Skip to main content
SpringerLink
Log in

Reconstructing rice phenology curves with frequency-based analysis and multi-temporal NDVI in double-cropping area in Jiangsu, China

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

Crop phenology retrieval in the double-cropping area of China is of great significance in crop yield estimation and water management under the influences of global change. In this study, rice phenology in Jiangsu Province, China was extracted from multi-temporal MODIS NDVI using frequency-based analysis. Pure MODIS pixels of rice were selected with the help of TM images. Discrete Fourier Transformation (DFT), Discrete Wavelet Transformation (DWT), and Empirical Mode Decomposition (EMD) were performed to decompose time series into components of different frequencies. Rice phenology in the double-cropping area is mainly located on the last 2 IMFs of EMD and the first 2‒3 frequencies of DFT and DWT. Compared with DFT and DWT, EMD is limited to fewer frequencies. Multi-temporal MODIS NDVI data combined with frequency-based analysis can retrieve rice phenology dates with on average 79% valid estimates. The sorting result for effective estimations from different methods is DWT (85%)>EMD (80%)>DFT (74%). Planting date (88%) is easier to estimate than harvesting date (70%). Rice planting date is easily affected by the former cropping mode within the same year in a double-cropping region. This study sheds light on understanding crop phenology dynamics in the frequency domain of multi-temporal MODIS data.

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

  • Andres L, Salas W, Skole D (1994). Fourier analysis of multi-temporal AVHRR data applied to a land cover classification. Int J Remote Sens, 15(5): 1115–1121

    Article  Google Scholar 

  • Carrao H, Gonalves P, Caetano M (2010). A nonlinear harmonic model for fitting satellite image time series: analysis and prediction of land cover dynamics. IEEE Trans Geosci Rem Sens, 48(4): 1919–1930

    Article  Google Scholar 

  • Chan K K Y, Xu B (2013). Perspective on remote sensing change detection of Poyang Lake wetland. Ann GIS, 19(4): 231–243

    Article  Google Scholar 

  • Chen C F, Chen C R, Son N T (2012). Investigating rice cropping practices and growing areas from MODIS data using empirical mode decomposition and support vector machines. GIsci Remote Sens, 49 (1): 117–138

    Article  Google Scholar 

  • Chen C, Son N, Chang L, Chen C (2011). Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam. Int J Remote Sens, 32(18): 5115–5134

    Article  Google Scholar 

  • Chen J, Jonsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens Environ, 91 (3‒4): 332–344

    Article  Google Scholar 

  • Cleland E, Chuine I, Menzel A, Mooney H, Schwartz M (2007). Shifting plant phenology in response to global change. Trends in Ecology and Evolution, 22(7): 357–365

    Article  Google Scholar 

  • Coughlin K, Tung K (2004). 11-year solar cycle in the stratosphere extracted by the empirical mode decomposition method. Adv Space Res, 34(2): 323–329

    Article  Google Scholar 

  • Delbart N, Le Toan T, Kergoat L, Fedotova V (2006). Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982‒2004). Remote Sens Environ, 101(1): 52–62

    Article  Google Scholar 

  • Genovese G, Vignolles C, Nègre T, Passera G (2001). A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain. Agronomie, 21(1): 91–111

    Google Scholar 

  • Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Yen N C, Tung C C, Liu H H (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of Medicine, 454(1971): 903–995

    Article  Google Scholar 

  • Huang N, Shen S (2005). Hilbert-Huang Transform and Its Applications. Singapore: World Scientific Pub Co Inc.

    Book  Google Scholar 

  • Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 83(1‒2): 195–213

    Article  Google Scholar 

  • Ito E, Araki M, Tith B, Pol S, Trotter C, Kanzaki M, Ohta S (2008). Leafshedding phenology in lowland tropical seasonal forests of cambodia as estimated from NOAA satellite images. IEEE Trans Geosci Rem Sens, 46(10): 2867–2871

    Article  Google Scholar 

  • Ivanov P, Rosenblum M, Peng C, Mietus J, Havlin S, Stanley H, Goldberger A (1996). Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis. Nature, 383(6598): 323–327

    Article  Google Scholar 

  • Justice C, Townshend J, Holben B, Tucker C (1985). Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens, 6(8): 1271–1318

    Article  Google Scholar 

  • Justice C, Townshend J, Vermote E F, Masuoka E, Wolfe R E, Saleous N, Roy D P, Morisette J T (2002). An overview of MODIS Land data processing and product status. Remote Sens Environ, 83(1‒2): 3–15

    Article  Google Scholar 

  • Kobayashi H, Dye D (2005). Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index. Remote Sens Environ, 97(4): 519–525

    Article  Google Scholar 

  • Lobell D B, Burke M B, Tebaldi C, Mastrandrea M D, Falcon W P, Naylor R L (2008). Prioritizing climate change adaptation needs for food security in 2030. Science, 319(5863): 607–610

    Article  Google Scholar 

  • Lu X, Liu R, Liu J, Liang S (2007). Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm Eng Remote Sensing, 73(10): 1129–1139

    Article  Google Scholar 

  • Martínez B, Gilabert MA (2009). Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens Environ, 113(9): 1823–1842

