Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data

Remote Sensing of Environment

Volumn 156, Issue , 2015, Pages 457-472

  Zhang, Xiaoyang ^a  

^a SOUTH DAKOTA STATE UNIVERSITY   (United States)

Author keywords

Daily EVI2 temporal trajectory; Hybrid model fitting; Long term AVHRR EVI2; Quality assessment

Indexed keywords

ADVANCED VERY HIGH RESOLUTION RADIOMETERS (AVHRR); SNOW; SURFACE MEASUREMENT; TIME SERIES; TRAJECTORIES;

ATMOSPHERIC CONTAMINATION; ENHANCED VEGETATION INDEX; GROSS PRIMARY PRODUCTIVITY; HYBRID MODEL; LONG-TERM AVHRR EVI2; NORMALIZED DIFFERENCE VEGETATION INDEX; QUALITY ASSESSMENT; TEMPORAL TRAJECTORIES;

VEGETATION;

ATMOSPHERIC POLLUTION; AVHRR; BIOPHYSICS; GROWING SEASON; LAND SURFACE; LONG-TERM CHANGE; NDVI; NET PRIMARY PRODUCTION; SATELLITE DATA; SPATIOTEMPORAL ANALYSIS; TIME SERIES; TRAJECTORY;

EID: 85027939181     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.10.012     Document Type: Article

References (90)

