Remote Sensing of Land Surface Phenology: A Prospectus

Chapter

Abstract

The process of observing land surface phenology (or LSP) using remote sensing satellites is fundamentally different from ground level observation of phenophase transition of specific organisms. The scale disparity between the spatial extent of the organisms and the spatial resolution of the sensor leads to an ill-defined mixture of target and background or signal and noise. Much progress has been made in the monitoring and modeling of land surface phenologies over the past decade. The chapter first provides a brief overview of land surface phenology, starting with the Landsat 1 in 1972, and then proceeds to a survey of current LSP products. The problem of indistinct phenometrics in remote sensing data is considered and the alternative phenometrics derived from the convex quadratic model are presented with an application in the North American Great Plains using MODIS data from 2001 to 2012. The chapter concludes with a view forward to outstanding challenges for LSP research in the coming decade.

References

  1. Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006) Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens Environ 104(1):88–95CrossRefGoogle Scholar
  2. Ahrends HE, Brügger R, Stöckli R, Schenk J, Michna P, Jeanneret F, Wanner H, Eugster W (2008) Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography. J Geophys Res 113, G04004CrossRefGoogle Scholar
  3. Anderson MC, Norman JM, Diak GR, Kustas WP, Mecikalski JR (1997) A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens Environ 60:195–216CrossRefGoogle Scholar
  4. Anderson MC, Kustas WP, Norman JM, Hain CR, Mecikalski JR, Schultz L, Gonzalez-Dugo MP, Cammalleri C, d’Urso G, Pimstein A, Gao F (2011) Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol Earth Syst Sci 15:223–239CrossRefGoogle Scholar
  5. Archibald S, Scholes RJ (2007) Leaf green-up in a semi-arid African savanna -separating tree and grass responses to environmental cues. J Veg Sci 18:583–594Google Scholar
  6. Badhwar GD (1984) Automatic corn-soybean classification using Landsat MSS data, I, near-harvest crop proportion estimation. Remote Sens Environ 14:15–29CrossRefGoogle Scholar
  7. Bartsch A, Kidd RA, Wagner W, Bartalis Z (2007) Temporal and spatial variability of the beginning and end of daily spring freeze/thaw cycles derived from scatterometer data. Remote Sens Environ 106(3):360–374CrossRefGoogle Scholar
  8. Bauer ME, Cipra JE, Anuta PE, Etheridge JB (1979) Identification and area estimation of agricultural crops by computer classification of Landsat MSS data. Remote Sens Environ 8:77–92CrossRefGoogle Scholar
  9. Beck PSA, Atzberger C, Høgda KA, Johansen B, Skidmore AK (2007) Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens Environ 100:321–334CrossRefGoogle Scholar
  10. Bradley BA, Olsson AD, Wang O, Dickson BG, Pelech L, Sesnie SE, Zachmann LJ (2012) Species detection vs. Habitat suitability: Are we biasing habitat suitability models with remotely sensed data? Ecol Model 244:57–64CrossRefGoogle Scholar
  11. Brown ME, de Beurs KM (2008) Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens Environ 112(5):2261–2271CrossRefGoogle Scholar
  12. Brown ME, de Beurs KM, Marshall M (2012) Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years. Remote Sensing of Environment 126:174–183CrossRefGoogle Scholar
  13. Brown JF, Wardlow BD, Tadesse T, Hayes MJ, Reed BC (2008) The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci Remote Sens 45(1):16–46CrossRefGoogle Scholar
  14. Brown ME, de Beurs KM, Vrieling A (2010) The response of African land surface phenology to large scale climate oscillations. Remote Sens Environ 114:2286–2296CrossRefGoogle Scholar
  15. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252CrossRefGoogle Scholar
  16. Choudhury BJ, Tucker CJ, Golus RE, Newcomb WW (1987) Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer’s data. Int J Remote Sens 8(3):533–538CrossRefGoogle Scholar
