A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images
文献类型：期刊论文
| 作者 | Chen, Hui2,3; Li, Huapeng3; Liu, Zhao2,3; Zhang, Ce1,6; Zhang, Shuqing3; Atkinson, Peter M.4,5,6 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 295页码:16 |
| 关键词 | Soybean mapping methods Automatic crop mapping Sentinel-2 Short-wave infrared (SWIR) Normalized difference vegetation index (NDVI) |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2023.113679 |
| 通讯作者 | Li, Huapeng(lihuapeng@iga.ac.cn) ; Zhang, Shuqing(zhangshuqing@iga.ac.cn) |
| 英文摘要 | As a critical source of food and one of the most economically significant crops in the world, soybean plays an important role in achieving food security. Large area accurate mapping of soybean has long been a vital, but challenging issue in remote sensing, relying heavily on large-volume and representative training samples, whose collection is time-consuming and inefficient, especially for large areas (e.g., national scale). Thus, methods are needed that can map soybean automatically and accurately from single-date remotely sensed imagery. In this research, a novel Greenness and Water Content Composite Index (GWCCI) was proposed to map soybean from just a single Sentinel-2 multispectral image in an end-to-end manner without employing training samples. By capitalizing on the product of the NDVI (related to greenness) and the short-wave infrared (SWIR) band (related to canopy water content), the GWCCI provides the required information with which to discriminate between soybean and other land cover types. The effectiveness of the proposed GWCCI was investigated in seven typical soybean planting regions within four major soybean-producing countries across the world (i.e., China, the United States, Brazil and Argentina), with diverse climates, cropping systems and agricultural landscapes. In the experiments, an optimal threshold of 0.17 was estimated and adopted by the GWCCI in the first study site (S1) in 2021, and then generalised to the other study sites over multiple years for soybean mapping. The GWCCI method achieved a consistently higher accuracy in 2021 compared to two conventional comparative classifiers (support vector machine (SVM) and random forest (RF)), with an average overall accuracy (OA) of 88.30% and a Kappa coefficient (k) of 0.77; significantly greater than those of RF (OA: 80.92%, k: 0.62) and SVM (OA: 80.29%, k: 0.60). Furthermore, the OA of the extended years was highly consistent with that of 2021 for study sites S2 to S7, demonstrating the great generalisation capability and robustness of the proposed approach over multiple years. The proposed GWCCI method is straightforward, reliable and robust, and represents an important step forward for mapping soybean, one of the most significant crops grown globally. |
| WOS关键词 | TIME-SERIES ; HYPERSPECTRAL INDEXES ; HEILONGJIANG PROVINCE ; CROP TYPES ; CORN ; LANDSAT ; LEAF ; FOREST ; SCALE ; CLASSIFICATION |
| 资助项目 | Strategic Priority Research Pro- gram of the Chinese Academy of Sciences[XDA28070500] ; National Key Research and Development Program of China[2021YFD1500100] ; Jilin Scientific and Technological Development Program[20220201158GX] ; Jilin Capital Construction Fund[2021C045-2] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001021738400001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | Strategic Priority Research Pro- gram of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; Jilin Scientific and Technological Development Program ; Jilin Capital Construction Fund |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/195327]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Huapeng; Zhang, Shuqing |
| 作者单位 | 1.UK Ctr Ecol & Hydrol, Lib Ave, Lancaster LA1 4AP, England 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China 5.Univ Southampton, Geog & Environm Sci, Southampton SO17 1BJ, England 6.Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England |
| 推荐引用方式 GB/T 7714 | Chen, Hui,Li, Huapeng,Liu, Zhao,et al. A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images[J]. REMOTE SENSING OF ENVIRONMENT,2023,295:16. |
| APA | Chen, Hui,Li, Huapeng,Liu, Zhao,Zhang, Ce,Zhang, Shuqing,&Atkinson, Peter M..(2023).A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images.REMOTE SENSING OF ENVIRONMENT,295,16. |
| MLA | Chen, Hui,et al."A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images".REMOTE SENSING OF ENVIRONMENT 295(2023):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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