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@Article{BernardesMorAdaGiaRud:2012:MoBiBe,
               author = "Bernardes, Tiago and Moreira, Maur{\'{\i}}cio Alves and Adami, 
                         Marcos and Giarolla, Ang{\'e}lica and Rudorff, Bernardo Friedrich 
                         Theodor",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS 
                         Remote Sensing Imagery",
              journal = "Remote Sensing",
                 year = "2012",
               volume = "4",
               number = "9",
                pages = "2492--2509",
             keywords = "Foliar biomass, Growing season, High yield, Landsat images, Leaf 
                         biomass, Minas Gerais, Minimum value, Previous year, Pure pixel, 
                         Reference map, Remote sensing imagery, Vegetation index, Wavelet 
                         filtering, Pixels, Radiometers, Remote sensing, Vegetation, 
                         Satellite imagery.",
             abstract = "Coffee is the second most valuable traded commodity worldwide. 
                         Brazil is the worlds largest coffee producer, responsible for one 
                         third of the world production. A coffee plot exhibits high and low 
                         production in alternated years, a characteristic so called 
                         biennial yield. High yield is generally a result of suitable 
                         conditions of foliar biomass. Moreover, in high production years 
                         one plot tends to lose more leaves than it does in low production 
                         years. In both cases some correlation between coffee yield and 
                         leaf biomass can be deduced which can be monitored through time 
                         series of vegetation indices derived from satellite imagery. In 
                         Brazil, a comprehensive, spatially distributed study assessing 
                         this relationship has not yet been done. The objective of this 
                         study was to assess possible correlations between coffee yield and 
                         MODIS derived vegetation indices in the Brazilian largest 
                         coffee-exporting province. We assessed EVI and NDVI MODIS products 
                         over the period between 2002 and 2009 in the south of Minas Gerais 
                         State whose production accounts for about one third of the 
                         Brazilian coffee production. Landsat images were used to obtain a 
                         reference map of coffee areas and to identify MODIS 250 m pure 
                         pixels overlapping homogeneous coffee crops. Only MODIS pixels 
                         with 100% coffee were included in the analysis. A wavelet-based 
                         filter was used to smooth EVI and NDVI time profiles. Correlations 
                         were observed between variations on yield of coffee plots and 
                         variations on vegetation indices for pixels overlapping the same 
                         coffee plots. The vegetation index metrics best correlated to 
                         yield were the amplitude and the minimum values over the growing 
                         season. The best correlations were obtained between variation on 
                         yield and variation on vegetation indices the previous year (R = 
                         0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although 
                         correlations were not enough to estimate coffee yield exclusively 
                         from vegetation indices, trends properly reflect the biennial 
                         bearing effect on coffee yield. Keywords: remote sensing; coffee 
                         yield; vegetation indices; wavelet filtering.",
                  doi = "10.3390/rs4092492",
                  url = "http://dx.doi.org/10.3390/rs4092492",
                 issn = "2072-4292",
                label = "lattes: 8408207746528834 1 BernardesAdMoAdGiRu:2012:MoBiBe",
             language = "en",
        urlaccessdate = "15 jan. 2021"
}


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