SeaWiFS Atmospheric Correction Algorithm Research Articles

Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements

The use of satellites to monitor the color of the ocean requires effective removal of the atmospheric signal. This can be performed by extrapolating the aerosol optical properties in the visible from the near-infrared (NIR) spectral region assuming that the seawater is totally absorbant in this latter part of the spectrum. However, the non-negligible water-leaving radiance in the NIR which is characteristic of turbid waters may lead to an overestimate of the atmospheric radiance in the whole visible spectrum with increasing severity at shorter wavelengths. This may result in significant errors, if not complete failure, of various algorithms for the retrieval of chlorophyll- a concentration, inherent optical properties and biogeochemical parameters of surface waters. This paper presents results of an inter-comparison study of three methods that compensate for NIR water-leaving radiances and that are based on very different hypothesis: 1) the standard SeaWiFS algorithm (Stumpf et al., 2003; Bailey et al., 2010) based on a bio-optical model and an iterative process; 2) the algorithm developed by Ruddick et al. (2000) based on the spatial homogeneity of the NIR ratios of the aerosol and water-leaving radiances; and 3) the algorithm of Kuchinke et al. (2009) based on a fully coupled atmosphere–ocean spectral optimization inversion. They are compared using normalized water-leaving radiance nL w in the visible. The reference source for comparison is ground-based measurements from three AERONET-Ocean Color sites, one in the Adriatic Sea and two in the East Coast of USA. Based on the matchup exercise, the best overall estimates of the nL w are obtained with the latest SeaWiFS standard algorithm version with relative error varying from 14.97% to 35.27% for λ = 490 nm and λ = 670 nm respectively. The least accurate estimates are given by the algorithm of Ruddick, the relative errors being between 16.36% and 42.92% for λ = 490 nm and λ = 412 nm, respectively. The algorithm of Kuchinke appears to be the most accurate algorithm at 412 nm (30.02%), 510 (15.54%) and 670 nm (32.32%) using its default optimization and bio-optical model coefficient settings. Similar conclusions are obtained for the aerosol optical properties (aerosol optical thickness τ(865) and the Ångström exponent, α(510, 865)). Those parameters are retrieved more accurately with the SeaWiFS standard algorithm (relative error of 33% and 54.15% for τ(865) and α(510, 865)). A detailed analysis of the hypotheses of the methods is given for explaining the differences between the algorithms. The determination of the aerosol parameters is critical for the algorithm of Ruddick et al. (2000) while the bio-optical model is critical for the algorithm of Stumpf et al. (2003) utilized in the standard SeaWiFS atmospheric correction and both aerosol and bio-optical model for the coupled atmospheric–ocean algorithm of Kuchinke. The Kuchinke algorithm presents model aerosol-size distributions that differ from real aerosol-size distribution pertaining to the measurements. In conclusion, the results show that for the given atmospheric and oceanic conditions of this study, the SeaWiFS atmospheric correction algorithm is most appropriate for estimating the marine and aerosol parameters in the given turbid waters regions.

Retrieval of ocean colour from high resolution multi‐spectral imagery for monitoring highly dynamic ocean features