  17. Crema ER, Bevan A, Lake MW (2010) A probabilistic framework for assessing spatio-temporal point patterns in the archaeological record. J Archaeol Sci 37(5):1118–1130CrossRefGoogle Scholar
  18. Davidson A, Csillag F (2003) A comparison of three approaches for predicting C4 species cover of northern mixed grass prairie. Remote Sens Environ 86:70–82CrossRefGoogle Scholar
  19. de Beurs KM, Henebry GM (2004) Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens Environ 89(4):497–509. doi:10.1016/j.rse.2003.11.006 CrossRefGoogle Scholar
  20. de Beurs KM, Henebry GM (2005a) A statistical framework for the analysis of long image time series. Int J Remote Sens 26(8):1551–1573CrossRefGoogle Scholar
  21. de Beurs KM, Henebry GM (2005b) Land surface phenology and temperature variation in the IGBP high-latitude transects. Glob Chang Biol 11(5):779–790CrossRefGoogle Scholar
  22. de Beurs KM, Henebry GM (2008a) Northern annular mode effects on the land surface phenologies of northern Eurasia. J Clim 21:4257–4279CrossRefGoogle Scholar
  23. de Beurs KM, Henebry GM (2008b) War, drought, and phenology: changes in the land surface phenology of Afghanistan since 1982. J Land Use Sci 3(2–3):95–111CrossRefGoogle Scholar
  24. de Beurs KM, Henebry GM (2010a) A land surface phenology assessment of the northern polar regions using MODIS reflectance time series. Can J Remote Sens 36(suppl 1):S87–S110CrossRefGoogle Scholar
  25. de Beurs KM, Henebry GM (2010b) Spatio-temporal statistical methods for modeling land surface phenology. In: Hudson IL, Keatley MR (eds) Phenological research: methods for environmental and climate change analysis. Springer, DordrechtGoogle Scholar
  26. de Beurs KM, Wright CK, Henebry GM (2009) Dual scale trend analysis distinguishes climatic from anthropogenic effects on the vegetated land surface. Environ Res Lett 4:045012CrossRefGoogle Scholar
  27. Delbart N, Kergoat L, Le Toan T, L’Hermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38CrossRefGoogle Scholar
  28. 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 100:52–62CrossRefGoogle Scholar
  29. Dethier B, Ashley MD, Blair B, Hopp RJ (1973) Phenology satellite experiment. In: Freden SC, EP Mercanti, MA Becker (eds) Symposium on significant results obtained from the Earth Resources Technology Satellite—1, vol I. Technical presentations, section A. NASA: Washington, DC, GPO NAS 1.21:327Google Scholar
  30. Dragoni D, Rahman AF (2012) Trends in fall phenology across the deciduous forests of the eastern USA. Agric For Meteorol 157:96–105CrossRefGoogle Scholar
  31. Ehrlich D, Estes JE, Singh A (1994) Applications of NOAA-AVHRR 1 km data for environmental monitoring. Int J Remote Sens 15(1):145–161CrossRefGoogle Scholar
  32. Fisher JI, Mustard JF (2007) Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens Environ 109(3):261–273CrossRefGoogle Scholar
  33. Fisher JI, Mustard JF, Vadeboncoeur MA (2006) Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens Environ 100(2):265–279CrossRefGoogle Scholar
  34. Friedl M, Henebry G, Reed B, Huete A, White M, Morisette J, Nemani R, Zhang X, Myneni R (2006) Land surface phenology. A Community White Paper requested by NASA. April 10. http://cce.nasa.gov/mtg2008_ab_presentations/Phenology_Friedl_whitepaper.pdf
  35. Frolking S, McDonald KC, Kimball JS, Way JB, Zimmermann R, Running SW (1999) Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons. J Geophys Res 104(D22):27895–27907CrossRefGoogle Scholar
  36. Frolking S, Milliman T, McDonald K, Kimball J, Zhao M, Fahnestock M (2006) Evaluation of the SeaWinds scatterometer for regional monitoring of vegetation phenology. J Geophys Res 11:D17302CrossRefGoogle Scholar
  37. Ganguly S, Friedl MA, Tan B, Zhang X, Verma M (2010) Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens Environ 114(8):1805–1816CrossRefGoogle Scholar
  38. Gao F, Morisette JT, Wolfe RE, Ederer G, Pedelty J, Masuoka E, Myneni R, Tan B, Nightingale J (2008) An algorithm to produce temporally and spatially continuous MODIS-LAI time series. IEEE Geosci Remote Sens Lett 5(1):60–64CrossRefGoogle Scholar
