Article(id=1311351171005112776, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250413, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741190400000, receivedDateStr=2025-03-06, revisedDate=1743523200000, revisedDateStr=2025-04-02, acceptedDate=1743868800000, acceptedDateStr=2025-04-06, onlineDate=1790580082894, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580082894, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580082894, creator=13701087609, updateTime=1790580082894, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=62, endPage=66, ext={EN=ArticleExt(id=1311351171239993801, articleId=1311351171005112776, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Remote Sensing-Based Dynamic Monitoring of Chlorophyll-a Concentration in Chaohu Lake Using Sentinel-2 Imagery, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Chlorophyll-a is a key indicator for assessing lake water quality, but traditional point-based water quality monitoring has limitations. Remote sensing technology can provide large-scale and dynamic water quality monitoring. Therefore, utilizing multispectral remote sensing technology and considering the spectral band characteristics of the Sentinel-2 satellite, three-band and four-band chlorophyll-a concentration inversion models were constructed based on ground-measured data. The leave-one-out cross-validation method was adopted to validate the model accuracy. The optimal model was applied to monitor the chlorophyll-a concentration in Chaohu Lake from 2019 to 2023. GIS spatial analysis and statistical methods were combined to systematically analyze the spatiotemporal variation characteristics of chlorophyll-a concentration in Chaohu Lake. The results showed that the three-band model based on b4/b5/b7 achieved the highest inversion accuracy. For the Sentinel-2A and Sentinel-2B three-band models, the determination coefficients (R2) were 0.763 and 0.766, the mean relative errors (MMRE) were 16.87% and 16.66%, and the root mean square errors (RRMSE) were 4.26 μg/L and 4.22 μg/L, respectively. Spatiotemporal analysis revealed that over the past five years, the chlorophyll-a concentration in Chaohu Lake exhibited significant spatial heterogeneity characterized by higher levels in the west and lower levels in the east, and seasonal fluctuations characterized by higher concentrations in summer and autumn and lower concentrations in spring and winter. An overall decreasing interannual trend was observed, and the inversion results were consistent with the water quality monitoring data for Chaohu Lake. The improved chlorophyll-a concentration inversion model established in this study demonstrates good accuracy and strong applicability. The proposed research method is feasible and effective, providing robust technical support for the monitoring, evaluation, and management of inland Type II water bodies such as Chaohu Lake using remote sensing.
, authors=Xin-yu CHEN
1, Shi-feng HUANG
1, 2, Rong-hua MA
3, authorsList=Xin-yu CHEN, Shi-feng HUANG, Rong-hua MA, authorCompany=null, correspAuthors=Shi-feng HUANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311351171730727370, articleId=1311351171005112776, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于Sentinel-2影像的巢湖叶绿素a浓度遥感动态监测, columnId=1207627659488632924, journalTitle=水电能源科学, columnName=水文水资源与环境, runingTitle=null, highlight=null, articleAbstract=
叶绿素a是衡量湖泊水质状况的重要指标,但传统点位水质监测存在局限性,遥感技术能够提供大范围、动态的水质监测。为此,利用多光谱遥感技术,针对Sentine1-2卫星谱段设置的特点,依托地面实测数据,构建三波段与四波段叶绿素a浓度反演模型,并采用留一交叉验证法对模型精度进行验证。基于最优模型开展2019~2023年巢湖叶绿素a浓度监测应用研究,结合GIS空间分析与统计方法系统解析巢湖叶绿素a浓度时空变化特征。结果表明,基于b4/b5/b7的三波段模型反演精度最优,基于Sentinel-2A/2B三波段模型的决定系数(R2)分别为0.763、0.766,平均相对误差(MMRE)分别为16.87%、16.66%,均方根误差(RRMSE)分别为4.26、4.22 μg/L。时空分析表明,近5年巢湖叶绿素a浓度呈现西高东低的显著空间异质性和夏秋高、春冬低的季节性波动,年际呈递减趋势,反演结果与巢湖水质监测情况相一致。所建立改进的叶绿素a浓度反演模型精度良好、适用性强,所提研究方法可行有效,可为巢湖等内陆Ⅱ类水体的水质遥感监测评价管理提供有力的技术支撑。
, authors=陈鑫雨
1, 黄诗峰
1, 2, 马荣华
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陈鑫雨(1997-),女,博士研究生,研究方向为水灾害遥感应用,E-mail: 1953024763@qq.com
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