@Article{OliveiraEpipRenn:2014:SpAnEs,
author = "Oliveira, Julio Cesar de and Epiphanio, Jos{\'e} Carlos Neves and
Renn{\'o}, Camilo Daleles",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Window regression: A spatial-temporal analysis to estimate pixels
classified as low-quality in MODIS NDVI time series",
journal = "Remote Sensing",
year = "2014",
volume = "6",
number = "4",
pages = "3123--3142",
month = "Apr.",
keywords = "Akaike information criterion, Data quality, Land use/land cover
change, Mean absolute percentage error, Moderate resolution
imaging spectroradiometer datum, MODIS, Normalized difference
vegetation index, Spatial temporals, Deforestation, Estimation,
Iterative methods, Noise abatement, Quality control, Radiometers,
Regression analysis, Satellite imagery, Time series, Time series
analysis, Pixels.",
abstract = "MODerate resolution Imaging Spectroradiometer (MODIS) data are
largely used in multitemporal analysis of various Earth-related
phenomena, such as vegetation phenology, land use/land cover
change, deforestation monitoring, and time series analysis. In
general, the MODIS products used to undertake multitemporal
analysis are composite mosaics of the best pixels over a certain
period of time. However, it is common to find bad pixels in the
composition that affect the time series analysis. We present a
filtering methodology that considers the pixel position (location
in space) and time (position in the temporal data series) to
define a new value for the bad pixel. This methodology, called
Window Regression (WR), estimates the value of the point of
interest, based on the regression analysis of the data selected by
a spatial-temporal window. The spatial window is represented by
eight pixels neighboring the pixel under evaluation, and the
temporal window selects a set of dates close to the date of
interest (either earlier or later). Intensities of noises were
simulated over time and space, using the MOD13Q1 product. The
method presented and other techniques (4253H twice, Mean Value
Iteration (MVI) and Savitzky-Golay) were evaluated using the Mean
Absolute Percentage Error (MAPE) and Akaike Information Criteria
(AIC). The tests revealed the consistently superior performance of
the Window Regression approach to estimate new Normalized
Difference Vegetation Index (NDVI) values irrespective of the
intensity of the noise simulated. © 2014 by the authors; licensee
MDPI, Basel, Switzerland.",
doi = "10.3390/rs6043123",
url = "http://dx.doi.org/10.3390/rs6043123",
issn = "2072-4292",
label = "scopus 2014-05 OliveiraEpipRenn:2014:SpAnEs",
language = "en",
targetfile = "remotesensing-06-03123.pdf",
urlaccessdate = "28 mar. 2024"
}