  39. Garrigues S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R, Plummer S, Shabanov NV, Myneni RB, Knyazikhin Y, Yang W (2008) Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J Geophys Res 113:G02028CrossRefGoogle Scholar
  40. Gazal R, White MA, Gillies R, Rodemaker E, Sparrow E, Gordon L (2008) GLOBE students, teachers, and scientists demonstrate variable differences between urban and rural leaf phenology. Glob Chang Biol 14(7):1568–1580CrossRefGoogle Scholar
  41. Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161:165–173PubMedCrossRefGoogle Scholar
  42. Gonsamo A, Chen JM, Wu C, Dragoni D (2012a) Predicting deciduous forest carbon uptake phenology by upscaling FLUXNET measurements using remote sensing data. Agric For Meteorol 165:127–135CrossRefGoogle Scholar
  43. Gonsamo A, Chen JM, Price DT, Kurz WA, Wu C (2012b) Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J Geophys Res 117, G03032CrossRefGoogle Scholar
  44. Goodin DG, Henebry GM (1997) Monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model. Remote Sens Environ 61:270–278CrossRefGoogle Scholar
  45. Goodin DG, Henebry GM (1998) Seasonality of finely-resolved spatial structure of NDVI and its component reflectances in tallgrass prairie. Int J Remote Sens 19:3213–3220CrossRefGoogle Scholar
  46. Goodin DG, Gao J, Henebry GM (2004) The effect of solar zenith angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie. IEEE Trans Geosci Remote Sens 42(1):154–165CrossRefGoogle Scholar
  47. Goward SN, Tucker CJ, Dye DG (1985) North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Plant Ecol 64(1):3–14CrossRefGoogle Scholar
  48. Gray LK, Gylander T, Mbogga MS, Chen P-Y, Hamann A (2011) Assisted migration to address climate change: recommendations for aspen reforestation in Western Canada. Ecol Appl 21(5):1591–1603PubMedCrossRefGoogle Scholar
  49. Gu Y, Wylie BK, Bliss NB (2013) Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO database over the Greater Platte River Basin, USA. Ecol Indic 24:31–36CrossRefGoogle Scholar
  50. Hargrove WW, Spruce JP, Gasser GE, Hoffman FM (2009) Toward a national early warning system for forest disturbances using remotely sensed phenology. Photogramm Eng Remote Sens 75(10):1150–1156Google Scholar
  51. Heilman JL, Kanemasu ET, Bagley JO, Rasmussen VP (1977) Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data. Remote Sens Environ 6:315–326CrossRefGoogle Scholar
  52. Henebry GM (2003) Grasslands of the North American great plains. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer, Dordrecht/BostonGoogle Scholar
  53. Henebry GM, Kux HJH (1995) Lacunarity as a texture measure for SAR imagery. Int J Remote Sens 16:565–571CrossRefGoogle Scholar
  54. Henebry GM, Kux HJH (1997) Spatio-temporal analysis of SAR image series from the Brazilian Pantanal. In: Proceedings of the 3rd ERS symposium on space at the service of our environment, SP-414, ESA, Noordwijk. http://earth.esa.int/workshops/ers97/papers/henebry1/index.html
  55. Henebry GM, Su H (1993) Using landscape trajectories to assess the effects of radiometric rectification. Int J Remote Sens 14:2417–2423CrossRefGoogle Scholar
  56. Henebry GM, Su H (1995) Observing spatial structure in the Flint Hills using AVHRR maximum biweekly NDVI composites. In: Proceedings of the 14th North American Prairie Conference. Kansas State University Press, Manhattan. http://images.library.wisc.edu/EcoNatRes/EFacs/NAPC/NAPC14/reference/econatres.napc14.ghenebry.pdf
  57. Hlavka CA, Haralick RM, Carlyle SM, Yokoyama R (1980) The discrimination of winter wheat using a growth-state signature. Remote Sens Environ 9:277–294CrossRefGoogle Scholar
  58. Hodges T (1990) Predicting crop phenology. CRC Press, Boca RatonGoogle Scholar
  59. Howard DM, Wylie BK, Tieszen LL (2012) Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA. Int J Remote Sens 33(19):6094–6108CrossRefGoogle Scholar
  60. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213CrossRefGoogle Scholar
