Minu S., Shetty A., Gomez Cécile. (2018). Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction. International Journal of Remote Sensing, 39 (22), p. 8246-8270. ISSN 0143-1161.
Titre du document
Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction
International Journal of Remote Sensing, 2018,
39 (22), p. 8246-8270 ISSN 0143-1161
Visible near-infrared and shortwave infrared data acquired by space-borne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (ELm) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an ELm technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an ELm technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and ELm) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R-2), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R-2 >= 0.75, RPD >= 2, and RPIQ >= 2.58 than single algorithms. HAC algorithms, developed using ELm technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping.
