地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (6): 965-970.doi: 10.3724/SP.J.1047.2014.00965

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基于反射峰面积的水体叶绿素遥感反演模拟研究

马万栋1(), 王桥1, 吴传庆1, 殷守敬1, 邢前国2, 朱利1, 吴迪1   

  1. 1. 环境保护部卫星环境应用中心,北京 100094
    2. 中国科学院烟台海岸带研究所,烟台 264003
  • 收稿日期:2013-10-29 修回日期:2014-01-21 出版日期:2014-11-10 发布日期:2014-11-01
  • 作者简介:

    作者简介:马万栋(1977- ), 男, 山东陵县人, 博士, 主要从事水环境遥感方面研究。E-mail:mawdcn@sohu.com

  • 基金资助:
    水专项课题(2014ZX07508001);国家自然科学基金项目(41271349);环保公益项目(201409100)

Research on Chlorophyll-a Retrieval Simulation in Waters Based on the Normalized Peak Area

MA Wandong1,*(), WANG Qiao1, WU Chuanqing1, YIN Shoujing1, XING Qianguo2, ZHU Li1, WU Di1   

  1. 1. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
    2. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
  • Received:2013-10-29 Revised:2014-01-21 Online:2014-11-10 Published:2014-11-01
  • Contact: MA Wandong E-mail:mawdcn@sohu.com
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

叶绿素浓度是水体富营养化状态的重要指标,也是水色遥感反演的水质参数之一。水体中叶绿素浓度的遥感反演主要是建立实测光谱和实测水质参数二者之间的关系模型,利用遥感影像进行叶绿素浓度的信息提取。传统的叶绿素浓度遥感反演受区域性和季节性的影响,反演精度不高,而且反演模型不具普适性,需对叶绿素光谱特征进行分析,建立高精度的反演模型。本文采用Hydrolight数据模拟了不同叶绿素浓度(1~200 µg·L-1)的水体在可见光近红外的反射波谱曲线,通过分析叶绿素的光谱特征选取了特征波段或波段组合,并建立了叶绿素浓度反演模型。研究表明,除反射峰波长模型外,反射峰面积模型、三波段模型、红光线高度模型等均能较好地反演叶绿素浓度。在不同叶绿素反演模型中,除红光线模型外,最优的是反射峰面积模型,其决定系数为0.9689,反演误差为25.25 µg·L-1;其次是三波段模型,其决定系数为0.9637,反演误差为10.66 µg·L-1。究其原因,三波段模型考虑了水体中非色素悬浮物、黄色物质及水体后向散射对叶绿素浓度反演造成的影响;反射峰面积模型除此之外还综合考虑了叶绿素散射效率的影响。

关键词: 叶绿素, 遥感反演, 反射峰面积模型, 三波段模型

Abstract:

The chlorophyll-a concentration of waters is one of the main retrieval parameters in the field of water color remote sensing. Based on the coefficients of absorption and backscattering of waters, Colored Dissolved Organic Matter(CDOM), tripton and chlorophyll-a, which are achieved using the Hydrolight software package, the remote sensing reflectance is simulated according to the forward radiation transfer models without the consideration of fluorescence peak. And then, the spectral curves of variable chlorophyll-a concentration are achieved. The spectral characteristics of chlorophyll-a are analyzed according to these remote sensing reflectance curves. Next, the retrieval models of chlorophyll-a are built based on analyzing the spectral characteristics within selected bands or certain band combinations. In this paper, the Normalized Peak Area (NPA) model and three-band model are analyzed and applied to retrieve the chlorophyll-a concentration. As a comparison, other retrieval models are also considered. According to the analysis and results, we find that the chlorophyll-a concentration could be better retrieved by the NPA model, the three-band model, and a few other models, except for the model of reflectance peak position. The least competent retrieval model for chlorophyll-a is the model of reflectance peak position with the R2 of 0.6513. Among all the retrieval models, the NPA model is the best model to retrieve cholorophyll-a concentration with the R2 of 0.9689 and the RMS error of 25.25µg·L-1. The second one is the three-band model with the R2 of 0.9637 and the RMS error of 10.66µg·L-1. The small retrieval error of the three-band model is due to the consideration of the backscattering impacts of tripton, CDOM and waters. The NPA model, in addition, has not only take into consideration of the backscattering impacts, but also the fluorescence efficiency and a variety of environmental factors when applied to retrieve chlorophyll-a concentration. In the end, we could conclude that the NPA could be utilized to retrieve chlorophyll-a concentration for simulated data. This conclusion should be further verified by using it with in situ experiments data.

Key words: chlorophyll-a, retrieval of remote sensing, the Normalized Peak Area model, the three-band model