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:10.1080/014311699
211499
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 nearinfrared 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:10.1080/014311608
02235829
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
124
J. M. Hanes et al.
greenness: an initial comparison. Int J Remote Sens 20:3451–3457. doi:10.1080/014311699
211499
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 nearinfrared 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:10.1080/014311608
02235829
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
124
J. M. Hanes et al.