Early drought detection by spectral analysis of satellite time series of precipitation and Normalized Difference Vegetation Index (NDVI)

Remote Sensing

Volumn 8, Issue 5, 2016, Pages

  Van Hoek, Mattijn ^a,b   Jia, Li ^a   Zhou, Jie ^a,b,c   Zheng, Chaolei ^a   Menenti, Massimo ^a,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Anomalies; Drought early detection; Phase delay; Spectral analysis

Indexed keywords

DROUGHT; FOURIER SERIES; FREQUENCY DOMAIN ANALYSIS; IMAGE PROCESSING; INTELLIGENT SYSTEMS; MONTE CARLO METHODS; SOIL MOISTURE; SPECTRUM ANALYSIS; TIME SERIES; VEGETATION;

AGRICULTURAL DROUGHT; ANOMALIES; AVAILABLE SOIL WATERS; CROSS-SPECTRAL ANALYSIS; FOURIER SERIES METHOD; NORMALIZED DIFFERENCE VEGETATION INDEX; PHASE DELAY; WATER HOLDING CAPACITY;

TIME SERIES ANALYSIS;

EID: 84971476078     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8050422     Document Type: Article

References (61)

1. Meehl, G.A.; Covey, C.; Taylor, K.E.; Delworth, T.; Stouffer, R.J.; Latif, M.; McAvaney, B.; Mitchell, J.F.B. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Am. Meteorol. Soc. 2007, 88, 1383-1394.
2. Rahmstorf, S.; Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. USA 2011, 108, 17905-17909.
3. Wilhite, D.A.; Glantz, M.H. Understanding: The drought phenomenon: The role of definitions. Water Int. 1985, 10, 111-120.
4. Beran, M.A.; Rodier, J.A. Hydrological Aspects of Drought: A Contribution to the International Hydrological Programme; Unesco: Paris, France, 1985.
5. Demuth, S.; Bakenhus, A. Hydrological Drought-A Literature Review; University of Freiburg: Freiburg, Germany, 1994.
6. Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang. 2011, 2, 45-65.
7. Tate, E.L.; Gustard, A. Drought definition: A hydrological perspective. In Drought and Drought Mitigation in Europe; Vogt, J.V., Somma, F., Eds.; Springer Netherlands: Dordrecht, The Netherlands, 2000; pp. 23-48.
8. Keyantash, J.; Dracup, J.A. The quantification of drought: An evaluation of drought indices. Bull. Am. Meteorol. Soc. 2002, 83, 1167-1180.
9. Heim, R.R. A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc. 2002, 83, 1149-1165.
10. Svoboda, M.; Lecomte, D.; Hayes, M.; Heim, R.R.; Gleason, K.; Angel, J.; Rippey, B.; Tinker, R.; Palecki, M.; Stooksbury, D.; et al. The drought monitor. Bull. Am. Meteorol. Soc. 2002, 83, 1181.
11. Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 2010, 23, 1696-1718.
12. Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202-216.
13. Rouse, J.W.; Haas, R.H.; Schell, J.A. Monitoring The Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation; Report RSC 1978-4; Texas A & M University, Remote Sensing Center: College Station, TX, USA, 1974.
14. Kogan, F.N. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 1995, 15, 91-100.
15. Peters, A.J.; Walter-Shea, E.A.; Ji, L.; Vina, A.; Hayes, M.; Svoboda, M.D. Drought monitoring with NDVI-based standardized vegetation index. Photogramm. Eng. Remote Sens. 2002, 68, 71-75.
16. Rhee, J.; Im, J.; Carbone, G.J. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ. 2010, 114, 2875-2887.
17. Zhang, A.; Jia, G. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens. Environ. 2013, 134, 12-23.
18. Shepherd, A. Drought contingency planning: Evaluating the effectiveness of plans. J. Water Res. Plan. Manag. 1998, 124, 246-251.
19. Wu, B.; Ci, L.J. Landscape change and desertification development in the MU US Sandland, Northern China. J. Arid Environ. 2002, 50, 429-444.
