Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Abstract

"Retrieval of absorption coefficients for a drinking water source using a green-red band quasianalytical algorithm," J. Appl. Remote Sens. 12(4), 042802 (2018), doi: 10.1117/1.JRS.12.042802. Abstract. This study proposed a green-red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green-red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, að510Þ. Second, a semianalytical model based on að510Þ and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI's performance was compared with that of QAA-v5 and another red-green QAAbased approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R 2 ¼ 0.81, mean absolute percentage error, MAPE ¼ 15.7%), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R 2 ¼ 0.56, MAPE ¼ 21.2%) and QAA-RGR (R 2 ¼ 0.67, MAPE ¼ 22.6%). In addition, to illustrate the QAA-GRI's assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former's extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

An Extended Quasi−Analytical Algorithm for Retrieving Absorption Coefficient Using 510–620 nm Bands from OLCI and MERIS Satellite Data

“…It clearly signifies that th majority of the in situ data belong to phytoplankton or CDOM−dominated waters, and the contribution of non−algal particles among the three components does not exceed 0.4 In particular, QHD is mainly characterized as a low−chlorophyll, CDOM−dominated wa ter system. a proposed in ou previous study [30]. It is a clear demonstration of the fact that the value of a pc (560) − a pc (620) is far less than the value of a w (560) − a w (620) (0.213 m −1 ), which implies that certain conditions have been met to establish the relation between GRI and a(510) proposed in our previous study [30].…”

“…An average value of r, 0.026, was adopted in this study according to Cui et al [43]; L g (λ) and ρ g (λ) stands for the radiance and reflectance from the Spectralon plaque, respectively. Measurements of the absorption coefficient of CDOM (a CDOM (λ)) were performed in the laboratory according to the ocean optical protocols [42], and were detailed in our previous study [30]. Briefly, CDOM samples were filtered with a pore size of 0.22 µm, then absorbance of the filtered water was measured over 250~860 nm at 1 nm increments using a Perkin Elmer (PE) lambda−35 spectrophotometer.…”

“…From the above analysis, the original QAA−GRI algorithm proposed in our previous study [30] can be extended to be applied to the two in situ datasets by improving the model between green-red index (GRI, see Table 1 step 2 in this study) and a(510). Semi−analytical 2 a(λ 0 )λ 0 = 510 nm = 0.4654 × GRI 0.55 GRI = 0.213 × Rrs (560)×Rrs (620) Rrs (560)−Rrs (620) ×…”

“…It has been proven that the QAA algorithm has great potential for a(λ) retrieval compared to empirical algorithms [27,28], and has already been adopted in NASA's software platform (SeaDAS) to process satellite ocean color data. Nevertheless, there are several QAA−based algorithms that have been proposed to improve the key model of a(λ 0 ) (λ 0 , reference band) estimation for diverse waters [19,20,29,30]. Up−to−date, semi−analytical algorithms are still widely used and effective algorithms for a(λ) estimation, even though a lot of efforts are required to further validate and refine the performance over different optical water types [31].…”

“…The QAA−GRI algorithm is enhanced by using a new empirical model between the green red index (GRI) and total absorption coefficient at 510 nm based on two in situ field measurements. By enhancement, the model can be applied to oceanic and inland waters rather than to a single water type in our previous study [30]. The algorithm is further applied to the OLCI and MERIS satellite data, and the temporal and spatial variations in total absorption coefficient were investigated and discussed over the East China Sea (ECS).…”