High-resolution cropland mapping in China's Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information
文献类型：期刊论文
| 作者 | Zhao, Jiafu1,3; Chen, Pengfei2,3 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 224页码:109225 |
| 关键词 | Cropland mapping A priori information Machine learning Sample migration Positive and unlabeled learning |
| DOI | 10.1016/j.compag.2024.109225 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The HuangHuai-Hai Plain, a critical grain production area in China, was used as the research area to produce highresolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding timeseries image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping. |
| WOS关键词 | ONE-CLASS CLASSIFICATION ; GOOGLE EARTH ENGINE ; LAND-COVER PRODUCT ; RANDOM FOREST ; ACCURACY ; EXTENT ; ALGORITHM ; DYNAMICS ; IMAGERY ; KAPPA |
| WOS研究方向 | Agriculture ; Computer Science |
| WOS记录号 | WOS:001346640400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209569]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Chen, Pengfei |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Jiafu,Chen, Pengfei. High-resolution cropland mapping in China's Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2024,224:109225. |
| APA | Zhao, Jiafu,&Chen, Pengfei.(2024).High-resolution cropland mapping in China's Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information.COMPUTERS AND ELECTRONICS IN AGRICULTURE,224,109225. |
| MLA | Zhao, Jiafu,et al."High-resolution cropland mapping in China's Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information".COMPUTERS AND ELECTRONICS IN AGRICULTURE 224(2024):109225. |
入库方式： OAI收割
来源：地理科学与资源研究所
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