A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data
文献类型：期刊论文
| 作者 | Liu, Zhenjie1; Liu, Jialin2; Su, Yingyue2; Xiao, Xiangming3; Dong, Jingwei4; Liu, Luo2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:14705-14717 |
| 关键词 | Deep learning Climate change Earth Observing System Phenology Crops Human activity recognition Terrain mapping Google Earth Engine (GEE) paddy rice phenology Sentinel-1/2 |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3573750 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Due to the influences combined with global climate change and human activity, paddy rice area and distribution have undergone dramatic changes. Currently, many approaches for paddy rice mapping rely on the prior knowledge of paddy rice phenology or require widely distributed ground samples of paddy rice, which are limited for large-scale applications. In this work, we propose a general paddy rice mapping (GPRM) model by combining biological characteristics, deep learning, and multisource remote sensing data. The proposed GPRM first utilizes the normalized difference vegetation index and land surface water index to acquire large-scale remote sensing dataset in key phenology periods of paddy rice, such as the transplanting period and peak vegetative growth period. Then, a general model using object-based deep neural networks is developed and trained by the remote sensing dataset and the ground reference data collected in one region (e.g., Guangdong Province), which can be directly applied for generating 10-m paddy rice maps in other regions with different climate conditions and complex cropping systems (e.g., Jiangxi Province and Heilongjiang Province). The results demonstrate that the GPRM can realize remarkable performance of paddy rice mapping in China. The overall accuracies are over 99%, and the user accuracy, producer accuracy, and Kappa coefficient vary from 0.77 to 0.93, 0.94 to 0.97, 0.9 to 0.95, respectively. Overall, the GPRM is has significant promise for large-scale paddy rice mapping with complex cropping systems, thus supporting global agricultural development strategies and food security. |
| URL标识 | 查看原文 |
| WOS关键词 | TIME-SERIES ; IMAGERY ; AREAS ; CHINA ; INDEX ; AGRICULTURE ; WATER ; SOUTH ; SAR |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001512581900005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214559]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Su, Yingyue; Liu, Luo |
| 作者单位 | 1.China Univ Geosci, Sch Comp Sci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430078, Peoples R China; 2.South China Agr Univ, Guangdong Prov Key Lab Agr Resources Utilizat, Guangzhou 510642, Peoples R China; 3.Univ Oklahoma, Ctr Earth Observat & Modeling, Dept Microbiol & Plant Biol, Norman, OK 73019 USA; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Zhenjie,Liu, Jialin,Su, Yingyue,et al. A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:14705-14717. |
| APA | Liu, Zhenjie,Liu, Jialin,Su, Yingyue,Xiao, Xiangming,Dong, Jingwei,&Liu, Luo.(2025).A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,14705-14717. |
| MLA | Liu, Zhenjie,et al."A General Model for Large-Scale Paddy Rice Mapping by Combining Biological Characteristics, Deep Learning, and Multisource Remote Sensing Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):14705-14717. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。