Abassi, M., 2009. Investigation of the spectral signature of forest species leaf: Fagus orientalis, Quercus castaneifolia, Carpinus betulus, Alnus subcordata, Parotia persica using field spectroradiometry. PhD thesis forestry and forest economic, Faculty of Natural Resources, University of Tehran, Iran. (In Persian)
- Bannari, A., Khurshid, K.S., Staenz, K., and Schwarz, J.W. 2007. A Comparison of hyperspectral chlorophyll indices for wheat crop chlorophyll content estimation using laboratory reflectance measurements. IEEE Trans Geosci Remote Sens. 45: 10. 3063-3074.
- Blackburn, G.A. 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66: 3. 273-285.
- Blackmer, T.M., Shepers, J.S., and Varvel, G.V. 1994. Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron J. 86: 6. 934-938.
- Broge, N.H., and Mortensen, J.V. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens. Environ. 81: 1. 45-57.
- Broge, N.H. and Leblanc, E. 2000. Comparing predictive power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76: 2. 156-172.
- Carmona, F., Rivas, R., and Fonnegra, D.C. 2015. Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. Eur. J. Remote Sens. 48: 1. 319-326.
- Chappelle, E.W., Kim, M.S., and McMurtrey III, J.E. 1992. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens. Environ. 39: 3.239-247.
- Clevers, J.G.P.W. 1986. The application of a vegetation index in correcting the infrared reflectance for soil background. Int. Arch. Photogramm. Rem. Sens, Balkema, Rotterdam, Boston. 26: 1. 221-
- Clevers, J.G.P.W., Kooistra, L., and Brande M.M.M. 2017. Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens. 9: 5. 1-15. Doi:10.3390/rs9050405.
- Cramer, W., and Field, C.B. 1999. The potsdam NPP model intercomparison. Glob Change Biol. 5 :1. 1-15.
- Croft, H., Arabian, J., Chen, J.M., Shang, J., and Liu, J. 2019. Mapping within‑field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat‑8 imagery. Precis Agric. 21: 4. 856-880. https://doi.org/10.1007/s11119-019-09698-y.
- Croft, H., Chen, J.M., and Zhang, Y. 2014. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol Complex. 17: 119-
- Dash, J., and Curran, P.J. 2004. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25: 23. 5403-5413.
- Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Brown de Colstoun, E., and McMurtrey III, J.E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74: 229-239.
- Flexas, J.L., Briantais, J.M., Cerovic, Z., Medrano, H., and Moya, I. 2000. Steady-state and maximum chlorophyll fluorescence responses to water stress in grapevine leaves. A new remote sensing system. Remote Sens. Environ. 73: 283-297.
- Gamon, J.A., and Surfus, J.S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143: 1. 105-117.
- Gitelson, A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 161: 165-173.
- Gitelson, A.A. and Merzlyak, M.N. 1997. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18: 2691-2697.
- Gitelson, A.A., Gritz, Y., and Merzlyak, M.N. 2003a. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160: 3. 271-282.
- Gitelson, A.A., Kaufman, J.Y., and Merzlyac, M.N. 1996. Use of a Green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58: 3. 289-298.
- Gitelson, A.A., Vina, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G.P., and Leavitt, B. 2003b. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett. 30: 5.1-4.
- Gitelson, A.A., Vina, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. 2005. Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett. 32: 8. 1-4.
- Gitelson, A.A., Zur, Y., Chivkunova, O.B., and Merzlyak, M.N. 2002 Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 75: 3. 272-281.
- Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81: (2-3). 416-426.
- Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., and Strachan, I.B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90: 337-352.
- Haboudane, D., Tremblay, N., Miller, J.R., and Vigneault, P. 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans Geosci Remote Sens. 46: 2. 423-437.
- Han, S., Hendrickson, L.L., and Ni, B. 2002. Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn. Trans ASAE. 45: 4. 1229-1239.
- Hatfield, J.L., Gitelson, A.A., Schepers, J.S., and Walthall, C.L. 2008. Application of spectral remote sensing for agronomic decisions. Agron J. 100: 117-131.
- Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25: 3. 295-309.
- Huete, A.R., Liu, H.Q., Batchily, K., and Leeuwen, W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59: 3. 440-451.
- Kim, M.S. 1994. The use of narrow spectral bands for improving remote sensing estimation of fractionally absorbed photosynthetically active radiation (fAPAR). Masters Thesis. Department of Geography, University of Maryland, College Park, MD.
