@InProceedings{CarvalhoNicoZimb:2017:ApNDCo,
               author = "Carvalho, Tania Maria de and Nicolete, Donizeti Aparecido Pastori 
                         and Zimback, C{\'e}lia Regina Lopes",
                title = "Modelagem digital de fra{\c{c}}{\~o}es granulom{\'e}tricas do 
                         solo na regi{\~a}o da Cuesta de Botucatu - SP: 
                         aplica{\c{c}}{\~a}o do NDVI como vari{\'a}vel auxiliar",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5281--5288",
         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 = "Many pedological information are required in land use planning, 
                         management of agroforestry activities and environmental studies, 
                         usually as a soil map. Currently, the soil attributes mapping 
                         applies quantitative modeling, and explanatory covariates 
                         representing the factors of soil forming equation. Legacy soil 
                         data were used at this modelling process and the Normalized 
                         Difference Vegetation Index (NDVI) is one of the most common 
                         radiometric indexes used as predictors for mapping soil size 
                         fractions. The aim of this work was to examine the potential of 
                         the NDVI for predicting sand and clay fractions of the soils, in 
                         an area where the vegetation are in recomposition process, using 
                         an hybrid model of digital soil modeling. The NDVI of two periods 
                         (coincident with soil sampling and current) along with terrain 
                         attributes were applied as auxiliary variables predictors of grain 
                         size fractions at two depths, using as target variable soil 
                         attributes data from a semi - detailed survey of soils. The 
                         regression-kriging technique (RK) was applied, using a multiple 
                         linear regression (RLM) and posterior sum with a kriging map of 
                         the residuals to obtain a prediction map. The values of the 
                         coefficient or determination were low, suggesting poor performance 
                         of the models. The results showed that the slope and profile 
                         curvature were the most significant variables in the prediction 
                         process and the NDVI coinciding with the soil sampling time was 
                         more important, especially for the subsurface layer.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59841",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM4J9",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM4J9",
           targetfile = "59841.pdf",
                 type = "Modelagem espacial",
        urlaccessdate = "27 abr. 2024"
}