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

Abstract

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.