湖泊/水库等内陆水体面积小、水质参数异质性显著；陆地卫星传感器具有空间分辨率高和刈幅大的特点，在陆地水体水色遥感中越来越受到重视。以现场实测光谱为基础，融合Landsat OLI光谱响应函数，模拟OLI传感器各波段的反射比数据Rrs（Remote Sensing Reflectance）；利用实测的水色组份吸收系数a和后向散射系数bb，模拟得到OLI传感器各波段的吸收和后向散射系数。以目前基于Landsat反演叶绿素常用的各种模型算法为例，基于生物光学模型，借助QAA，揭示其中的内在机理，并确定OLI每个波段的代表性波长；以此为基础，设计新的可用于长时间序列估算的高精度算法模型。

It is well known that inland water bodies such as lakes and reservoirs are small and optical variations are significant in spatial. Satellite image data for the land remote sensing plays a more and more important role in water color remote sensing of inland waters because of the high spatial resolution and a large spatial coverage. Based on the spectral response function of Landsat OLI data, remote sensing reflectance (Rrs) of each band mounted on the OLI sensor will be simulated by the in situ Rrs. Additionally, the absorption coefficients (a) and backscattering coefficients (bb) at each band will be also simulated using in situ/lab absorptions and backscattering coefficients. Take the widely used chlorophyll-a inversion algorithms as an example, the inherent mechanism will be revealed based on the bio-optical algorithms with the help of the Qusi-Analytical Model (QAA), and then the representative wavelength of each band is derived. Based on the mentioned above, it tries to develop a novel and high-precision chlorophyll-a inversion algorithm being applicable to a time-series satellite images with broad-bands.