Abstract
Abiotic stresses are one of the major factors affecting crop production in many parts of the globe. The need of the hour is to reduce the yield losses due to these abiotic stresses. In this connection, early detection and corrective measures can help to reduce the impact of stresses on crop growth and yield. The recent developments in remote sensing particularly hyperspectral remote sensing hold a major key in early detection of abiotic stress over a larger area with less involvement of cost, time and labour. The works relevant to abiotic stress characterization particularly water and nutrient stress based on plant spectral reflectance are dealt in this chapter. The research work done previously elucidates that the water and nutrient monitoring through remote sensing is possible. The remote sensing-based techniques can lead to the development of real-time management of water and nutrient stress, thereby reducing the yield losses due these stresses.
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References
Araus JL, Casadesus J, Bort J (2001) Recent tools for the screening of physiological traits determining yield. In: Reynolds MP, Ortiz-Monasterio JI, McNab A (eds) Application of physiology in wheat breeding. CIMMYT, Mexico
Ashraf M, Athar HR, Harris PJC, Kwon TR (2008) Some prospective strategies for improving crop salt tolerance. Adv Agron 97:45–110. https://doi.org/10.1016/S0065-2113(07)00002-8
Asner GP, Martin RE (2008) Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens Environ 112:3958–3970. https://doi.org/10.1016/j.rse.2008.07.003
Asrar GQ, Fuchs M, Kanemasu ET, Hatfield JL (1984) Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1. Agron J 76:300–306
Babar MA, Reynolds MP, Van Ginkel M et al (2006) Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci 46:578–588. https://doi.org/10.2135/cropsci2005.0059
Bauer ME, Daughtry CST, Biehl LL et al (1986) Field spectroscopy of agricultural crops. IEEE Trans Geosci Remote Sens GE-24:65–75. https://doi.org/10.1109/TGRS.1986.289589
Blackburn GA (1998) Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int J Remote Sens 19:657–675. https://doi.org/10.1080/014311698215919
Blackburn GA (1999) Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens Environ 70:224–237
Blackburn GA (2007) Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation. Int J Remote Sens 28:2831–2855
Boyer JS (1982) Plant productivity and environment. Science 218:443–448. https://doi.org/10.1126/science.218.4571.443
Broge NH, Leblanc E (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ 76:156–172
Campbell PKE, Middleton EM, McMurtrey JE, Chappelle EW (2007) Assessment of vegetation stress using reflectance or fluorescence measurements. J Environ Qual 36:832–845
Carter GA (1993) Responses of leaf spectral reflectance to plant stress. Am J Bot 80:239–243
Ceccato P, Flasse S, Tarantola S et al (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ 77:22–33. https://doi.org/10.1016/S0034-4257(01)00191-2
Ceccato P, Flasse S, Grégoire J-M (2002) Designing a spectral index to estimate vegetation water content from remote sensing data. Remote Sens Environ 82:198–207. https://doi.org/10.1016/S0034-4257(02)00036-6
Chaerle L, De Boever F, Van Der Straeten D (2002) Infrared detection of early biotic and wound stress in plants. Thermol Int 12:100–106
Champagne CM, Staenz K, Bannari A et al (2003) Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies. Remote Sens Environ 87:148–160. https://doi.org/10.1016/S0034-4257(03)00137-8
Chapman SC (2008) Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161:195–208
Chen X, Vierling L, Deering D (2005) A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sens Environ 98:63–79
Chen S, Li D, Wang Y et al (2011) Spectral characterization and prediction of nutrient content in winter leaves of litchi during flower bud differentiation in southern China. Precis Agric 12:682–698
Cheng T, Rivard B, Sánchez-Azofeifa A (2011) Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ 115:659–670. https://doi.org/10.1016/j.rse.2010.11.001
Cho MA, Skidmore A, Corsi F et al (2007) Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int J Appl Earth Obs Geoinf 9:414–424. https://doi.org/10.1016/j.jag.2007.02.001
Chuvieco E, Riaño D, Aguado I, Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of landsat thematic mapper reflectance data: applications in fire danger assessment. Int J Remote Sens 23:2145–2162. https://doi.org/10.1080/01431160110069818
Cohen Y, Alchanatis V, Zusman Y et al (2010) Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite. Precis Agric 11:520–537
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. https://doi.org/10.1016/0034-4257(89)90069-2
