@Article{ZhuLuVictDutr:2016:MaFrCr,
author = "Zhu, Changming and Lu, Dengsheng and Victoria, Daniel and Dutra,
Luciano Vieira",
affiliation = "{Jiangsu Normal University} and {Michigan State University} and
{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Mapping fractional cropland distribution in Mato Grosso, Brazil
using time series MODIS enhanced vegetation index and Landsat
Thematic Mapper data",
journal = "Remote Sensing",
year = "2016",
volume = "8",
number = "1",
pages = "Number 22",
keywords = "Crop phenology analysis, Fractional cropland distribution,
Landsat, Mato grosso, MODIS EVI, Seasonal dynamic index.",
abstract = "Mapping cropland distribution over large areas has attracted great
attention in recent years, however, traditional pixel-based
classification approaches produce high uncertainty in cropland
area statistics. This study proposes a new approach to map
fractional cropland distribution in Mato Grosso, Brazil using time
series MODIS enhanced vegetation index (EVI) and Landsat Thematic
Mapper (TM) data. The major steps include: (1) remove noise and
clouds/shadows contamination using the Savizky-Gloay filter and
temporal resampling algorithm based on the time series MODIS EVI
data; (2) identify the best periods to extract croplands through
crop phenology analysis; (3) develop a seasonal dynamic index
(SDI) from the time series MODIS EVI data based on three key
stages: sowing, growing, and harvest; and (4) develop a regression
model to estimate cropland fraction based on the relationship
between SDI and Landsat-derived fractional cropland data. The root
mean squared error of 0.14 was obtained based on the analysis of
randomly selected 500 sample plots. This research shows that the
proposed approach is promising for rapidly mapping fractional
cropland distribution in Mato Grosso, Brazil.",
doi = "10.3390/rs8010022",
url = "http://dx.doi.org/10.3390/rs8010022",
issn = "2072-4292",
language = "en",
targetfile = "zhu_mapping.pdf",
urlaccessdate = "27 abr. 2024"
}