Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition

Gilerson, Alex; Zhou, Jing; Hlaing, S.; Ioannou, Ioannis; Schalles, John F.; Gross, Barry; Moshary, Fred; Ahmed, Sam

doi:10.1364/oe.15.015702

Cited by 98 publications

(78 citation statements)

References 29 publications

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“…The reflectance spectrum showed a weak trough at 665 nm, which was caused by the Chl-a absorption maximum; this magnitude was affected by the Chl-a concentration and fluorescence contribution. 44 The reflectance peak at 680 nm resulted from the minimal combined absorption of phytoplankton, NAP, and water. 45 In the NIR band, the reflectance reached a minimum, approaching zero, which was caused by the increasing absorption by water and a relatively small scattering by NAP due to the low concentration of TSM.…”

Section: Remote Sensing Reflectancementioning

confidence: 99%

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

Shi

Tao

Mao

et al. 2018

J. Appl. Rem. Sens.

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"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.

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Section: Remote Sensing Reflectancementioning

confidence: 99%

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

Shi

Tao

Mao

et al. 2018

J. Appl. Rem. Sens.

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“…The key difference among these models is the parameters of specific absorption and backscattering [9]. The chlorophyll concentration and a ph (λ) have a proportional relationship [71] that can be expressed as…”

Section: Simulating the Remote Sensing Reflectancementioning

confidence: 99%

“…The NAP concentration was calculated by subtracting the PSS (=0.0687 × Chla) from the TSS. The reflectance caused by chlorophyll fluorescence emission for Case 2 waters was also considered as proposed by Gilerson et al [71]. Fluorescence reflectance follows a Gaussian shape with a peak at 685 nm, a full-width half maximum of 25 nm and a standard deviation, σ, of 10.6 nm [74].…”

Section: Simulating the Remote Sensing Reflectancementioning

confidence: 99%

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Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

et al. 2017

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Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms' indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R 2 = 0.692, root mean square error (RMSE) = 21.4 mg m −3 ), Lake Kasumigaura (R 2 = 0.866, RMSE = 11.3 mg m −3 ), and MERIS data over Lake Kasumigaura (R 2 = 0.57, RMSE = 36.5 mg m −3 ). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

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“…In general, the use of model type I is inaccurate and sometimes useless in optically complex waters due to the spectral overlap between phytoplankton and other dominant constituents affecting light attenuation (e.g., sediments, CDOM) [3,4,7]. Conversely, ocean color algorithms relying on chlorophyll-derived fluorescence signatures are less influenced by the presence of optical components having a strong light absorption (e.g., CDOM) or scattering (e.g., detritus) at the Soret bands [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Local and Global Spaceborne Chlorophyll Indices in the St. Lawrence Estuary

et al. 2012

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Spaceborne chlorophyll indices based on red fluorescence (wavelength = 680 nm) and water leaving radiance (L w ) in the visible spectrum (i.e., 400-700 nm) were evaluated in the St Lawrence Estuary (SLE) during September of 2011. Relationships between chlorophyll concentration (chl) and fluorescence were constructed based on fluorescence line height (FLH) measurements derived from a compact laser-based spectrofluorometer developed by ENEA (CASPER) and using spectral bands corresponding to the satellite sensor MERIS (MEdium Resolution Imaging Spectrometer). Chlorophyll concentration as estimated from CASPER (chl CASPER ) was relatively high NE of the MTZ (upper Estuary), and nearby areas influenced by fronts or freshwater plumes derived from secondary rivers (lower estuary). These findings agree with historical shipboard measurements. In general, global chl products calculated from L w had large biases (up to 27-fold overestimation and 50-fold underestimation) with respect to chl CASPER values. This was attributed to the smaller interference of detritus (mineral + organic non-living particulates) and chromophoric dissolved organic matter on chl CASPER estimates. We encourage the use of spectrofluorometry for developing and validating remote sensing models of chl in SLE waters and other coastal environments characterized by relatively low to moderate (<10 g·m

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Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition

Cited by 98 publications

References 29 publications

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

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

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

A Comparison between Local and Global Spaceborne Chlorophyll Indices in the St. Lawrence Estuary

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