Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia
文献类型：期刊论文
| 作者 | Liu, Meng5,6; Wang, Juanle3,4,5; Fetisov, Denis2; Li, Kai4,5; Xu, Chen5,6; Jiang, Jiawei1,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 16期号:10页码:1670 |
| 关键词 | crop classification food security Sentinel-2 random forest sample label |
| DOI | 10.3390/rs16101670 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources. |
| WOS关键词 | GOOGLE EARTH ENGINE |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001231656800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205389]  |
| 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 | Wang, Juanle |
| 作者单位 | 1.China Univ Min & Technol, Sch Geosci & Surveying Engn, Beijing 100083, Peoples R China 2.Russian Acad Sci, Inst Complex Anal Reg Problems, Far Eastern Branch, Birobidzhan 679016, Russia 3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 6.Jiangsu Ocean Univ, Sch Marine Technol & Geomat, Lianyungang 222005, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Meng,Wang, Juanle,Fetisov, Denis,et al. Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia[J]. REMOTE SENSING,2024,16(10):1670. |
| APA | Liu, Meng,Wang, Juanle,Fetisov, Denis,Li, Kai,Xu, Chen,&Jiang, Jiawei.(2024).Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia.REMOTE SENSING,16(10),1670. |
| MLA | Liu, Meng,et al."Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia".REMOTE SENSING 16.10(2024):1670. |
入库方式： OAI收割
来源：地理科学与资源研究所
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