen Yaqun Liu Huadong Zang Kiril Manevski Fangmiao Chen Yadong Yang Junyong Ge Zhaohai Zeng |
| author_sort | Zhenwei Hou |
| collection | DOAJ |
| description | 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<sup>2</sup>: 0.85–0.88; RMSE: 0.42–0.58 × 10<sup>4</sup> 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°, with sesame extending to 4–10° 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. |
| format | Article |
| id | doaj-art-7f238ee6abd84d7797185abbc6d2cb3e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7f238ee6abd84d7797185abbc6d2cb3e2025-08-20T01:56:45ZengMDPI AGRemote Sensing2072-42922025-05-011710170710.3390/rs17101707High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth EngineZhenwei Hou0Bangqian Chen1Yaqun Liu2Huadong Zang3Kiril Manevski4Fangmiao Chen5Yadong Yang6Junyong Ge7Zhaohai Zeng8State Key Laboratory of Maize Bio-Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, ChinaRubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences (CATAS), Hainan Danzhou Agro-Ecosystem National Observation and Research Station, State Key Laboratory Incubation Base for Cultivation and Physiology of Tropical Crops, Haikou 571101, ChinaKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Maize Bio-Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, ChinaDepartment of Agroecology, Aarhus University, Blichers Allé, 508830 Tjele, DenmarkAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Maize Bio-Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, ChinaZhangjiakou Academy of Agricultural Sciences, Zhangjiakou 075000, ChinaState Key Laboratory of Maize Bio-Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, ChinaThe 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<sup>2</sup>: 0.85–0.88; RMSE: 0.42–0.58 × 10<sup>4</sup> 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°, with sesame extending to 4–10° 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.https://www.mdpi.com/2072-4292/17/10/1707crop classificationmulti-satellite imagerycrop distributioncropping patternsfarming–pastoral ecotone |
| spellingShingle | Zhenwei Hou Bangqian Chen Yaqun Liu Huadong Zang Kiril Manevski Fangmiao Chen Yadong Yang Junyong Ge Zhaohai Zeng High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine Remote Sensing crop classification multi-satellite imagery crop distribution cropping patterns farming–pastoral ecotone |
| title | High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine |
| title_full | High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine |
| title_fullStr | High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine |
| title_full_unstemmed | High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine |
| title_short | High Resolution Crop Type and Rotation Mapping in Farming–Pastoral Ecotone in China Using Multi-Satellite Imagery and Google Earth Engine |
| title_sort | high resolution crop type and rotation mapping in farming pastoral ecotone in china using multi satellite imagery and google earth engine |
| topic | crop classification multi-satellite imagery crop distribution cropping patterns farming–pastoral ecotone |
| url | https://www.mdpi.com/2072-4292/17/10/1707 |
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