Abstract
Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the land surface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called land surface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
References
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 104:88–95. doi:10.1016/j.rse.2006.05.003
Anderson BT, Strahler A (2008) Visualizing weather and climate. Wiley, New York
Baldocchi DD, Black TA, Curtis PS, Falge E, Fuentes JD, Granier A, Gu L, Knohl A, Pilegaard K, Schmid HP, Valentini R, Wilson K, Wofsy S, Xu L, Yamamoto S (2005) Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data. Int J Biometeorol 49:377–387. doi:10.1007/s00484-005-0256-4
Beck PSA, Atzberger C, Høgda KA, Johansen B, Skidmore AK (2006) Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens Environ 100:321–334. doi:10.1016/j.rse.2005.10.021
Bradley BA, Jacob RW, Hermance JF, Mustard JF (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens Environ 106:137–145. doi:10.1016/j.rse.2006.08.002
Burrows S, Gower S, Clayton M, Mackay D, Ahl D, Norman JM & Diak G (2002) Application of geostatistics to characterize leaf area index (LAI) from flux tower to landscape scales using a cyclic sampling design. Ecosystems, 5:667–679
Cao C, Xiong X, Wu A, Wu X (2008) Assessing the consistency of AVHRR and MODIS L1B reflectance for generating fundamental climate data records. J Geophys Res Atmos 113:D09114. doi:10.1029/2007JD009363
Castro KL, Sanchez-Azofeifa GA (2008) Changes in spectral properties, chlorophyll content and internal mesophyll structure of senescing Populus balsamifera and Populus tremuloides leaves. Sensors 8:51–69
Clark RN, Swayze GA, Wise R, Livo KE, Hoefen TM, Kokaly RF, Sutley SJ (2007) USGS digital spectral library splib06a. U.S. Geological Survey, Data series 231
de Beurs KM, Henebry GM (2004) Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sens Environ 89:497–509. doi:10.1016/j.rse.2003.11.006
DeFries R, Hansen M, Townshend J (1995) Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sens Environ 54:209–222
Delbart N, Kergoat L, Le Toan T, Lhermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97:26–38. doi:10.1016/j.rse.2005.03.011
Eidenshink JC (1992) The 1990 conterminous United States AVHRR data set. Photogram Eng Rem S 58:809–813
Elmore AJ, Guinn SM, Minsley BJ, Richardson AD (2012) Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob Change Biol 18:656–674. doi:10.1111/j.1365-2486.2011.02521.x
Evangelista PH, Stohlgren TJ, Morisette JT, Kumar S (2009) Mapping invasive tamarisk (Tamarix): a comparison of single-scene and time-series analyses of remotely sensed data. Remote Sens 1:519–533. doi:10.3390/rs1030519
Filella I, Peñuelas J (1994) The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int J Remote Sens 15:1459–1470
Fischer A (1994) A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sens Environ 48:220–230
Fisher JI, Mustard JF (2007) Cross-scalar satellite phenology from ground, landsat, and MODIS data. Remote Sens Environ 109:261–273. doi:10.1016/j.rse.2007.01.004
Fisher JI, Mustard JF, Vadeboncoeur MA (2006) Green leaf phenology at landsat resolution: scaling from the field to the satellite. Remote Sens Environ 100:265–279. doi:10.1016/j.rse.2005.10.022
Fitzjarrald D, Acevedo OC, Moore KE (2001) Climatic consequences of leaf presence in the Eastern United States. J Clim 14:598–614. doi:10.1175/1520-0442(2001)014<0598:CCOLPI>2.0.CO;2
Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Peñuelas J, Valentini R (1995) Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol Appl 5:28–41
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 114:1805–1816. doi:10.1016/j.rse.2010.04.005
Gao B (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266
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 S 5:60–64. doi:10.1109/LGRS.2007.907971
Gonsamo A, Chen JM, Price DT, Kurz WA, Wu C (2012) Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J Geophys Res 117:G03032. doi:10.1029/2012JG002070
Goward SN, Tucker CJ & Dye DG (1985) North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio (The Hague), 64:3–14
Graham EA, Riordan EC, Yuen EM, Estrin D, Rundel PW (2010) Public Internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Glob Change Biol 16:3014–3023. doi:10.1111/j.1365-2486.2010.02164.x
Gray TI & McCrary DG (1981) The environmental vegetative index: the tool potentially useful for arid land management. Proceedings of the Fifth Conference on Biometeorology. Anaheim, California. p. 205
Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. doi:10.1111/j.1461-0248.2005.00792.x
Hanes JM (2012) Spring leaf phenology and the diurnal temperature range in a temperate maple forest. Int J Biometeorol. doi:10.1007/s00484-012-0603-1
