75
Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical
characteristics of vegetation. J Plant Physiol 161:165–173
Gitelson AA, Vina A, Verma SB, Rundquist DC, Arkebauer TJ, Keydan G, Leavitt B, Ciganda
V, Burba GG, Suyker AE (2006) Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity.
J Geophys Res-Atmos 111:13
Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship Between Remotely-sensed
Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation
Indices Can and Cannot Tell Us About the Landscape. Sensors (Basel) 8:2136–2160
Goetz SJ, Bunn AG, Fiske GJ, Houghton RA (2005) Satellite-observed photosynthetic trends
across boreal north america associated with climate and fire disturbance. Proc Natl Acad Sci
U S A 102:13521–13525
Goetz SJ, Fiske GJ, Bunn AG (2006) Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada. Remote Sens Environ 101:352–365
Gökkaya K, Thomas V, Noland TL, McCaughey H, Morrison I, Treitz P (2015) Prediction of
macronutrients at the canopy level using spaceborne imaging spectroscopy and LiDAR data in
a mixedwood boreal forest. Remote Sens 7:9045–9069
Goward SN, Huemmrich KF (1992) Vegetation canopy PAR absorptance and the normalized
difference vegetation index – An assessment using the SAIL model. Remote Sens Environ
39:119–140
Green DS, Erickson JE, Kruger EL (2003) Foliar morphology and canopy nitrogen as predictors of
light-use efficiency in terrestrial vegetation. Agric For Meteorol 115:165–173
Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, Faust JA, Pavri
BE, Chovit CJ, Solis MS, Olah MR, Williams O (1998) Imaging spectroscopy and the Airborne
Visible Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ 65:227–248
Grossman YL, Ustin SL, Jacquemoud S, Sanderson EW, Schmuck G, Verdebout J (1996) Critique
of stepwise multiple linear regression for the extraction of leaf biochemistry information from
leaf reflectance data. Remote Sens Environ 56:182–193
Hilker T, Coops NC, Wulder MA, Black TA, Guy RD (2008) The use of remote sensing in light
use efficiency based models of gross primary production: A review of current status and future
requirements. Sci Total Environ 404:411–423
Huemmrich KF (2013) Simulations of seasonal and latitudinal variations in leaf inclination angle
distribution: implications for remote sensing. J Adv Remote Sens 02:9
Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near-infrared and
middle-infrared reflectances. Remote Sens Environ 30:43–54
Inoue Y, Penuelas J, Miyata A, Mano M (2008) Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and
CO2 flux measurements in rice. Remote Sens Environ 112:156–172
IPCC (2018) Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report
on the impacts of global warming of 1.5°C above pre-industrial levels and related global
greenhouse gas emission pathways, in the context of strengthening the global response to the
threat of climate change, sustainable development, and efforts to eradicate poverty [MassonDelmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. MoufoumaOkia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E.
Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. World Meteorological Organization,
Geneva, Switzerland, 32 pp.
IPBES (2018) Summary for policymakers of the regional assessment report on biodiversity and
ecosystem services for the Americas of the Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany
Jacquemoud S, Baret F (1990) PROSPECT – a model of leaf optical-properties of spectra. Remote
Sens Environ 34:75–91
Jacquemoud S, Baret F, Andrieu B, Danson FM, Jaggard K (1995) Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote Sens Environ 52:163–172
3 Scaling Functional Traits from Leaves to Canopies
Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical
characteristics of vegetation. J Plant Physiol 161:165–173
Gitelson AA, Vina A, Verma SB, Rundquist DC, Arkebauer TJ, Keydan G, Leavitt B, Ciganda
V, Burba GG, Suyker AE (2006) Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity.
J Geophys Res-Atmos 111:13
Glenn EP, Huete AR, Nagler PL, Nelson SG (2008) Relationship Between Remotely-sensed
Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation
Indices Can and Cannot Tell Us About the Landscape. Sensors (Basel) 8:2136–2160
Goetz SJ, Bunn AG, Fiske GJ, Houghton RA (2005) Satellite-observed photosynthetic trends
across boreal north america associated with climate and fire disturbance. Proc Natl Acad Sci
U S A 102:13521–13525
Goetz SJ, Fiske GJ, Bunn AG (2006) Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada. Remote Sens Environ 101:352–365
Gökkaya K, Thomas V, Noland TL, McCaughey H, Morrison I, Treitz P (2015) Prediction of
macronutrients at the canopy level using spaceborne imaging spectroscopy and LiDAR data in
a mixedwood boreal forest. Remote Sens 7:9045–9069
Goward SN, Huemmrich KF (1992) Vegetation canopy PAR absorptance and the normalized
difference vegetation index – An assessment using the SAIL model. Remote Sens Environ
39:119–140
Green DS, Erickson JE, Kruger EL (2003) Foliar morphology and canopy nitrogen as predictors of
light-use efficiency in terrestrial vegetation. Agric For Meteorol 115:165–173
Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, Faust JA, Pavri
BE, Chovit CJ, Solis MS, Olah MR, Williams O (1998) Imaging spectroscopy and the Airborne
Visible Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ 65:227–248
Grossman YL, Ustin SL, Jacquemoud S, Sanderson EW, Schmuck G, Verdebout J (1996) Critique
of stepwise multiple linear regression for the extraction of leaf biochemistry information from
leaf reflectance data. Remote Sens Environ 56:182–193
Hilker T, Coops NC, Wulder MA, Black TA, Guy RD (2008) The use of remote sensing in light
use efficiency based models of gross primary production: A review of current status and future
requirements. Sci Total Environ 404:411–423
Huemmrich KF (2013) Simulations of seasonal and latitudinal variations in leaf inclination angle
distribution: implications for remote sensing. J Adv Remote Sens 02:9
Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near-infrared and
middle-infrared reflectances. Remote Sens Environ 30:43–54
Inoue Y, Penuelas J, Miyata A, Mano M (2008) Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and
CO2 flux measurements in rice. Remote Sens Environ 112:156–172
IPCC (2018) Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report
on the impacts of global warming of 1.5°C above pre-industrial levels and related global
greenhouse gas emission pathways, in the context of strengthening the global response to the
threat of climate change, sustainable development, and efforts to eradicate poverty [MassonDelmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. MoufoumaOkia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E.
Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. World Meteorological Organization,
Geneva, Switzerland, 32 pp.
IPBES (2018) Summary for policymakers of the regional assessment report on biodiversity and
ecosystem services for the Americas of the Intergovernmental Science-Policy Platform on
Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany
Jacquemoud S, Baret F (1990) PROSPECT – a model of leaf optical-properties of spectra. Remote
Sens Environ 34:75–91
Jacquemoud S, Baret F, Andrieu B, Danson FM, Jaggard K (1995) Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy
reflectance data. Application to TM and AVIRIS sensors. Remote Sens Environ 52:163–172
3 Scaling Functional Traits from Leaves to Canopies