Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling

Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling

2013

Korea's Geostationary Ocean Colour Imager (GOCI) has very high temporal resolution as well as wide spatial coverage. There is thus great interest in testing its applicability for monitoring land areas in addition to ocean areas. GOCI has eight spectral bands, from blue to near-infrared. These bands can be sensitive to vegetation change, but their wavelength ranges are slightly different from those of the extensively studied Moderate Resolution Imaging Spectroradiometer (MODIS). This study examines whether GOCI data can be applied for land monitoring and how GOCI data should be processed so as to reflect the spectral characteristics of land surfaces as detected by polar-orbit satellite sensors. Several image processing steps were performed for the GOCI data, including atmospheric correction and semi-empirical bidirectional reflectance distribution function modelling, before the results were compared with the MODIS land-surface product. Among the four GOCI normalized difference vegetation index (NDVI) products tested in this study, the GOCI NDVI with viewing-angle-adjusted reflectance showed the best agreement with MODIS NDVI calculated from normalized reflectance, with the lowest root mean square error of 0.126. Additionally, its temporal trends over forest and mixed vegetation areas were similar to those of MODIS NDVI during the study period from September to December.

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