A novel framework for river organic carbon retrieval through satellite data and machine learning
文献类型：期刊论文
| 作者 | Tian, Shang6,7,8,9; Sha, Anmeng6,7,8,9; Luo, Yingzhong9; Ke, Yutian5; Spencer, Robert4; Hu, Xie3; Ning, Munan2; Zhao, Yi9; Deng, Rui9; Gao, Yang1 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2025-03-01 |
| 卷号 | 221页码:109-123 |
| 关键词 | Particulate organic carbon Dissolved organic carbon Remote sensing Machine learning River |
| ISSN号 | 0924-2716 |
| DOI | 10.1016/j.isprsjprs.2025.01.028 |
| 产权排序 | 9 |
| 文献子类 | Article |
| 英文摘要 | Rivers transport large amounts of carbon, serving as a critical link between terrestrial, coastal, and atmospheric biogeochemical cycles. However, our observations and understanding of long-term river carbon dynamics in large-scale remain limited. Integrating machine learning with remote sensing offers an effective approach for quantifying organic carbon (OC) from space. Here, we develop the Aquatic-Organic Carbon (Aqua-OC), a dynamic machine learning retrieval framework designed to estimate reach-scale river OC using nearly half a century of analysis-ready Landsat archives. We first integrate a globally representative river OC dataset, comprising 299,330 measurements of dissolved organic carbon (DOC) and 101,878 measurements of particulate organic carbon (POC). This dataset is then used to evaluate the performance of four machine learning methods, i. e., random forest (RF), extreme gradient boosting (XGBoost), Support vector regression (SVR), and deep neural network (DNN), using an optical water type classification strategy. We further leverage multimodal input features to enhance the Aqua-OC framework and OC retrieval accuracy by considering various factors related to OC sources and environmental conditions. The results demonstrate that the Aqua-OC can effectively estimate DOC (R2 = 0.68, RMSE = 2.88 mg/L, Bias = 2.63 %, Error = 12.52 %) and POC (R2 = 0.76, RMSE = 1.76 mg/L, Bias = 6.31 %, Error = 21.36 %). Additionally, the Mississippi River Basin case study demonstrates Aqua-OC's capability to map nearly four decades of reach-scale OC changes at a basin scale. This study provides a generalized method for satellite-based river OC retrieval at fine spatial and long-term temporal scales, thus offering an effective tool to quantify the rivers' role in the global carbon cycle. |
| URL标识 | 查看原文 |
| WOS关键词 | MATTER CDOM ; WATER ; PERFORMANCE ; INDEX ; ABSORPTION ; LANDSAT-8 ; QUALITY ; FLUXES ; COLOR ; LAND |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001424395400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212301]  |
| 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 | Li, Dongfeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen, Guangdong, Peoples R China; 3.Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; 4.Florida State Univ, Dept Earth Ocean & Atmospher Sci, Tallahassee, FL USA; 5.CALTECH, Div Geol & Planetary Sci, Pasadena, CA USA; 6.Peking Univ, Inst Tibetan Plateau, Beijing 100871, Peoples R China; 7.Peking Univ, Inst Carbon Neutral, Beijing 100871, Peoples R China; 8.Peking Univ, State Environm Protect Key Lab All Mat Flux River, Beijing 100871, Peoples R China; 9.Peking Univ, Coll Environm Sci & Engn, Key Lab Water & Sediment Sci, Minist Educ, Beijing 100871, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Tian, Shang,Sha, Anmeng,Luo, Yingzhong,et al. A novel framework for river organic carbon retrieval through satellite data and machine learning[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2025,221:109-123. |
| APA | Tian, Shang.,Sha, Anmeng.,Luo, Yingzhong.,Ke, Yutian.,Spencer, Robert.,...&Li, Dongfeng.(2025).A novel framework for river organic carbon retrieval through satellite data and machine learning.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,221,109-123. |
| MLA | Tian, Shang,et al."A novel framework for river organic carbon retrieval through satellite data and machine learning".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 221(2025):109-123. |
入库方式： OAI收割
来源：地理科学与资源研究所
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