Hanes JM, Schwartz MD (2011) Modeling land surface phenology in a mixed temperate forest using MODIS measurements of leaf area index and land surface temperature. Theor Appl Climatol 105:37–50. doi:10.1007/s00704-010-0374-8
Hanes JM, Richardson AD, Klostermann S (2013) Mesic temperate deciduous forest phenology. In: Schwartz MD (ed) Phenology: an integrative environmental science, 2nd edn. Springer, New York
Hayden BP (1998) Ecosystem feedbacks on climate at the landscape scale. Philos Trans R Soc Lond B 353:5–18. doi:10.1098/rstb 1998.0186
Henebry GM & Su H (1995) Observing spatial structure in the Flint Hills using AVHRR maximum biweekly NDVI composites. Proceedings of 14th North American Prairie Conference. Kansas State University Press, Manhattan, KS. pp. 143-151
Henebry GM & de Beurs KM (2013) Remote Sensing of Land Surface Phenology: A Prospectus. In: Schwartz MD (ed) Phenology: an integrative environmental science, 2nd edn. Springer, New York
Henricksen BL, Durkin JW (1986) Growing period and drought early warning in Africa using satellite data. Int J Remote Sens 7:1583–1608. doi:10.1080/01431168608948955
Herfindal I, Solberg EJ, Sæther B-E, Høgda KA, Andersen R (2006) Environmental phenology and geographical gradients in moose body mass. Oecologia 150:213–224. doi:10.1007/s00442-006-0519-8
Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7:1417–1434
Huemmrich KF, Black TA, Jarvis PG, McCaughey JH, Hall FG (1999) High temporal resolution NDVI phenology from micrometeorological radiation sensors. J Geophys Res Atmos 104:27935–27944. doi:10.1029/1999JD900164
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309. doi:10.1016/0034-4257(88)90106-X
Huete AR, Liu HQ (1994) An error and sensitivity analysis of the atmospheric-and soil-correcting variants of the NDVI for the MODIS-EOS. IEEE T Geosci Remote 32:897–905. doi:10.1109/36.298018
Huete A, Justice C, van Leeuwen W (1999) MODIS vegetation index (MOD 13) algorithm theoretical basis document. Version 3. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf. Accessed 8 Aug 2012
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 83:195–213. doi:10.1016/S0034-4257(02)00096-2
Hufkens K, Friedl M, Sonnentag O, Braswell BH, Milliman T, Richardson AD (2012) Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens Environ 117:307–321. doi:10.1016/j.rse.2011.10.006
Ivits E, Cherlet M, Tóth G, Sommer S, Mehl W, Vogt J, Micale F (2012) Combining satellite derived phenology with climate data for climate change impact assessment. Global Planet Change 88–89:85–97. doi:10.1016/j.gloplacha.2012.03.010
James ME & Kalluri SNV (1994) The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring. Remote Sensing of Environment, 15(17):3347–3363. doi:10.1080/01431169408954335
Jönsson P, Eklundh L (2004) TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci 30:833–845. doi:10.1016/j.cageo.2004.05.006
Ju JC, Roy DP, Shuai YM, Schaaf C (2010) Development of an approach for generation of temporally complete daily nadir MODIS reflectance time series. Remote Sens of Environ 114:1–20. doi:10.1016/j.rse.2009.05.022
Justice CO, Holben BN, Gwynne MD (1986) Monitoring East African vegetation using AVHRR data. Int J Remote Sens 7:1453–1474
Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G & Strahler A (1998) The Moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36:1228–1249
Justice CO (1986) Monitoring the Grasslands of Semiarid Africa Using NOAA AVHRR Data - Editorial. Int J Remote Sens 7:1385–1390
Kang S, Running SW, Lim J-H, Zhao M, Park C-R, Loehman R (2003) A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: an application of MODIS leaf area index. Remote Sens Environ 86:232–242. doi:10.1016/S0034-4257(03)00103-2
Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE T Geosci Remote 30:261–270. doi:10.1109/36.134076
Knapp AK, Carter GA (1998) Variability in leaf optical properties among 26 species from a broad range of habitats. Am J Bot 85:940–946
Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159. doi:10.1016/S0034-4257(70)80021-9
Kovalskyy V, Roy DP, Zhang XY, 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 3:325–334. doi:10.1080/01431161.2011.593581
Lee DW, O’Keefe J, Holbrook NM, Feild TS (2003) Pigment dynamics and autumn leaf senescence in a New England deciduous forest, Eastern USA. Ecol Res 18:677–694. doi:10.1111/j.1440-1703.2003.00588.x
Liang L, Schwartz MD (2009) Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landscape Ecol 24:465–472. doi:10.1007/s10980-009-9328-x
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 115:143–157. doi:10.1016/j.rse.2010.08.013
