@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 = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} 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",
targetfile = "remotesensing-04-02492.pdf",
urlaccessdate = "30 abr. 2024"
}