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.
Highlights
The MODIS sensor (MODerate resolution Imaging Spectroradiometer) can be considered as a landmark in the technological and scientific advances for global, regional and local analysis, and acquisition and monitoring of phenomena related to atmosphere, oceans, and terrestrial surface [1].The MODIS sensor stands out for its high temporal resolution, moderate spatial resolution and diverse spectral bands ranging from visible to thermal infrared
The method proposed to reconstruct the MODIS Normalized Difference Vegetation Index (NDVI) time series is divided into two steps: (A) the first step comprises a descriptive analysis of the time series, which is based on the quality information of the MOD13Q1 product
The noise in NDVI time series caused by cloud contamination and atmospheric variability is considered a problem for the analyses of environmental changes, where the time series are used as input data in the models
Summary
The MODIS sensor (MODerate resolution Imaging Spectroradiometer) can be considered as a landmark in the technological and scientific advances for global, regional and local analysis, and acquisition and monitoring of phenomena related to atmosphere, oceans, and terrestrial surface [1].The MODIS sensor stands out for its high temporal resolution, moderate spatial resolution and diverse spectral bands ranging from visible to thermal infrared. The MODIS sensor (MODerate resolution Imaging Spectroradiometer) can be considered as a landmark in the technological and scientific advances for global, regional and local analysis, and acquisition and monitoring of phenomena related to atmosphere, oceans, and terrestrial surface [1]. Among the MODIS Land products, the MODIS Vegetation Index (MOD13) is of particular interest for vegetation phenology research. It comprises the Normalized Difference Vegetation Index (NDVI). The MOD13 includes products according to the acquisition data period and spatial resolution. MOD13Q1 is composed of vegetation indices with a spatial resolution of 250 m and 16-day compositions [3]
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