  61. Isaacson BN, Serbin SP, Townsend PA (2012) Detections of relative differences in phenology of forest species using Landsat and MODIS. Landsc Ecol 27:529–543CrossRefGoogle Scholar
  62. Jakubauskas ME, Legates DR, Kastens JH (2002) Crop identification using harmonic analysis of time-series AVHRR NDVI data. Comput Electron Agric 37(1–3):127–139CrossRefGoogle Scholar
  63. Jenkerson CB, Schmidt GL (2008) eMODIS product access for large scale monitoring. In: Proceedings of the 17th Pecora symposium, paper 19. http://www.asprs.org/a/publications/proceedings/pecora17/0019.pdf
  64. Jenkerson C, Maiersperger T, Schmidt G (2010) eMODIS: A user-friendly data source. USGS open-file report 2010–1055. http://pubs.usgs.gov/of/2010/1055/pdf/OF2010-1055.pdf
  65. Johnston CA (2013) Wetland losses due to row crop expansion in the Dakota Prairie Pothole region. Wetlands 33(1):175–182CrossRefGoogle Scholar
  66. Jolly WM, Nemani R, Running SW (2005) A generalized, bioclimatic index to predict foliar phenology in response to climate. Glob Chang Biol 11:619–632CrossRefGoogle Scholar
  67. Jones MO, Jones LA, Kimball JS, McDonald KS (2011) Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens Environ 115:1102–1114CrossRefGoogle Scholar
  68. Jones MO, Kimball JS, Jones LA, McDonald KC (2012) Satellite passive microwave detection of North America start of season. Remote Sens Environ 123:324–333CrossRefGoogle Scholar
  69. Jonsson P, Eklundh L (2002) Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans Geosci Remote Sens 40:1824–1832CrossRefGoogle Scholar
  70. Jonsson P, Eklundh L (2004) TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci 30(8):833–845CrossRefGoogle Scholar
  71. Justice CO, Townshend JRG, Holben BN, Tucker CJ (1985) Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens 6(8):1271–1318CrossRefGoogle Scholar
  72. Justice CO, Holben BN, Gwynne MD (1986) Monitoring east African vegetation using AVHRR data. Int J Remote Sens 7(11):1453–1474CrossRefGoogle Scholar
  73. Justice CO, Townshend JRG, Choudhury BJ (1989) Comparison of AVHRR and SMMR data for monitoring vegetation phenology on a continental scale. Int J Remote Sens 10(10):1607–1632CrossRefGoogle Scholar
  74. Justice CO, Vermote E, Townshend JRG, DeFries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, ZhengMing W, Huete AR, van Leeuwen W, Wolfe RE, Giglio L, Muller J, Lewis P, Barnsley MJ (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36(4):1228–1249CrossRefGoogle Scholar
  75. Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE, Saleous N, Roy DP, Morisette JT (2002) An overview of MODIS land data processing and product status. Remote Sens Environ 83(1–2):3–15CrossRefGoogle Scholar
  76. Kanemasu ET (1974) Seasonal canopy reflectance patterns of wheat, sorghum, and soybean. Remote Sens Environ 3:43–47CrossRefGoogle Scholar
  77. Kanemasu ET, Niblett CL, Manges H, Lenhert D, Newman MA (1974) Wheat: its growth and disease severity as deduced from ERTS-1. Remote Sens Environ 3:255–260CrossRefGoogle Scholar
  78. Kathuroju N, White MA, Symanzik J, Schwartz MD, Powell JA, Nemani RR (2007) On the use of the Advanced Very High Resolution Radiometer for development of prognostic land surface phenology models. Ecol Model 201(1):144–156CrossRefGoogle Scholar
  79. Kim Y, Kimball JS, McDonald KC, Glassy J (2011) Developing a global data record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEE Trans Geosci Remote Sens 49(3):949–960CrossRefGoogle Scholar
  80. Knapp WW, Dethier BE (1976) Satellite monitoring of phenological events. Int J Biometeorol 20(3):230–239CrossRefGoogle Scholar
  81. Kovalskyy V, Henebry GM (2012a) A new concept for simulation of vegetated land surface dynamics: the event driven phenology model part I. Bio Geosci 9:141–159Google Scholar
  82. Kovalskyy V, Henebry GM (2012b) Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology: the event driven phenology model part II. Bio Geosci 9:161–177. doi:10.5194/bg-9-161-2012 Google Scholar
