Simultaneous retrieval of oceanic and atmospheric parameters for ocean color imagery by spectral optimization: a validation

Chomko, R.; Gordon, Howard R.; Maritorena, Stéphane; Siegel, David A.

doi:10.1016/s0034-4257(02)00108-6

Cited by 60 publications

(31 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improvement of the matchup statistics when examining reflectance ratios supports the use of band-ratio techniques to derive geophysical products, such as the chlorophyll concentration, in parallel to more sophisticated methods using all bands and the absolute values of reflectances [e.g., Chomko et al, 2003;Schiller and Doerffer, 1999].…”

Section: Resultsmentioning

confidence: 93%

Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS‐A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)

et al. 2008

View full text Add to dashboard Cite

[1] The match-up of satellite-derived reflectances with in situ observations is crucial to evaluate their quality and temporal stability. To contribute to this effort, a project has been set up to collect a data set of in situ radiometric and bio-optical quantities, in support to satellite ocean color calibration and validation. The project has been named ''BOUSSOLE'', and one of its key elements is a deep-sea optics mooring collecting data on a near-continuous basis since September 2003. This buoy is deployed in the deep clear waters of the northwestern Mediterranean Sea, and is visited on a monthly basis for servicing and acquisition of complementary data. The characteristics of the work area establish the site as a satisfactory location for validating satellite ocean color observations. A description of the data processing protocols is provided, followed by an analysis of the uncertainty of the buoy radiometry measurements. The results of a match-up analysis of the marine reflectances, diffuse attenuation coefficients, and chlorophyll concentrations for three major missions, i.e., MERIS, SeaWiFS, and MODIS-A, are then analyzed. They show poor performances for the bluest band (412 nm) of the three sensors, and performances within requirements at 443 and 490 nm for SeaWiFS and MODIS-A. These results suggest that a vicarious calibration should be introduced for the MERIS sensor. This analysis also demonstrates that a major effort is still required to improve atmospheric correction procedures whatever the mission.

show abstract

Section: Resultsmentioning

confidence: 93%

Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS‐A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)

et al. 2008

View full text Add to dashboard Cite

show abstract

“…A number of atmospheric correction methods, such as the spectral optimisation algorithm (SOA) [164,165] and the NeuroVaria (neuro-variational) method [166,167], have made use of a simple model that uses a Junge power-law distribution to characterise the size of aerosol particles, together with a complex refractive index that is wavelength independent. For the spectral optimisation algorithm, the diameter of the particles is D, and dN is the number of particles per unit volume in the size interval D ± dD/2 [164]:…”

Section: Atmospheric Contributionsmentioning

confidence: 99%

“…As models based on the Junge power-law distribution do not require a comprehensive set of in situ measurements, they have the potential to be applied universally. One limitation of these simple models is that they assume that the particles are spherical, which does not accurately account for the diversity of aerosol particles [165].…”

Section: Atmospheric Contributionsmentioning

confidence: 99%

“…Spectral inversion approaches simultaneously retrieve bio-optical water properties and correct for atmospheric effects in one step [165,222]. The spectral optimisation algorithm (SOA) [164,165] employs a simple aerosol model which determines the size of aerosol particles with a Junge power-law distribution.…”

Section: Spectral Inversion Approachesmentioning

confidence: 99%

“…The spectral optimisation algorithm (SOA) [164,165] employs a simple aerosol model which determines the size of aerosol particles with a Junge power-law distribution. This distribution is a simplification, which enables SOA to achieve optimisation when there are a limited number of spectral bands [223].…”

Section: Spectral Inversion Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

View full text Add to dashboard Cite

Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

show abstract

Requirement of minimal signal‐to‐noise ratios of ocean color sensors and uncertainties of ocean color products

Lee

et al. 2017

JGR Oceans

View full text Add to dashboard Cite

Using simulations, error propagation theory, and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), we determined the minimal signal‐to‐noise ratio (SNR) required for ocean color measurements and product uncertainties at different spatial and temporal scales. First, based on typical top‐of‐atmosphere (TOA) radiance over the ocean, we evaluate the uncertainties in satellite‐derived Rrs in the visible wavelengths (ΔRrs(vis)) due to sensor noise in both the near‐infrared (NIR) and the visible bands. While the former induces noise in Rrs(vis) through atmospheric correction, the latter has a direct impact on Rrs(vis). Such estimated uncertainties are compared with inherent ΔRrs(vis) uncertainties from in situ measurements and from the operational atmosphere correction algorithm. The comparison leads to a conclusion that once SNR(NIR) is above 600:1, an SNR(vis) better than 400:1 will not make a significant reduction in product uncertainties at pixel level under typical conditions for a solar zenith angle of 45°. Then, such uncertainties are found to decrease significantly in data products of oceanic waters when the 1 km pixels from individual images are binned to lower spatial resolution (e.g., 4 km) or temporal resolution (e.g., monthly). Although these findings do not suggest that passive ocean color sensors should have SNR(vis) around 400:1, they do support the argument for more trade space in higher spatial and/or spectral resolutions once this minimal 400:1 SNR(vis) requirement is met.

show abstract

Simultaneous retrieval of oceanic and atmospheric parameters for ocean color imagery by spectral optimization: a validation

Cited by 60 publications

References 25 publications

Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS‐A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)

Assessment of uncertainty in the ocean reflectance determined by three satellite ocean color sensors (MERIS, SeaWiFS and MODIS‐A) at an offshore site in the Mediterranean Sea (BOUSSOLE project)

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

Requirement of minimal signal‐to‐noise ratios of ocean color sensors and uncertainties of ocean color products

Contact Info

Product

Resources

About