@Article{AraiSaDuCaHoSh:2020:VeFrIm,
author = "Arai, Eg{\'{\i}}dio and Sano, Edson Eyji and Dutra, Andeise
Cerqueira and Cassol, Henrique Luis Godinho and Hoffmann,
T{\^a}nia Beatriz and Shimabukuro, Yosio Edemir",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Empresa
Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} 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 = "Vegetation fraction images derived from PROBA-V data for rapid
assessment of annual croplands in Brazil",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "7",
pages = "e1152",
month = "Apr.",
keywords = ": linear spectral mixing model, Mato Grosso State, cropland
mapping, maximum fraction values mosaic.",
abstract = "This paper presents a new method for rapid assessment of the
extent of annual croplands in Brazil. The proposed method applies
a linear spectral mixing model (LSMM) to PROBA-V time series
images to derive vegetation, soil, and shade fraction images for
regional analysis. We used S10-TOC (10 days synthesis, 1 km
spatial resolution, and top-of-canopy) products for Brazil and
S5-TOC (five days synthesis, 100 m spatial resolution, and
top-of-canopy) products for Mato Grosso State (Brazilian Legal
Amazon). Using the time series of the vegetation fraction images
of the whole year (2015 in this case), only one mosaic composed
with maximum values of vegetation fraction was generated, allowing
detecting and mapping semi-automatically the areas occupied by
annual crops during the year. The results (100 m spatial
resolution map) for the Mato Grosso State were compared with
existing global datasets (Finer Resolution Observation and
MonitoringGlobal Land Cover (FROM-GLC) and Global Food
SecuritySupport Analyses Data (GFSAD30)). Visually those maps
present a good agreement, but the area estimated are not
comparable since the agricultural class definition are different
for those maps. In addition, we found 11.8 million ha of
agricultural areas in the entire Brazilian territory. The area
estimation for the Mato Grosso State was 3.4 million ha for 1 km
dataset and 5.3 million ha for 100 m dataset. This difference is
due to the spatial resolution of the PROBA-V datasets used. A
coefficient of determination of 0.82 was found between PROBA-V 100
m and Landsat-8 OLI area estimations for the Mato Grosso State.
Therefore, the proposed method is suitable for detecting and
mapping annual croplands distribution operationally using PROBA-V
datasets for regional analysis.",
doi = "10.3390/rs12071152",
url = "http://dx.doi.org/10.3390/rs12071152",
issn = "2072-4292",
language = "en",
targetfile = "arai_remote sensing.pdf",
urlaccessdate = "13 maio 2024"
}