  83. Kovalskyy V, Roy DP, Zhang X, Ju J (2012) The suitability of multi-temporal web-enabled Landsat data NDVI for phenological monitoring—a comparison with flux tower and MODIS NDVI. Remote Sens Lett 3(4):325–334CrossRefGoogle Scholar
  84. Leopold A, Jones SE (1947) A phenological record for Sauk and Dane counties, Wisconsin, 1935–1945. Ecol Monogr 17(1):81–122CrossRefGoogle Scholar
  85. Li H, Wang X, Hamann A (2010) Genetic adaptation of aspen (Populus tremuloides) populations to spring risk environments: a novel remote sensing approach. Can J For Res 40(11):2082–2090. doi:10.1139/X10-153 CrossRefGoogle Scholar
  86. Liang L, Schwartz MD (2009) Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landsc Ecol 24:465–472CrossRefGoogle Scholar
  87. Liang L, Schwartz MD, Fei S (2011) Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens Environ 115(1):143–157CrossRefGoogle Scholar
  88. Lloyd D (1990) A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int J Remote Sens 11(12):2269–2279CrossRefGoogle Scholar
  89. Loveland TR, Merchant JW, Brown JF, Ohlen DO, Reed BC, Olson P, Hutchinson J (1995) Seasonal land-cover regions of the United States. Ann Assoc Am Geogr 85(2):339–355CrossRefGoogle Scholar
  90. Loveland TR, Cochrane MA, Henebry GM (2008) Landsat still contributing to environmental research. Trends Ecol Evolut 23(4):182–183CrossRefGoogle Scholar
  91. McDonald KC, Kimball JS, Njoku E, Zimmermann R, Zhao M (2004) Variability in springtime thaw in the terrestrial high latitudes: monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interact 8:1–23CrossRefGoogle Scholar
  92. McManus KM, Morton DC, Masek JG, Wang D, Sexton JO, Nagol JR, Ropars P, Boudreau S (2010) Satellite-based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010. Glob Chang Biol 18(7):2313–2323CrossRefGoogle Scholar
  93. Mitchell R, Fritz J, Moore K, Moser L, Vogel K, Redfearn D, Wester D (2001) Predicting forage quality in switchgrass and big bluestem. Agron J 93:118–124CrossRefGoogle Scholar
  94. Morisette JT, Baret F, Privette JL, Myneni RB, Nickeson JE, Garrigues S, Shabanov NV, Weiss M, Fernandes RA, Leblanc DG, Kalacska M, Sanchez-Azofeifa GA, Chubey M, Rivard B, Stenberg P, Rautiainen M, Voipio P, Manninen T, Pilant AN, Lewis TE, Iiames JS, Colombo R, Meroni M, Busetto L, Cohen WB, Turner DP, Warner ED, Petersen GW, Seufert G, Cook R (2006) Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE T Geosci Remote 44(7):1804–1817CrossRefGoogle Scholar
  95. Morisette JT, Richardson AD, Knapp AK, Fisher JI, Graham E, Abatzoglou J, Wilson BE, Breshears DD, Henebry GM, Hanes JM, Liang L (2008) Unlocking the rhythm of the seasons in the face of global change: challenges and opportunities for phenological research in the 21st century. Front Ecol Environ 5(7):253–260. doi:10.1890/070217 Google Scholar
  96. Morisette JT, Nightingale J, Nickeson J (2010) Assessing the accuracy of landscape-scale phenology products: an international workshop on the validation of satellite-based phenology products; Dublin, Ireland, 18 June 2010. Eos 91(44):407CrossRefGoogle Scholar
  97. Moulin S, Kergoat L, Viovy N, Dedieu G (1997) Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J Clim 10:1154–1170CrossRefGoogle Scholar
  98. Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsch A, Friedl M, Morisette JT, Votava P, Nemani RR, Running SW (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83(1–2):214–223CrossRefGoogle Scholar
  99. Neale CMU, McFarland MJ, Chang K (1990) Land-surface-type classification using microwave brightness temperatures from the Special Sensor Microwave/Imager. IEEE Trans Geosci Remote Sens 28(5):829–838CrossRefGoogle Scholar
  100. Noormets A (2009) Phenology of ecosystems processes. Springer, New YorkCrossRefGoogle Scholar
  101. Nuttonson MV (1955) Wheat-climate relationships and the use of phenology in ascertaining the thermal and photo-thermal requirements of wheat. American Institute of Crop Ecology, Washington, DCGoogle Scholar
