Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln, 303 Hardin Hall, Lincoln, USA; School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA.
Water Res. 2012 Mar 15;46(4):993-1004. doi: 10.1016/j.watres.2011.11.068. Epub 2011 Dec 16.
Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll-a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll-a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll-a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll-a concentration, with a root mean square error of 5.54 mg m(-3) for chlorophyll-a concentrations in the range 2.27-81.17 mg m(-3).
基于使用野外分光计测量的红光和近红外（NIR）反射率的算法已被证明可准确估计混浊高生产力水域中的叶绿素-a 浓度，而不论水的生物光学特性的变化如何。本研究的目的是研究当应用于多时间机载反射率数据时，NIR-红模型的性能，该数据由高光谱传感器机载成像光谱仪（AISA）获取，在数据获取日期存在不均匀的大气影响。结果表明，NIR-红模型无需大气校正即可捕获水面叶绿素-a 的空间分布。然而，可变的大气影响确实会影响叶绿素-a 反演的准确性。对 AISA 数据应用了两种大气校正程序，即快速直线大气光谱调整（FLAASH）和快速大气校正（QUAC），并比较了它们的结果。QUAC 产生了稳健的大气校正，这使得 NIR-红算法能够准确估计叶绿素-a 浓度，对于 2.27-81.17 mg m(-3) 范围内的叶绿素-a 浓度，其均方根误差为 5.54 mg m(-3)。
