二类水体组份的遥感定量反演一直是水色遥感的难点和热点问题,原因在于其水体组分(纯水、叶绿素、悬浮物及CDOM)之间复杂的相互作用。本文引入光谱分解算法,通过Hydrolight软件模拟叶绿素、悬浮物和CDOM的标准反射率光谱,解决光谱分解算法中"纯端元"难以获取的问题。在此基础上建立了二类水体组分光谱分解反演模型。模型表明,光谱分解系数之间有很强的独立性,能够作为独立变量估算对应组分浓度。对光谱分解建模数据建立波段比值模型,用不同时期的同步实测数据,对光谱分解算法和比值模型进行比较验证,结果表明,光谱分解模型和波段比值模型的悬浮物反演平均相对误差分别为:22.4%和37.7%。验证结果:光谱分解算法相对于传统的经验模型,具有一定的季节通用性。叶绿素浓度估算模型的平均相对误差为31.7%,反演精度不高。其因是由于叶绿素浓度相对于非藻类悬浮物浓度过低,平均浓度分别为17.3μg/L和78.6mg/L,叶绿素的光谱信息在一定程度上被高悬浮物掩盖。
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.
[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.
