Rice and Greenhouse Identification in Plateau Areas Incorporating Sentinel-1/2 Optical and Radar Remote Sensing Data from Google Earth Engine
文献类型：期刊论文
| 作者 | Zhang, Tao1; Tang, Bo-Hui1,2; Huang, Liang1,3; Chen, Guokun1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 14期号:22页码:18 |
| 关键词 | classification Sentinel-1 2 principal component analysis time series Google Earth Engine |
| DOI | 10.3390/rs14225727 |
| 通讯作者 | Tang, Bo-Hui(tangbh@kust.edu.cn) |
| 英文摘要 | Affected by geographical location and climatic conditions, crop classification in the Yunnan Plateau of China is greatly restricted by the low utilization rate of annual optical data, complex crop planting structure, and broken cultivated land. This paper combines monthly Sentinel-2 optical remote sensing data with Sentinel-1 radar data to minimize cloud interference to conduct crop classification for plateau areas. However, pixel classification will inevitably produce a "different spectrum of the same object, foreign objects in the same spectrum". A principal component feature synthesis method is developed for multi-source remote sensing data (PCA-MR) to improve classification accuracy. In order to compare and analyze the classification effect of PCA-MR combined with multi-source remote sensing data, we constructed 11 classification scenarios using the Google Earth Engine platform and random forest algorithm (RF). The results show that: (1) the classification accuracy is 79.98% by using Sentinel-1 data and 91.18% when using Sentinel-2 data. When integrating Sentinel-1 and Sentinel-2 data, the accuracy is 92.31%. By analyzing the influence of texture features on classification under different feature combinations, it was found that optical texture features affected the recognition accuracy of rice to a lesser extent. (2) The errors will be reduced if the PCA-MR feature is involved in the classification, and the classification accuracy and Kappa coefficient are improved to 93.47% and 0.92, respectively. |
| WOS关键词 | WATER INDEX NDWI ; RANDOM FOREST ; MACHINE ; CHINA ; CLASSIFICATION ; GROWTH |
| 资助项目 | Platform Construction Project of High-Level Talent in KUST ; Yunnan Fundamental Research Projects[202201AT070164] ; Yunnan Fundamental Research Projects[202101AU070161] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000887746800001 |
| 出版者 | MDPI |
| 资助机构 | Platform Construction Project of High-Level Talent in KUST ; Yunnan Fundamental Research Projects |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187369]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Bo-Hui |
| 作者单位 | 1.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Yunnan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Surveying & Mapping Geoinformat Technol Res Ctr P, Kunming 650093, Yunnan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tao,Tang, Bo-Hui,Huang, Liang,et al. Rice and Greenhouse Identification in Plateau Areas Incorporating Sentinel-1/2 Optical and Radar Remote Sensing Data from Google Earth Engine[J]. REMOTE SENSING,2022,14(22):18. |
| APA | Zhang, Tao,Tang, Bo-Hui,Huang, Liang,&Chen, Guokun.(2022).Rice and Greenhouse Identification in Plateau Areas Incorporating Sentinel-1/2 Optical and Radar Remote Sensing Data from Google Earth Engine.REMOTE SENSING,14(22),18. |
| MLA | Zhang, Tao,et al."Rice and Greenhouse Identification in Plateau Areas Incorporating Sentinel-1/2 Optical and Radar Remote Sensing Data from Google Earth Engine".REMOTE SENSING 14.22(2022):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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