1. Barlage M., Zeng X.B. The effects of observed fractional vegetation cover on the land surface climatology of the community land model. Journal of Hydrometeorology 2004, 5:823-830.
2. Beck P.S.A., Atzberger C., Hogda K.A., Johansen B., Skidmore A.K. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sensing of Environment 2006, 100:321-334.
3. Ben-Ze'Ev E., Karnieli A., Agam N., Kaufman Y., Holben B. Assessing vegetation condition in the presence of biomass burning smoke by applying the Aerosol-free Vegetation Index (AFRI) on MODIS images. International Journal of Remote Sensing 2006, 27:3203-3221.
4. Blackburn G.A., Milton E.J. Seasonal-variations in the spectral reflectance of deciduous tree canopies. International Journal of Remote Sensing 1995, 16:709-720.
5. Bolton D.K., Friedl M.A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology 2013, 173:74-84.
6. Bradley B.A., Jacob R.W., Hermance J.F., Mustard J.F. A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment 2007, 106:137-145.
7. Bradley A.V., Millington A.C. Spatial and temporal scale issues in determining biomass burning regimes in Bolivia and Peru. International Journal of Remote Sensing 2006, 27:2221-2253.
8. Interactive geography 2002, 4. R.B. Bunnett (Ed.).
9. Busetto L., Meroni M., Colombo R. Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series. Remote Sensing of Environment 2008, 112:118-131.
10. Carlson T.N., Ripley D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 1997, 62:241-252.
11. Carrao H., Goncalves P., Caetano M. A nonlinear harmonic model for fitting satellite image time series: Analysis and prediction of land cover dynamics. IEEE Transactions on Geoscience and Remote Sensing 2010, 48:1919-1930.
12. Chen J., Jonsson P., Tamura M., Gu Z.H., Matsushita B., Eklundh L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment 2004, 91:332-344.
13. de Beurs K.M., Henebry G.M. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment 2004, 89:497-509.
14. de Beurs K.M., Henebry G.M. Land surface phenology and temperature variation in the International Geosphere-Biosphere Program high-latitude transects. Global Change Biology 2005, 11:779-790.
15. de Beurs K.M., Henebry G.M. Spatio-temporal statistical methods for modeling land surface phenology. Phenological research-Methods for environmental and climate change analysis 2010, 177-208. Spring, New York. I.L.H.A.M.R. Keatley (Ed.).
16. de Jong R., de Bruin S., de Wit A., Schaepman M.E., Dent D.L. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment 2011, 115:692-702.
17. Ek M.B., Mitchell K.E., Lin Y., Rogers E., Grunmann P., Koren V., et al. Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research-Atmospheres 2003, 108.
18. Elmore A.J., Guinn S.M., Minsley B.J., Richardson A.D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Global Change Biology 2012, 18:656-674.
19. Fensholt R., Rasmussen K., Nielsen T.T., Mbow C. Evaluation of earth observation based long term vegetation trends-Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment 2009, 113:1886-1898.
20. Friedl M.A., Muchoney D., McIver D., Gao F., Hodges J.C.F., Strahler A.H. Characterization of North American land cover from NOAA-AVHRR data using the EOS MODIS land cover classification algorithm. Geophysical Research Letters 2000, 27:977-980.
21. Friedl M.A., Sulla-Menashe D., Tan B., Schneider A., Ramankutty N., Sibley A., et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment 2010, 114:168-182.
22. Gitelson A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology 2004, 161:165-173.
23. Goward S.N., Markham B., Dye D.G., Dulaney W., Yang A.J. Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer. Remote Sensing of Environment 1991, 35:257-277.
24. Gutman G., Ignatov A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing 1998, 19:1533-1543.
25. Hargrove W.W., Spruce J.P., Gasser G.E., Hoffman F.M. Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering and Remote Sensing 2009, 75:1150-1156.
26. Harris A., Dash J. The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation. Remote Sensing of Environment 2010, 114:1856-1862.
27. Holben B.N. Characteristics of maximum value composite images from temporal AVHRR data. International Journal of Remote Sensing 1986, 7:1417-1434.
28. Houborg R., Soegaard H., Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Sensing of Environment 2007, 106:39-58.
29. 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 Sensing of Environment 2002, 83:195-213.
30. Huete A.R., Didan K., Shimabukuro Y.E., Ratana P., Saleska S.R., Hutyra L.R., et al. Amazon rainforests green-up with sunlight in dry season. Geophysical Research Letters 2006, 33.
31. Huete A., Miura T., Yoshioka H., Ratana P., Broich M. Indices of vegetation activity. Biophysical applications of satellite remote sensing 2014, New York, New York. J.M. Hanes (Ed.).
