Noise reduction of NDVI time series: An empirical comparison of selected techniques

Hird, Jennifer N.; McDermid, Gregory J.

doi:10.1016/j.rse.2008.09.003

Cited by 474 publications

(336 citation statements)

References 40 publications

Supporting

Mentioning

285

Contrasting

Unclassified

Order By: Relevance

“…This has also been reported in other works where the SG adaptive filter has been successfully used in minimizing noise in NDVI time-series [33] and [12] and Leaf area index time-series [11]. …”

Section: Analysis Of MVI Time-series Filteringsupporting

confidence: 78%

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Chakroun¹

2017

OJAppS

View full text Add to dashboard Cite

Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive SavitskyGolay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

show abstract

Section: Analysis Of MVI Time-series Filteringsupporting

confidence: 78%

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Chakroun¹

2017

OJAppS

View full text Add to dashboard Cite

show abstract

“…The filter is adaptive since the window size "n" can be adjusted during the processing if there is a large variation in an interval around a given VI value; in this case, the filtering is redone with a smaller "n" size window. This latter filter has been used with success in minimizing noise in NDVI [48,49]. Our time series data of various VI had been processed within the Timesat software tool; we also find a better performance of the adaptive Savitzky-Golay method over the local model functions.…”

Section: Modis Images Processingmentioning

confidence: 85%

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

2015

View full text Add to dashboard Cite

Abstract:The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference 1938Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed.

show abstract

“…From the database, low quality time series were simulated by introducing noises according to [8,36]. This data simulation aims to assess the performance of the noise reduction process on the points considered as of low quality.…”

Section: Data Simulation and Analysismentioning

confidence: 99%

“…However, in [7,8], they show that the noises caused by different atmospheric conditions or by bidirectional effects related to the geometry of data acquisition pose some problems for the temporal profile analysis. These problems must be minimized before using the vegetation index temporal profile.…”

Section: Introductionmentioning

confidence: 99%

Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series

2014

View full text Add to dashboard Cite

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.

show abstract

Noise reduction of NDVI time series: An empirical comparison of selected techniques

Cited by 474 publications

References 40 publications

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

Window Regression: A Spatial-Temporal Analysis to Estimate Pixels Classified as Low-Quality in MODIS NDVI Time Series

Contact Info

Product

Resources

About