Skip to main content
SpringerLink
Log in

Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping. However, the temporal crop signatures generated from these data were always accompanied by noise. In this study, a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented. The detail crop planting patterns in Hebei Plain, China were classified using denoised time-series MODIS NDVI data at 250 m resolution. The denoising approach improved original MODIS NDVI product significantly in several periods, which may affect the accuracy of classification. The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation, statistical data and high resolution image. The field investigation accuracy was 85% at pixel level. At county-level, for winter wheat, there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R 2 = 0.814, p < 0.01). Moreover, the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period. The overall accuracy achieved 91.01%. The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Achard F E H, Mayaux P, 2001. Tropical forest mapping from coarse spatial resolution satellite data: Production and accuracy assessment issues. International Journal of Remote Sensing, 22(14): 2741–2762. doi: 10.1080/01431160120548

    Google Scholar 

  • Addison P S, 2002. The Illustrated Wavelet Transform Handbook. Bristol: The Institute of Physics Publishing.

    Book  Google Scholar 

  • Bartholome E, Belward A, 2005. GLC2000: A new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26(9): 1959–1977. doi: 10.1080/01431160412331291297

    Article  Google Scholar 

  • Beck P, Atzberer C, Høgda K et al., 2006. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment, 100(3): 321–334. doi: 10.1016/j.rse.2005.10.021

    Article  Google Scholar 

  • Boles S, Xiao X, Liu J et al., 2004. Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment, 90(4): 477–489. doi: 10.1016/j.rse.2004.01.016

    Article  Google Scholar 

  • Canisius F, Turral H, Molden D, 2007. Fourier analysis of historical NOAA time series data to estimate bimodal agriculture. International Journal of Remote Sensing, 28(24): 5503–5522. doi: 10.1080/01431160601086043

    Article  Google Scholar 

  • Cao Zhenguo, Yang Jingxiang, Zhao Wenhai, 2007. Rural Statistical Yearbook of Hebei Province. Beijing: China Statistics Press, 476–522. (in Chinese)

    Google Scholar 

  • Chawla N, Bowyer K, Hall L et al., 2002. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16: 321–357. doi: 10.1613/jair.953

    Google Scholar 

  • Chen J, Jonsson P, Tamura M et al., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91(3–4): 332–344. doi: 10.1016/j.rse.2004.03.014

    Article  Google Scholar 

  • Didan K, Huete A, 2006. MODIS vegetation index product series collection 5 change summary. Available at: http://landweb.nascom.nasa.gov/QAwww/forPage/MOD13VIC5ChangesDocument062806.pdf.

  • Friedl M, Mciver D, Hodges J et al., 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83(1–2): 287–302. doi: 10.1016/ S0034-4257(02)00078-0

    Article  Google Scholar 

  • Galford G, Mustard J, Melillo J et al., 2008. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sensing of Environment, 112(5): 576–587. doi: 10.1016/j.rse.2007.05.017

    Article  Google Scholar 

  • Hird J, Mcdermid G, 2009. Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1): 248–258. doi: 10.1016/j.rse.2008. 09.003

    Article  Google Scholar 

  • Homer C, Huang C, Yang L et al., 2004. Development of a 2001 national land-cover database for the United States. Photogrammetric Engineering and Remote Sensing, 70(7): 829–840.

    Google Scholar 

  • Huete A, Justice C, Leeuwen W V, 1999. MODIS vegetation index (MOD13) algorithm theoretical basis document (Version 3). Available at: http://modis.gsfc.nasa.gov/data/atbd/atbdmod13.pdf (accessed 20 March 2009).

  • Jönsson P, Eklundh L, 2002. Seasonality extraction by function- fitting to time series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824–1832. doi: 10.1109/TGRS.2002.802519

    Article  Google Scholar 

  • 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. doi: 10.1016/j.rse. 2009.11.001

    Article  Google Scholar 

  • Liu C M, Yu J J, Kendy E, 2001. Groundwater exploitation and its impact on the environment in the North China Plain. Water International, 26(2): 265–272. doi: 10.1080/02508060108686913

    Article  Google Scholar 

  • Lobell D, Asner G, 2004. Cropland distributions from temporal unmixing of MODIS data. Remote Sensing of Environment, 93(3): 412–422. doi: 10.1016/j.rse.2004.08.002

    Article  Google Scholar 

  • Lotsch A, Tian Y, Friedl M et al., 2003. Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors. International Journal of Remote Sensing, 24(10): 1997–2016. doi: 10.1080/01431160210154858

    Article  Google Scholar 

  • Lovell J L, Graetz R D, 2001. Filtering pathfinder AVHRR land NDVI data for Australia. International Journal of Remote Sensing, 22(13): 2649–2654. doi: 10.1080/01431160116874

    Google Scholar 

  • Lu X, Liu R, Liu J et al., 2007. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogrammetric Engineering and Remote Sensing, 73(10): 1129–1139.

