Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-12, Vol.16 (23), p.4608
Main Authors: Feng, Pengju, Song, Kaishan, Wen, Zhidan, Tao, Hui, Yu, Xiangfei, Shang, Yingxin
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 23
container_start_page 4608
container_title Remote sensing (Basel, Switzerland)
container_volume 16
creator Feng, Pengju
Song, Kaishan
Wen, Zhidan
Tao, Hui
Yu, Xiangfei
Shang, Yingxin
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.
doi_str_mv 10.3390/rs16234608
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_2948e60a9dea40b5afb9f93ca37254a5</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A819954519</galeid><doaj_id>oai_doaj_org_article_2948e60a9dea40b5afb9f93ca37254a5</doaj_id><sourcerecordid>A819954519</sourcerecordid><originalsourceid>FETCH-LOGICAL-d1698-605eff1165a3b11bf4c08d002c32969e82ef64d239ba0098af8a415e67f6b8d03</originalsourceid><addsrcrecordid>eNpNj01PwzAMhisEEhPswi-IxLkjX00bbtM2YNLQpA1uSJXbJl2mNRlJi8S_J2wcsA-27NeP7SS5I3jCmMQPPhBBGRe4uEhGFOc05VTSy3_5dTIOYY-jMUYk5qPkY6M61yu0VTYY26JF6E0HvXEWOY1m8_Ur0s6jrbPtbgC0MV_Knzo7Y-ERzU3ovamG34GAwDZo2R0Ppj4Rwm1ypeEQ1Pgv3iTvT4u32Uu6Wj8vZ9NV2hAhi1TgTGlNiMiAVYRUmte4aDCmNaNSSFVQpQVvKJMVYCwL0AVwkimRa1FFIbtJlmdu42BfHn38wH-XDkx5KjjfluB7Ux9USSUvlMAgGwUcVxnoSmrJamA5zThkkXV_Zh29-xxU6Mu9G7yN55eM8Lg2x5hE1eSsaiFCjdWu91BHb1RnameVNrE-LYiUGc-IZD9003y8</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3144157001</pqid></control><display><type>article</type><title>Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications</title><source>Publicly Available Content Database</source><creator>Feng, Pengju ; Song, Kaishan ; Wen, Zhidan ; Tao, Hui ; Yu, Xiangfei ; Shang, Yingxin</creator><creatorcontrib>Feng, Pengju ; Song, Kaishan ; Wen, Zhidan ; Tao, Hui ; Yu, Xiangfei ; Shang, Yingxin</creatorcontrib><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.</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. These findings are pivotal for a deeper understanding of the role of river systems in the global carbon cycle.</description><subject>Agricultural production</subject><subject>Algorithms</subject><subject>Aquatic ecosystems</subject><subject>Aquatic plants</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Carbon cycle (Biogeochemistry)</subject><subject>CDOM</subject><subject>Climate change</subject><subject>Decomposition</subject><subject>Dissolved organic carbon</subject><subject>Dissolved organic matter</subject><subject>Ecosystems</subject><subject>Flow velocity</subject><subject>Land use</subject><subject>Machine learning</subject><subject>natural condition</subject><subject>Optical properties</subject><subject>Radiation</subject><subject>Regression analysis</subject><subject>Remote sensing</subject><subject>River ecology</subject><subject>River systems</subject><subject>Rivers</subject><subject>Seasonal distribution</subject><subject>Seasonal variations</subject><subject>Songhua river</subject><subject>Spatial variations</subject><subject>Urban areas</subject><subject>Water quality</subject><subject>Watersheds</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNj01PwzAMhisEEhPswi-IxLkjX00bbtM2YNLQpA1uSJXbJl2mNRlJi8S_J2wcsA-27NeP7SS5I3jCmMQPPhBBGRe4uEhGFOc05VTSy3_5dTIOYY-jMUYk5qPkY6M61yu0VTYY26JF6E0HvXEWOY1m8_Ur0s6jrbPtbgC0MV_Knzo7Y-ERzU3ovamG34GAwDZo2R0Ppj4Rwm1ypeEQ1Pgv3iTvT4u32Uu6Wj8vZ9NV2hAhi1TgTGlNiMiAVYRUmte4aDCmNaNSSFVQpQVvKJMVYCwL0AVwkimRa1FFIbtJlmdu42BfHn38wH-XDkx5KjjfluB7Ux9USSUvlMAgGwUcVxnoSmrJamA5zThkkXV_Zh29-xxU6Mu9G7yN55eM8Lg2x5hE1eSsaiFCjdWu91BHb1RnameVNrE-LYiUGc-IZD9003y8</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Feng, Pengju</creator><creator>Song, Kaishan</creator><creator>Wen, Zhidan</creator><creator>Tao, Hui</creator><creator>Yu, Xiangfei</creator><creator>Shang, Yingxin</creator><general>MDPI AG</general><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>DOA</scope></search><sort><creationdate>20241201</creationdate><title>Remote Sensing Estimation of CDOM for Songhua River of China: Distributions and Implications</title><author>Feng, Pengju ; Song, Kaishan ; Wen, Zhidan ; Tao, Hui ; Yu, Xiangfei ; Shang, Yingxin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d1698-605eff1165a3b11bf4c08d002c32969e82ef64d239ba0098af8a415e67f6b8d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Agricultural production</topic><topic>Algorithms</topic><topic>Aquatic ecosystems</topic><topic>Aquatic plants</topic><topic>Carbon</topic><topic>Carbon cycle</topic><topic>Carbon cycle (Biogeochemistry)</topic><topic>CDOM</topic><topic>Climate change</topic><topic>Decomposition</topic><topic>Dissolved organic carbon</topic><topic>Dissolved organic matter</topic><topic>Ecosystems</topic><topic>Flow velocity</topic><topic>Land use</topic><topic>Machine learning</topic><topic>natural condition</topic><topic>Optical properties</topic><topic>Radiation</topic><topic>Regression analysis</topic><topic>Remote sensing</topic><topic>River ecology</topic><topic>River systems</topic><topic>Rivers</topic><topic>Seasonal distribution</topic><topic>Seasonal variations</topic><topic>Songhua river</topic><topic>Spatial variations</topic><topic>Urban areas</topic><topic>Water quality</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Pengju</creatorcontrib><creatorcontrib>Song, Kaishan</creatorcontrib><creatorcontrib>Wen, Zhidan</creatorcontrib><creatorcontrib>Tao, Hui</creatorcontrib><creatorcontrib>Yu, Xiangfei</creatorcontrib><creatorcontrib>Shang, Yingxin</creatorcontrib><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Feng, Pengju</au><au>Song, Kaishan</au><au>Wen, 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>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2024-12, Vol.16 (23), p.4608
issn 2072-4292
2072-4292
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_2948e60a9dea40b5afb9f93ca37254a5
source Publicly Available Content Database
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T19%3A39%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Remote%20Sensing%20Estimation%20of%20CDOM%20for%20Songhua%20River%20of%20China:%20Distributions%20and%20Implications&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Feng,%20Pengju&rft.date=2024-12-01&rft.volume=16&rft.issue=23&rft.spage=4608&rft.pages=4608-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs16234608&rft_dat=%3Cgale_doaj_%3EA819954519%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-d1698-605eff1165a3b11bf4c08d002c32969e82ef64d239ba0098af8a415e67f6b8d03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3144157001&rft_id=info:pmid/&rft_galeid=A819954519&rfr_iscdi=true