20. Sepulcre-Canto, G.; Horion, S.; Singleton, A.; Carrao, H.; Vogt, J. Development of a combined drought indicator to detect agricultural drought in Europe. Natl. Hazards Earth Syst. Sci. 2012, 12, 3519-3531.
21. Ji, L.; Peters, A.J. Assessing vegetation response to drought in the northern great plains using vegetation and drought indices. Remote Sens. Environ. 2003, 87, 85-98.
22. Gessner, U.; Naeimi, V.; Klein, I.; Kuenzer, C.; Klein, D.; Dech, S. The relationship between precipitation anomalies and satellite-derived vegetation activity in central Asia. Glob. Planet. Chang. 2013, 110, 74-87.
23. Stoica, P.; Moses, R.L. Spectral Analysis of Signals; Prentice Hall, Inc.: Bergen, NJ, USA, 2004.
24. Bloomfield, P. The spectrum. In Fourier Analysis of Time Series; John Wiley & Sons: New York, NY, USA, 2005; pp. 133-166.
25. Percival, D.B.; Walden, A.T. Spectral Analysis for Physical Applications; Cambridge University Press: Cambridge, UK, 1993.
26. Sun, F.T.; Miller, L.M.; D'Esposito, M. Measuring temporal dynamics of functional networks using phase spectrum of FMRI data. NeuroImage 2005, 28, 227-237.
27. Qian, W.; Lin, X. Regional trends in recent precipitation indices in China. Meteorol. Atmos. Phys. 2005, 90, 193-207.
28. Hu, G.; Jia, L. Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations. Remote Sens. 2015, 7, 3056-3087.
29. Zou, X.; Zhai, P.; Zhang, Q. Variations in droughts over China: 1951-2003. Geophys. Res. Lett. 2005, 32.
30. Yu, M.; Li, Q.; Hayes, M.J.; Svoboda, M.D.; Heim, R.R. Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951-2010? Int. J. Climatol. 2014, 34, 545-558.
31. Lu, E.; Luo, Y.; Zhang, R.; Wu, Q.; Liu, L. Regional atmospheric anomalies responsible for the 2009-2010 severe drought in China. J. Geophys. Res. Atmos. 2011, 116, D21114.
32. Barriopedro, D.; Gouveia, C.M.; Trigo, R.M.;Wang, L. The October 2009 drought in China: Possible causes and impacts on vegetation. J. Hydrometeorol. 2012, 13, 1251-1267.
33. 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, 195-213.
34. Huete, A.R.; Didan, K.; Shimabukuro, Y.E.; Ratana, P.; Saleska, S.R.; Hutyra, L.R.; Yang, W.; Nemani, R.R.; Myneni, R. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 2006, 33.
35. Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R. Mapping agroecological zones and time lag in vegetation growth by means of fourier analysis of time series of NDVI images. Adv. Space Res. 1993, 13, 233-237.
36. Verhoef, W.; Menenti, M.; Azzali, S. Cover a colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981-1992). Int. J. Remote Sens. 1996, 17, 231-235.
37. Menenti, M.; Jia, L.; Azzali, S.; Roerink, G.; Gonzalez-Loyarte, M.; Leguizamon, S. Analysis of vegetation response to climate variability using extended time series of multispectral satellite images. In Remote Sensing Optical Observation of Vegetation Properties; Maselli, F., Menenti, M., Brivio, P.A., Eds.; Research Signpost: Trivandrum, India, 2010; pp. 131-164.
38. Roerink, G.; Menenti, M.; Verhoef, W. Reconstructing cloudfree NDVI composites using fourier analysis of time series. Int. J. Remote Sens. 2000, 21, 1911-1917.
39. Jia, L.; Li, J.; Menenti, M. Drought Monitoring And Prediction by Time Series Analysis of Greenness and Thermal Anomalies at Large Scale. Available online: http://adsabs.harvard.edu/abs/2009EGUGA.11.9659J (accessed on 13 May 2016).
