An empirical algorithm for light absorption by ocean water based on color

Lee, Zhongping; Carder, Kendall L.; Steward, Robert G.; Peacock, Thomas G.; Davis, Curtiss O.; Patch, Jennifer S.

doi:10.1029/98jc01946

Cited by 85 publications

(44 citation statements)

References 43 publications

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“…Currently, COSYNA is being extended to the western part of the Baltic Sea (in cooperation with a new partner, GEOMAR, Helmholtz Centre for Ocean Research) by installing an Underwater-Node System in spring 2016 in the Eckernförde Bight near the location of GEOMAR's long established Boknis Eck time-series station (Lennartz et al, 2014). COSYNA already contributes to observations of other coastal areas in the world, such as the Lena delta, the Bohai Sea in China, or with instruments on research vessels and cruise ships operating in various parts of the world ocean.…”

Section: Discussionmentioning

confidence: 99%

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The Coastal Observing System for Northern and Arctic Seas (COSYNA)

et al. 2017

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Abstract. The Coastal Observing System for Northern and Arctic Seas (COSYNA) was established in order to better understand the complex interdisciplinary processes of northern seas and the Arctic coasts in a changing environment. Particular focus is given to the German Bight in the North Sea as a prime example of a heavily used coastal area, and Svalbard as an example of an Arctic coast that is under strong pressure due to global change.The COSYNA automated observing and modelling system is designed to monitor real-time conditions and provide short-term forecasts, data, and data products to help assess the impact of anthropogenically induced change. Observations are carried out by combining satellite and radar remote sensing with various in situ platforms. Novel sensors, instruments, and algorithms are developed to further improve the understanding of the interdisciplinary interactions between physics, biogeochemistry, and the ecology of coastal seas. New modelling and data assimilation techniques are used to integrate observations and models in a quasi-operational system providing descriptions and forecasts of key hydrographic variables.

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Section: Discussionmentioning

confidence: 99%

“…Standard algorithms are used widely to determine the optically dominant water constituents and the chlorophyll a concentration in clear oceanic waters (Carder et al, 1991;Lee et al, 1998;Gohin et al, 2002). These simple band-ratio algorithms, however, often fail in optically complex coastal waters.…”

Section: Satellite Oceanographymentioning

confidence: 99%

The Coastal Observing System for Northern and Arctic Seas (COSYNA)

et al. 2017

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“…Because the reference band used in the original QAA was shifted from 555 to 510 nm, the coefficients of the spectral slope (Y) calculation (step 4) were fine-tuned with an optimization algorithm. 47 The newly developed algorithm for retrieving aðλÞ, QAA-GRI, is summarized in Table 2.…”

Section: Qaa-gri Calibration In Lake Qiandaohumentioning

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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“…To solve this problem, many methods have been developed for remotely retrieving the IOPs. Those methods consist of empirical [14]- [17] and semianalytical algorithms [18]- [24].…”

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Validation of a Quasi-Analytical Algorithm for Highly Turbid Eutrophic Water of Meiliang Bay in Taihu Lake, China

Zha

et al. 2009

IEEE Trans. Geosci. Remote Sensing

102

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In many hydrological studies and applications, it is desirable to know the absorption property of water bodies. In order to derive this inherent optical property of waters from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was calibrated and validated for the highly turbid water of Taihu Lake in China. A data set collected on November 8, 2007, from Meiliang Bay of Lake Taihu was first used to calibrate a regional QAA algorithm for this area, and other two independent data sets, which were collected on August 22, 2006, and November 10, 2008, from the same area, were used to further validate the local algorithm. By shifting the reflectance wavelength from the red region to near infrared, the local QAA algorithm works well for this highly turbid water, the percent difference between the derived and measured absorption coefficients is less than 20% for all 13 samples in the data set of 2007, and most of them are less than 10%. The regional calibrated algorithm also has great result in deriving the absorption for the data set of 2008. However, the performance of the local algorithm also has various seasonal properties. It failed in deriving absorption for the data set of August 2006, unless the reference wavelength is shifted to even more long ones. It has been suggested in this paper that the seasonal and regional information is necessary for using the QAA algorithm in different optical property waters.

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An empirical algorithm for light absorption by ocean water based on color

Cited by 85 publications

References 43 publications

The Coastal Observing System for Northern and Arctic Seas (COSYNA)

The Coastal Observing System for Northern and Arctic Seas (COSYNA)

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

Validation of a Quasi-Analytical Algorithm for Highly Turbid Eutrophic Water of Meiliang Bay in Taihu Lake, China

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