Lloyd D (1990) A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int J Remote Sens 11:2269–2279
Loveland TR, Merchant JW, Ohlen DO, Brown JF (1991) Development of a land-cover characteristics database for the conterminous U.S. Photogram Eng Rem S 57:1453–1463
Morisette JT (2010) Toward a standard nomenclature for imagery spatial resolution. Int J Remote Sens 31:2347–2349. doi:10.1080/01431160902994457
Morisette JT, Richardson AD, Knapp AK, Fisher JI, Graham EA, Abatzoglou J, Wilson BE, Breshears DD, Henebry GM, Hanes JM, Liang L (2009) Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front Ecol Environ 7:253–260. doi:10.1890/070217
Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702. doi:10.1038/386698a0
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 83:214–231
Ollinger SV (2011) Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol 189:375–394. doi:10.1111/j.1469-8137.2010.03536.x
Park K-A, Bayarsaikhan U, Kim K-R (2012) Effects of El Niño on spring phenology of the highest mountain in North-East Asia. Int J Remote Sens 33:5268–5288. doi:10.1080/01431161.2012.657362
Peñuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3:151–156. doi:10.1016/S1360-1385(98)01213-8
Pouliot D, Latifovic R, Fernandes R, Olthof I (2011) Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests. Remote Sens Environ 115:158–166. doi:10.1016/j.rse.2010.08.014
Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714. doi:10.2307/3235884
Reed BC, Schwartz MD, Xiao X (2009) Remote sensing phenology: status and the way forward. In: Noormets A (ed) Phenology of ecosystem processes: applications in global change research. Springer, Heidelberg
Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith M-L (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334. doi:10.1007/s00442-006-0657-z
Richardson AD, Braswell BH, Hollinger DY, Jenkins JP, Ollinger SV (2009a) Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417–1428. doi:10.1890/08-2022.1
Richardson AD, Hollinger DY, Dail DB, Lee JT, Munger JW, O’Keefe J (2009b) Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol 29:321–331. doi:10.1093/treephys/tpn040
Richardson AD, Anderson RS, 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, Poulter B, Raczka BM, Ricciuto DM, Sahoo AK, Schaefer K, Tian H, Vargas R, Verbeeck H, Xiao J, Xue Y (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American carbon program site synthesis. Glob Change Biol 18:566–584. doi:10.1111/j.1365-2486.2011.02562.x
Rosenzweig C, Casassa G, Karoly DJ, Imeson A, Liu C, Menzel A, Rawlins S, Root TL, Seguin B, Tryjanowski P (2007) Assessment of observed changes and responses in natural and managed systems. Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Rouse J-W, Haas R-H, Schell J-A, Deering D-W, Harlan J-C (1974) Monitoring the vernal advancements and retrogradation (Greenwave effect) of nature vegetation. NASA/GSFC final report, NASA, Greenbelt
Roy DP, Ju J, Kline K, Scaramuzza PL, Kovalskyy V, Hansen M, Loveland TR, Vermote R, Zhang C (2010) Web-enabled landsat data (WELD): landsat ETM + composited mosaics of the conterminous United States. Remote Sens Environ 114:35–49. doi:10.1016/j.rse.2009.08.011
Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54:547–560
Saleska SR, Didan K, Huete AR, da Rocha HR (2007) Amazon Forests Green-Up During 2005 Drought. Science 318(5850):612
Samanta A, Ganguly S, Hashimoto H, Devadiga S, Vrmote E, Knyazikhin Y, Nemani R, Myneni R (2010) Amazon forests did not green-up during the 2005 drought. Geophys Res Lett 37:L05401. doi:10.1029/2009GL042154
Samanta A, Ganguly S, Vermote E, Nemani RR, Myneni RB (2012)Â Why is remote sensing of Amazon forest greenness so challenging? Earth Int 16(2). Paper 7. doi:10.1175/2012EI440.1
Schwartz MD (1990) Detecting the onset of spring: a possible application of phenological models. Climate Res 1:23–29
Schwartz MD (1994) Monitoring global change with phenology: the case of the spring green wave. Int J Biometeorol 38:18–22. doi:10.1007/BF01241799
Schwartz MD (1996) Examining the spring discontinuity in daily temperature ranges. J Climate 9:803–808. doi:10.1175/1520-0442(1996)009<0803:ETSDID>2.0.CO;2
Schwartz MD (1997) Spring index models: an approach to connecting satellite and surface phenology. In: Lieth H, Schwartz MD (eds) Phenology of seasonal climates. Backhuys, Netherlands
Schwartz MD, Hanes JM (2010) Intercomparing multiple measures of the onset of spring in eastern North America. Int J Climatol 30:1614–1626. doi:10.1002/joc.2008
Schwartz MD, Reed BC (1999) Surface phenology and satellite sensor-derived onset of greenness: an initial comparison. Int J Remote Sens 20:3451–3457. doi:10.1080/014311699211499
