Pitfalls in atmospheric correction of ocean color imagery: how should aerosol optical properties be computed?

Yan, Banghua; Stamnes, Knut; Li, Wei; Chen, Bingquan; Stamnes, Jakob J.; Tsay, Si‐Chee

doi:10.1364/ao.41.000412

Cited by 37 publications

(20 citation statements)

References 30 publications

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“…While several factors may be responsible for poor performance of the atmospheric correction procedure (Gordon, 1997;Siegel, Wang, Maritorena, & Robinson, 2000;Yan et al, 2002), it is of interest to evaluate first whether the discrepancies observed between the satellite-derived and in situ-derived L wn values in the Baltic show any relation to water properties themselves. This evaluation is illustrated by plotting the ratio of satellite L wn to in situ L wn for five MODIS wavebands as a function of three water properties measured in situ, L wn , K d (490), and Chl a (Fig.…”

Section: Comparison Of Satellite-derived Data Products With In Situ Mmentioning

confidence: 99%

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

Darecki

Stramski

2004

Remote Sensing of Environment

331

209

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Section: Comparison Of Satellite-derived Data Products With In Situ Mmentioning

confidence: 99%

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

Darecki

Stramski

2004

Remote Sensing of Environment

331

209

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“…The atmospheric molecules scattering is viewed as Rayleigh scattering with the depolarization factor of 0.0279. For the aerosol, a more 5 realistic scattering multi-component approach is adopted within the aerosol model, wherein the refractive index of each component of aerosol particle is calculated considering hygroscopic growth (Shettle and Robert, 1979;Yan et al, 2002). It is assumed that the external mixture aerosol model consists of fine particles, sea spray particles, and dust particles (which are defined as non-spherical particles); and that an internal mixture of water-soluble, dust-like and soot aerosols exist within the fine particles, of which the refractive index are calculated by the sum of each internal component contribution based on its 10 volume fraction.…”

Section: Atmospheric Modulementioning

confidence: 99%

Simultaneous determination of aerosol optical thickness and water leaving radiance from multispectral measurements in coastal waters

Shi¹,

Nakajima²

2017

Preprint

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Abstract. Retrieval of aerosol optical properties and water leaving radiance over ocean is changeling since the latter mostly 10 accounts for ~10% of satellite observed signal and can be easily contaminated by the atmospheric scattering. Such an effort would be more difficulty in turbid coastal waters due to the existence of optically complex oceanic substances or high aerosol loading. In an effort to solve such problems, we present an optimization approach for the simultaneous determination of aerosol optical thickness (AOT) and normalized water leaving radiance (nL w ) from multi-spectral measurements. In this algorithm, a coupled atmosphere-ocean radiative transfer model combined with a comprehensive bio-optical oceanic module 15 is used to jointly simulate the satellite observed reflectance at the top of atmosphere and water leaving radiance just above the ocean surface. Then a full-physical nonlinear optimization method is adopted to retrieve AOT and nL w in one step. The algorithm is validated using Aerosol Robotic Network Ocean Color (AERONET-OC) products selected from eight OC sites distributed over different waters, consisting of observation cases covered both in and out of sun glint from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Results show a good consistency between retrieved and in situ 20 measurements in each site. It is demonstrated that more accurate AOT are determined based on the simultaneous retrieval method, particularly in shorter wavelengths and sun glint conditions, where the averaged percentage difference (APD) of retrieved AOT generally reduce by approximate 10% in visible bands compared with those derived from the standard atmospheric correction (AC) scheme. It is caused that all the spectral measurements can be used jointly to increase the information content in the inversion of AOT and the wind speed is also simultaneously retrieved to compensate the specular 25 reflectance error estimated from the rough ocean surface model. For the retrieval of nL w , over atmospheric correction can be avoided to have a significant improvement for the inversion of nL w at 412 nm. Furthermore, generally better estimates of band ratios of nL w (443)/nL w (554) and nL w (488)/nL w (554), which are employed in the inversion of chlorophyll a concentration (Chl), are obtained using simultaneous retrieval approach with less root mean square errors and relative differences than those derived from the standard AC approach in comparison to the AERONET-OC products, as a result that 30 the APD value of retrieved Chl decreases by about 5%. On the other hand, the standard AC scheme yields a more accurate retrieval of nL w at 488 nm, prompting a further optimization of oceanic bio-optical module of current model. Atmos. Chem. Phys. Discuss., https://doi

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“…The above corrections are necessary because the aerosols are many types (strongly absorbing and non-absorbing) and may consist of a multi-component mixture of particles with different chemical compositions (that change with differing humidity and altitude) and affinities to water (Yan et al, 2003). It has been noted that under these circumstances, models with the SeaDAS algorithm remove too much L cpl (λ i ) from sensor-observed radiance which eventually leads to a nonnegligible error in the retrieval of ocean colour .…”

Section: P Shanmugam: Caas: An Atmospheric Correction Algorithm For mentioning

confidence: 99%

CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

Shanmugam

2012

Ann. Geophys.

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Abstract. The current SeaDAS atmospheric correction algorithm relies on the computation of optical properties of aerosols based on radiative transfer combined with a nearinfrared (NIR) correction scheme (originally with assumptions of zero water-leaving radiance for the NIR bands) and several ancillary parameters to remove atmospheric effects in remote sensing of ocean colour. The failure of this algorithm over complex waters has been reported by many recent investigations, and can be attributed to the inadequate NIR correction and constraints for deriving aerosol optical properties whose characteristics are the most difficult to evaluate because they vary rapidly with time and space. The possibility that the aerosol and sun glint contributions can be derived in the whole spectrum of ocean colour solely from a knowledge of the total and Rayleigh-corrected radiances is developed in detail within the framework of a Complex water Atmospheric correction Algorithm Scheme (CAAS) that makes no use of ancillary parameters. The performance of the CAAS algorithm is demonstrated for MODIS/Aqua imageries of optically complex waters and yields physically realistic water-leaving radiance spectra that are not possible with the SeaDAS algorithm. A preliminary comparison with in-situ data for several regional waters (moderately complex to clear waters) shows encouraging results, with absolute errors of the CAAS algorithm closer to those of the SeaDAS algorithm. The impact of the atmospheric correction was also examined on chlorophyll retrievals with a Case 2 water bio-optical algorithm, and it was found that the CAAS algorithm outperformed the SeaDAS algorithm in terms of producing accurate pigment estimates and recovering areas previously flagged out by the later algorithm. These findings suggest that the CAAS algorithm can be used for applications focussing in quantitative assessments of the biological and biogeochemical properties in complex waters, and can easily be extended to other sensors such as OCM-2, MERIS and GOCI.

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Pitfalls in atmospheric correction of ocean color imagery: how should aerosol optical properties be computed?

Cited by 37 publications

References 30 publications

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

Simultaneous determination of aerosol optical thickness and water leaving radiance from multispectral measurements in coastal waters

CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

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