Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration
The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2417 |
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| author | Pengnan Xiao Yong Zhou Jianping Qian Yujie Liu Xigui Li |
| author_facet | Pengnan Xiao Yong Zhou Jianping Qian Yujie Liu Xigui Li |
| author_sort | Pengnan Xiao |
| collection | DOAJ |
| description | The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. |
| format | Article |
| id | doaj-art-68331cae383e4aa39cf6ef50ea215b67 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-68331cae383e4aa39cf6ef50ea215b672025-08-20T03:07:56ZengMDPI AGRemote Sensing2072-42922025-07-011714241710.3390/rs17142417Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample MigrationPengnan Xiao0Yong Zhou1Jianping Qian2Yujie Liu3Xigui Li4Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, ChinaInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaCollege of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, ChinaCollege of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, ChinaThe accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies.https://www.mdpi.com/2072-4292/17/14/2417crop planting patternGoogle Earth EngineJianghan Plain |
| spellingShingle | Pengnan Xiao Yong Zhou Jianping Qian Yujie Liu Xigui Li Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration Remote Sensing crop planting pattern Google Earth Engine Jianghan Plain |
| title | Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration |
| title_full | Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration |
| title_fullStr | Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration |
| title_full_unstemmed | Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration |
| title_short | Spatiotemporal Mapping and Driving Mechanism of Crop Planting Patterns on the Jianghan Plain Based on Multisource Remote Sensing Fusion and Sample Migration |
| title_sort | spatiotemporal mapping and driving mechanism of crop planting patterns on the jianghan plain based on multisource remote sensing fusion and sample migration |
| topic | crop planting pattern Google Earth Engine Jianghan Plain |
| url | https://www.mdpi.com/2072-4292/17/14/2417 |
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