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叶绿素a浓度是水质状况评价的一个重要指标,而遥感是大面积反演叶绿素a浓度的重要手段。由于采用基于经验模型的标准算法对二类水体叶绿素a浓度的反演值往往偏高,因此本文基于半分析模型GSM01(GarverSiegel-Maritorena-01),在对模型参数进行调节的基础上,对东海2008年5月11日Aqua MODIS、Terra MODIS、SeaWiFS 3种传感器各波段遥感反射率进行融合,来反演叶绿素a浓度,并将反演结果与自适应加权平均算法获得的叶绿素a浓度数据进行对比。结果表明,基于GSM01融合的多传感器叶绿素a浓度反演,拥有4个优势:(1)GSM01模型反演叶绿素a浓度值范围更符合实测结果,由于该模型考虑水体各组分的散射吸收特性对光谱反射率的影响,避免因高浓度悬浮物质影响造成的近岸水体叶绿素a浓度过高问题;(2)通过融合多传感器反射率数据,用于叶绿素a浓度反演的波段从6个增至18个,光谱信息变丰富,模型求解的自由度提高,叶绿素a浓度反演的精度提高。模型通过误差最小化准则,将不同传感器反演的差异降至最小,保证反演结果的空间连续性;(3)与自适应加权平均采用的融合策略不同,GSM01模型直接利用各传感器遥感反射率数据进行融合而不是针对叶绿素a浓度数据进行融合,避免了误差的传递;(4)GSM01模型可自由组合输入的反射率数据,具有更强的灵活性。
Chlorophyll a concentration is an important indicator of water quality assessment, and remote sensing is a large area inversion of chlorophyll a concentration of an important means. Based on the semi-analytical model GSM01 (Garver Siegel-Maritorena-01), based on the empirical model-based standard algorithm, the inversion values of chlorophyll-a concentrations in the second-type water bodies tend to be high. Based on the adjustment of the model parameters, Aqua MODIS, Terra MODIS and SeaWiFS were fused on May 11, 2008 to retrieve the concentration of chlorophyll-a, and the inversion results were compared with those obtained by the adaptive weighted averaging method . The results show that the multi-sensor chlorophyll a concentration inversion based on the GSM01 fusion has four advantages: (1) The GS01 model inverts the range of chlorophyll-a concentration more in line with the measured results. Since this model considers the scattering absorption of each component Spectral reflectance to avoid over-concentration of chlorophyll-a in nearshore waters due to high concentrations of suspended matter; (2) By incorporating multi-sensor reflectance data, the band for the inversion of chlorophyll-a concentrations increased from 6 to 18, the spectral information is rich, the degree of freedom for solving the model is improved, and the accuracy of chlorophyll a concentration inversion is improved. The model minimizes the difference between different sensor inversion by the error minimization criterion and ensures the spatial continuity of the inversion results. (3) Different from the fusion strategy adopted by adaptive weighted averaging, the GSM01 model directly uses the remote sensing reflectance Data fusion rather than chlorophyll a concentration data fusion, to avoid the error transmission; (4) GSM01 model can be freely combined input reflectivity data, with more flexibility.