@Article{SousaBragBragDant:2014:InRaVe,
               author = "Sousa, Leandro Fontes de and Braga, Celia Campos and Braga, Ramon 
                         Campos and Dantas, Milena Pereira",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Interrelationship between rainfall and vegetation index by remote 
                         sensing",
              journal = "Journal of Hyperspectral Remote Sensing",
                 year = "2014",
               volume = "4",
               number = "3",
                pages = "87 - 99",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "IVDN, Least Square Method, rainfall.",
             abstract = "Considering the importance of vegetation and influence of climatic 
                         factors in development, especially precipitation, the purpose of 
                         this study was to find a function that best represents the 
                         relationship between rainfall and NDVI in the Para{\'{\i}}ba 
                         state. We used daily images from the sensor AVHRR / NOAA system 
                         with spatial resolution of 4 km and MODIS / Aqua with spatial 
                         resolution of 1km product and monthly precipitation data of 250 
                         stations for the years 2007, 2008 and 2009. The method of least 
                         squares regression to find the curve that best fitted the dataset 
                         was used. The Student t test was applied to the correlation 
                         coefficients \α = 0.05 level of significance. The results 
                         indicate relationship that best represents the behavior of NDVI 
                         depending on rainfall is a polynomial second degree curve with 
                         better correlations during the dry season (June to September). 
                         Generally the NDVIAVHRR showed better correlation with rainfall 
                         than NDVIMODIS. In the rainy season they have been weaker because 
                         when vegetation reaches maximum force, the NDVI is practically 
                         stable. On average the highest correlations (r) found for the two 
                         satellites between 0.69 and 0.86 regardless of the year it was wet 
                         or dry. It is noteworthy that these adjustments were a little 
                         better for the polynomial model.",
                 issn = "2237-2202",
                label = "lattes: 4547675870020096 3 SousaBragBragDant:2014:INRAVE",
             language = "en",
           targetfile = "32-251-1-PB.pdf",
                  url = "http://www.ufpe.br/jhrs",
        urlaccessdate = "11 maio 2024"
}