Darvishzadeh R, Skidmore A, Schlerf M et al (2008) LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogramm Remote Sens 63:409–426. https://doi.org/10.1016/j.isprsjprs.2008.01.001
Das B, Sahoo RN, Pargal S et al (2015) Spectral based non-invasive estimation of plant chlorophyll content. J Agric Phys 15:88–102
Das B, Sahoo RN, Pargal S et al (2017) Comparison of different uni- and multi-variate techniques for monitoring leaf water status as an indicator of water-deficit stress in wheat through spectroscopy. Biosyst Eng 160:69–83. https://doi.org/10.1016/j.biosystemseng.2017.05.007
Das B, Sahoo RN, Pargal S et al (2018) Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics. Spectrochim Acta Part A Mol Biomol Spectrosc 192:41–51. https://doi.org/10.1016/j.saa.2017.10.076
Datt B (1999) Remote sensing of water content in eucalyptus leaves. Aust J Bot 47:909. https://doi.org/10.1071/BT98042
Doraiswamy PC, Moulin S, Cook PW, Stern A (2003) Crop yield assessment from remote sensing. Photogramm Eng Remote Sens 69:665–674. https://doi.org/10.14358/PERS.69.6.665
Eitel JUH, Gessler PE, Smith AMS, Robberecht R (2006) Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For Ecol Manag 229:170–182. https://doi.org/10.1016/j.foreco.2006.03.027
El-Shikha DM, Waller P, Hunsaker D et al (2007) Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric Water Manag 92:183–193
Fabre S, Lesaignoux A, Olioso A, Briottet X (2011) Influence of water content on spectral reflectance of leaves in the 3–15- μm domain. IEEE Geosci Remote Sens Lett 8:143–147. https://doi.org/10.1109/LGRS.2010.2053518
Ferwerda JG, Skidmore AK (2007) Can nutrient status of four woody plant species be predicted using field spectrometry? ISPRS J Photogramm Remote Sens 62:406–414
Galvez-Sola L, GarcÃa-Sánchez F, Pérez-Pérez JG et al (2015) Rapid estimation of nutritional elements on citrus leaves by near infrared reflectance spectroscopy. Front Plant Sci 6:1–8
Gao B-C (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gates DM, Keegan HJ, Schleter JC, Weidner VR (1965) Spectral properties of plants. Appl Opt 4:11–20
Gausman HW (1974) Leaf reflectance of near-infrared. Photogramm Eng 40:183–191
Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17
Gerber F, Marion R, Olioso A et al (2011) Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4μm to 5.7μm with the PROSPECT-VISIR model. Remote Sens Environ 115:404–414. https://doi.org/10.1016/j.rse.2010.09.011
Gitelson A, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol 143:286–292. https://doi.org/10.1016/S0176-1617(11)81633-0
Gitelson AA, Merzlyak MN (1997) Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens 18:2691–2697
Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58:289–298. https://doi.org/10.1016/S0034-4257(96)00072-7
Goetz AFH, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing. Science 228:1147–1153. https://doi.org/10.1126/science.228.4704.1147
Guilioni L, Jones HG, Leinonen I, Lhomme JP (2008) On the relationships between stomatal resistance and leaf temperatures in thermography. Agric For Meteorol 148:1908–1912. https://doi.org/10.1016/j.agrformet.2008.07.009
Gutierrez M, Reynolds MP, Klatt AR (2010) Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. J Exp Bot 61:3291–3303
Hansen PM, Schjoerring JK (2003) Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ 86:542–553. https://doi.org/10.1016/S0034-4257(03)00131-7
Hardisky MA, Klemas V, Smart RM (1983) The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm Eng Remote Sensing 49:77–83
Harry V (2006) Principles of soil and plant water relations. Geoderma 133:478
Hatfield JL, Gitelson AA, Schepers JS, Walthall CL (2008) Application of spectral remote sensing for agronomic decisions. Agron J 100:S–117
Havlin JL, Beaton JD, Tisdale SL, Nelson WL (2005) Soil fertility and fertilizers: an introduction to nutrient management. Pearson Prentice Hall, Upper Saddle River
Herrmann I, Karnieli A, Bonfil DJ et al (2010) SWIR-based spectral indices for assessing nitrogen content in potato fields. Int J Remote Sens 31:5127–5143
Horler DNH, Docray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288. https://doi.org/10.1080/01431168308948546
Huang Z, Turner BJ, Dury SJ et al (2004) Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens Environ 93:18–29. https://doi.org/10.1016/j.rse.2004.06.008
Hunt ER Jr, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30:43–54
Hunt ERJ, Rock BN (1989) Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens Environ 30:43–54. https://doi.org/10.1016/0034-4257(89)90046-1
Jensen JR (2009) Remote sensing of the environment: an earth resource perspective 2/e. Pearson Education India, Chennai