Retrieval of ocean colour information from a space borne Multi‐spectral Camera (MSC) on KOMPSAT‐2 is investigated to study and characterize small‐scale biogeophysical features that are very rich and dynamic in nature in the coastal oceans rather than the interior. Prior to the derivation of this information from space‐borne ocean colour observations, the path radiance largely from the atmospheric path and air–sea interface should be removed from the total signal recorded at the top of the atmosphere (TTOA ). In this study, the ‘path extraction’ method is introduced for the atmospheric correction of ocean colour images. The potential use of path extraction was demonstrated on Landsat TM and SeaWiFS images of highly turbid coastal waters of Korea. The path‐extracted water‐leaving radiance was then compared with the water‐leaving radiance spectra derived from the standard SeaWiFS atmospheric correction algorithm. It was noticed that the path‐extracted water‐leaving radiance resembled in situ spectra while the same was found highly degraded throughout the visible wavebands by adopting the standard SeaWiFS atmospheric correction algorithm. Algorithms for the retrieval of ocean colour information are explored from remotely sensed reflectance (Rrs ) in the visible wavelength bands of a Multi‐spectral Camera. A large set of remote sensing reflectances are generated by random number functions using an Rrs model, which relates bb /(a+bb ) to Rrs as functions of inherent optical properties, such as absorption and backscattering coefficients of six water components including water, phytoplankton (chl), dissolved organic matter (DOM), suspended sediment (SS) concentration, heterotropic organisms (he) and an unknown component, possibly represented by bubbles or other particulates unrelated to the first five components. Since the Kompsat‐2 MSC and Landsat‐5 TM bands are spectrally similar, these Rrs values are then modelled to the equivalent remote sensing reflectances at MSC and Landsat TM bands using a spectral band model. The empirical relationships between the spectral ratios of modelled Rrs (e.g. Rrs (MSC band1)/Rrs (MSC band2) and Rrs (MSC band1‐centre)/Rrs (MSC band2‐centre)) and chlorophyll concentrations are established in order to derive ⟨chl⟩ algorithms for both Landsat TM and MSC bands. Similarly, ⟨SS⟩ algorithms are obtained by relating a single band reflectance (e.g. Rrs (MSC band2) and Rrs (MSC Band2‐centre)) to the suspended sediment concentrations. Finally, a comparative analysis is made between the Landsat TM and MSC bands as well as narrow (centre‐wavelength) and broad band (full bandwidth) width of algorithms. From this study, it was observed that the Rrs spectra of three MSC spectral bands are found to be slightly superior to the Landsat TM bands in terms of spectral sensitivity to varying constituent concentrations. A small discrepancy between the reflectance ratios of broad and narrow bands was noticed in MSC and Landsat TM. The coefficient of determination (R 2) for log‐transformed data [⟨chl⟩ N = 500] was interestingly found to be R 2 = 0.90 for both Landsat TM and MSC. Similarly, the R 2 value for log‐transformed data [⟨SS⟩ N = 500] was 0.93 and 0.92 for Landsat TM and MSC, respectively. The modelled Rrs spectra were in good agreement with our in situ spectra obtained from the southern coastal Sea of Korea during 1998 and 1999. The algorithms presented are expected to explore the fine details of the complex coastal oceanic features from the ocean colour images of Multi‐spectral Camera and Landsat TM.

SeaWiFS data analysis and match-ups with in situ chlorophyll concentrations in Danish waters

For the year 1999 all Sea viewing Wide Field of view Sensor (SeaWiFS) scenes of the Danish waters from the North Sea to the Baltic Sea were browsed, and a total of 47 SeaWiFS scenes with reasonably low cloud cover and, therefore, potential in situ match-ups were found and processed. The in situ data used as match-ups were collected on routine monitoring cruises by Danish and Swedish environmental authorities. A few stations in the North Sea, Skagerak and the western Baltic Sea were sampled, while most stations were located in Kattegat and the inner Danish waters. A turbid water SeaWiFS atmospheric correction algorithm was applied, since the standard SeaWiFS algorithm for chlorophyll-a (CHL) has been shown to be fairly inaccurate in turbid coastal waters. This is due to both inaccurate atmospheric and to relatively high and variable abundance of yellow substance. The application of the turbid atmospheric correction substantially improved the SeaWiFS CHL estimates. Regressions between SeaWiFS estimates using the OC2 and OC4 algorithms used in the SeaDAS software (versions 3.3 and 4.0, respectively) and in situ CHL values were made as well, and regression with a number of other possible reflectance ratios with SeaWiFS channels. The best correlation was found to be R2=0.54 using a double-ratio algorithm using both R510/R555 and R443/R670, while the OC4v4 had the second best correlation of R2=0.39. Among other single ratios, the R510/R555 had the highest correlation with CHL, which was expected since this is also the ratio that OC4v4 most often switches to in the waters investigated here. The range of CHL concentrations in this study was rather limited (all but three points from 0.5–3 mg m−3) so there is a need for inclusion of more data to expand the concentration range. This should be possible using also data from 2000, 2001 and onwards and, hereafter, a more ‘stable’ empirical algorithm can be derived for the Danish waters.

