Lichtenthaler, H. K. 1987. Chlorophyll and carotenoids: Pigments of photosynthetic biomembranes. Methods in Enzymology 148:331–382.
Peng, Y., and Gitelson, A. A. 2011. Application of chlorophyll-related vegetation indices
for remote estimation of maize productivity. Agricultural and Forest Meteorology
151:1267–1276.
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C. 1974. Monitoring the
vernal advancement of retrogradation of natural vegetation. National Aeronautics and Space
Administration, Goddard Space Flight Center (NASA/GSFC), Type III, Final Report.
Sims, D. A., and Gamon, J. A. 2002. Relationships between leaf pigment content and spectral
reflectance across a wide range of species, leaf structures and developmental stages. Remote
Sensing of Environment 81:337–354.
Wong, K. 2012. Remote Sensing of Tall Grasslands: Estimating Vegetation Biochemical
Contents at Multiple Spatial Scales and Investigating Vegetation Temporal Response to
Climate Conditions. Ph.D. Dissertation, University of Toronto.
Wong, K., and He, Y. 2013. Estimating grassland chlorophyll content using remote sensing
data at the species, canopy, and landscape scales. Canadian Journal of Remote Sensing
39:1–12.
Wu, C., Niu, Z., Tang, Q., and Huang, W. 2008. Estimating chlorophyll content from
hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology 148:1230–1241.
Yoder, B. J., and Pettigrew-Crosby, R. E. 1995. Predicting nitrogen and chlorophyll content and
concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote
Sensing of Environment 53:199–211.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 1999.
Canopy optical indices from infinite reflectance and canopy reflectance models for forest
condition monitoring: Application to hyperspectral CASI data. Paper presented at the
International Geoscience and Remote Sensing Symposium (IGARSS) 3:1878–1881.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 2000.
Chlorophyll fluorescence effects on vegetation apparent reflectance: II. Laboratory and
airborne canopy-level measurements with hyperspectral data. Remote Sensing of Environment 74:596–608.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 2002.
Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on
hyperspectral imagery. Journal of Environmental Quality 31:1433–1441.
Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjón, A., and Agüera, J. 2004. Hyperspectral
indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote
Sensing of Environment 90:463–476.
Zhang, Y., Chen, J. M., Miller, J. R., and Noland, T. L. 2008. Leaf chlorophyll content retrieval
from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment
112:3234–3247.
REFERENCES
137
Peng, Y., and Gitelson, A. A. 2011. Application of chlorophyll-related vegetation indices
for remote estimation of maize productivity. Agricultural and Forest Meteorology
151:1267–1276.
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C. 1974. Monitoring the
vernal advancement of retrogradation of natural vegetation. National Aeronautics and Space
Administration, Goddard Space Flight Center (NASA/GSFC), Type III, Final Report.
Sims, D. A., and Gamon, J. A. 2002. Relationships between leaf pigment content and spectral
reflectance across a wide range of species, leaf structures and developmental stages. Remote
Sensing of Environment 81:337–354.
Wong, K. 2012. Remote Sensing of Tall Grasslands: Estimating Vegetation Biochemical
Contents at Multiple Spatial Scales and Investigating Vegetation Temporal Response to
Climate Conditions. Ph.D. Dissertation, University of Toronto.
Wong, K., and He, Y. 2013. Estimating grassland chlorophyll content using remote sensing
data at the species, canopy, and landscape scales. Canadian Journal of Remote Sensing
39:1–12.
Wu, C., Niu, Z., Tang, Q., and Huang, W. 2008. Estimating chlorophyll content from
hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology 148:1230–1241.
Yoder, B. J., and Pettigrew-Crosby, R. E. 1995. Predicting nitrogen and chlorophyll content and
concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote
Sensing of Environment 53:199–211.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 1999.
Canopy optical indices from infinite reflectance and canopy reflectance models for forest
condition monitoring: Application to hyperspectral CASI data. Paper presented at the
International Geoscience and Remote Sensing Symposium (IGARSS) 3:1878–1881.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 2000.
Chlorophyll fluorescence effects on vegetation apparent reflectance: II. Laboratory and
airborne canopy-level measurements with hyperspectral data. Remote Sensing of Environment 74:596–608.
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., and Sampson, P. H. 2002.
Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on
hyperspectral imagery. Journal of Environmental Quality 31:1433–1441.
Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjón, A., and Agüera, J. 2004. Hyperspectral
indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote
Sensing of Environment 90:463–476.
Zhang, Y., Chen, J. M., Miller, J. R., and Noland, T. L. 2008. Leaf chlorophyll content retrieval
from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment
112:3234–3247.
REFERENCES
137