Haboudane, D., J.R. Miller, and N. Tremblay, et al.(2002). Integrated
narrow-band vegetation indices for prediction of crop relative
chlorophyll content for application to precision agriculture,
Remote Sensing of Environment
, 81(2):416-426.
Herrmann, I., U. Shapira, S. Kinast, et al. (2013). Ground-level
hyperspectral imagery for detecting weeds in wheat fields,
Precision Agriculture
, 14(6):637-659.
Horler, D.N.H., M. Dockray, J. Barber, et al. (1983). Red edge
measurements for remotely sensing plant relative chlorophyll
content,
Advances in Space Research
, 3(2):273-277.
Huete, A., K. Didan, T, Miura T, et al. (2002).Overview of the
radiometric and biophysical performance of the MODIS
vegetation indices,
Remote Sensing of Environment
, 83(1-2):195-
213.
Hunt, E. R., C.S.T. Daughtry,and L. Li, (2016). Feasibility of estimating
leaf water content using spectral indices from WorldView-3’s
near-infrared and shortwave infrared bands, Taylor & Francis,
Inc. 2016.
Inoue, Y., J. Peñuelas, A. Miyata M. Mano, (2008). Normalized
difference spectral indices for estimating photosynthetic
ef ciency and capacity at a canopy scale derived from
hyperspectral and co 2 ux measurements in rice, Remote
Sensing of Environment, (2008), 112:156-172.
Inoue, Y., E. Sakaiya, Y. Zhu, et al. (2012). Diagnostic mapping
of canopy nitrogen content in rice based on hyperspectral
measurements,
Remote Sensing of Environment
, 126:210-221.
Jiménez, F.,(2012). Non-destructive determination of impact bruising
on table olives using Vis–NIR spectroscopy,
Biosystems
Engineering
, 113(4):371-378.
Jin, C., G. Song M.G. Shen, et al. (2009).Estimating aboveground
biomass of grassland having a high canopy cover: An exploratory
analysis of in situ hyperspectral data,
International Journal of
Remote Sensing
, 30(24):6497-6517.
Karunaratne, S.B., T.F.A. Bishop, J.A. Baldock, et al. (2014),
Catchment scale mapping of measureable soil organic carbon
fractions,
Geoderma
, 219-220:14-23.
Kim, M.S., C.S.T.Daughtry, E.W. Chappelle, et al. (1994).The use
of high spectral resolution bands for estimating absorbed
photosynthetically active radiation (APAR) 415-434,3-4.
Li, Z., X. Jin, J. Wang et al. (2015). Estimating winter wheat (Triticum
aestivum) LAI and leaf relative chlorophyll content from canopy
reflectance data by integrating agronomic prior knowledge with
the PROSAIL model,
International Journal of Remote Sensing
,
36(10):2634-2653.
Maimaitiyiming, M., A. Ghulam A. Bozzolo, et al. (2017). Early
detection of plant physiological responses to different levels of
water stress using reflectance spectroscopy,
Remote Sensing
,
(7):745.
Marshall, P., T. Thenkabail, T. Biggs, et al. (2016).Hyperspectral
narrowband and multispectral broadband indices for remote
sensing of crop evapotranspiration and its components
(transpiration and soil evaporation),
Agricultural and Forest
Meteorology
, 218:122-134.
Nijat, K., Q.D. Shi, J.Z. Wang et al. (2017). Estimation of spring wheat
relative chlorophyll content based on hyperspectral features and
PLSR model,
Transactions of the Chinese Society of Agricultural
Engineering
, 33(22).
Peng, X., T. Shi, A. Song, et al. (2014). Estimating soil organic carbon
using Vis/NIR spectroscopy with SVMR and SPA methods,
Remote Sensing
, 2014, 6(4):2699-2717.
Penuelas, J., F. Baret, and I. Filella, (1995). Semiempirical indexes
to assess carotenoids chlorophyll-a ratio from leaf spectral
reflectance,
Photosynthetica
, 31(2):221-230.
Peñuelas, J., J. Gamon, A. Fredeen, et al. (1994).Re ectance indices
associated with physiological changes in nitrogen-and water-
limited sun ower leaves,
Remote Sensing of Environment
,
48:135-46.
Pôças, I., A. Rodrigues, S. Gonçalves, et al. (2015). Predicting
grapevine water status based on hyperspectral reflectance
vegetation indices,
Remote Sensing
, 2015, 7(12):16460-16479.
