Algorithms Based on Spectral Decomposition Algorithm for Retrieval of Constituents in Taihu Lake

  • Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210046, China

Received date: 2011-05-12

  Revised date: 2011-08-10

  Online published: 2011-10-25


Quantitative remote sensing inversion of constituents in Case II waters has been a difficult and hot issue. It is due to the complex interaction among the constituents (e.g. pure water, phytoplankton, non-phytoplanktonic suspended solids and CDOM). In this paper we analyze the spectral decomposition algorithm of the optically active substances (OAS) in Taihu Lake. It contains two steps. First step is to obtain standard reflectance spectra of end-members (Chl a, NPSS, CDOM and pure Water) by simulation experience using Hydrolight; second step is to establish a number of equations (according to the numbers of end-members) obtained from different wavelengths and derive the decomposition coefficients to be used as the independent variables in the SDA-based estimation model. The results show that the decomposition coefficients obtained from spectral decomposition algorithm are highly independent. Moreover, we build the band ratio model based on the same data as the spectral decomposition algorithm. Then we apply the two models to the experimental data in November, 2008, and get the average relative error of 22.4% and 37.7%, which shows that to some extent the spectral decomposition algorithm has more seasonal versatility than that of band ratio model. The average relative error of chlorophyll concentration estimation model is 31.7%, which shows low inversion accuracy. The reason may be due to the concentration of chlorophyll is too low compared to non-algal suspended solids concentration. The average concentrations were 17.3μg/L and 78.6mg/L. So, to some extent, the spectral information of chlorophyll is concealed by the high suspended solids.

Cite this article

LU Chaoping, LV Heng, LI Yunmei . Algorithms Based on Spectral Decomposition Algorithm for Retrieval of Constituents in Taihu Lake[J]. Journal of Geo-information Science, 2011 , 13(5) : 687 -694 . DOI: 10.3724/SP.J.1047.2011.00687


[1] Morel A. In-water and Remote Measurements of Color[J]. Boundary Layer Meteorol,1980,18:177-201.

[2] Dekker A G., Peters S W M. The Use of Thematic Mapper for the Analysis of Entropic Lakes: A Case Study in the Netherlands[J]. International Remote Sensing, 1993,14:799-821.

[3] 李素菊. 利用分析方法建立湖泊光学水质参数反演算法研究 .北京大学,2003.

[4] 唐军武.海洋光学特性模拟与遥感模型 .中国科学院遥感应用研究所,1999,9-10.

[5] Sathyendranath S (Ed.). IOCCG Report 3: Remote Sensing of Ocean Color in Coastal, and Other Optically-Complex Waters . International Ocean Color Coordinating Group, 2000.

[6] Lindell T, Pierson D, Premazzi G, et al. Manual for Monitoring European Lakes Using Remote Sensing Techniques[J]. European Commission Joint Research Centre, Environment Institute, Salmon. Part III Remote Sensing of Lakes, 1999,81-112.

[7] Miyazaki, Shimizu, Yasuoka. High-speed Spectral Diameter for Remote Sensing[J]. Applied Optics, 1987, 26 (22): 4761-4766.

[8] Svab E, Tyler A N, Preston T, et al. Characterizing the Spectral Reflectance of Algae in Lake Waters with High Suspended Sediment Concentrations[J]. International Journal of Remote Sensing, 2005, 26(5): 919-928.

[9] Morel A, Prieur L. Analysis of Variations in Ocean Color[J]. Limnology and Oceanography, 1977, 22(4): 709-722.

[10] Oyama Y, Matsushita B, Fukushima, et al. A New Algorithm for Estimating Chlorophyll-a Concentration from Multi-spectral Satellite Data in Case II Waters: A Simulation Based on a Controlled Laboratory Experiment[J]. International Journal of Remote Sensing, 2007, 28(7): 1437-1453.

[11] 肖青, 闻建光,柳钦火.混合光谱分解模型提取水体叶绿素含量的研究[J]. 遥感学报,2006,10(4):559-567.

[12] 郑有飞,范旻昊,张雪芬.基于MODIS遥感数据的混合像元分解技术研究和应用[J].南京气象学院学报, 2008, 31(2):145-150.

[13] 钱乐祥,崔海山.运用归一化光谱混合模型分析城市地表组成[J].国土资源遥感, 2006, 6:65-68.

[14] 唐军武,田国良,汪小勇,等.水体光谱测量与分析,Ⅰ水面以上测量法[J]. 遥感学报, 2004, 8(1):37-43.

[15] Gons H, Burger-Wiersma J Otten T, et al. Coupling of Phytoplankton and Detritus in a Shallow, Entropic Lake (Lake Loosdrecht, The Netherlands)[J].Hydrobiologia, 1992, 233(1-3):51-59.