  102. Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D (2012) Winter wheat area estimation from MODIS-EVI time series data using the crop proportion phenology index. Remote Sens Environ 19:232–242CrossRefGoogle Scholar
  103. Pedelty J, Devadiga S, Masuoka E, Brown M, Pinzon J, Tucker C, Vermote E, Prince S, Nagol J, Justice C, Roy D, Ju J, Schaaf C, Liu J, Privette J, Pinheiro A (2007) Generating a long-term land data record from the AVHRR and MODIS instruments. In: Proceeding of the IEEE international geoscience and remote sensing symposium 2007 (IGARSS 2007), pp 1021–1025. Available at: http://ltdr.nascom.nasa.gov/ltdr/docs/LTDR_IGARSS2007_paper.pdf
  104. Peñuelas J, Filella I, Zhang X, Llorens L, Ogaya R, Lloret F, Comas P, Estiarte M, Terradas J (2004) Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytol 161(3):837–846CrossRefGoogle Scholar
  105. Pervez MS, Brown JF (2010) Mapping irrigated lands at 250-m scale by merging MODIS data and national agricultural statistics. Remote Sens 2(10):2388–2412CrossRefGoogle Scholar
  106. Pitman AJ, Noblet-Ducoudré N, Cruz FT, Davin EL, Bonan GB, Brovkin V, Claussen M, Delire C, Ganzeveld L, Gayler V, van den Hurk BJJM, Lawrence PJ, van der Molen MK, Müller C, Reick CH, Seneviratne SI, Strengers BJ, Voldoire A (2009) Uncertainties in climate responses to past land cover change: first results from the LUCID intercomparison study. Geophys Res Lett 36, L14814CrossRefGoogle Scholar
  107. Prigent C, Rossow W, Matthews E (1997) Microwave land surface emissivities estimated from SSM/I observations. J Geophys Res 102(D18):21867–21890CrossRefGoogle Scholar
  108. Ramsay JO, Silverman BW (2005) Functional data analysis, 2e. Springer, New YorkGoogle Scholar
  109. Ratcliffe JH (2000) Aoristic analysis: the spatial interpretation of unspecific temporal events. Int J Geograp Inf Sci 14(7):669–679CrossRefGoogle Scholar
  110. Ratcliffe JH (2002) Aoristic signatures and the spatio-temporal analysis of high volume crime patterns. J Quant Criminol 18(1):23–43CrossRefGoogle Scholar
  111. Ratcliffe JH, McCullagh MJ (1998) Aoristic crime analysis. Int J Geog Inf Sci 12(7):751–764CrossRefGoogle Scholar
  112. Rea J, Ashley M (1976) Phenological evaluations using landsat-1 sensors. Int J Biometeorol 20(3):240–248CrossRefGoogle Scholar
  113. Reed BC (2006) Trend analysis of time-series phenology of North America derived from satellite data. GISci Remote Sens 43(1):24–38CrossRefGoogle Scholar
  114. Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Olhen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5(5):703–714CrossRefGoogle Scholar
  115. Reed BC, White MA, Brown JF (2003) Remote sensing phenology. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer, Dordrecht/BostonGoogle Scholar
  116. Reed BC, Schwartz MD, Xiao X (2009) Remote sensing phenology. In: Noormets A (ed) Phenology of ecosystem processes. Springer, New YorkGoogle Scholar
  117. Richardson AD, Bailey AS, Denny EG, Martin CW, O’Keefe J (2006) Phenology of a northern hardwood forest canopy. Glob Chang Biol 12(7):1174–1188CrossRefGoogle Scholar
  118. Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith ML (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152(2):323–334PubMedCrossRefGoogle Scholar
  119. Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol App 19:1417–1428CrossRefGoogle Scholar
  120. Richardson AD, Anderson RC, Arain MA, Barr AG, Bohrer G, Chen G, Chen JM, Ciais P, Davis KJ, Desai AR, Dietze MC, Dragoni D, Garrity SR, Gough CM, Grant R, Hollinger DY, Margolis HA, McCaughey H, Migliavacca M, Monson RK, Munger JW, Poulte B, Raczka BM, Ricciuto DM, Sahoo AK, Schaefer K, Tian H, Vargas R, Verbeeck H, Xiao J, Xue J (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob Chang Biol 18:566–584CrossRefGoogle Scholar
  121. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173CrossRefGoogle Scholar
  122. Roy DP, Ju J, Kline K, Scaramuzza PL, Kovalskyy V, Hansen M, Loveland TR, Vermote E, Zhang C (2010) Web-enabled Landsat Data (WELD): Landsat ETM + composited mosaics of the conterminous United States. Remote Sens Environ 114(1):35–49CrossRefGoogle Scholar