Rollin, E.M., and E.J. Milton, (1998). Processing of high spectral
resolution reflectance data for the retrieval of canopy water
content information,
Remote Sensing of Environment
, 65(1):86-
92.
Rondeaux, S.B.,(1996). Optimization of soil-adjusted vegetation
indices,
Furrow Irrigation and Salinization
, VDM Verlag Dr.
Müller, 1996:8369-8375.
Rouse, J.W., Jr, R. Haas, et al. (1973) Monitoring Vegetation Systems
in the Great Plains with Erts,
Proceedings of the Third Earth
Resources Technology Satellite-1 Symposium
, Washington, D.C.,
10-14 December.
Shibayama, M., T.A. Akiyama, (1986).A Spectroradiometer For Field
Use: VI. Radiometric estimation for chlorophyll index of rice
canopy,
Japanese Journal of Crop Science
, 55(4):433-438.
Sims D.A., and J.A. Gamon, (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(2):337-354.
Stagakis, S., N. Markos, O Sykioti, et al. (2010). Monitoring canopy
biophysical and biochemical parameters in ecosystem scale
using satellite hyperspectral imagery: An application on a
Phlomis fruticosa Mediterranean ecosystem using multiangular
CHRIS/PROBA observations,
Remote Sensing of Environment
,
114(5):977-994.
Stratoulias, D., H. Balzter, A. Zlinszky et al. (2015). Assessment
of ecophysiology of lake shore reed vegetation based on
chlorophyll uorescence, eld spectroscopy and hyperspectral
airborne imagery, Remote Sensing of Environment, 157:72-84.
Sun, J., S. Shi, J. Yang J, et al., (2018). Analyzing the performance
of PROSPECT model inversion based on different spectral
information for leaf biochemical properties retrieval,
ISPRS
Journal of Photogrammetry & Remote Sensing
, 2018:135:74-83.
Sid’ko, A. F., I.Y. Botvich, T.I. Pisman, et al. (2017). Estimation
of relative chlorophyll content and yield of wheat crops
from reflectance spectra obtained by ground-based remote
measurements.
Field Crops Research
, 207:24-29.
Uddling, J., J. Gelang-Alfredesson, K. Piikki et al. (2007). Evaluating
the relationship between leaf chlorophyll concentration and
SPAD-502 chlorophyll meter readings,
Photosynthesis. Research
,
91(1):37.
Ulissi, V., F. Antonucci, P. Benincasa et al., (2011). Nitrogen
concentration estimation in tomato leaves by VIS-NIR non-
destructive spectroscopy,
Sensors
, 11(6):6411-6424.
Ustin, S.L., M.O. Smith, S. Jacquemoud, et al. (1999) Geobotany:
Vegetation mapping for Earth sciences, Remote sensing for the
earth sciences:
Manual of Remote Sensing
, 1999, 3:189-233.
Wold, S., M. Sjöström and L. Eriksson, (2001). PLS-regression: A basic
tool of chemometrics,
Chemometrics & Intelligent Laboratory
Systems
, 58(2):109-130. (48).
Yao, X., Y. Xia, W. Jia et al. (2013). Comparison and Intercalibration
of Vegetation indices from different sensors for monitoring
above-ground plant nitrogen uptake in winter Wheat.
Sensors
,
13(3):3109-30.
Zhang, K., F. Runyuan, W. Qi, et al. (2015). Effects of simulated
warning and precipitation change on growth characteristics and
grain yield of spring wheat in semi-arid area,
Transactions of the
Chinese Society of Agricultural Engineering
, 2015, 31: 161-170.
Zarco-Tejada, P.J., A. Berjón R. López-Lozano, et al. (2005). Assessing
vineyard condition with hyperspectral indices: Leaf and canopy
reflectance simulation in a row-structured discontinuous canopy,
Remote Sensing of Environment
, 99(3):271-287.
Zarco-Tejada, P. J., J.R. Miller, T.L. Noland et al. (2001). Scaling-
up and model inversion methods with narrowband optical
indices for relative chlorophyll content estimation in closed
forest canopies with hyperspectral data,
IEEE Transactions on
Geoscience and Remote Sensing
,39(7):1491-1507.
Zhang, X., T.Q.S.R. (2010), Analysis of directional characteristics
of winter wheat canopy spectra,
Spectroscopy and Spectral
Analysis
, 30(6):1600-1605.
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