@InProceedings{MarianoFoscMore:2014:SiMeMa,
               author = "Mariano, Denis Araujo and Foschiera, William and Moreira, 
                         Maur{\'{\i}}cio Alves",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "A simple method for mapping annual agricultural areas by analyzing 
                         MODIS Land Surface Temperature time-series",
            booktitle = "Mem{\'o}rias",
                 year = "2014",
         organization = "Simp{\'o}sio Internacional Selper, 16.",
            publisher = "SELPER",
              address = "Medell{\'{\i}}n",
             keywords = "Agriculture, Land surface temperature, MODIS, Remote Sensing, 
                         thermal, time-series.",
             abstract = "The current paper presents a method for mapping summer annual 
                         agriculture (maize and soybean) by analyzing Land Surface 
                         Temperature (LST) time-series derived from the Moderate Resolution 
                         Imaging Spectroradiometer (MODIS). We used Terra and Aqua LST 
                         daytime and nighttime data (M_D11A2) with 8-day temporal and 1km 
                         spatial resolution. The physical basis behind the method is the 
                         heat transfer between soil, plant and atmosphere over time. There 
                         are two approaches, inter-daily and intra-daily LST variation. We 
                         tested daytime and day-night difference time-series, being the 
                         latter more efficient on detecting annual agriculture. Regarding 
                         the satellites, Aqua proves on being more efficient due the 
                         passage hour for daytime. In sense, the couple Difference/Aqua 
                         yielded better results. However, the performance is strongly 
                         dependent on the contiguity of agricultural areas due to the high 
                         thermal mixing susceptibility. Another drawback is the spatial 
                         resolution (1 km) which depending on the situation fails on 
                         detecting low acreage crops. Despite the limitations, the idea 
                         shows potential to be coupled to traditional vegetation indices 
                         based methods for furthering the biophysical meaning and 
                         relationships between vegetation and remote sensing. It also 
                         brings new findings about vegetation thermal behavior throughout 
                         the time..",
  conference-location = "Medell{\'{\i}}n",
      conference-year = "29 set. - 3 oct. 2014",
                label = "lattes: 4721908858063120 1 MarianoFoscMore:2014:SiMeMa",
             language = "en",
         organisation = "SELPER",
           targetfile = "A-simple-method-for-mapping-annual-agricultural-areas.pdf",
                  url = "http://www.selpercolombia2014.com/papers/Fotogrametria-PDI-Fusion-de-datos/FP1-A-simple-method-for-mapping-annual-agricultural-areas.pdf",
        urlaccessdate = "27 abr. 2024"
}