32. Ingwersen J., Steffens K., Hogy P., Warrach-Sagi K., Zhunusbayeva D., Poltoradnev M., et al. Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand. Agricultural and Forest Meteorology 2011, 151:345-355.
33. Jiang Z.Y., Huete A.R., Didan K., Miura T. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 2008, 112:3833-3845.
34. Jin M. Analysis of land skin temperature using AVHRR observations. Bulletin of the American Meteorological Society 2004, 85. (587-+).
35. Jonsson P., Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing 2002, 40:1824-1832.
36. Julien Y., Sobrino J.A. Comparison of cloud-reconstruction methods for time series of composite NDVI data. Remote Sensing of Environment 2010, 114:618-625.
37. Justice C.O., Roman M.O., Csiszar I., Vermote E.F., Wolfe R.E., Hook S.J., et al. Land and cryosphere products from Suomi NPP VIIRS: Overview and status. Journal of Geophysical Research-Atmospheres 2013, 118:9753-9765.
38. Kogan F.N. Global drought watch from space. Bulletin of the American Meteorological Society 1997, 78:621-636.
39. Kogan F., Gitelson A., Zakarin E., Spivak L., Lebed L. AVHRR-based spectral vegetation index for quantitative assessment of vegetation state and productivity: Calibration and validation. Photogrammetric Engineering and Remote Sensing 2003, 69:899-906.
40. Kovalskyy V., Henebry G.M., Roy D.P., Adusei B., Hansen M., Senay G., et al. Evaluation of a coupled event-driven phenology and evapotranspiration model for croplands in the United States northern Great Plains. Journal of Geophysical Research-Atmospheres 2013, 118:5065-5081.
41. Lloyd D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing 1990, 11:2269-2279.
42. Loveland T.R., Reed B.C., Brown J.F., Ohlen D.O., Zhu Z., Yang L., et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 2000, 21:1303-1330.
43. Maselli F. Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses. Remote Sensing of Environment 2001, 75:29-38.
44. Melaas E.K., Friedl M.A., Zhu Z. Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM plus data. Remote Sensing of Environment 2013, 132:176-185.
45. Migliavacca M., Sonnentag O., Keenan T.F., Cescatti A., O'Keefe J., Richardson A.D. On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model. Biogeosciences 2012, 9:2063-2083.
46. Miura T., Huete A.R., van Leeuwen W.J.D., Didan K. Vegetation detection through smoke-filled AVIRIS images: An assessment using MODIS band passes. Journal of Geophysical Research-Atmospheres 1998, 103:32001-32011.
47. Mondal P. Quantifying surface gradients with a 2-band Enhanced Vegetation Index (EVI2). Ecological Indicators 2011, 11:918-924.
48. Moody A., Johnson D.M. Land-surface phenologies from AVHRR using the discrete fourier transform. Remote Sensing of Environment 2001, 75:305-323.
49. Myneni R.B., Keeling C.D., Tucker C.J., Asrar G., Nemani R.R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 1997, 386:698-702.
50. Namias J. Spring and Summer 1988 drought over the contiguous United States-Causes and prediction. Journal of Climate 1991, 4:54-65.
51. Olsson L., Eklundh L., Ardo J. A recent greening of the Sahel-Trends, patterns and potential causes. Journal of Arid Environments 2005, 63:556-566.
52. Pedelty J., Vermote E.F., Devadiga S., Roy D., Schaaf C., Privette J., et al. Generating a long-term land data record from the AVHRR and MODIS instruments. IEEE International Geoscience and Remote Sensing Symposium 2007, 1021-1025. IGARSS 2007.
53. Piao S.L., Mohammat A., Fang J.Y., Cai Q., Feng J.M. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Global Environmental Change-Human and Policy Dimensions 2006, 16:340-348.
54. Pinty B., Verstraete M.M. GEMI: A non-linear index to monitor global vegetation from satellites. Vegetation 1992, 101:15-20.
55. Pinzon J.E., Tucker C.J. A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series. Remote Sensing 2014, 6:6929-6960.
56. Ratkowsky D.A. Nonlinear regression modeling-A unifiedpractical approach 1983, Marcel Dekker, New York and Basel.
57. Reed B.C., Brown J.F., Vanderzee D., Loveland T.R., Merchant J.W., Ohlen D.O. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 1994, 5:703-714.
58. Richards F.J. A flexible growth function for empirical use. Journal of Experimental Botany 1959, 10:290-300.
59. Rocha A.V., Shaver G.R. Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology 2009, 149:1560-1563.
60. Roerink G.J., Menenti M., Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing 2000, 21:1911-1917.
61. Roujean J.L., Leroy M., Deschamps P.Y., Podaire A. Evidence of surface reflectance bidirectional effects from a NOAA/AVHRR multitemporal data set. International Journal of Remote Sensing 1992, 13:685-698.
