The Advanced Hyper-spectral Imager (AHSI) data on-board GF-5 satellite fills up China's lack of hyper-spectral earth observation technology. However, using GF-5 AHSI data for remote sensing applications, especially quantitative remote sensing applications, a challenging problem is to retrieve surface reflectance through atmospheric correction. In this paper, the MODerate spectral resolution atmospheric TRANsmittance algorithm and computer model (MODTRAN) radiative transfer model was used to perform atmospheric correction experiments (AHSI-AC) for GF-5 AHSI data. And we validated the atmospheric correction results based on measured surface reflectance and Landsat 8 surface reflectance data. Using field-measure spectra for validation the AHSI-AC method has a relative error of about 10% in most bands. Compared with the ENVI-FLAASH algorithm, the accuracy of the blue-green bands and the short-wave infrared bands are significantly improved, and the red and near-infrared bands are similar. Using Landsat 8 data for intercomparison, the large homogeneous, non-vegetated pixels were selected, and the overall accuracy of the 515 selected validation points reached 81.7%.
Surface albedo determines radiative forcing and is a key parameter for driving Earth’s climate. Better characterization of surface albedo for individual land cover types can reduce the uncertainty in estimating changes to Earth’s radiation balance due to land cover change. This paper presents albedo look-up maps (LUMs) using a multiscale hierarchical approach based on moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo products and Landsat imagery. Ten years (2001 to 2011) of MODIS BRDF/albedo products were used to generate global albedo climatology. Albedo LUMs of land cover classes defined by the International Geosphere-Biosphere Programme (IGBP) at multiple spatial resolutions were generated. The albedo LUMs included monthly statistics of white-sky (diffuse) and black-sky (direct) albedo for each IGBP class for visible, near-infrared, and shortwave broadband under both snow-free and snow-covered conditions. The albedo LUMs were assessed by using the annual MODIS IGBP land cover map and the projected land use scenarios from the Intergovernmental Panel on Climate Change land-use harmonization project. The comparisons between the reconstructed albedo and the MODIS albedo data product show good agreement. The LUMs provide high temporal and spatial resolution global albedo statistics without gaps for investigating albedo variations under different land cover scenarios and could be used for land surface modeling.
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