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Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications
Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintainin...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (23), p.4608 |
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description | Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of a[sub.CDOM](355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R[sup.2] = 0.85, RMSE = 0.71 m[sup.−1]). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R[sup.2] = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: a[sub.CDOM](355) in spring (6.23 m[sup.−1]) was higher than that in summer (5.3 m[sup.−1]) and autumn (4.74 m[sup.−1]). The a[sub.CDOM](355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle. |
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Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of a[sub.CDOM](355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R[sup.2] = 0.85, RMSE = 0.71 m[sup.−1]). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R[sup.2] = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: a[sub.CDOM](355) in spring (6.23 m[sup.−1]) was higher than that in summer (5.3 m[sup.−1]) and autumn (4.74 m[sup.−1]). The a[sub.CDOM](355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs16234608</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural production ; Algorithms ; Aquatic ecosystems ; Aquatic plants ; Carbon ; Carbon cycle ; Carbon cycle (Biogeochemistry) ; CDOM ; Climate change ; Decomposition ; Dissolved organic carbon ; Dissolved organic matter ; Ecosystems ; Flow velocity ; Land use ; Machine learning ; natural condition ; Optical properties ; Radiation ; Regression analysis ; Remote sensing ; River ecology ; River systems ; Rivers ; Seasonal distribution ; Seasonal variations ; Songhua river ; Spatial variations ; Urban areas ; Water quality ; Watersheds</subject><ispartof>Remote sensing (Basel, Switzerland), 2024-12, Vol.16 (23), p.4608</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3144157001/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3144157001?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Feng, Pengju</creatorcontrib><creatorcontrib>Song, Kaishan</creatorcontrib><creatorcontrib>Wen, Zhidan</creatorcontrib><creatorcontrib>Tao, Hui</creatorcontrib><creatorcontrib>Yu, Xiangfei</creatorcontrib><creatorcontrib>Shang, Yingxin</creatorcontrib><title>Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications</title><title>Remote sensing (Basel, Switzerland)</title><description>Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of a[sub.CDOM](355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R[sup.2] = 0.85, RMSE = 0.71 m[sup.−1]). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R[sup.2] = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: a[sub.CDOM](355) in spring (6.23 m[sup.−1]) was higher than that in summer (5.3 m[sup.−1]) and autumn (4.74 m[sup.−1]). The a[sub.CDOM](355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. 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Zhidan</au><au>Tao, Hui</au><au>Yu, Xiangfei</au><au>Shang, Yingxin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2024-12-01</date><risdate>2024</risdate><volume>16</volume><issue>23</issue><spage>4608</spage><pages>4608-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>Rivers are crucial pathways for transporting organic carbon from land to ocean, playing a vital role in the global carbon cycle. Dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) are major components of dissolved organic matter and have significant impacts on maintaining the stability of river ecosystems and driving the global carbon cycle. In this study, the in situ samples of a[sub.CDOM](355) and DOC collected along the main stream of the Songhua River were matched with Sentinel-2 imagery. Multiple linear regression and five machine learning models were used to analyze the data. Among these models, XGBoost demonstrated a superior, highly stable performance on the validation set (R[sup.2] = 0.85, RMSE = 0.71 m[sup.−1]). The multiple linear regression results revealed a strong correlation between CDOM and DOC (R[sup.2] = 0.73), indicating that CDOM can be used to indirectly estimate DOC concentrations. Significant seasonal variations in the CDOM distribution in the Songhua River were observed: a[sub.CDOM](355) in spring (6.23 m[sup.−1]) was higher than that in summer (5.3 m[sup.−1]) and autumn (4.74 m[sup.−1]). The a[sub.CDOM](355) values in major urban areas along the Songhua River were generally higher than those in non-urban areas. Using the predicted DOC values and annual flow data at the sites, the annual DOC flux in Harbin was calculated to be approximately 0.2275 Tg C/Yr. Additionally, the spatial variation in annual CDOM was influenced by both natural changes in the watershed and human activities. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs16234608</doi><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural production Algorithms Aquatic ecosystems Aquatic plants Carbon Carbon cycle Carbon cycle (Biogeochemistry) CDOM Climate change Decomposition Dissolved organic carbon Dissolved organic matter Ecosystems Flow velocity Land use Machine learning natural condition Optical properties Radiation Regression analysis Remote sensing River ecology River systems Rivers Seasonal distribution Seasonal variations Songhua river Spatial variations Urban areas Water quality Watersheds |
title | Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications |
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