连云港主要入海河流叶绿素a及综合营养状态指数遥感定量反演

doi:10.3964/j.issn.1000-0593(2023)10-3314-07

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摘要：入海河流是陆源污染物向海洋输送过程的关键环节，其富营养化对区域生态环境及人类生活安全构成严重威胁。由于习近平生态文明思想的深入贯彻，“河长制”、“湾长制”等管理制度的广泛推行，以及污染防治攻坚战的全面打响，入海河流与近岸海域的水质状况得到稳步改善，但仍存在波动，污染防治形势依然严峻，实时有效的大范围遥感监测能力亟待加强。随着近年来高分卫星与无人机遥感技术的迅速发展，如何将定量遥感技术应用于河流水体的生态环境要素监测，从而推进污染防治水平进一步提升已成为该领域研究热点。基于2022年6月期间在连云港境内蔷薇河、临洪河、古泊善后河及灌河获取的叶绿素a、总磷、总氮浓度等实测数据，并采用Sentinel-2A MSI L2A卫星影像，开展了连云港主要入海河流叶绿素a及综合营养状态指数[TLI(Σ)]遥感定量反演研究。结果表明，叶绿素a浓度、综合营养状态指数与可见光波段反射率的相关性明显高于其他波段，尤以490、560、665 nm三个波段最佳，其中R(λ)与Chl a的相关系数分别为-0.697、-0.681、-0.693，R(λ)与TLI(Σ)的相关系数分别为-0.728、-0.744、-0.706，可作为建模的敏感波段；经反演模型的精度对比发现，以R(665)为自变量，在叶绿素a浓度对数坐标下的乘幂模型为其遥感定量反演的最优模型(R²=0.67, MAPE=47.34%, RMSE=12.89 μg·L^-1)，而以R(560)为自变量的乘幂模型是综合营养状态指数遥感定量反演的最优模型(R²=0.61, MAPE=4.36%, RMSE=3.45)；将最优模型应用于2022年6月25日Sentinel-2A MSI L2A影像，得到连云港主要入海河流叶绿素a浓度和综合营养状态指数的空间分布结果，发现蔷薇河/临洪河、古泊善后河及灌河均处于富营养化状态，叶绿素a浓度和综合营养状态指数均以蔷薇河/临洪河最高，古泊善后河次之，灌河最低，且河流上游反演结果普遍高于下游。

关键词：叶绿素a；综合营养状态指数；遥感定量反演；入海河流；连云港

Abstract：Eutrophication of sea-going rivers seriously threatens the regional ecological environment and human safety as they transport pollutants from land-based sources to the sea. With the deep implementation of Xi Jinping's ideology of ecological civilization, the widespread application of management systems such as the “river chiefs” and the “bay chiefs”, as well as the comprehensive fight against pollution, the water quality of rivers and near-shore waters entering the sea has steadily improved. Despite this, water quality fluctuates, and pollution prevention remains vital. There is an urgent need to strengthen real-time and effective large-scale remote sensing monitoring to track and consolidate treatment effectiveness. Recently, as high-resolution satellite and UAV remote sensing technology has rapidly developed, a research hot topic in the field has been the application of quantitative remote sensing monitoring of water column components in river water bodies to promote further improvement of pollution prevention and control. In this paper, a quantitative remote sensing inversion study of chlorophyll a and trophic level index (TLI(Σ)) of the main seagoing rivers in Lianyungang was conducted utilizing field measurements of chlorophyll a, total phosphorus, and total nitrogen content in the Qiangwei River, Linhong River, Gubo Shanhou River, and Guan River during June 2022, as well as Sentinel-2A MSI L2A satellite images. A significant correlation was found between chlorophyll concentration, TLI(Σ), and visible band reflectance. In particular, this was evident in the three bands of 490, 560, and 665 wavelengths, which can be used as sensitive bands for modeling. Analysis showed that the correlation coefficients of R(λ) and Chl a were -0.697, -0.681 and -0.693, respectively, and the correlation coefficients of R(λ) and TLI(Σ) were -0.728, -0.744, and -0.706. The accuracy comparison of the inversion models revealed that the multiplicative power model with R(665) as the independent variable in logarithmic coordinates of chlorophyll concentration was the optimal model for its remote sensing quantitative inversion (R²=0.67, MAPE=47.34%, RMSE=12.89 μg·L^-1), while the multiplicative power model with R(560) as the independent variable model was the optimal model for the quantitative remote sensing inversion of TLI(Σ) (R²=0.61, MAPE=4.36%, RMSE=3.45). Using Sentinel-2A MSI L2A images acquired on 25 June 2022, models were applied to calculate the spatial distribution of chlorophyll concentration and TLI(Σ) of the main seagoing rivers in Lianyungang. As a result, the Qiangwei/Linghong River had the highest chlorophyll concentration and the highest TLI(Σ), followed by the Gubo Shanhou River and the Guan River, with the lowest. The inversion results were generally higher in the upper reaches of the rivers than in the lower reaches.

Key words：Chlorophyll a;Trophic level index;Remote sensing quantitative retrieval;Seagoing rivers;Lianyungang

收稿日期: 2022-10-19 修订日期: 2023-05-06

中图分类号:

X87

基金资助: 国家自然科学基金项目(42076186，41906156)资助

通讯作者: 王祥 E-mail: xwang@nmemc.org.cn

作者简介: 王林，1981年生，国家海洋环境监测中心副研究员 e-mail: wanglin198110@yeah.net

引用本文:

王林，王祥，周超，王新新，孟庆辉，陈艳拢. 连云港主要入海河流叶绿素a及综合营养状态指数遥感定量反演[J]. 光谱学与光谱分析, 2023, 43(10): 3314-3320.
WANG Lin, WANG Xiang, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320.

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