利用Hyperion高光谱数据的三波段法反演太湖叶绿素a浓度

以2004-08-19太湖野外试验所获取的水质数据(叶绿素a浓度7.8~154.3μg.L-1,总悬浮物浓度65.0~190.2 mg.L-1,N=38)和同步的Hyperion星载高光谱数据为研究对象,利用三波段算法反演太湖水体的叶绿素a浓度.通过分析太湖固有光学量的特点,提出适用于太湖的3个特征波段的选择依据,并对波段进行优化计算,在此基础上建立了三波段统计模型,最后对模型的反演精度进行分析与评价.结果表明,Hyperion的B34(691.37 nm)、B37(721.90 nm)和B50(854.18 nm)组成三波段模型变量与叶绿素a浓度具有最高的相关系数(r=0.934),模型的决定系数(R2)和均方根误差(RMSE)分别为0.872和13.93μg.L-1,其反演精度优于传统经验统计模型,如比值模型(R2=0.844,RMSE=15.41μg.L-1)和一阶微分模型(R2=0.831,RMSE=16.00μg.L-1).研究结果证实了三波段法适用于内陆富营养化浑浊水体和Hyperion高光谱数据,为今后更精确地反演内陆水体的叶绿素a浓度提供了参考依据.

Remote Chlorophyll a Retrieval in Taihu Lake by Three-band Model Using Hyperion Hyperspectral Data

To retrieve chlorophyll a (Chla) concentration in Taihu Lake by three-band model, a field study was conducted on August 19,2004 to collect water samples (N=38),which contained widely variable Chla (7.8-154.3 μg·L~(-1)) and total suspended solids(65.0-190.2μg·L~(-1)dry wt),and the synchronous Hyperion images was also acquired as remote sensing data. After obtaining the approximate range of wavelengths for the three bands by analyzing the inherent optical properties of Taihu Lake, the three-band models were spectrally tuned to select the bands for most accurate Chla estimation. Finally Hyperion B34(691.37 nm),B37(721.90 nm)and B50 (854.18 nm) were selected to establish a three-band model. The results show that strong linear relationship is found between analytically measured Chla and the three-band model(r=0.934),which accounts for 87.2% of variation in Chla and allows estimation of Chla with a root mean square error (RMSE) of 13.93 μg·L~(-1).whereas the traditional two-band models accounts for lower accuracies of Chla estimation (spectral ratio,R~2=0.844,RMSE =15.41 μg·L~(-1),and reflectance fimt-derivative,R~2=0.831,RMSE=16.00 μg·L~(-1)).The findings prove that the three-band model is applicable for Chla retrieval in turbid, productive inland waters and by using Hyperion hyperspectral data.