High Carbon Emission Simulated in the Permafrost Degradation Regions of the Qinghai-Tibet Plateau by Remote Sensing and Deep Learning Modules
文献类型：期刊论文
| 作者 | Ni, Chenrui1,2,4,6; Zhang, Zhengjia2; Zhu, Biao1,4,6; Liu, Zhenhai3,5; Wang, Shaoqiang2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4302712 |
| 关键词 | Permafrost Soil Carbon Degradation Remote sensing Vegetation mapping Estimation Normalized difference vegetation index Landsat Attention mechanism carbon release intensity deep learning (DL) permafrost degradation soil organic carbon (SOC) soil organic carbon (SOC) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3593421 |
| 产权排序 | 6 |
| 文献子类 | Article |
| 英文摘要 | The Qinghai-Tibet plateau (QTP) stores a significant amount of organic carbon in permafrost regions, and the temporal dynamic changes under permafrost degradation remain uncertain. In this study, integrating on-site and multisource remote sensing data, we proposed a dual-input small-sample deep learning (DL) framework for estimating the soil organic carbon (SOC) density and storage at a depth of 0-3 m in permafrost regions based on attention mechanisms and DL methods. Our model achieved an improvement of 10.6%-22.9% in the accuracy of SOC estimation compared to previous studies in the shallow (0-30 cm) and deep (0-100 cm) layers of permafrost regions, respectively. The SOC storage over permafrost regions to a depth of 3 m was 14.27 +/- 4.38 and 12.26 +/- 2.02 Pg in 2005 and 2020, respectively, suggesting a release of 2.01 +/- 0.48 Pg of SOC over the past 15 years. The carbon release intensities per unit area in thermokarst lakes (TLs) and retrogressive thaw slumps (RTSs) are approximately 24.6x and 21.0x higher than the mean value of whole permafrost regions, respectively. The factor analysis revealed that precipitation, normalized difference vegetation index (NDVI), and mean annual ground temperature (MAGT) are the primary controlling factors when estimating the shallow layers (0-30 cm). In contrast, at deeper layers, the spatial distribution of shallower SOC, soil water content, and DEM possess greater weight. This finding is crucial for modeling SOC storage and its dynamics in the permafrost regions on the QTP. |
| URL标识 | 查看原文 |
| WOS关键词 | SOIL ORGANIC-CARBON ; STORAGE ; MAP |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001550785400003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215522]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhang, Zhengjia |
| 作者单位 | 1.Peking Univ, Key Lab Earth Surface Proc, Minist Educ, Beijing 100871, Peoples R China; 2.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China; 3.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Beijing 100045, Peoples R China; 4.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100045, Peoples R China 6.Peking Univ, Inst Ecol, Beijing 100871, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ni, Chenrui,Zhang, Zhengjia,Zhu, Biao,et al. High Carbon Emission Simulated in the Permafrost Degradation Regions of the Qinghai-Tibet Plateau by Remote Sensing and Deep Learning Modules[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4302712. |
| APA | Ni, Chenrui,Zhang, Zhengjia,Zhu, Biao,Liu, Zhenhai,&Wang, Shaoqiang.(2025).High Carbon Emission Simulated in the Permafrost Degradation Regions of the Qinghai-Tibet Plateau by Remote Sensing and Deep Learning Modules.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4302712. |
| MLA | Ni, Chenrui,et al."High Carbon Emission Simulated in the Permafrost Degradation Regions of the Qinghai-Tibet Plateau by Remote Sensing and Deep Learning Modules".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4302712. |
入库方式： OAI收割
来源：地理科学与资源研究所
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