  123. 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):366–374CrossRefGoogle Scholar
  124. Sakamoto T, Wardlow BD, Gitelson AA, Verma SB, Suyker AE, Arkebauer TJ (2010) A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens Environ 114(10):2146–2159CrossRefGoogle Scholar
  125. Schwartz MD (2003) Phenology: an integrative environmental science. Kluwer, Dordrecht/BostonCrossRefGoogle Scholar
  126. Schwartz MD, Reed BC (1999) Surface phenology and satellite sensor-derived onset of greenness: an initial comparison. Int J Remote Sens 20(7):3451–3457CrossRefGoogle Scholar
  127. Schwartz MD, Reed BC, White MA (2002) Assessing satellite-derived start of season measures in the conterminous USA. Int J Climatol 22(14):1793–1805CrossRefGoogle Scholar
  128. Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the northern hemisphere. Glob Chang Biol 12(2):343–351CrossRefGoogle Scholar
  129. Smart AJ, Schacht WH, Moser LE (2001) Predicting leaf/stem ratio and nutritive value in grazed and nongrazed big bluestem. Agron J 93:1243–1249CrossRefGoogle Scholar
  130. Soudani K, le Maire G, Dufrêne E, François C, Delpierre N, Ulrich E, Cecchini S (2008) Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remote Sens Environ 112(5):2643–2655CrossRefGoogle Scholar
  131. Spruce JP, Sader S, Ryan RE, Smoot J, Kuper P, Ross K, Prados D, Russell J, Gasser G, McKellip R, Hargrove WW (2011) Assessment of MODIS NDVI time series data products for detecting forest defoliation from gypsy moth outbreaks. Remote Sens Environ 115:427–437CrossRefGoogle Scholar
  132. Stöckli R, Rutishauser T, Dragoni D, O’Keefe J, Thornton PE, Jolly M, Lu L, Denning AS (2008) Remote sensing data assimilation for a prognostic phenology model. J Geophys Res 113, G04021CrossRefGoogle Scholar
  133. Stöckli R, Rutishauser T, Baker I, Liniger MA, Denning AS (2011) A global reanalysis of vegetation phenology. J Geophys Res 116, G03020CrossRefGoogle Scholar
  134. Stohlgren TJ, Ma P, Kumar S, Rocca M, Morisette JT, Jarnevich CS, Benson N (2010) Ensemble habitat mapping of invasive plant species. Risk Anal 30(2):224–235PubMedCrossRefGoogle Scholar
  135. Swets DL, Reed BC, Rowland JR, Marko SE (1999) A weighted least-squares approach to temporal smoothing of NDVI. In: Proceedings of the 1999 ASPRS annual conference. http://phenology.cr.usgs.gov/pubs/ASPRS%20Swets%20et%20al%20Smoothing.pdf
  136. Tan B, Morisette J, Wolfe R, Gao F, Nightingale JM, Pedelty J, Ederer G (2011) User guide for MOD09PHN and MOD15PHN. Version 3.0. http://accweb.nascom.nasa.gov/project/docs/User_guide_C5_PHN.pdf. Accessed 3 Feb 2011
  137. Townshend JRG, Justice CO, Choudhury BJ, Tucker CJ, Kalb VT, Goff TE (1989) A comparison of AVHRR and SMMR data for continental land cover characterization. Int J Remote Sens 10(10):1633–1642CrossRefGoogle Scholar
  138. Tuanmu M-N, Viña A, Bearer S, Xu W, Ouyang Z, Zhang H, Liu J (2010) Mapping understory vegetation using phenological characteristics derived from remotely sensed data. Remote Sens Environ 114:1833–1844CrossRefGoogle Scholar
  139. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  140. Tucker CJ, Elgin JH Jr, McMurtrey JE III, Fan CJ (1979) Monitoring corn and soybean crop development with hand-held radiometer spectral data. Remote Sens Environ 8:237–248CrossRefGoogle Scholar
  141. Tucker CJ, Townshend JRG, Goff TE (1985) African land-cover classification using satellite data. Science 227:369–375PubMedCrossRefGoogle Scholar
  142. Tucker CJ, Slayback DA, Pinzon JE, Los SO, Myneni RB, Taylor MG (2001) Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol 45:184–190PubMedCrossRefGoogle Scholar
  143. Tucker CJ, Pinzon JE, Brown ME, Slayback MA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498CrossRefGoogle Scholar
  144. US Census Bureau (2012) Table 858. Crops—supply and use: 2000 to 2010. Statistical abstract of the United States. http://www.census.gov/compendia/statab/2012/tables/12s0858.pdf
