Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China
Abstract
Keywords: remote sensing, phenology, paddy rice, time series MODIS EVI, growth monitoring, Savitzky-Golay filter, wavelet transform
DOI: 10.3965/j.ijabe.20140706.005
Citation: Li S H, Xiao J T, Ni P, Zhang J, Wang H S, Wang J X. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int J Agric & Biol Eng, 2014; 7(6): 28-36.
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Chen C F, Son N T, Chang L Y. Monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam using time-series MODIS data. Adv. Space Res. 2012; 49(2): 292–301.
Coats B. Global rice production. Rice origin, history, technology and production. Hoboken, NJ, USA: John Wiley and Sons, 2003; pp. 247–470.
Bi L, Zhang B, Liu G, Li Z, Liu Y, Ye C, et al. Long-term effects of organic amendments on the rice yields for double rice cropping systems in subtropical China. Agric. Ecosyst. Environ., 2009; 129(4): 534–541.
Liu L, Wang E, Zhu Y, Tang L, Cao W. Effects of warming and autonomous breeding on the phenological development and grain yield of double-rice systems in China. Agric. Ecosyst. Environ., 2013; 165: 28–38.
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., 2005; 96(3): 366–374.
Kang S, Running S W, Lim J H, Zhao M, Park C R, Loehman R. A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index. Remote Sens. Environ., 2003; 86(2): 232–242.
Wu W, Yang P, Tang H, Zhou Q, Chen Z, Ryosuke S. Characterizing spatial patterns of phenology in cropland of China based on remotely sensed data. Agr. Sci. China, 2010; 9(1): 101–112.
Garrity S R, Bohrer G, Maurer K D, Mueller K L, Vogel C S, Curtis P S. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol., 2011; 151(12): 1741–1752.
Melaas E K, Richardson A D, Friedl M A, Dragoni D, Gough C M, Herbst M, et al. Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems. Agric. For. Meteorol., 2013; 171: 46–56.
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., 2014; 147: 79–88.
Haw C L, Ismail W I W, Kairunniza-Bejo S, Putih A, Shamshiri R. Colour vision to determine paddy maturity. Int. J. Agric. & Biol. Eng., 2014; 7(5): 55–63.
Pei W, Lan Y B, Luo X W, Zhou Z Y, Wang Z, Wang Y. Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system. Int. J. Agric. & Biol. Eng., 2014; 7(2): 75–81.
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., 2010; 114(7): 1388–1402.
Li B, Li L, Qin Y, Liang L, Li J, Liu Y, et al. Impacts of climate variability on streamflow in the upper and middle reaches of the Taoer River based on the Budyko Hypothesis. Resources Science, 2011; 33(1): 70–76. (in Chinese with English abstract)
Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979; 8(2): 127–150.
Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002; 83(1): 195–213.
Sakamoto T, Wardlow B D, Gitelson A A, Verma S B, Suyker A E, Arkebauer T J. A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ., 2010; 114(10): 2146–2159.
Zhang X, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 2003; 84(3): 471–475.
Zhang X, Friedl M A, Schaaf C B. Global vegetation phenology from moderate resolution imaging spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res., 2006; 111(G4): G04017.
Hmimina G, Dufrêne E, Pontailler J Y, Delpierre N, Aubinet M, Caquet B, et al. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sens. Environ., 2013; 132: 145– 158.
Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N, Yokozawa M. Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens. Environ., 2006; 100(1): 1–16.
Motohka T, Nasahara K N, Miyata A, Mano M, Tsuchida S. Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. Int. J. Remote Sens., 2009; 30(17): 4343–4357.
Boschetti M, Stroppiana D, Brivio P A, Bocchi S. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int. J. Remote Sens., 2009; 30(18): 4643–4662.
Wang H, Chen J, Wu Z, Lin H. Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting. Int. J. Remote Sens. 2012; 33(6): 1905–1916.
Xiao W, Sun Z, Wang Q, Yang Y. Evaluating MODIS phenology product for rotating croplands through ground observations. J. Appl. Remote Sens. 2013; 7(1): 073562– 073562.
Chen J, Jo¨nsson 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., 2004; 91(3): 332–344.
Chen C F, Son N T, Chen C R, Chang L Y. Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier. J. Appl. Remote Sens., 2011; 5(1): 053525–053525-15.
Yang H, Zhang D Y, Huang L S, Zhao J L. Wavelet-based threshold denoising for imaging hyperspectral data. Int. J. Agric. & Biol. Eng., 2014; 7(3): 36–42.
Galford G L, Mustard J F, Melillo J, Gendrin A, Cerri C C, Cerri C E P. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens. Environ., 2008; 112(2): 576–587.
About Jiangxi. http://english.jiangxi.gov.cn/AboutJiangxi/ 200808/t20080824_83092.htm.
Savitzky A, Golay M J E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem.,
; 36(8): 1627–1639.
Blackburn G A, Ferwerda J G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens. Environ. 2008; 112(4): 1614–1632.
Wiman H, Qin Y. Fast Compression and Access of LiDAR Point Clouds Using Wavelets. In 2009 Joint Urban Remote Sensing Event, pp.718–723, Shanghai, China.
Daubechies I. Ten lectures on wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics 61, 1992; pp.377, PA: Soc. Ind. Appl. Math, Philadelphia.
Martinez B, Gilabert M A. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens. Environ. 2009; 113(9): 1823–1842.
Torrence C, Compo G P. A practical guide to wavelet analysis. B. Am. Meteorol. Soc. 1998; 79(1): 61–78.
Misiti M, Misiti Y, Oppenheim G, Poggi J M. Wavelet Toolbox User’s Guide. The Math Works Ins., 1996.
Pu R, Gong P. Wavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping. Remote Sens. Environ., 2004; 91(2): 212–224.
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., 1994; 5(5): 703–714.
Boschetti M, Stroppiana D, Brivio P A, Bocchi S. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int. J. Remote Sens., 2009; 30(18): 4643–4662.
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