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Water Optical Properties and Water Color Remote Sensing in Optically Deep and Shallow Waters of Lake Taihu, China.

机译：太湖光学深浅水域的水光学特性和水彩色遥感

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摘要

In this study, Lake Taihu in Jiangsu Province of China, a typical large freshwater lake, is selected as the study area. Based on the field spectral measurements and laboratory analyses performed in October 2008, water optical properties and water color/quality remote sensing retrieval models in Lake Taihu were investigated. It was recognized that water quality varied a lot in different areas. Waters in Lake Taihu were classified as optically deep waters (ODWs) and optically shallow waters (OSWs). ODWs are the waters where the water depth is more than three times the measured Secchi Disk Depth (SDD), otherwise they are OSWs. Cyanobacteria blooms happen frequently in ODWs and the water is eutrophicated heavily. Whereas water is very clear with rare cyanobacteria blooms but many aquatic plants in OSWs. Focused on the two types of water areas respectively, the inherent optical properties (lOPs), apparent optical properties (lOPs) and reflectance spectra were analyzed, as well as their relationships to water quality parameters. Local optical parameters f and Q, which play significant roles in water quality parameters retrieval models, were also determined.;Measured remote sensing reflectance data were used to establish two-band and three-band models for chlorophyll-a (Chl-a) concentration estimation, results showed both models were suitable in ODWs. However, aquatic plants in OSWs had great influence on spectra, resulting in the inapplicability of the established models at these sites. Absorption and backscattering coefficients were used to remove those influences and simulate new set of remote sensing reflectance based on radiative transfer theory, which were proved reliable to establish Chl-a retrieval algorithms. Three-band model established by simulated spectra showed more satisfactory performance in whole ODWs, and performance of two-band model in OSWs was also enhanced much.;Several models were established to estimate total suspended solids (TSS) concentrations. Single band model, two-band model and first derivative model all showed good results with R2 greater than 0.8, RMSE lower than 8.3 g m-3 and mean RE lower than 35%. There is no obvious difference found between ODWs and OSWs when validating these models, indicating that aquatic plants has little influence on spectral bands which are sensitive to TSS concentrations. Though several empirical bands performed well but were unsteady, thus quasi-analytical algorithm (QAA) was modified on its reference wavelength and several parameters to estimate TSS from backscattering coefficients of total particles . The modified QAA algorithm was more stable than emprirical ones with fair accuracy.;In ODWs, a semi-empirical algorithm proposed by Simis (2005) was used to roughly estimate Chl-a and cyanobacteria phycocyanin (PC) concentrations. Estimated Chl-a was matched with measured Chl-a quite well but measured PC was lacked for validation. However, preliminary results were obtained on the spectral response of PC on reflectance, which would be helpful on algal biomass estimation. In OSWs, the contribution rates of the bottom and aquatic plants on spectra were also analyzed based on different water characteristics. Several groups of measured data with different depth, SDD transparency, aquatic plants height and coverage, were selected and compared thoroughly to estimate approximate contribution of bottom and aquatic plants on measured remote sensing reflectance in OSWs, which make well basis on establishment of accurate Chl-a retrieval models in OSWs of Lake Taihu.;To realize water quality monitoring of Lake Taihu remotely, Landsat-7 ETM+ imagery and MODIS Terra and Aqua data were applied to map the relative distribution of Chl-a and TSS. ETM+ band 4 was the most sensitive band to Chl-a concentrations, and the ratio of ETM+ band2/bandl had the strongest correlation with TSS concentrations. For MODIS data, the ratio of band2/bandl was most correlated with Chl-a concentrations, and band 1 was most sensitive to TSS concentrations. The mapping results showed rational distribution patterns for both Chl-a and TSS in Lake Taihu, except Chl-a distribution in OSWs where aquatic plants overestimated the retrieved Chl-a values. Mapping water quality parameters by combining satellite data could give more understandable and comprehensive explanation on water quality distribution patterns both spatially and temporally, and is also of great significance on cyanobacteria blooms monitoring.
机译：本研究选择典型的大型淡水湖泊-中国江苏省的太湖作为研究区域。根据2008年10月进行的现场光谱测量和实验室分析，研究了太湖的水光学特性和水色/质量遥感检索模型。人们认识到，不同地区的水质差异很大。太湖水域分为光学深水（ODW）和光学浅水（OSW）。 ODW是水深超过测量的Secchi盘深度（SDD）的三倍的水，否则为OSW。蓝藻水华在ODWs中经常发生，并且水被严重富营养化。尽管水非常清澈，但蓝藻却很少见，但开放式作业系统中有许多水生植物。分别针对两种类型的水域，分析了其固有的光学特性（lOPs），表观光学特性（lOPs）和反射光谱，以及它们与水质参数的关系。还确定了局部光学参数f和Q，这些参数在水质参数检索模型中起着重要作用。;使用测得的遥感反射率数据建立叶绿素a（Chl-a）浓度的两波段和三波段模型估计，结果表明这两种模型均适用于ODW。但是，OSW中的水生植物对光谱影响很大，导致已建立的模型在这些地点不适用。利用吸收系数和后向散射系数消除了这些影响，并基于辐射转移理论模拟了一套新的遥感反射率，证明了建立Chl-a检索算法的可靠性。通过模拟光谱建立的三波段模型在整个ODWs中表现出令人满意的性能，并且在OSWs中的两波段模型的性能也得到了很大的提高。;建立了几个模型来估计总悬浮固体（TSS）浓度。单频带模型，两频带模型和一阶导数模型均显示出良好的结果，R2大于0.8，RMSE小于8.3 g m-3，平均RE小于35％。验证这些模型时，ODW和OSW之间没有发现明显差异，这表明水生植物对对TSS浓度敏感的光谱带几乎没有影响。尽管几个经验带表现良好但不稳定，因此对准参考算法（QAA）的参考波长和几个参数进行了修改，以根据总粒子的反向散射系数估算TSS。改进后的QAA算法比常规算法更稳定，具有相当的准确性。在ODW中，使用Simis（2005）提出的半经验算法粗略估计Chl-a和蓝藻藻蓝蛋白（PC）的浓度。估计的Chl-a与测得的Chl-a匹配得很好，但缺乏测得的PC来进行验证。然而，关于PC对反射率的光谱响应获得了初步结果，这将有助于藻类生物量的估算。在OSW中，还根据不同的水质特征分析了底部和水生植物在光谱上的贡献率。选择并深度比较了几组具有不同深度，SDD透明度，水生植物高度和覆盖率的测量数据，以估计底部和水生植物对OSW中测得的遥感反射率的近似贡献，这为建立准确的Chl-为了实现太湖水质的远程监测，采用Landsat-7 ETM +影像和MODIS Terra和Aqua数据绘制了Chl-a和TSS的相对分布。 ETM +带4是对Chl-a浓度最敏感的带，而ETM +带2 /带1的比率与TSS浓度具有最强的相关性。对于MODIS数据，band2 / band1的比率与Chl-a浓度最相关，而band 1对TSS浓度最敏感。测绘结果显示了太湖中Chl-a和TSS的合理分布模式，但OSW中的Chl-a分布是水生植物高估了所获取的Chl-a值。通过结合卫星数据测绘水质参数，可以对水质的时空分布格局进行更易理解，更全面的解释，对蓝藻水华的监测具有重要意义。

著录项

  • 作者

    Xi, Hongyan.;

  • 作者单位

    The Chinese University of Hong Kong (Hong Kong).;

  • 授予单位 The Chinese University of Hong Kong (Hong Kong).;
  • 学科 Geophysics.;Environmental Sciences.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 194 p.
  • 总页数 194
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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