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通过对2010年5月2日太湖HJ-1A卫星超光谱影像的几何纠正和6S模型辐射校正,以及水体实测光谱数据和影像光谱数据分析,将太湖28个水体采样点光谱数据分别进行归一化处理和一阶微分处理后,选取和水质参数相关系数最大的波段或波段组合建立反演模型,获得太湖叶绿素a浓度以及悬浮物浓度的空间分布图.研究表明,超光谱影像B73波段(682.785nm)处的归一化光谱数据和叶绿素a浓度相关性最高,泥沙遥感参数(Sr)和悬浮物浓度相关性最高,与水体实测光谱数据的相关性分析结果相同,模型预测值和实测值的平均相对误差均在30%之内,水质空间分布图与实地调查结果一致,因此,HJ-1A卫星超光谱数据可以借鉴水体实测光谱数据不同水质参数敏感波段的分析结果,很好地应用于水质定量遥感.
The spectral data of 28 water bodies in Taihu Lake were normalized respectively by geometric correction and 6S model radiometric correction of HJ-1A satellite on May 2, 2010, as well as actual measured spectral data and image spectral data of Taihu Lake After the treatment and the first-order differential processing, the inversion model was established by using the band or band combination with the highest correlation coefficient with the water quality parameters to obtain the spatial distribution of chlorophyll-a concentration and suspended matter concentration in the Taihu Lake.The results show that the B73 band (682.785nm ) Had the highest correlation with chlorophyll a concentration. The correlation between sediment remote sensing parameters (Sr) and suspended matter concentration was the highest, which was the same as that of water spectral data. The predicted value and the measured value The average relative error is within 30%, and the spatial distribution of water quality is consistent with the field survey. Therefore, HJ-1A satellite hyperspectral data can draw on the analysis results of sensitive bands of different water quality parameters of measured spectral data of water body, and are well applied to water quality Quantitative remote sensing.