62. Sellers P.J. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing 1985, 6:1335-1372.
63. Stowe L.L., Davis P.A., McClain E.P. Scientific basis and initial evaluation of the CLAVR-1 global clear cloud classification algorithm for the advanced very high resolution radiometer. Journal of Atmospheric and Oceanic Technology 1999, 16:656-681.
64. Tan B., Morisette J.T., Wolfe R.E., Gao F., Ederer G.A., Nightingale J., et al. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2011, 4:361-371.
65. Trenberth K.E., Branstator G.W., Arkin P.A. Origins of the 1988 North American Drought. Science 1988, 242:1640-1645.
66. Tsend-Ayush J., Miura T., Didan K., Barreto-munoz A. Compatibility analysis of Spot-4 vegetation and terra modis vegetation index products for long-term data records. Proceedings of IEEE international geoscience and remote sensing symposium 2012, (Munich, Gernamy).
67. Tucker C.J., VanPraet C.L., Sharman M.J., Van Ittersum G. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel, 1980-1984. Remote Sensing of Environment 1985, 17:233-249.
68. Unsalan C., Boyer K.L. Linearized vegetation indices based on a formal statistical framework. IEEE Transactions on Geoscience and Remote Sensing 2004, 42:1575-1585.
69. Vermote E., Justice C., Csiszar I., Eidenshink J., Myneni R., Baret F., et al. A terrestrial surface climate data record for global change studies. AGU Fall 2009, (San Francisco).
70. Vermote E., Kaufman Y.J. Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views. International Journal of Remote Sensing 1995, 16:2317-2340.
71. Wan Z.M., Zhang Y.L., Zhang Q.C., Li Z.L. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment 2002, 83:163-180.
72. White M.A., de Beurs K.M., Didan K., Inouye D.W., Richardson A.D., Jensen O.P., et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology 2009, 15:2335-2359.
73. White M.A., Thornton P.E., Running S.W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles 1997, 11:217-234.
74. Willmott C.J. On the validation of modes. Physical Geography 1981, 2:184-194.
75. Wittich K.P., Hansing O. Area-averaged vegetative cover fraction estimated from satellite data. International Journal of Biometeorology 1995, 38:209-215.
76. Wu X.Q., Sullivan J.T., Heidinger A.K. Operational calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared channels. Canadian Journal of Remote Sensing 2010, 36:602-616.
77. Xiao J.F., Moody A. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment 2005, 98:237-250.
78. Xiao X.M., Zhang Q.Y., Saleska S., Hutyra L., De Camargo P., Wofsy S., et al. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sensing of Environment 2005, 94:105-122.
79. Yang S.S., Logan J., Coffey D.L. Mathematical formulas for calculating the base temperature for growing degree-days. Agricultural and Forest Meteorology 1995, 74:61-74.
80. Yin H., Udelhoven T., Fensholt R., Pflugmacher D., Hostert P. How Normalized Difference Vegetation Index (NDVI) trends from Advanced Very High Resolution Radiometer (AVHRR) and Systeme Probatoire d'Observation de la Terre VEGETATION (SPOT VGT) Time Series differ in agricultural areas: An inner Mongolian case study. Remote Sensing 2012, 4:3364-3389.
81. Zhang X.Y., 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. Journal of Geophysical Research-Biogeosciences 2006, 111.
82. Zhang X., Friedl M., Schaaf C. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing 2009, 30:2061-2074.
83. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology 2004, 10:1133-1145.
84. Zhang X.Y., Friedl M.A., Schaaf C.B., Strahler A.H., Hodges J.C.F., Gao F., et al. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 2003, 84:471-475.
85. Zhang X.Y., Kondragunta S., Schmidt C., Kogan F. Near real time monitoring of biomass burning particulate emissions (PM2.5) across contiguous United States using multiple satellite instruments. Atmospheric Environment 2008, 42:6959-6972.
86. Zhang Q.Y., Middleton E.M., Cheng Y.B., Landis D.R. Variations of foliage chlorophyll fAPAR and foliage non-chlorophyll fAPAR (fAPAR(chl), fAPAR(non-chl)) at the Harvard Forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6:2254-2264.
87. Zhang X., Tan B., Yu Y. Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010. International Journal of Biometeorology 2014, 58:547-564.
88. Zhang X.Y., Tarpley D., Sullivan J.T. Diverse responses of vegetation phenology to a warming climate. Geophysical Research Letters 2007, 34.
89. Zhang Q.Y., Xiao X.M., Braswell B., Linder E., Baret F., Moore B. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sensing of Environment 2005, 99:357-371.
90. Zhou L.M., Tucker C.J., Kaufmann R.K., Slayback D., Shabanov N.V., Myneni R.B. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research-Atmospheres 2001, 106:20069-20083.