Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data
文献类型：期刊论文
| 作者 | Tian, Wenjie5; Zhang, Lili4,5; Yu, Tao5; Yao, Dong3; Zhang, Wenhao2; Wang, Chunmei5 |
| 刊名 | ATMOSPHERE
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 15期号:8页码:985 |
| 关键词 | DNN XCO2 China regional fine scale high resolution |
| DOI | 10.3390/atmos15080985 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0 degrees-60 degrees N, 70 degrees-140 degrees E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people's understanding of CO2 spatiotemporal variations, supporting carbon-related research. |
| WOS关键词 | INDUCED CHLOROPHYLL FLUORESCENCE ; CARBON ; RETRIEVAL ; CO2 ; EMISSIONS ; TCCON ; OCO-2 |
| WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
| WOS记录号 | WOS:001306896700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207996]  |
| 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 | Zhang, Lili |
| 作者单位 | 1.North China Inst Aerosp Engn, Langfang 065000, Peoples R China 2.QiLu Aerosp Informat Res Inst, Jinan 250010, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 5.Hainan Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Wenjie,Zhang, Lili,Yu, Tao,et al. Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data[J]. ATMOSPHERE,2024,15(8):985. |
| APA | Tian, Wenjie,Zhang, Lili,Yu, Tao,Yao, Dong,Zhang, Wenhao,&Wang, Chunmei.(2024).Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data.ATMOSPHERE,15(8),985. |
| MLA | Tian, Wenjie,et al."Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data".ATMOSPHERE 15.8(2024):985. |
入库方式： OAI收割
来源：地理科学与资源研究所
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