A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine
文献类型：期刊论文
| 作者 | Guo, Linghui2; Zhao, Sha2; Gao, Jiangbo3; Zhang, Hebing2; Zou, Youfeng2; Xiao, Xiangming1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 14期号:21页码:20 |
| 关键词 | crop type mapping random forest vegetation indices phenological characteristics Jiaozuo |
| DOI | 10.3390/rs14215458 |
| 通讯作者 | Gao, Jiangbo(gaojiangbo@igsnrr.ac.cn) |
| 英文摘要 | High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09-2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification. |
| WOS关键词 | GLOBAL LAND-COVER ; SENTINEL-2 IMAGES ; AREA ; CLASSIFICATION ; MODIS ; CHINA ; ALGORITHMS ; INDEX ; NDVI ; OLI |
| 资助项目 | Science and Technology Project of the Henan Province[212102310028] ; National Natural Science Foundation[42271124] ; National Natural Science Foundation[41977284] ; Qinghai Kunlun High-end Talents Project, Young Backbone Teachers of Henan Polytechnic University, China[2020XQG-02] ; Key Scientific Research Project of Colleges and Universities in Henan Province[20A170009] ; Key Scientific Research Project of Colleges and Universities in Henan Province[21A440013] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000885868100001 |
| 出版者 | MDPI |
| 资助机构 | Science and Technology Project of the Henan Province ; National Natural Science Foundation ; Qinghai Kunlun High-end Talents Project, Young Backbone Teachers of Henan Polytechnic University, China ; Key Scientific Research Project of Colleges and Universities in Henan Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187418]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Gao, Jiangbo |
| 作者单位 | 1.Univ Oklahoma, Ctr Spatial Anal, Dept Microbiol & Plant Biol, Norman, OK 73019 USA 2.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, 11A Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Linghui,Zhao, Sha,Gao, Jiangbo,et al. A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine[J]. REMOTE SENSING,2022,14(21):20. |
| APA | Guo, Linghui,Zhao, Sha,Gao, Jiangbo,Zhang, Hebing,Zou, Youfeng,&Xiao, Xiangming.(2022).A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine.REMOTE SENSING,14(21),20. |
| MLA | Guo, Linghui,et al."A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine".REMOTE SENSING 14.21(2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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