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@Article{SanchezIpiaPCACLSMSFQ:2020:CoClCo,
               author = "Sanchez Ipia, Alber Hamersson and Picoli, Michelle Cristina 
                         Ara{\'u}jo and C{\^a}mara, Gilberto and Andrade Neto, Pedro 
                         Ribeiro de and Chaves, Michel Eust{\'a}quio Dantas and Lechler, 
                         Sarah and Soares, Anderson Reis and Marujo, Rennan de Freitas 
                         Bezerra and Sim{\~o}es, Rolf Ezequiel de Oliveira and Ferreira, 
                         Karine Reis and Queiroz, Gilberto Ribeiro",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {University of M{\"u}nster} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Comparison of cloud cover detection algorithms on sentinel-2 
                         images of the Amazon tropical forest",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "8",
                pages = "e1284",
                month = "Apr.",
             keywords = "remote sensing, amazon forest, clouds, Sentinel–2, Fmask, Sen2Cor, 
                         MAJA, s2cloudless.",
             abstract = "Tropical forests regulate the global water and carbon cycles and 
                         also host most of the worlds biodiversity. Despite their 
                         importance, they are hard to survey due to their location, extent, 
                         and particularly, their cloud coverage. Clouds hinder the spatial 
                         and radiometric correction of satellite imagery and also 
                         diminishing the useful area on each image, making it difficult to 
                         monitor land change. For this reason, our purpose is to identify 
                         the cloud detection algorithm best suited for the Amazon 
                         rainforest on Sentinel2 images. To achieve this, we tested four 
                         cloud detection algorithms on Sentinel2 images spread in five 
                         areas of the Amazonia. Using more than eight thousand validation 
                         points, we compared four cloud detection methods: Fmask 4, MAJA, 
                         Sen2Cor, and s2cloudless. Our results point out that FMask 4 has 
                         the best overall accuracy on images of the Amazon region (90%), 
                         followed by Sen2Cors (79%), MAJA (69%), and S2cloudless (52%). We 
                         note the choice of method depends on the intended use. Since MAJA 
                         reduces the number of false positives by design, users that aim to 
                         improve the producers accuracy should consider its use.",
                  doi = "10.3390/RS12081284",
                  url = "http://dx.doi.org/10.3390/RS12081284",
                 issn = "2072-4292",
             language = "en",
           targetfile = "sanchez_comparison.pdf",
        urlaccessdate = "2024, Mar. 28"
}


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