多年度同时相"星-地"协同遥感反演鄱阳湖叶绿素a浓度
Retrieval of Chlorophyll-a Concentration in Poyang Lake by Using Multi-annual and Simultaneous Satellite-Ground Remote Sensing
郑璞 1许新发 1许小华 1陈鑫雨 2张任高3
作者信息
- 1. 江西省水利科学院,南昌 330029;江西省鄱阳湖流域生态水利技术创新中心,南昌 330029
- 2. 中国水利水电科学研究院,北京 100038
- 3. 东华理工大学测绘工程学院,南昌 330013
- 折叠
摘要
作为长江最大的通江湖泊,鄱阳湖水生态安全影响着整个长江流域.叶绿素a浓度是水生态富营养化的重要指标,利用遥感技术开展叶绿素a浓度常规监测具有重要意义.以鄱阳湖为试验区,以表征富营养化程度的叶绿素a浓度为反演指标,基于2015-2020年6 a的观测数据以及Landsat系列影像,在规范建模时相以及"星-地"协同匹配的情况下,通过分析对比不同波段与叶绿素a浓度的相关指数,构建了适用于夏季鄱阳湖水体叶绿素a浓度反演模型.经6组独立数据验证后R2均值为0.86,RMSE均值为1.01 μg/L,MAPE均值为17.6％,认为所采用的方法可以较好地适用于鄱阳湖区夏季(丰水期)叶绿素a浓度的的反演,同时也为具备长期观测条件下内陆二类水体叶绿素a浓度监测提供了新的参考方法.
Abstract
Poyang Lake,situated in north-central Jiangxi Province,is the largest freshwater lake in China and con-nects to the Yangtze River.Given its size and location,the lake's water ecological security profoundly impacts the entire Yangtze River basin.Chlorophyll-a concentration serves as a crucial indicator of water ecological eutrophica-tion,underscoring the importance of routine remote sensing monitoring.This study focuses on Poyang Lake,using chlorophyll-a as a proxy for eutrophication.By analyzing six years of observation data(2015-2020)and LandSat images,a retrieval model for summer chlorophyll-a concentration in Poyang Lake was developed.This model em-ploys standard modeling techniques and ensures"satellite-ground"coordination to accurately match data.Through correlation analysis between various wavelength bands and chlorophyll-a concentration,the study constructs a robust retrieval model.Verification with six independent datasets yielded a mean R2 value of 0.86,RMSE of 1.01 μg/L,and MAPE of 17.6％.These results validate the efficacy of the method for chlorophyll-a retrieval in Poyang Lake and suggest its potential application as a reference for long-term monitoring of chlorophyll-a in similar inland ClassⅡ water bodies.
关键词
遥感反演/鄱阳湖/叶绿素a/相关性分析Key words
remote sensing/Poyang lake/chlorophyll-a/correlation analysis引用本文复制引用
基金项目
江西省科技厅重大科技研发专项(20213AAG01012)
江西省科技厅重点研发计划项目(20212BBG71008)
出版年
2024
国家科技期刊平台
NSTL
万方数据