Article(id=1314923042279613062, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751731200000, receivedDateStr=2025-07-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791431683368, onlineDateStr=2026-10-08, pubDate=1773936000000, pubDateStr=2026-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791431683368, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791431683368, creator=13701087609, updateTime=1791431683368, updator=13701087609, issue=Issue{id=1314922986466009203, tenantId=1146029695717560320, journalId=1234093305789726721, year='2026', volume='46', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='1773936000000', pubDateStr='2026-03-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1791431670061, creator='13701087609', updateTime=1791431955369, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314924183251935526, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314924183251935527, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1314922986466009203, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1509, endPage=1521, ext={EN=ArticleExt(id=1314923042757763719, articleId=1314923042279613062, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Time-series monitoring and prediction of cyanobacteria blooms based on FY3D/MERSI data, columnId=1234106388083954308, journalTitle=China Environmental Science, columnName=Environmental Ecology, runingTitle=null, highlight=null, articleAbstract=

Conventional remote sensing platforms like Landsat and Sentinel face inherent limitations in achieving continuous dynamic monitoring of cyanobacterial blooms due to their inadequate temporal resolution. Similarly, existing FY satellite-based monitoring and prediction approaches are constrained by unidimensional analyses and oversimplified models dependent on single-variable inputs. To overcome these challenges, this study developed a comprehensive multidimensional monitoring and prediction system utilizing high-temporal-resolution FY3D/MERSI data for Taihu Lake cyanobacterial blooms. Our methodology integrated spectral indices with an optimized Otsu algorithm for accurate bloom detection, coupled with innovative dual-scale (pixel and sub-pixel) quantification techniques. Through this framework, we conducted systematic multidimensional analysis of Taihu Lake's cyanobacterial blooms (2019~2023), examining critical parameters including bloom intensity, frequency patterns, and outbreak severity dynamics. Concurrently, integrating meteorological and water quality factors, we constructed a BP neural network model to predict bloom areas. Validation analyses demonstrated the strong performance of FY3D/MERSI data in cyanobacterial bloom extraction, showing excellent agreement with in-situ measurements (R² = 0.98, RMSE = 21.6km²). Cyanobacterial bloom areas in Lake Tai exhibited seasonal double-peak patterns, with peaks occurring in May and October. Bloom severity predominantly ranged from mild to moderate, while severe blooms remained at low levels throughout the year. Outbreak frequency was significantly higher in the western, southern coastal, and central lake areas than in the eastern region, and higher frequencies occurred during summer and autumn. Inter annual outbreak intensity showed a gradual decreasing trend, with the proportion of "no cyanobacterial blooms" rising to 89% in 2023. Correlation analysis indicated that factors such as temperature and relative humidity promote bloom outbreaks, while turbidity and fluorescent dissolved organic matter exerted inhibitory effects. The BP neural network model demonstrated robust predictive capability, achieving a root mean square error (RMSE) of 27.07km² and a Pearson correlation coefficient (r) of 0.94 on the validation set. Notably, 83% of predictions fell within ±25km² of observed values. These metrics collectively confirm the model's effectiveness in capturing the dynamic spatiotemporal trends of cyanobacterial bloom areas.

, authors=Ruo-lin ZHANG¹, Meng WANG^{2, *}, Qing-yan MENG^{3, 4}, Yun-xiao SUN⁵, Xi-tun YUAN¹, Lin-lin ZHANG³, Han-tian WU⁶, Zhen-hui SUN⁵, Jia-long WANG⁵, authorsList=Ruo-lin ZHANG, Meng WANG, Qing-yan MENG, Yun-xiao SUN, Xi-tun YUAN, Lin-lin ZHANG, Han-tian WU, Zhen-hui SUN, Jia-long WANG, authorCompany=null, correspAuthors=Meng WANG, 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=1314923043181388424, articleId=1314923042279613062, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于FY3D/MERSI数据的蓝藻水华时序监测与预测研究, columnId=1234106388268503686, journalTitle=中国环境科学, columnName=环境生态, runingTitle=null, highlight=null, articleAbstract=

针对Landsat、Sentinel等遥感平台因时间分辨率较低，难以实现蓝藻水华的连续动态监测；而基于FY卫星数据的蓝藻监测与预测普遍存在监测维度单一、预测模型依赖单一类型变量等问题，基于FY3D/MERSI高时间分辨率遥感数据，构建太湖蓝藻水华多维度时序监测与预测体系；结合遥感指数与大津算法提取蓝藻水华信息，通过像元级与亚像元级双尺度计算量化面积，并从强度、频率、暴发程度等维度系统分析2019~2023年太湖蓝藻水华时空特征．同时整合气象与水质因子，构建BP神经网络模型预测水华面积．结果表明，利用FY3D/MERSI数据提取水华具有较高精度，与实测数据的决定系数R²达0.98，均方根误差为21.6km²．太湖蓝藻水华面积呈季节性双峰特征，峰值出现在5月和10月；水华等级以轻度和中度为主，重度水华全年维持低水平；西部、南部沿岸及湖心区暴发频率显著高于东部，夏秋季节暴发频率较高；年际暴发程度呈逐步减弱趋势，2023年“无蓝藻水华”占比升至89%．相关性分析显示，温度、相对湿度等因子促进水华暴发，浊度、荧光可溶性有机物等起抑制作用．BP神经网络模型验证集均方根误差为27.07km²，皮尔逊相关系数0.94,83%的样本预测误差控制在±25km²内，多项指标表明该模型能够有效捕捉蓝藻水华面积动态变化趋势．

, authors=张若琳¹, 王萌^{2, *}, 孟庆岩^{3, 4}, 孙云晓⁵, 原喜屯¹, 张琳琳³, 吴晗天⁶, 孙震辉⁵, 王佳龙⁵, authorsList=张若琳, 王萌, 孟庆岩, 孙云晓, 原喜屯, 张琳琳, 吴晗天, 孙震辉, 王佳龙, authorCompany=null, correspAuthors=王萌, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=RuEJZSY3qK0XmMbThDyQSA==, magXml=Lc5/DToloSMn8K9NZKnFEQ==, pdfUrl=null, pdf=hozsqL5IrMdHkBN+h21biQ==, pdfFileSize=2165313, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=v5SAt7XXtGA0kXfXWTWvtQ==, mapNumber=null, fund=null)}, authors=[Author(id=1314923044460651166, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923042279613062, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zrl991213@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314923044540342944, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923042279613062, authorId=1314923044460651166, language=EN, stringName=Ruo-lin ZHANG, firstName=Ruo-lin, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314923044611646113, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314923042279613062, authorId=1314923044460651166, language=CN, stringName=张若琳, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.西安科技大学测绘科学与技术学院，陕西 西安 710054, bio={"content":"

张若琳（1999-），女，陕西铜川人，西安科技大学硕士研究生，从事卫星遥感水体藻类监测评估应用研究．zrl991213@163.com.

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张若琳（1999-），女，陕西铜川人，西安科技大学硕士研究生，从事卫星遥感水体藻类监测评估应用研究．zrl991213@163.com.

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