@InProceedings{SilveiraEberSancGalv:2017:AnCoNu,
               author = "Silveira, Hilton Lu{\'{\i}}s Ferraz da and Eberhardt, Isaque 
                         Daniel Rocha and Sanches, Ieda Del Arco and Galv{\~a}o, 
                         L{\^e}nio Soares",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An{\'a}lise da cobertura de nuvens no nordeste do Brasil e seus 
                         impactos no sensoriamento remoto agr{\'{\i}}cola operacional",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "400--407",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "For the agricultural sector, the monitoring of crops throughout 
                         their development stages is required for many applications, such 
                         as the evaluation of their phytosanitary condition and 
                         production/yield. In the analysis of optical remote sensing data, 
                         cloudiness directly affects the quantity and quality of the 
                         images. Clouds or their shadows can cover the surface. In 
                         addition, there is also residual scattering effects that can alter 
                         the spectral response of the surface detected by the satellite 
                         sensors. To address this challenge, this study aims to 
                         characterize the cloud cover of the Northeast of Brazil along the 
                         year, as well as to discuss the impacts on the agricultural 
                         monitoring of the main producing areas of the region. The study 
                         area was divided into 6 homogeneous sub-regions in terms of 
                         agricultural production and production potential. To calculate the 
                         cloud cover, the MOD35 product from the Moderate Resolution 
                         Imaging Spectroradiometer (MODIS) sensor was used. The analysis 
                         was conducted, between July 2000 and June 2016. The resulting 
                         images were grouped according to the month of acquisition. For 
                         each pixel, the mean and variance of the occurrence of clouds were 
                         calculated using the homogeneous distribution of Bernoulli. Based 
                         on the set of 12 maps with monthly averages of cloud covering, it 
                         was noticed that some regions along the coast and in the northwest 
                         of Maranh{\~a}o State had 80% of cloud cover in great part of the 
                         year. Despite the several months without rainfall, cloud cover in 
                         the semiarid sub-region was above 40% in the dry season. Finally, 
                         in the sub-region known as Mapitoba, cloud cover was reduced with 
                         seasonality to values below 20% in August. In all Northeast of 
                         Brazil, cloud cover is greater than 70% between January and 
                         April.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59691",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS447P",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS447P",
           targetfile = "59691.pdf",
                 type = "Agricultura e pecu{\'a}ria",
        urlaccessdate = "28 abr. 2024"
}