@InProceedings{BernardesMorVerShiLui:2013:VaMoEs,
               author = "Bernardes, Tiago and Moreira, Maur{\'{\i}}cio Alves and Verona, 
                         Jane Delane and Shimabukuro, Yosio Edemir and Luiz, Alfredo 
                         Jos{\'e} Barreto",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Vari{\'a}veis e modelos para estimativa da produtividade do 
                         cafeeiro a partir de {\'{\i}}ndices de vegeta{\c{c}}{\~a}o 
                         derivados de imagens Landsat",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "720--727",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Coffee fields present a specific pattern of productivity resulting 
                         in high and low production in alternated years. Branches grown the 
                         first phenological year will produce coffee beans the second 
                         phenological year. In high-production years a plant works mostly 
                         to grain-filling to the detriment of new branches which will be 
                         responsible for production the following year. In low-production 
                         years the plant works rather to grow new branches which will 
                         produce beans the subsequent year. This feature can be related to 
                         the foliar biomass, which can be estimated through remote sensing 
                         derived vegetation indices. Several studies report this feature 
                         must be incorporated in modeling coffee yield coupled with 
                         agrometeorogical models. In this paper we derived Landsat 
                         vegetation indices related to coffee plots in order to obtain 
                         relationships to yield of the same coffee plots. Vegetation 
                         indices and biophysical variables were selected through stepwise 
                         regression in order to obtain the best regression models to 
                         estimate coffee yield. Outcomes of stepwise regression statistic 
                         showed that general models based on vegetation indices were not 
                         good to estimate coffee yield. Although coffee yield cannot be 
                         estimated exclusively from these models, they can be usefull 
                         coupled with agrometeorogical models for estimating coffee 
                         yield.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "1437",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GLB6",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GLB6",
           targetfile = "p1437.pdf",
                 type = "Agricultura",
        urlaccessdate = "10 maio 2024"
}