Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data
Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data
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DOI：
10.1016/j.isprsjprs.2008.04.005
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发表时间：
2009-01-01
影响因子：
12.7
通讯作者：
Imai, Akio
中科院分区：
文献类型：
--
作者：
Oyama, Youichi;Matsushita, Bunkei;Imai, Akio
案例2水的遥感远不如案例1水成功，这主要是由于光学活性物质之间的复杂相互作用（例如，浮游植物、悬浮沉积物、有色溶解有机物和水）。为了解决这个问题，我们开发了一个光谱分解算法（SDA），基于光谱线性混合建模方法。通过水槽实验，我们发现基于SDA的水质参数估算模型比传统的经验模型（如单波段、波段比或波段算术计算）更能准确估算水质参数，具有上级优势。在本文中，我们开发了一种方法，应用SDA陆地卫星5 TM数据上霞浦湖，富营养化的湖泊，其特征在于高浓度的悬浮泥沙，叶绿素a（Chl-a）和非浮游植物悬浮泥沙（NPSS）分布图。结果表明，基于SDA的估算模型可以通过水池试验得到。此外，通过将该估计模型与卫星SRS（标准反射光谱：即，光谱端元），我们可以直接将该模型应用于卫星图像。同样的SDA为基础的估计模型叶绿素-a浓度被应用到两个Landsat-5 TM图像，一个在1994年4月和其他在2006年2月获得。两者之间的平均叶绿素a估计误差为9.9%，这一结果表明基于SDA的估计模型的潜在稳健性。2006年Landsat-5 TM图像NPSS浓度的平均估计误差为15.9%。成功地将基于SDA的估计模型应用于卫星数据的关键点是用于获得每个端元的合适的卫星SRS的方法。(c)2008年国际摄影测量和遥感学会。（摄影测量和遥感学会）。Elsevier B. V.出版，保留所有权利。
The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member. (c) 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.