A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation

International Journal of Applied Earth Observation and Geoinformation

Volumn 47, Issue , 2016, Pages 91-101

  Qiu, Bingwen ^a   Feng, Min ^a   Tang, Zhenghong ^b  

^a FUZHOU UNIVERSITY   (China)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Continuous wavelet transform; Noise reduction; Smoothness; Time series; Vegetation phenology

Indexed keywords

MODIS; PHENOLOGY; REMOTE SENSING; SMOOTHING; TIME SERIES; VEGETATION; WAVELET ANALYSIS;

CHINA;

EID: 84988700490     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2015.11.009     Document Type: Article

References (44)

1. Atkinson, P.M., Jeganathan, C., Dash, J., Atzberger, C., Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens. Environ. 123 (2012), 400–417.
2. Atzberger, C., Eilers, P.H., Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements. Int. J. Remote Sens. 32 (2011), 3689–3709.
3. Atzberger, C., Eilers, P.H.C., A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America. Int. J. Digital Earth 4 (2011), 365–386.
4. Atzberger, C., Klisch, A., Mattiuzzi, M., Vuolo, F., Phenological metrics derived over the european continent from NDVI3G data and MODIS time series. Remote Sens. 6 (2013), 257–284.
5. Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K., Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens. Environ. 100 (2006), 321–334.
6. Beurs de, K.M., Henebry, G.M., Spatio-temporal statistical methods for modelling land surface phenology. Hudson, I.L., Keatley, M.R., (eds.) Phenological Research, 2010, Springer 177–208.
7. Bridhikitti, A., Overcamp, T.J., Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery. Agric. Ecosyst. Environ. 146 (2012), 113–120.
8. Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91 (2004), 332–344.
9. Cheng, T., Rivard, B., Snchez-Azofeifa, A., Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ. 115 (2011), 659–670.
10. Dash, J., Jeganathan, C., Atkinson, P.M., The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sens. Environ. 114 (2010), 1388–1402.
11. Daubechies, I., The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36 (1990), 961–1005.
12. Du, P., Kibbe, W.A., Lin, S.M., Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 22 (2006), 2059–2065.
13. Ebadi, L., Shafri, H.Z.M., Mansor, S.B., Ashurov, R., A review of applying second-generation wavelets for noise removal from remote sensing data. Environ. Earth Sci. 70 (2013), 2679–2690.
14. Eilers, P.H.C., A perfect smoother. Anal. Chem. 75 (2003), 3631–3636.
15. Gang, Y., Huanfeng, S., Liangpei, Z., Zongyi, H., Xinghua, L., A moving weighted harmonic analysis method for reconstructing high-quality SPOT VEGETATION NDVI time-series data. IEEE Trans. 53 (2015), 6008–6021.
16. Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., Verma, M., Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens. Environ. 114 (2010), 1805–1816.
17. Geng, L., Ma, M., Wang, X., Yu, W., Jia, S., Wang, H., Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe River Basin, China. Remote Sens. 6 (2014), 2024–2049.
18. Hird, J.N., McDermid, G.J., Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ. 113 (2009), 248–258.
19. Jönsson, P., Eklundh, L., TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci. 30 (2004), 833–845.
20. Jiang, Z., Huete, A.R., Didan, K., Miura, T., Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112 (2008), 3833–3845.
21. Jonsson, P., Eklundh, L., Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. 40 (2002), 1824–1832.
22. Julien, Y., Sobrino, J.A., Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sens. Environ. 114 (2010), 618–625.
23. Kandasamy, S., Baret, F., Verger, A., Neveux, P., Weiss, M., A comparison of methods for smoothing and gap filling time series of remote sensing observations—application to MODIS LAI products. Biogeosciences 10 (2013), 4055–4071.
24. Lu, X., Liu, R., Liu, J., Liang, S., Removal of noise by Wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm. Eng. Rem. Sens., 73, 2007, 1129.
25. Martínez, B., Gilabert, M.A., Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 113 (2009), 1823–1842.
26. Meroni, M., Fasbender, D., Kayitakire, F., Pini, G., Rembold, F., Urbano, F., Verstraete, M.M., Early detection of biomass production deficit hot-spots in semi-arid environment using FAPAR time series and a probabilistic approach. Remote Sens. Environ. 142 (2014), 57–68.
27. Michishita, R., Jin, Z., Chen, J., Xu, B., Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure. ISPRS J Photogramm. Remote Sens. 91 (2014), 17–28.
28. Qiu, B.W., Zeng, C.Y., Tang, Z.H., Chen, C.C., Characterizing spatiotemporal non-stationarity in vegetation dynamics in china using MODIS EVI dataset. J. Environ. Monit. Assess. 185 (2013), 9019–9035.
29. Qiu, B.W., Zhong, M., Tang, Z.H., Chen, C.C., Spatiotemporal variability of vegetation phenology with reference to altitude and climate in the subtropical mountain and hill region, China. Chin. Sci. Bull. 58 (2013), 2883–2892.
30. Qiu, B.W., Zhong, M., Tang, Z.H., Wang, C.Y., A new methodology to map double-cropping croplands based on continuous wavelet transform. Int. J. Appl. Earth Obs. Geoinf. 26 (2014), 97–104.
31. Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., Measuring phenological variability from satellite imagery. J. Veg. Sci. 5 (1994), 703–714.
32. Roerink, G.J., Menenti, M., Verhoef, W., Reconstructing cloudfree NDVI composites using Fourier analysis of time series. Int. J. Remote Sens. 21 (2000), 1911–1917.
33. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96 (2005), 366–374.
34. Savitzky, A., Golay, M.J.E., Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36 (1964), 1627–1639.
35. Schmidt, K.S., Skidmore, A.K., Smoothing vegetation spectra with wavelets. Int. J. Remote Sens. 25 (2004), 1167–1184.
36. Torrence, C., Compo, G.P., A practical guide to Wavelet analysis. Bull. Am. Meteorol. Soc. 79 (1998), 61–78.
37. Verger, A., Baret, F., Weiss, M., Kandasamy, S., Vermote, E., The CACAO method for smoothing, gap filling, and characterizing seasonal anomalies in satellite time series. IEEE Trans. Geosci. Remote Sens. 51 (2013), 1963–1972.
38. Walker, J.J., de Beurs, K.M., Wynne, R.H., Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sens. Environ. 144 (2014), 85–97.
39. White, M.A., Thornton, P.E., Running, S.W., A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem. Cycles 11 (1997), 217–234.
40. White, M.A., Beurs, D., Kirsten, M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., O'Keefe, J., Zhang, G., Nemani, R.R., Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biol. 15 (2009), 2335–2359.
41. Wu, C., Gonsamo, A., Gough, C.M., Chen, J.M., Xu, S., Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sens. Environ. 147 (2014), 79–88.
42. Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., Moore, B., Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ. 95 (2005), 480–492.
43. Zhang, X., Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sens. Environ. 156 (2015), 457–472.
44. Zhou, J., Jia, L., Menenti, M., Reconstruction of global MODIS NDVI time series: performance of harmonic analysis of time series (HANTS). Remote Sens. Environ. 163 (2015), 217–228.