Schwartz MD, Reed BC, White MA (2002) Assessing satellite-derived start-of-season measures in the conterminous USA. Int J Climatol 22:1793–1805. doi:10.1002/joc.819
Schwartz MD, Hanes JM, Liang L (2013) Comparing carbon flux and high-resolution spring phenological measurements in a northern mixed forest. Agr Forest Meteorol 169:136–147
Schneider SR, McGinnis SR & Gatlin JA (1981) Use of NOAA/AVHRR visible and near-infrared data for land remote sensing. NOAA Technical Report, NESS 84, USDC, Washington, D.C
Shellito BA (2012) Introduction to geospatial technologies. W.H. Freeman and Company, New York
Slaton MR, Hunt EMR Jr, Smith WK (2001) Estimating near-infrared leaf reflectance from leaf structural characteristics. Am J Bot 88:278–284
Sonnentag O, Hufkens K, Teshera-Sterne C, Young AM, Friedl M, Braswell BH, Milliman T, O’Keefe J, Richardson AD (2012) Digital repeat photography for phenological research in forest ecosystems. Agric For Meteorol 152:159–177
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:G04021. doi:10.1029/2008JG000781
Stöckli R, Rutishauser T, Baker I, Liniger MA, Denning AS (2011) A global reanalysis of vegetation phenology. J Geophys Res 116:G03020. doi:10.1029/2010JG001545
Tan B, Morisette JT, Wolfe RE, Gao F, Ederer GA, Nightingale J, Pedelty JA (2008) Vegetation phenology metrics derived from temporally smoothed and gap-filled MODIS data. Proc IGARSS 3:593–596. doi:10.1109/IGARSS.2008.4779417
Tan B, Morisette JT, Wolfe RE, Gao F, Ederer GA, Nightingale J, Pedelty JA (2010) An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE J Sel Top Appl 4:361–371. doi:10.1109/JSTARS.2010.2075916
Tarpley J (1991) The NOAA global vegetation index product—A review. Global and Planetary Change, 4:189–194
Thayn JB, Price KP (2008) Julian dates and introduced temporal error in remote sensing vegetation phenology studies. Int J Remote Sens 29:6045–6049. doi:10.1080/01431160802235829
Townshend JRG & Tucker CJ (1981) Utility of AVHRR of NOAA 6 and 7 for vegetation mapping. In Matching Remote Sensing Technologies and their Applications Proceedings (London: Remote Sensing Society), p. 97
Tucker CJ, Pinzon JE, Brown ME, Slayback DA, 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 26:4485–4498. doi:10.1080/01431160500168686
Wang Q, Tenhunen J, Dinh NQ, Reichstein M, Otieno D, Granier A, Pilegarrd K (2005) Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites. Remote Sens Environ 96:475–484. doi:10.1016/j.rse.2005.04.003
White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochem Cycles 11:217–234. doi:10.1029/97GB00330
White MA, Schwartz MD, Running SW (1999) Young students, satellites aid understanding of climate–biosphere link. EOS Trans 81(1):5. doi:10.1029/00EO00001
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 5:260–273. doi:10.1007/s10021-001-0070-8
White MA, Hoffman F, Hargrove WW, Nemani RR (2005) A global framework for monitoring phenological responses to climate change. Geophys Res Lett 32:L04705. doi:10.1029/2004GL021961
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 1982–2006. Glob Change Biol 15:2335–2359. doi:10.1111/j.1365-2486.2009.01910.x
Wu J, Loucks OL (1995) From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q Rev Biol 70:439–466
Yang W, Huang D, Tan B, Stroeve JC, Shabanov NV, Knyazikhin Y, Nemani RR, Myneni RB (2006) Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000–2005. IEEE Trans Geosci Remote Sens 44:1829–1842. doi:10.1109/TGRS.2006.871214
Zhang X, Goldberg MD (2011) Monitoring fall foliage coloration dynamics using time-series satellite data. Remote Sens Environ 115:382–391. doi:10.1016/j.rse.2010.09.009
Zhang X, Hodges JCF, Schaaf CB, Friedl MA, Strahler AH, Gao F (2001) Global vegetation phenology from AVHRR and MODIS data. Proc IGARSS 5:2262–2264. doi:10.1109/IGARSS.2001.977969
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 84:471–475. doi:10.1016/S0034-4257(02)00135-9
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Schneider A (2004) The footprint of urban climates on vegetation phenology. Geophys Res Lett 31:L12209. doi:10.1029/2004GL020137
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hanes, J.M., Liang, L., Morisette, J.T. (2014). Land Surface Phenology. In: Hanes, J. (eds) Biophysical Applications of Satellite Remote Sensing. Springer Remote Sensing/Photogrammetry. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25047-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-25047-7_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25046-0
Online ISBN: 978-3-642-25047-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)