Correction of artifacts in the SeaWiFS atmospheric correction: removing discontinuity in the derived products

It has been found that, at the certain solar and sensor-viewing geometry and for certain atmosphere conditions, discontinuity lines appear in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) retrieved atmospheric and ocean color products. Such discontinuity lines, which do not happen very often, are apparently artifacts from the atmospheric correction scheme that uses the lookup tables to process SeaWiFS data. In this paper, a brief description of the SeaWiFS atmospheric correction algorithm, in particular, the technique that is used in retrieving the aerosol models and aerosol radiance contributions in the visible wavelengths, is provided. Results from some specific simulations that explain the causes of the discontinuity lines in the derived products are presented. We show that these discontinuities appearing in the derived SeaWiFS products are the result of the atmospheric correction due to aerosol model effects. To remove the discontinuities, a simple modification to the current atmospheric correction algorithm is proposed and tested with both simulated and real SeaWiFS data. The modification has been implemented in the SeaWiFS fourth data reprocessing in August 2002.

Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters

The standard SeaWiFS atmospheric correction algorithm, designed for open ocean water, has been extended for use over turbid coastal and inland waters. Failure of the standard algorithm over turbid waters can be attributed to invalid assumptions of zero water-leaving radiance for the near-infrared bands at 765 and 865 nm. In the present study these assumptions are replaced by the assumptions of spatial homogeneity of the 765:865-nm ratios for aerosol reflectance and for water-leaving reflectance. These two ratios are imposed as calibration parameters after inspection of the Rayleigh-corrected reflectance scatterplot. The performance of the new algorithm is demonstrated for imagery of Belgian coastal waters and yields physically realistic water-leaving radiance spectra. A preliminary comparison with in situ radiance spectra for the Dutch Lake Markermeer shows significant improvement over the standard atmospheric correction algorithm. An analysis is made of the sensitivity of results to the choice of calibration parameters, and perspectives for application of the method to other sensors are briefly discussed.

A Sensitivity Study of the SeaWiFS Atmospheric Correction Algorithm: Effects of Spectral Band Variations

With the success of launch and initial data processing of Sea-viewing Wide Field-of-view Sensor (SeaWiFS), there is a great interest in ocean color community to intercompare ocean color data between different ocean color sensors. It is well known that the atmospheric correction, which removes about 90% of sensor-measured signals contributed from atmosphere in the visible, is the key procedure in ocean color imagery data processing. Therefore, it is useful to evaluate the SeaWiFS atmospheric correction algorithm applying to various ocean color sensors. The SeaWiFS atmospheric correction algorithm uses lookup tables which were generated with over ∼25,000 radiative transfer model runs for different aerosol optical and microphysical properties, solar and viewing geometries, and, in particular, at the eight SeaWiFS spectral bands. Since different ocean color sensors usually have different band spectral characterizations, it is rather difficult to apply the SeaWiFS atmospheric correction algorithm to other sensors if one needs to regenerate lookup tables at spectral bands different from SeaWiFS. In this article, we evaluate the accuracy of the SeaWiFS atmospheric correction algorithm for various ocean color sensors using the current SeaWiFS lookup tables. The focus in on the following satellite ocean color sensors: the Modular Optoelectronic Scanner (MOS), the Ocean Color and Temperature Sensor (OCTS), and the Polarization and Directionality of the Earth’s Reflectances (POLDER). These sensors have a slightly different spectral bands compared with the SeaWiFS. It was found that, with an appropriate calculation of the Rayleigh scattering contributions at each sensor’s spectral band and a simple modification in computing the diffuse transmittance of the ocean-atmosphere system, the SeaWiFS atmospheric correction applied to other sensors is as accurate as for SeaWiFS for the solar zenith angles θ 0⩽60°.