@InProceedings{SugawaraAdamRudoRosa:2009:AvTrMé,
               author = "Sugawara, Luciana Miura and Adami, Marcos and Rudorff, Bernardo 
                         Friedrich Theodor and Rosa, Viviane Gomes Cardoso da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais/SP} and {Instituto 
                         Nacional de Pesquisas Espaciais/SP} and {Instituto Nacional de 
                         Pesquisas Espaciais/SP} and {Instituto Nacional de Pesquisas 
                         Espaciais/SP}",
                title = "Avalia{\c{c}}{\~a}o de tr{\^e}s m{\'e}todos de estimativa de 
                         {\'{\i}}ndice de {\'a}rea foliar aplicados {\`a} 
                         cana-de-a{\c{c}}{\'u}car",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "499--506",
         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 = "remote sensing, MODIS, LAI, NDVI, sugarcane, sensoriamento remoto, 
                         MODIS, IAF, NDVI, cana-de-a{\c{c}}{\'u}car.",
             abstract = "Remote sensing satellite images have the potential to monitor 
                         agricultural crops throughout the growing season and are, 
                         therefore, an important tool to provide relevant information for 
                         agricultural crop forecasting models. Leaf area index (LAI) is one 
                         of the most important variables to monitor the development of 
                         agricultural crops and can be estimated from remote sensing data. 
                         This work aims to compare three LAI computing methods using MODIS 
                         surface reflectance data. In 2006, 2117 samples were selected over 
                         planted sugarcane areas in Sao Paulo state. In 2007, these same 
                         areas were selected again this time over first ratoon sugarcane 
                         areas. After that, NDVI (Normalized Difference Vegetation Index) 
                         was calculated from MODIS/Terra surface reflectance 8-day 
                         composite (MOD09Q1) and linked to LAI via three different 
                         mathematical functions. For the two years of sugarcane growing 
                         season, NDVI ranged from 0 to 0.99. Larger NDVI values were 
                         observed throughout the rainy season. The harvest event varied for 
                         the sugarcane sampled fields in both years; however, the start 
                         point of the growing season was almost the same for both years. 
                         Different LAI values were obtained for each LAI computing method; 
                         however, these differences were merely mathematical. Methods 1 and 
                         3 had good performance to estimate LAI whereas method 2 
                         overestimates LAI values. Prior to the use of MODIS/Terra 8-day 
                         composite it is essential to detect and eliminate or minimize 
                         images noise.",
  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@80/2008/11.18.00.14",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.18.00.14",
           targetfile = "499-506.pdf",
                 type = "Agricultura",
        urlaccessdate = "18 jun. 2024"
}