长江科学院院报 ›› 2024, Vol. 41 ›› Issue (7): 48-56.DOI: 10.11988/ckyyb.20230292

• 水环境与水生态 • 上一篇    下一篇

多年度同时相“星-地”协同遥感反演鄱阳湖叶绿素a浓度

郑璞1,2, 许新发1,2, 许小华1,2, 陈鑫雨3, 张任高4   

  1. 1.江西省水利科学院,南昌 330029;
    2.江西省鄱阳湖流域生态水利技术创新中心,南昌 330029;
    3.中国水利水电科学研究院,北京 100038;
    4.东华理工大学 测绘工程学院,南昌 330013
  • 收稿日期:2023-03-22 修回日期:2023-08-09 出版日期:2024-07-01 发布日期:2024-07-08
  • 通讯作者: 许小华(1978-),男,江西会昌人,正高级工程师,硕士,主要从事水信息学研究工作。E-mail:Zgxxh79@163.com
  • 作者简介:郑 璞(1982-),男,河北南宫人,工程师,博士,主要从事遥感水文学研究。E-mail:zhengpu1982@hotmail.com
  • 基金资助:
    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)

Retrieval of Chlorophyll-a Concentration in Poyang Lake by Using Multi-annual and Simultaneous Satellite-Ground Remote Sensing

ZHENG Pu1,2, XU Xin-fa1,2, XU Xiao-hua1,2, CHEN Xin-yu3, ZHANG Ren-gao4   

  1. 1. Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China;
    2. Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin,Nanchang 330029,China;
    3. China Institute of Water Resources and Hydropower Research,Beijing 100038, China;
    4. School of Surveying and Mapping Engineering, East China University of Technology, Nanchang 330013, China
  • Received:2023-03-22 Revised:2023-08-09 Published:2024-07-01 Online:2024-07-08

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。

关键词: 遥感反演, 鄱阳湖, 叶绿素a, 相关性分析

Abstract: Poyang Lake, situated in north-central Jiangxi Province, is the largest freshwater lake in China and connects 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 eutrophication, 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 employs 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 Class II water bodies.

Key words: remote sensing, Poyang lake, chlorophyll-a, correlation analysis

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