@Article{OldoniSanPicCovFro:2020:AgReSe,
               author = "Oldoni, Lucas Volochen and Sanches, Ieda Del'Arco and Picoli, 
                         Michelle Cristina Ara{\'u}jo and Covre, Renan Moreira and Fronza, 
                         Jos{\'e} Guilherme",
          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)}",
                title = "LEM+ dataset: for agricultural remote sensing applications",
              journal = "Data in Brief",
                 year = "2020",
               volume = "33",
                pages = "e106553",
             keywords = "Field reference data, Time series analysis, Remote sensing, Double 
                         crop system, Tropical agriculture.",
             abstract = "Remote sensing allows obtaining information on agriculture 
                         regularly with non-invasive measurement approaches. Field data is 
                         crucial for adequate agricultural monitoring by remote sensing. 
                         However, public available field data are scarce, mainly in 
                         tropical regions, where agriculture is highly dynamic. The present 
                         publication aims to support the reduction of this gap. The LEM+ 
                         dataset provides information monthly about 16 land use classes for 
                         1854 fields from October 2019 to September 2020 (one Brazilian 
                         agricultural year) from Lu{\'{\i}}s Eduardo Magalh{\~a}es (LEM) 
                         and other municipalities in the west of Bahia state, Brazil. The 
                         reference data were collected in two fieldworks (March 2020 first 
                         crop season, and August 2020 second crop season). The boundaries 
                         of the fields visited in situ were delimited using Sentinel-2 
                         false color compositions (near infrared - red - green) at 10 m 
                         spatial resolution. The land use classes were labeled monthly 
                         based on information collected in situ (agricultural land use and 
                         photographs) and by visual interpretation of Sentinel-2 false 
                         color composition (near infrared - shortwave infrared - red) and 
                         MODIS/Terra (Normalized Difference Vegetation Index) time series. 
                         The dataset can be useful for the development of new pattern 
                         recognition methods for agricultural land use mapping and 
                         monitoring, comparison of different classification methods, and 
                         optical and SAR remote sensing time series analysis. This dataset 
                         contributes to complement previous initiatives [1,2] to make 
                         tropical agriculture field reference data publicly available.",
                  doi = "10.1016/j.dib.2020.106553",
                  url = "http://dx.doi.org/10.1016/j.dib.2020.106553",
                 issn = "2352-3409",
                label = "lattes: 2456184661855977 2 OldoniSanPicCovFro:2020:AgReSe",
             language = "en",
           targetfile = "oldoni_lem.pdf",
        urlaccessdate = "28 abr. 2024"
}