@Article{ParreirasBoSaViSaVi:2022:ExHaLa,
               author = "Parreiras, T. C. and Bolfe, Edson L. and Sano, Edson S. and 
                         Victoria, Daniel C. and Sanches, Ieda Del'Arco and Vicente, Luiz 
                         E.",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Embrapa 
                         Agricultura Digital} and {Embrapa Cerrados} and {Embrapa 
                         Agricultura Digital} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Embrapa Meio Ambiente}",
                title = "Exploring the Harmonized Landsat Sentinel (HLS) datacube to map an 
                         agricultural landscape in the brazilian savanna",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2022",
               volume = "43",
               number = "B3",
                pages = "967--673",
                month = "June",
             keywords = "Agriculture, Cerrado Biome, Classification, Harmonized Landsat 
                         Sentinel, Random Forest.",
             abstract = "Brazil has established itself as one of the world leaders in food 
                         production. Different types of remote sensing mapping techniques 
                         have been undertaken to support rural planning in the country. 
                         However, due to the complex dynamics of Brazilian agriculture, 
                         especially in the Cerrado biome (tropical savanna), there is a 
                         need for more feasible crop discrimination and monitoring 
                         initiatives, which require a consistent time series of remote 
                         sensing data at medium meter and potentially up to 3 day Landsat 8 
                         and Sentinel-2 satellite time series, minimizing the cloud cover 
                         limitations for rainfed agricultural monitoring. This paper aims 
                         to explore the potential of the Harmonized Landsat 8 Sentinel-2 
                         (HLS) data cube to map agricultural landscapes in the Brazilian 
                         Cerrado. The HLS multispectral bands from 27 scenes with less than 
                         10% cloud cover, from October 2020 to September 2021, encompassing 
                         one entire crop growing season, were processed by the Random 
                         Forest algorithm to produce a map with four land use/cover classes 
                         (annual crops, sugarcane, renovated sugarcane fields, cultivated 
                         pastures, and native Cerrado). We performed accuracy assessment 
                         through 10-fold cross-validation and confusion matrix analyses. 
                         The results showed a high level of overall accuracy and Kappa 
                         coefficient, both with 99%, as well as high user's and producer's 
                         accuracies of at least 99%. The HLS dataset has been continuously 
                         improved, showing very promising results for rainfed agricultural 
                         mapping and monitoring.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2022-967-2022",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2022-967-2022",
                 issn = "1682-1750",
             language = "en",
           targetfile = "isprs-archives-XLIII-B3-2022-967-2022.pdf",
        urlaccessdate = "19 maio 2024"
}