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@Article{MartinsSoNoBaPiArKa:2018:AsAtCo,
               author = "Martins, Vitor S. and Soares, Jo{\~a}o Vianei and Novo, Evlyn 
                         M{\'a}rcia Le{\~a}o de Moraes and Barbosa, Cl{\'a}udio Clemente 
                         Faria and Pinto, Cibele T. and Arcanjo, Jeferson de Souza and 
                         Kaleita, Amy",
          affiliation = "{Iowa State University (ISU)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {South Dakota State Universit} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Iowa State University (ISU)}",
                title = "Continental-scale surface reflectance product from CBERS-4 MUX 
                         data: Assessment of atmospheric correction method using coincident 
                         Landsat observations",
              journal = "Remote Sensing of Environment",
                 year = "2018",
               volume = "218",
                pages = "55--68",
                month = "Dec.",
             keywords = "CBERS, Surface reflectance, CMPAC, Landsat-8, MODIS VIIRS.",
             abstract = "A practical atmospheric correction algorithm, called Coupled 
                         Moderate Products for Atmospheric Correction (CMPAC), was 
                         developed and implemented for the Multispectral Camera (MUX) 
                         on-board the China-Brazil Earth Resources Satellite (CBERS-4). 
                         This algorithm uses a scene-based processing and sliding window 
                         technique to derive MUX surface reflectance (SR) at continental 
                         scale. Unlike other optical sensors, MUX instrument imposes 
                         constraints for atmospheric correction due to the absence of 
                         spectral bands for aerosol estimation from imagery itself. To 
                         overcome this limitation, the proposed algorithm performs a 
                         further processing of atmospheric products from Moderate 
                         Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared 
                         Imaging Radiometer Suite (VIIRS) sensors as input parameters for 
                         radiative transfer calculations. The success of CMPAC algorithm 
                         was fully assessed and confirmed by comparison of MUX SR data with 
                         the Landsat-8 OLI Level-2 and Aerosol Robotic Network 
                         (AERONET)-derived SR products. The spectral adjustment was 
                         performed to compensate for the differences of relative spectral 
                         response between MUX and OLI sensors. The results show that MUX SR 
                         values are fairly similar to operational Landsat-8 SR products 
                         (mean difference < 0.0062, expressed in reflectance). There is a 
                         slight underestimation of MUX SR compared to OLI product (except 
                         the NIR band), but the error metrics are typically low and 
                         scattered points are around the line 1:1. These results suggest 
                         the potential of combining these datasets (MUX and OLI) for 
                         quantitative studies. Further, the robust agreement of MUX and 
                         AERONET-derived SR values emphasizes the quality of moderate 
                         atmospheric products as input parameters in this application, with 
                         root-mean-square deviation lower than 0.0047. These findings 
                         confirm that (i) CMPAC is a suitable tool for estimating surface 
                         reflectance of CBERS MUX data, and (ii) ancillary products support 
                         the application of atmospheric correction by filling the gap of 
                         atmospheric information. The uncertainties of atmospheric 
                         products, negligence of the bidirectional effects, and two aerosol 
                         models were also identified as a limitation. Finally, this study 
                         presents a framework basis for atmospheric correction of CBERS-4 
                         MUX images. The utility of CBERS data comes from its use, and this 
                         new product enables the quantitative remote sensing for land 
                         monitoring and environmental assessment at 20 m spatial 
                         resolution.",
                  doi = "10.1016/j.rse.2018.09.017",
                  url = "http://dx.doi.org/10.1016/j.rse.2018.09.017",
                 issn = "0034-4257",
             language = "en",
           targetfile = "martins_continental.pdf",
        urlaccessdate = "27 nov. 2020"
}


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