Role of sensor noise in hyperspectral remote sensing of natural waters: Application to retrieval of phytoplankton pigments

Levin, I. M.; Levina, Elizaveta; Gilbert, Gary D.; Stewart, S.

doi:10.1016/j.rse.2005.01.001

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Moses et al, 2012). Levin et al (2005) found that when noise issues are considered, a sensor with 28 channels yields a retrieval accuracy that is similar to or even slightly better than that of a 54-channel sensor with half the spectral resolution.…”

Section: Comparison With Hyperspectral Datamentioning

confidence: 99%

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

Moses

Gitelson

Berdnikov

et al. 2012

Remote Sensing of Environment

125

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a b s t r a c tWe present here results that strongly support the use of MERIS-based NIR-red algorithms as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. The study was carried out as one of the steps in testing the potential of the universal applicability of previously developed NIR-red algorithms, which were earlier calibrated using a limited set of MERIS imagery and in situ data from the Azov Sea and the Taganrog Bay, Russia, and data that were synthetically generated using a radiative transfer model. We used an extensive set of MERIS imagery and in situ data collected over a period of three years in the Azov Sea and the Taganrog Bay for this validation task. We found that the two-band and three-band NIR-red algorithms gave consistently highly accurate estimates of chl-a concentration, with a mean absolute error of 4.32 mg m − 3 and 4.71 mg m − 3 , respectively, and a root mean square error as low as 5.92 mg m − 3 , for data with chl-a concentrations ranging from 1.09 mg m − 3 to 107.82 mg m − 3 . This obviates the need for case-specific reparameterization of the algorithms, as long as the specific absorption coefficient of phytoplankton in the water does not change drastically, and presents a strong case for the use of NIR-red algorithms as standard algorithms that can be routinely applied for near-real-time quantitative monitoring of chl-a concentration in the Azov Sea and the Taganrog Bay, and potentially elsewhere, which will be a real boon to ecologists, natural resource managers and environmental decision-makers. (W.J. Moses), agitelson2@unl.edu (A.A. Gitelson), berdnikov@ssc-ras.ru (S. Berdnikov), saprygin@ssc-ras.ru (V. Saprygin), povazhny@mmbi.krinc.ru (V. Povazhnyi).

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Section: Comparison With Hyperspectral Datamentioning

confidence: 99%

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

Moses

Gitelson

Berdnikov

et al. 2012

Remote Sensing of Environment

125

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“…The real-world SNR is even lower than the theoretical value on hyperspectral cameras because they simultaneously image the entire spectrum, so the lowest intensity spectra will have a lower SNR than the highest intensity pixel . The low SNR can significantly alter the accuracy of the computed ocean color products, either through the ocean color algorithms directly or through the environmental noise correction algorithms [Levin et al, 2005;Levin and Levina, 2007;Moses et al, 2012a. Therefore, the impact of optoelectronic and photonic noise needs to be considered in the most sensitive ocean color applications before using lightweight hyperspectral cameras to map optical constituents.…”

Section: Identified Limitations In Fine-scale Optical Constituent Estmentioning

confidence: 99%

“…The optical and electronics noise sources directly affect the estimated water-leaving radiance, the noise can then propagate through software algorithms to produce imprecise estimates of OACs and inaccurate correction of surface reflected light (glint) [Levin et al, 2005;Levin and Levina, 2007]. OAC algorithms typically utilize band ratios of the remote sensing reflectance, which is the camera measured radiance divided by the irradiometer measured irradiance.…”

Section: Considerationsmentioning

confidence: 99%

Computational approaches for sub-meter ocean color remote sensing

O'Shea¹

2021

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“…This algorithm will also account for sensor noise and maritime atmospheric variations. Earlier versions of this approach are presented in Levin et al (2005). The accuracy of OAM retrieval will be estimated and an analysis will be made of possible ways to increase retrieval accuracy by varying sensor parameters and expanding a priori information about observational conditions.…”

Section: Approachmentioning

confidence: 99%

Inverse Problems in Optical Remote Sensing of Coastal Waters

Dolina¹,

Долин²,

Лучинин³

et al. 2006

Self Cite

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Grant Number: N000140610741 http://www.hydrooptics.spb.ru LONG-TERM GOALSThe long-term goal of the Project is to develop robust inversion algorithms to extract estimates of the optical and physical properties of sea water layers, the sea floor, and the sea surface from LIDAR (Light Detection and Ranging) returns, and optical imagery of the ocean bottom and depths and to improve the quality of images of subsurface objects collected over the sea. OBJECTIVESThe project objectives are: 1) to develop new models of water, bottom, and surface returned LIDAR signals and to develop new mathematical inverse techniques to extract from actual or simulated LIDAR returns the inherent optical properties of water (IOP) and to estimate parameters of internal waves (IW) from IW-induced disturbances of IOP stratification; 2) to develop theoretical mathematical techniques and experimental procedures to improve the quality of passive images of the coastal sea floor by correcting distortion due to light refraction through rough sea surfaces; and 3) to develop mathematical techniques for the retrieval of the quantity of optically active materials (chlorophyll, dissolved organic matter, and sediment) from the color spectrum of sea water optical returns.

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Role of sensor noise in hyperspectral remote sensing of natural waters: Application to retrieval of phytoplankton pigments

Cited by 11 publications

References 11 publications

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters — The Azov Sea case study

Computational approaches for sub-meter ocean color remote sensing

Inverse Problems in Optical Remote Sensing of Coastal Waters

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