Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 1975–1982 | Cite as

An Adaptive Noise Reduction Method for NDVI Time Series Data Based on S–G Filtering and Wavelet Analysis

  • Jianyun ZhaoEmail author
  • Xiaohua Zhang
Research Article


In order to reduce the noise in advanced very high-resolution radiometer global inventory modeling and mapping studies normalized difference vegetation index (NDVI) version 3g time series data, we propose an adaptive noise reduction method based on the Savitzky–Golay filter and wavelet analysis and on curve-fitting and spectrum analysis. The noise reduction effect was analyzed and evaluated. Studies have shown that the denoising of data using asymmetric Gaussian and double logistic curve-fitting methods results in the loss of details of the NDVI changes and is not conducive to the extraction of vegetation phenotypic characteristics. The wavelet function, wavelet decomposition layer number, and the threshold determination method have a large influence on the noise reduction effect, and the adaptive method has high noise reduction efficiency and effectively reduces the noise in the NDVI data. The proposed method does not require the determination of the smoothing window size and threshold for each year, which represents an advantage for processing large amounts of data.


NDVI Denoise S–G filtering Wavelet analysis Adaptive 



This work was supported by the Natural Science Foundation of Qinghai Science and Technology Agency of China (No. 2017-ZJ-744), Chunhui Planning Project of the Education Ministry of China (No. Z2016076).


  1. Atzberger, C., Klisch, A., Mattiuzzi, M., & Vuolo, F. (2013). Phenological metrics derived over the European Continent from NDVI3g data and MODIS time series. Remote Sensing, 6(1), 257–284.CrossRefGoogle Scholar
  2. Bradley, B. A., Jacob, R. W., Hermance, J. F., & Mustard, J. F. (2007). A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment, 106(2), 137–145.CrossRefGoogle Scholar
  3. Cristea, N. C., Lundquist, J. D., Loheide, S. P., Lowry, C. S., & Moore, C. E. (2014). Modelling how vegetation cover affects climate change impacts on streamflow timing and magnitude in the snowmelt-dominated upper Tuolumne Basin, Sierra Nevada. Hydrological Processes, 28(12), 3896–3918.CrossRefGoogle Scholar
  4. Defries, R., & Townshend, J. (1994). NDVI-derived land cover classification at global scales. International Journal of Remote Sensing, 15(17), 3567–3586.CrossRefGoogle Scholar
  5. Fsiii, C., Bretharte, M., Hobbie, S. E., & Zhong, H. (1996). Plant functional types as predictors of transient responses of arctic vegetation to global change. Journal of Vegetation Science, 7(3), 347–358.CrossRefGoogle Scholar
  6. Geng, L., Ma, M., & Wang, H. (2017). An effective compound algorithm for reconstructing MODIS NDVI time series data and its validation based on ground measurements. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 9(8), 3588–3597.CrossRefGoogle Scholar
  7. Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566.CrossRefGoogle Scholar
  8. Gu, J., Li, X., Huang, C., & Okin, G. S. (2009). A simplified data assimilation method for reconstructing time-series MODIS NDVI data. Advances in Space Research, 44(4), 501–509.CrossRefGoogle Scholar
  9. Hang-Yan, L. I., Ming-Guo, M. A., & Tan, J. L. (2010). Integrated reconstruction methods of time-series NDVI dataset. Remote Sensing Technology & Application, 25(6), 891–896.Google Scholar
  10. Ina, H., Takeda, M., & Kobayashi, S. (1982). Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. Review of Scientific Instruments, 72(12), 156–160.Google Scholar
  11. Julien, Y., & Sobrino, J. A. (2010). Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment, 114(3), 618–625.CrossRefGoogle Scholar
  12. Mohajer, S., Tian, C., & Diggavi, S. N. (2013). Asymmetric multilevel diversity coding and asymmetric gaussian multiple descriptions. IEEE Transactions on Information Theory, 56(9), 4367–4387.CrossRefGoogle Scholar
  13. Paruelo, J. M., Garbulsky, M. F., Guerschman, J. P., & Jobbágy, E. G. (2004). Two decades of normalized difference vegetation index changes in South America: Identifying the imprint of global change. International Journal of Remote Sensing, 25(14), 2793–2806.CrossRefGoogle Scholar
  14. Pinzon, J. E., & Tucker, C. J. (2014). A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sensing, 6(8), 6929–6960.CrossRefGoogle Scholar
  15. Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911–1917.CrossRefGoogle Scholar
  16. Seong, J. C. (2000). Multi-temporal NDVI change patterns and global land cover dynamics. Journal of the Korean Association of Geographic Information Studies, 3(3), 20–30.Google Scholar
  17. Shen, B., Fang, S., & Li, G. (2014). Vegetation coverage changes and their response to meteorological variables from 2000 to 2009 in Naqu, Tibet, China. Canadian Journal of Remote Sensing, 40(1), 67–74.CrossRefGoogle Scholar
  18. Smith, T. M., Shugart, H. H., Bonan, G. B., & Smith, J. B. (1992). Modeling the potential response of vegetation to global climate change. Advances in Ecological Research, 22, 93–98.CrossRefGoogle Scholar
  19. Song, C., You, S., Linghong, K. E., & Liu, G. (2011). Analysis on three NDVI time-series reconstruction methods and their applications in North Tibet. Journal of Geo-Information Science, 13(1), 133–143.CrossRefGoogle Scholar
  20. Song, W., & Deng, X. (2017). Land-use/land-cover change and ecosystem service provision in China. Science of the Total Environment, 576, 705–719.CrossRefGoogle Scholar
  21. Verma, R., & Dutta, S. (2013). Vegetation dynamics from denoised NDVI using empirical mode decomposition. Journal of the Indian Society of Remote Sensing, 41(3), 555–566.CrossRefGoogle Scholar
  22. Wang, J., Dong, J., Liu, J., Huang, M., Li, G., Running, S., et al. (2014). Comparison of gross primary productivity derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia. Remote Sensing, 6(3), 2108–2133.CrossRefGoogle Scholar
  23. Zhang, S., Wang, Y., Hongjun, L., et al. (2011). Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China. Chinese Geographical Science, 21(3), 322.CrossRefGoogle Scholar
  24. Zhou, J., Jia, L., & Menenti, M. (2015). Reconstruction of global MODIS NDVI time series: Performance of harmonic analysis of time SERIEs (HANTS). Remote Sensing of Environment, 163, 217–228.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Department of Geologic EngineeringQinghai UniversityXiningPeople’s Republic of China
  2. 2.Hiroshima Institute of TechnologyHiroshimaJapan

Personalised recommendations