author = "Arai, Eg{\'{\i}}dio and Sano, Edson Eyji and Dutra, Andeise 
                         Cerqueira and Cassol, Henrique Luis Godinho and Hoffmann, 
                         T{\^a}nia Beatriz and Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Empresa 
                         Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} 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 
                title = "Vegetation fraction images derived from PROBA-V data for rapid 
                         assessment of annual croplands in Brazil",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "7",
                pages = "e1152",
                month = "Apr.",
             keywords = ": linear spectral mixing model, Mato Grosso State, cropland 
                         mapping, maximum fraction values mosaic.",
             abstract = "This paper presents a new method for rapid assessment of the 
                         extent of annual croplands in Brazil. The proposed method applies 
                         a linear spectral mixing model (LSMM) to PROBA-V time series 
                         images to derive vegetation, soil, and shade fraction images for 
                         regional analysis. We used S10-TOC (10 days synthesis, 1 km 
                         spatial resolution, and top-of-canopy) products for Brazil and 
                         S5-TOC (five days synthesis, 100 m spatial resolution, and 
                         top-of-canopy) products for Mato Grosso State (Brazilian Legal 
                         Amazon). Using the time series of the vegetation fraction images 
                         of the whole year (2015 in this case), only one mosaic composed 
                         with maximum values of vegetation fraction was generated, allowing 
                         detecting and mapping semi-automatically the areas occupied by 
                         annual crops during the year. The results (100 m spatial 
                         resolution map) for the Mato Grosso State were compared with 
                         existing global datasets (Finer Resolution Observation and 
                         MonitoringGlobal Land Cover (FROM-GLC) and Global Food 
                         SecuritySupport Analyses Data (GFSAD30)). Visually those maps 
                         present a good agreement, but the area estimated are not 
                         comparable since the agricultural class definition are different 
                         for those maps. In addition, we found 11.8 million ha of 
                         agricultural areas in the entire Brazilian territory. The area 
                         estimation for the Mato Grosso State was 3.4 million ha for 1 km 
                         dataset and 5.3 million ha for 100 m dataset. This difference is 
                         due to the spatial resolution of the PROBA-V datasets used. A 
                         coefficient of determination of 0.82 was found between PROBA-V 100 
                         m and Landsat-8 OLI area estimations for the Mato Grosso State. 
                         Therefore, the proposed method is suitable for detecting and 
                         mapping annual croplands distribution operationally using PROBA-V 
                         datasets for regional analysis.",
                  doi = "10.3390/rs12071152",
                  url = "http://dx.doi.org/10.3390/rs12071152",
                 issn = "2072-4292",
             language = "en",
           targetfile = "arai_remote sensing.pdf",
        urlaccessdate = "12 abr. 2021"