An Effective Compound Algorithm for Reconstructing MODIS NDVI Time Series Data and Its Validation Based on Ground Measurements

In this study, a compound technique was developed using eight denoising techniques for reconstructing high-quality normalized difference vegetation index (NDVI) time series data. The new algorithm consists of two major procedures: 1) detecting noisy data according to variation in the modification rates of eight selected denoising techniques and 2) using the medians of the denoised values of the eight techniques to replace the noisy data. The eight techniques include the modified best index slope extraction (M-BISE) technique, the Savitzky-Golay (S-G) technique, the mean value iteration (MVI) filter, the asymmetric Gaussian (A-G) technique, the double logistic (D-L) technique, the changing-weight (CW) filter, the interpolation for data reconstruction (IDR) technique, and the Whittaker smoother (WS) technique. The technique was tested with moderate resolution imaging spectroradiometer (MODIS) NDVI time series data derived from MOD09GQ of the Heihe River Basin in China. In situ NDVI data were obtained during one nearly complete growing season for six land-use types in the study area. Analysis of the temporal and spatial characteristics of the reconstructed data revealed that the compound technique performs better than the other techniques. In addition, the lower root-mean-square error (RMSE) of the compound technique, which was calculated using ground measurements, demonstrated the improved performance of the new technique. The main advantage of the new technique is its ability to effectively denoise data and maintain fidelity such that it can be widely used for other NDVI time series data and for other study areas.

[1]  Gaofeng Zhu,et al.  The hydrochemical characteristics and evolution of groundwater and surface water in the Heihe River Basin, northwest China , 2008 .

[2]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[3]  Hao He,et al.  A Changing-Weight Filter Method for Reconstructing a High-Quality NDVI Time Series to Preserve the Integrity of Vegetation Phenology , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Zhenyu Jin,et al.  A Novel Compound Smoother—RMMEH to Reconstruct MODIS NDVI Time Series , 2013, IEEE Geoscience and Remote Sensing Letters.

[5]  Jude H. Kastens,et al.  Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data , 2013 .

[6]  J. M. de Mello,et al.  The Assessment of Vegetation Seasonal Dynamics using Multitemporal NDVI and EVI images derived from MODIS , 2007, 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images.

[7]  Takeshi Motohka,et al.  Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements , 2011, Remote. Sens..

[8]  Tobias Landmann,et al.  MODIS-based change vector analysis for assessing wetland dynamics in Southern Africa , 2013 .

[9]  P. Eilers.  A perfect smoother. , 2003, Analytical chemistry.

[10]  Wout Verhoef,et al.  Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images , 1993 .

[11]  Paul F. Velleman,et al.  Definition and Comparison of Robust Nonlinear Data Smoothing Algorithms , 1980 .

[12]  Hideki Kobayashi,et al.  Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index , 2005 .

[13]  Jinwei Dong,et al.  Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 , 2013, Proceedings of the National Academy of Sciences.

[14]  Kazuhito Ichii,et al.  Multi-temporal analysis of deforestation in Rondônia state in Brazil using Landsat MSS, TM, ETM+ and NOAA AVHRR imagery and its relationship to changes in the local hydrological environment , 2003 .

[15]  M. Menenti,et al.  Assessment of climate impact on vegetation dynamics by using remote sensing , 2003 .

[16]  J. Carreiras,et al.  Evaluation of compositing algorithms over the Brazilian Amazon using SPOT-4 VEGETATION data , 2003 .

[17]  José A. Sobrino,et al.  Comparison of cloud-reconstruction methods for time series of composite NDVI data , 2010 .

[18]  Frédéric Baret,et al.  Developments in the 'validation' of satellite sensor products for the study of the land surface , 2000 .

[19]  Mingguo Ma,et al.  Validation of the MODIS NDVI Products in Different Land-Use Types Using In Situ Measurements in the Heihe River Basin , 2014, IEEE Geoscience and Remote Sensing Letters.

[20]  A. Fischer.  A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters , 1994 .

[21]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[22]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[23]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[24]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[25]  J. L. Lovell,et al.  Filtering Pathfinder AVHRR Land NDVI data for Australia , 2001 .

[26]  Zhao Han-bing,et al.  Study on the seasonal dynamics of zonal vegetation of NDVI/EVI of costal zonal vegetation based on MODIS data: A case study of Spartina alterniflora salt marsh on Jiangsu Coast, China , 2011 .

[27]  Zhenyu Jin,et al.  Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure , 2014 .

[28]  Jan Verbesselt,et al.  Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology , 2013, Remote. Sens..

[29]  Yuping Lei,et al.  Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China , 2011 .

[30]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[31]  Xiaoliang Lu,et al.  Removal of Noise by Wavelet Method to Generate High Quality Temporal Data of Terrestrial MODIS Products , 2007 .

[32]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[33]  R. Fensholt,et al.  Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series , 2012 .

[34]  Hideki Saito,et al.  Land Cover Change Mapping of the Mekong River Basin Using NOAA Pathfinder AVHRR 8-km Land Dataset , 2007 .

[35]  Masayuki Matsuoka,et al.  A proposal of the Temporal Window Operation (TWO) method to remove high-frequency noises in AVHRR NDVI time series data , 1999 .

[36]  Sunyurp Park,et al.  Cloud and cloud shadow effects on the MODIS vegetation index composites of the Korean Peninsula , 2013 .

[37]  Mingguo Ma,et al.  Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China , 2014, Remote. Sens..

[38]  F. Veroustraete,et al.  Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China , 2006 .

[39]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[40]  Chunlin Huang,et al.  A Simplified Data Assimilation Method for Reconstructing Time-Series MODIS NDVI Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[41]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[42]  A. Fischer.  A simple model for the temporal variations of NDVI at regional scale over agricultural countries. Validation with ground radiometric measurements , 1994 .

[43]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[44]  John F. Mustard,et al.  Extracting Phenological Signals From Multiyear AVHRR NDVI Time Series: Framework for Applying High-Order Annual Splines With Roughness Damping , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Douglas A. Stow,et al.  Daily MODIS products for analyzing early season vegetation dynamics across the North Slope of Alaska , 2010 .

[46]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[47]  Liangxu Wang,et al.  Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China , 2012, Frontiers of Earth Science.

[48]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[49]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[50]  Nan Jiang,et al.  A phenology-preserving filtering method to reduce noise in NDVI time series , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.