    Article  Google Scholar 

  • Mo X, Liu S, Lin Z, Xu Y, Xiang Y, McVicar T (2005). Prediction of crop yield, water consumption and water use efficiency with a SVATcrop growth model using remotely sensed data on the North China Plain. Ecol Modell, 183(2‒3): 301–322

    Article  Google Scholar 

  • Molla M, Rahman M, Sumi A, Banik P (2006). Empirical mode decomposition analysis of climate changes with special reference to rainfall data. Discrete Dyn Nat Soc, 2006: 1–17

    Article  Google Scholar 

  • Peng S, Tang Q, Zou Y (2009). Current status and challenges of rice production in China. Plant Prod Sci, 12(1): 3–8

    Article  Google Scholar 

  • Percival D, Walden A (2006). Wavelet Methods for Time Series Analysis. Cambridge University Press

    Google Scholar 

  • Qian S, Chen D (1999). Joint time-frequency analysis. IEEE Signal Process Mag, 16(2): 52–67

    Article  Google Scholar 

  • Raddatz R, Cummine J (2003). Inter-annual variability of moisture flux from the prairie agro-ecosystem: impact of crop phenology on the seasonal pattern of tornado days. Boundary-Layer Meteorol, 106(2): 283–295

    Article  Google Scholar 

  • Rilling G, Flandrin P, Goncalvés P (2003). On empirical mode decomposition and its algorithms. In Proceedings of the 6th IEEE/ EURASIP Workshop on Nonlinear Signal and Image Processing, Grado, Italy 2003

    Google Scholar 

  • Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N, Yokozawa M (2006). 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, 100(1): 1–16

    Article  Google Scholar 

  • Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005). A crop phenology detection method using time-series MODIS data. Remote Sens Environ, 96(3‒4): 366–374

    Article  Google Scholar 

  • Santoso S, Powers E J, Grady W M, Hofmann P (1996). Power quality assessment via wavelet transform analysis. IEEE Trans Power Deliv, 11(2): 924–930

    Article  Google Scholar 

  • Schmidhuber J, Tubiello F N (2007). Global food security under climate change. Proc Natl Acad Sci USA, 104(50): 19703–19708

    Article  Google Scholar 

  • Shen J, Liu J, Lin X, Zhao R, Xu S (2011). Cropland extraction from very high spatial resolution satellite imagery by object-based classification using improved mean shift and one-class support vector machines. Sens Lett, 9(3): 997–1005

    Article  Google Scholar 

  • Slayback D, Pinzon J, Los S, Tucker C (2003). Northern hemisphere photosynthetic trends 1982‒99. Glob Change Biol, 9(1): 1–15

    Article  Google Scholar 

  • Tan G, Shibasaki R (2003). Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration. Ecol Modell, 168(3): 357–370

    Article  Google Scholar 

  • Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z (2006). Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agric Meteorol, 138(1‒4): 82–92

    Article  Google Scholar 

  • Torrence C, Compo G (1998). A practical guide to wavelet analysis. Bull Am Math Soc, 79(1): 61–78

    Article  Google Scholar 

  • Vasudevan K, Cook F (2000). Empirical mode skeletonization of deep crustal seismic data: theory and applications. J Geophys Res, D, Atmospheres, 105(B4): 7845–7856

    Article  Google Scholar 

  • Wang H S, Chen J S,Wu Z F, Lin H (2012a). Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting. Int J Remote Sens, 33(6): 1905–1916

    Article  Google Scholar 

  • Wang H S, Lin H, Chen J S, Chen F L (2012b). Study on the relationship between sub-pixel percentage cover and multi-temporal NDVI. Int J Remote Sens, 33(17): 5615–5628

    Article  Google Scholar 

  • Wang H S, Lin H, Liu D S (2014). Remotely sensed drought index and its responses to meteorological drought in Southwest China. Remote Sens Lett, 5(5): 413–422

    Article  Google Scholar 

  • Wang H S, Rogers J C, Munroe D K (2015). Commonly used drought indices as indicators of soil moisture in China. J Hydrometeorol, 16 (3): 1397–1408

    Article  Google Scholar 

  • Wolfe R E, Nishihama M, Fleig A J, Kuyper J A, Roy D P, Storey J C, Patt F S (2002). Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens Environ, 83(1‒2): 31–49

    Article  Google Scholar 

  • Xiao X M, Boles S, Liu J Y, Zhuang D F, Frolking S, Li C S, Salas W, Moore B III (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ, 95(4): 480–492

    Article  Google Scholar 

  • You X, Meng J, Zhang M, Dong T (2013). Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method. Remote Sens, 5(7): 3190–3211

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Hongshuo Wang & Xiaodong Zhang

  2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China

    Hongshuo Wang & Hui Lin

  3. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China

    Hui Lin

  4. Department of Geography, The Ohio State University, Columbus, OH, 43210, USA

    Darla K. Munroe

  5. School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, 221116, China

    Pengfei Liu

Authors
  1. Hongshuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Darla K. Munroe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaodong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pengfei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Lin, H., Munroe, D.K. et al. Reconstructing rice phenology curves with frequency-based analysis and multi-temporal NDVI in double-cropping area in Jiangsu, China. Front. Earth Sci. 10, 292–302 (2016). https://doi.org/10.1007/s11707-016-0552-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-016-0552-9

Keywords

Search

Navigation