  145. Vina A, Gitelson AA (2011) Sensitivity to foliar Anthocyanin content of vegetation indices using green reflectance. IEEE Geosci Remote Sens Lett 8(3):464–468CrossRefGoogle Scholar
  146. Viña A, Henebry GM (2005) Spatio-temporal change analysis to identify anomalous variation in the vegetated land surface: ENSO effects in tropical South America. Geophys Res Lett 32, L21402. doi:10.1029/2005GL023407 CrossRefGoogle Scholar
  147. Viña A, Henebry GM, Gitelson AA (2004) Satellite monitoring of vegetation dynamics: sensitivity enhancement by the Wide Dynamic Range Vegetation Index. Geophys Res Lett 31, L04503. doi:10.1029/2003GL019034 CrossRefGoogle Scholar
  148. Walker J, de Beurs KM, Wynne RH, Gao F (2012) An evaluation of data fusion products for the analysis of dryland forest phenology. Remote Sens Environ 117:381–393CrossRefGoogle Scholar
  149. Wang JR (1985) Effect of vegetation on soil moisture sensing observed from orbiting microwave radiometers. Remote Sens Environ 17:141–151CrossRefGoogle Scholar
  150. Wardlow BD, Egbert SL (2008) Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains. Remote Sens Environ 112(3):1096–1116CrossRefGoogle Scholar
  151. Weiss M, Baret F, Garrigues S, Lacaze R (2007) LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products. Remote Sens Environ 110(3):317–331CrossRefGoogle Scholar
  152. White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem Cycle 11(2):217–234CrossRefGoogle Scholar
  153. White MA, Nemani RR, Thornton PE, Running SW (2002) Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems 5:260–277CrossRefGoogle Scholar
  154. White MA, Hoffman F, Hargrove WW, Nemani RR (2005) A global framework for monitoring phenological responses to climate change. Geophys Res Lett 32, L04705CrossRefGoogle Scholar
  155. White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’Keefe J, Zhang G, Nemani RR, van Leeuwen WJD, Brown JF, de Wit A, Schaepman M, Lin X, Dettinger M, Bailey AS, Kimball J, Schwartz MD, Baldocchi DD, Lee JT, Lauenroth WK (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006. Glob Chang Biol 15(10):2335–2359CrossRefGoogle Scholar
  156. Worley-Firley S (2012) USFS eastern threat center develops forest technology and tools. Natl Woodl 2012(Fall):12–15Google Scholar
  157. Wright CK, Wimberly MC (2013) Recent land cover change in the western corn belt threatens grasslands and wetlands. PNAS (in review following revision) Published online before print February 19, 2013, doi:10.1073/pnas.1215404110
  158. Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10CrossRefGoogle Scholar
  159. Yates H, Strong A, McGinnis D Jr, Tarpley D (1986) Terrestrial observations from NOAA operational satellites. Science 231:463–470PubMedCrossRefGoogle Scholar
  160. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475CrossRefGoogle Scholar
  161. Zhang X, Friedl MA, Schaaf CB, Strahler AH (2004) Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Glob Chang Biol 10(7):1133–1145CrossRefGoogle Scholar
  162. Zhang X, Friedl MA, Schaaf CB, Strahler AH, Liu Z (2005) Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research: Atmospheres 110(D12, 27) doi:10.1029/2004JD005263
  163. Zhang X, Friedl MA, Schaaf CB (2006) Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J Geophys Res 111, G04017CrossRefGoogle Scholar
  164. Zhang X, Friedl MA, Schaaf CB (2009) Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. Int J Remote Sens 30(8):2061–2074CrossRefGoogle Scholar
  165. Zhang X, Goldberg MD, Yunyue Y (2012) Prototype for monitoring and forecasting fall foliage coloration in real time from satellite data. Agric For Meteorol 158–159:21–29CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2013

Authors and Affiliations

  1. 1.Geographic Information Science Center of ExcellenceSouth Dakota State UniversityBrookingsUSA
  2. 2.Department of Geography and Environmental SustainabilityThe University of OklahomaNormanUSA

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