- Kooistra, L., and Clevers, J.G.P.W. 2016. Estimating potato leaf chlorophyll content using ratio vegetation indices. Remote Sens Lett. 7: 611-620.
- Le Maire, G., Francois, C., and Dufrene, E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 89: 1-28.
- Li, F., Miao, Y., Hennig, S.D., Gnyp, M.L., Chen, X., Jia, L., and Bareth, G. 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis Agric. 11: 4. 335-357.
- Lichtenthaler, H.K. 1987. Chlorophyll and carotenoids: Pigments of photosynthetic biomembranes. Methods. Enzymol. 148: 350-387.
- Lichtenthaler, H.K., and Buschmann, C. 2001. Chlorophylls and carotenoids: Measurement and characterization by UV–VIS spectroscopy. Current protocols in food analytical chemistry (pp. F4.3.1-F4.3.8). New York: John Wiley and Sons.
- Liu, J. and Moore, J.M. 1990. Hue image RGB colour composition. A simple technique to suppress shadow and enhance spectral signature. Int. J. Remote Sens. 11: 8. 1521-1530.
- Merzlyak, M.N., Gitelson, A.A., Chivkunova O.B., and Rakitin, V.Y. 1999. Non-destructive optical detection of leaf senescence and fruit ripening. Physiol Plant. 106: 135-141.
- Miraglio, T., Adeline, K., Huesca, M., Ustin, S., and Briottet, X. 2020. Monitoring LAI, chlorophylls, and carotenoids content of awoodland savanna using hyperspectral imagery and 3d radiative transfer modeling. Remote Sens. 12: 1. 1-28.
- Mosleh Ghahfarokhi, Z. 2016. Soil digital mapping, land suitability and optimization cultivation model for major products plains of Shahrekord. PhD thesis pedology, Faculty of Agriculture, University of Shahrekord, Iran. (In Persian)
- Nagy, A., Feher, J., and Tamas, J. 2018. Wheat and maize yield forecasting for the Tisza River Catchment using MODIS NDVI time series and reported crop statistics. Comput. Electron. Agric. 151: 10. 41-49.
- Nguyen, H., Kim, J., Nguyen, A., Shin, J.C., and Lee, B. 2006. Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of Rice. Precis Agric. 7: 4. 249-264.
- Pearson, R.L. and Miller, L.D. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Remote Sens. Environ. 8: 1348-1355.
- Penuelas, J., Baret, F., and Filella, I. 1995. Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetica. 31: 2. 221-230.
- Penuelas, J., Gamon, J.A., Freeden, A.L., Merino, J. and Field, C.1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sens. Environ. 48: 2. 135-146.
- Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sens. Environ. 48: 2. 119-126.
- Ranjan, A.K. and Parida, B.R. 2020. Estimating biochemical parameters of paddy using satellite and near-proximal sensor data in Sahibganj Province, Jharkhand (India). Remote Sens App Soc Environ. 18: 1. 1-12.
- Rondeaux, G., Steven, M., and Baret, F. 1996. Optimization of soil- adjusted vegetation indices. Remote Sens. Environ. 55: 2. 95-107.
- Roujean, J.L., and Breon, F.M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51:3. 375-384.
- Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Pp. 309-317. In: S.C. Freden, E.P. Mercanti, and M. Becker (eds) Third Earth Resources Technology Satellite–1 Syposium. Volume I: Technical Presentations, NASA SP-351, NASA, Washington, D.C.
- Sage, R.F., Pearcy, R.W., and Seemann, J.R. 1987. The nitrogen use efficiency of C3 and C4 plants III. Leaf nitrogen effects on the activity of carboxylating enzymes in Chenopodium album (L.) and Amaranthus retroflexus (L.). J. Plant Physiol. 85: 2. 355-359.
- 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 Sens. Environ. 81: 2-3.337-354.
- Sinclair, T.R., and Rufty, T.W. 2012. Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Glob Food Sec. 1: 2. 94-98.
- Verrelst, J., Camps-Valls, G., Munoz-Mari, J., Rivera, J.P., Veroustraete, F., Clevers, J.G., and Moreno, J. 2015. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties, A review. ISPRS J. Photogramm. Remote Sens. 108: 273-290.
- Vincini, M., Frazzi, E., and D’Alessio, P. 2008. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis Agric. 9: 303-319.
- Wu, C., Niu, Z., Tang, Q., and Huang, W. 2008. Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric. For. Meteorol. 148: 8-9. 1230-1241.
Xue, J., and Su, B. 2017. Significant
|