@InProceedings{Ramírez-LópezDemaTerrBort:2009:SoGrIn,
               author = "Ram{\'{\i}}rez-L{\'o}pez, Leonardo and Dematt{\^e}, Jos{\'e} 
                         Alexandre Melo and Terra, Fabr{\'{\i}}cio da Silva and 
                         Bortoletto, Marco Antonio Melo",
          affiliation = "{Universidade de S{\~a}o Paulo/ESALQ} and {Universidade de 
                         S{\~a}o Paulo/ESALQ} and {Universidade de S{\~a}o Paulo/ESALQ} 
                         and {Universidade de S{\~a}o Paulo/ESALQ}",
                title = "Sensoriamento remoto no mapeamento digital da fertilidade do solo: 
                         solucionando um grande inconveniente em agricultura de 
                         precis{\~a}o",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "363--370",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "espectroscopia de solos, quimiometr{\'{\i}}a, variabilidade 
                         espacial, amostragem estratificada e em grade.",
             abstract = "Precision agriculture requires high soil sampling density to 
                         assess the soil spatial variability and therefore the cost for 
                         asses this variability is significantly high. In this way, the aim 
                         of this study was to demonstrate that with a small number of soil 
                         samples (with low costs) statistical models for the quantification 
                         of soil attributes in a large number of foil samples based on the 
                         soil spectral reflectance can be obtained with suitable accuracy 
                         for the description of the spatial variability of attributes 
                         related to the soil fertility. In the study area of 473 ha 900 
                         soil samples were collected in two depths. A soil analysis was 
                         carried out and were obtained Ca, Mg, K and CIC, was also 
                         calculated the base sum and soil textural fractions were also 
                         determined. For each soil sample a spectral reflectance was 
                         obtained. Models for the quantification of soil attributes were 
                         calibrated by partial least squares regression (PLS). These models 
                         were calibrated with three different quantity of soil samples, for 
                         the selection of these samples was used two different methods. The 
                         performance each model was evaluated by cross validation. Finally 
                         these models were applied to all spectral data and was obtained 
                         the predicted soil attribute values in each soil sample. The 
                         predicted soil attributes (by remote sensing) and the soil 
                         attributes form conventional soil analysis were mapped using 
                         geoestat{\'{\i}}stica methods. This study demonstrates that the 
                         remote sensing can be solve a great problem in precision 
                         agriculture and digital soil mapping.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.18.01.33",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.18.01.33",
           targetfile = "363-370.pdf",
                 type = "Agricultura",
        urlaccessdate = "17 maio 2025"
}