Gilerson Alexander A, Gitelson Anatoly A, Zhou Jing, Gurlin Daniela, Moses Wesley, Ioannou Ioannis, Ahmed Samir A
Optical Remote Sensing Laboratory, Department of Electrical Engineering, The City College of the City University of New York, 140 St & Convent Ave, New York, New York 10031, USA.
Opt Express. 2010 Nov 8;18(23):24109-25. doi: 10.1364/OE.18.024109.
Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3=[Rrs(-1)(665)-Rrs(-1)(708)]×Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl]>1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning.
利用红光和近红外波段估算叶绿素a浓度[Chl]的遥感算法，在内陆和沿海水域可能比使用蓝光和绿光波段的算法更有效。我们使用了与各种水参数相关的反射光谱和固有光学特性的综合合成数据集，以及来自美国内布拉斯加州多个湖泊的非常一致的现场数据集，测试了这种双波段和三波段红-近红外算法。用MERIS波段测试的双波段算法是Rrs(708)/Rrs(665)和Rrs(753)/Rrs(665)。带有MERIS波段的三波段算法形式为R3 = [Rrs(-1)(665) - Rrs(-1)(708)]×Rrs(753)。结果表明，Rrs(708)/Rrs(665)和R3与[Chl]的关系在很大程度上不依赖于CDOM和非藻类颗粒的吸收，也不依赖于水体成分的后向散射特性，并且可以根据各波段的水体吸收系数以及665nm处的浮游植物比吸收系数来定义。对于[Chl]>1mg/m3建立了后者与[Chl]的关系，然后进一步用于开发与现场数据匹配良好且无需区域调整的算法。
