一种基于MODIS影像可业务化运行的巢湖水体叶绿素a估算算法

现有水色卫星主要是针对大洋清洁水体设计,内陆浑浊水体多数波段经常饱和;而发展可以业务化运行的内陆水体叶绿素a算法,为生产实践服务,一直是水色遥感的重点和难点之一.利用2013年巢湖星地同步数据(N=55),通过经验正交函数(empirical orthogonal function,EOF)分析方法,选用MODIS唯一不饱和的4个波段(469、555、645、859 nm)数据进行分解,然后回归建模;并使用第三方独立的巢湖实测数据(N=40)进行验证(R2=0.63,URMSE=85.46%).结果表明:该算法用于MODIS影像上,空间分布合理,季节差异明显,且在高悬浮物水体、不同气溶胶条件下均有很好的抗扰动性.实践证明EOF算法可以应用于业务化运行的内陆水体叶绿素a浓度估算,并对其他水色参数反演具有一定的借鉴意义.

An operational algorithm to estimate chlorophyll-a concentrations in Lake Chaohu from MODIS imagery

Ocean color satellite is mainly designed for oceanic waters, but most of their bands are easily saturated in highly turbid waters. It's a great challenge to monitor water quality using satellites, because there are no operational algorithm to estimate chlorophyll-a concentration (Chl.a) in inland waters. In this paper, a novel approach based on the Empirical Orthogonal Function (EOF) analysis was developed using the spectral variance from the Rayleigh-corrected reflectance data (R_rc) at 469, 555, 645 and 859 nm, and Chl.a was estimated by considering the regression relationships between the spectral variance and 55 concurrent field measurements. The validation was then performed using independent data (N=40) from other laboratory, with R²=0.63, URMSE=85.46%. Application of the algorithm to MODIS images showed that spatial distribution patterns and seasonal changes are reasonable. Besides, this algorithm is immune to perturbations from thick aerosols and different sediments. Hence, we suggest that EOF algorithm could be applied to estimate Chl.a concentrations in inland waters.