40. Zhou, J.; Jia, L.; Hu, G.; Menenti, M. Evaluation of harmonic analysis of time series (HANTS): Impact of gaps on time series reconstruction. In Proceedings of the Earth Observation and Remote Sensing Applications (EORSA), 2012 Second International Workshop, Shanghai, Chian, 8-11 June 2012.
41. Zhou, J.; Jia, L.; Menenti, M. Reconstruction of global MODIS NDVI time series: Performance of harmonic analysis of time series (HANTS). Remote Sens. Environ. 2015, 163, 217-228.
42. Hu, Q.; Yang, D.; Li, Z.; Mishra, A.K.;Wang, Y.; Yang, H. Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges. Int. J. Remote Sens. 2014, 35, 1272-1294.
43. Arino, O.; Ramos Perez, J.J.; Kalogirou, V.; Bontemps, S.; Defourny, P.; Van Bogaert, E. Global Land Cover Map for 2009 (Globcover 2009); ESA and CUL: Paris, France, 2012.
44. Dai, Y.; Shangguan, W.; Duan, Q.; Liu, B.; Fu, S.; Niu, G. Development of a China dataset of soil hydraulic parameters using pedotransfer functions for land surface modeling. J. Hydrometeorol. 2013, 14, 869-887.
45. Li, Q.S.; Willardson, L.S.; Deng, W.; Li, X.J.; Liu, C.J. Crop water deficit estimation and irrigation scheduling in western Jilin province, Northeast China. Agric. Water Manag. 2005, 71, 47-60.
46. Challis, R.; Kitney, R. Biomedical signal processing (in four parts). Med. Biol. Eng. Comput. 1991, 29, 1-17.
47. Salter, P.J.; Goode, J.E. Crop Responses to Water at Different Stages of Growth; Research Review No. 2; Commonwealth Agricultural Bureaux: Dorset, UK, 1967.
48. Iwasaki, H. Study on influence of rainfall distribution on NDVI anomaly over the arid regions in Mongolia using an operational weather radar. SOLA 2006, 2, 168-171.
49. Nicholson, S.E.; Davenport, M.L.; Malo, A.R. A comparison of the vegetation response to rainfall in the sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Clim. Chang. 1990, 17, 209-241.
50. Eklundh, L. Estimating relations between Avhrr Ndvi and rainfall in east Africa at 10-day and monthly time scales. Int. J. Remote Sens. 1998, 19, 563-570.
51. Camberlin, P.; Martiny, N.; Philippon, N.; Richard, Y. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sens. Environ. 2007, 106, 199-216.
52. Propastin, P.; Kappas, M. Spatiotemporal drifts in AVHRR/NDVI-precipitation relationship and their linkage to land use change in central Kazakhstan. EARSeL eProceedings 2008, 7, 30-45.
53. Nezlin, N.; Kostianoy, A.; Li, B.-L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. J. Arid Environ. 2005, 62, 677-700.
54. Granger, C.W. Investigating causal relations by econometric models and cross-spectral methods. Econ. J. Econom. Soc. 1969.
55. Breitung, J.; Candelon, B. Testing for short-and long-run causality: A frequency-domain approach. J. Econ. 2006, 132, 363-378.
56. Sugihara, G.; May, R.; Ye, H.; Hsieh, C.-H.; Deyle, E.; Fogarty, M.; Munch, S. Detecting causality in complex ecosystems. Science (N.Y.) 2012, 338, 496-500.
57. Grinsted, A.; Moore, J.C.; Jevrejeva, S. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process. Geophys. 2004, 11, 561-566.
58. Torrence, C.; Compo, G.P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 1998, 79, 61-78.
59. 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. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Royal Soc. 1998, 454, 903-995.
60. Li, B.; Tao, S.; Dawson, R.W. Relations between AVHRR NDVI and ecoclimatic parameters in China. Int. J. Remote Sens. 2002, 23, 989-999.
61. Bond-Lamberty, B.; Bunn, A.G.; Thomson, A.M. Multi-year lags between forest browning and soil respiration at high northern latitudes. PLoS ONE 2012, 7, e50441.