    Google Scholar 

  • Lunetta R, Knight J, Ediriwickrema J et al., 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105(2): 142–154. doi: 10.1016/ j.rse.2006.06.018

    Article  Google Scholar 

  • Ma M, Veroustraete F, 2006. Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China. Advances in Space Research, 37(4): 835–840. doi: 10.1016/j.asr. 2005.08.037

    Article  Google Scholar 

  • Mallat S, 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7): 674–693. doi: 10. 1109/34.192463

    Article  Google Scholar 

  • Roerink G J, Menenti M, Verhoef W, 2000. Reconstructing cloud free NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9): 1911–1917. doi: 10.1080/014311600209814

    Article  Google Scholar 

  • Sakamoto T, Yokozawa M, Toritani H et al., 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3–4): 366–374. doi: 10. 1016/j.rse.2005.03.008

    Article  Google Scholar 

  • Savitzky A, Golay M J E, 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627–1639. doi: 10.1021/ac60214a047

    Article  Google Scholar 

  • Sellers P, Tucker C, Collatz G et al., 1994. A global 1° by 1° NDVI data set for global studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing, 15(17):3519–3545. doi: 10.1080/01431169408954343

    Article  Google Scholar 

  • Torrence C, Compo G, 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1):61–78.

    Article  Google Scholar 

  • USGS (U.S. Geological Survey), 2009. Land processes distributed active archive center (LP DAAC). Available at: https://lpdaac.usgs.gov/lpdaac/get_data/data_pool (accessed 20 March 2009).

  • Viovy N, Arino O, Belward A S, 1992. The best index slope extraction (BISE): A method for reducing noise in NDVI time-series. International Journal of Remote Sensing, 13(8):1585–1590. doi: 10.1080/01431169208904212

    Article  Google Scholar 

  • Wardlow B, Egbert S, 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains. Remote Sensing of Environment, 112(3): 1096–1116. doi: 10.1016/j.rse.2007.07.019

    Article  Google Scholar 

  • Wardlow B, Egbert S, Kastens J, 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108(3): 290–310. doi: 10.1016/j.rse.2006.11.021

    Article  Google Scholar 

  • Wessels K, Fries D R, Dempewolf J et al., 2004. Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil. Remote Sensing of Environment, 92(1): 67–83. doi: 10.1016/j.rse.2004.05.002

    Article  Google Scholar 

  • Wu G, De Leeuw J, Skidmore A et al., 2008. Comparison of MODIS and Landsat TM5 images for mapping tempo-spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve, China. International Journal of Remote Sensing, 29(8): 2183–2198. doi: 10.1080/01431160701422254

    Article  Google Scholar 

  • Xiao X, Braswell B, Zhang Q et al., 2003. Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia. Remote Sensing of Environment, 84(3): 385–392. doi: 10.1016/S0034-4257(02)00129-3

    Article  Google Scholar 

  • Zhang M W, Zhou Q B, Chen Z X et al., 2008. Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data. International Journal of Applied Earth Observations and Geoinformation, 10(4): 476–485. doi: 10.1016/j.jag.2007.11.002

    Article  Google Scholar 

  • Zhang X, Friedl M, Schaaf C et al., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471–475. doi: 10.1016/S0034-4257(02)00135-9

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inner Mongolia Agricultural University, Hohhot, 010018, China

    Shengwei Zhang, Liping Wang & Hongbin Zhao

  2. Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, 050021, China

    Shengwei Zhang, Yuping Lei & Hongjun Li

Authors
  1. Shengwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuping Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongbin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuping Lei.

Additional information

Foundation item: Under the auspices of Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-449, KSCX-YW-09), National Natural Science Foundation of China (No. 40971025, 40901030, 50969003)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, S., Lei, Y., Wang, L. et al. Crop classification using MODIS NDVI data denoised by wavelet: A case study in Hebei Plain, China. Chin. Geogr. Sci. 21, 322–333 (2011). https://doi.org/10.1007/s11769-011-0472-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-011-0472-2

Keywords

Search

Navigation