Ji HY, Wang PX, Yan TL (2007) [Estimations of chlorophyll and water contents in live leaf of winter wheat with reflectance spectroscopy]. Guang pu xue yu guang pu fen xi = Guang pu 27:514–516
Johansson J, Hagman O, Fjellner B-A (2003) Predicting moisture content and density distribution of Scots pine by microwave scanning of sawn timber. J Wood Sci 49:312–316. https://doi.org/10.1007/s10086-002-0493-7
Knox NM, Skidmore AK, Prins HHT et al (2012) Remote sensing of forage nutrients: combining ecological and spectral absorption feature data. ISPRS J Photogramm Remote Sens 72:27–35
Li L, Ustin SL, Lay M (2005) Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA. USA Int J Remote Sens 26:5193–5207. https://doi.org/10.1080/01431160500218911
Li F, Mistele B, Hu Y et al (2014) Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Eur J Agron 52:198–209
Li D, Wang C, Liu W et al (2016) Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra. Eur J Agron 80:182–194
Lichtenthaler HK, Gitelson A, Lang M (1996) Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. J Plant Physiol 148:483–493
Mahajan GR, Sahoo RN, Pandey RN et al (2014) Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precis Agric 15:499–522
Mahajan GR, Pandey RN, Sahoo RN et al (2016) Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precis Agric 15:499–522. https://doi.org/10.1007/s11119-016-9485-2
Malingreau JP (1989) The vegetation index and the study of vegetation dynamics. In: Toselli F (ed) Applications of remote sensing to agrometeorology. Springer, Dordrecht, pp 285–303
Maron JL, Crone E (2006) Herbivory: effects on plant abundance, distribution and population growth. Proc R Soc B Biol Sci 273:2575–2584. https://doi.org/10.1098/rspb.2006.3587
McGwire K, Minor T, Fenstermaker L (2000) Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sens Environ 72:360–374
Möller M, Alchanatis V, Cohen Y et al (2006) Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58:827–838
Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436
Mulla DJ (2013) Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114:358–371
Munjal R, Dhanda SS (2005) Physiological evaluation of wheat (Triticum aestivum L) genotypes for drought resistance. Indian J Genet Plant Breed 65:307–308
Mutanga O, Skidmore AK (2004) Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. Int J Appl Earth Obs Geoinf 5:87–96. https://doi.org/10.1016/j.jag.2004.01.001
Mutanga O, Skidmore AK, Kumar L, Ferwerda J (2005) Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. Int J Remote Sens 26:1093–1108. https://doi.org/10.1080/01431160512331326738
Nguyen HT, Lee B-W (2006) Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. Eur J Agron 24:349–356. https://doi.org/10.1016/j.eja.2006.01.001
Oerke E-C (2006) Crop losses to pests. J Agric Sci 144:31. https://doi.org/10.1017/S0021859605005708
Osborne SL, Schepers JS, Francis DD, Schlemmer MR (2002) Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agron J 94:1215–1221
Pagani A, EcheverrÃa HE (2011) Performance of sulfur diagnostic methods for corn. Agron J 103:413–421
Peñuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3:151–156. https://doi.org/10.1016/S1360-1385(98)01213-8
Peñuelas J, Gamon JA, Griffin KL, Field CB (1993) Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sens Environ 46:110–118. https://doi.org/10.1016/0034-4257(93)90088-F
Peñuelas J, Gamon JA, Fredeen AL et al (1994) Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens Environ 48:135–146. https://doi.org/10.1016/0034-4257(94)90136-8
Peñuelas J, Pinol J, Ogaya R, Filella I (1997) Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int J Remote Sens 18:2869–2875. https://doi.org/10.1080/014311697217396
Pimstein A, Karnieli A, Bonfil D (2007) Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis. J Appl Remote Sens 1:13530
Pimstein A, Karnieli A, Bansal SK, Bonfil DJ (2011) Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. F Crop Res 121:125–135
Ponzoni FJ, De JL, Goncalves M (1999) Spectral features associated with nitrogen, phosphorus, and potassium deficiencies in Eucalyptus saligna seedling leaves. Int J Remote Sens 20:2249–2264
Prasad B, Carver BF, Stone ML et al (2007) Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Sci 47:1416–1425. https://doi.org/10.2135/cropsci2006.08.0546
Pu R, Ge S, Kelly NM, Gong P (2003) Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia ) leaves. Int J Remote Sens 24:1799–1810. https://doi.org/10.1080/01431160210155965
Ramoelo A, Skidmore AK, Cho MA et al (2012) Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor. Int J Appl Earth Obs Geoinf 19:151–162
Ramoelo A, Skidmore AK, Cho MA et al (2013) Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS J Photogramm Remote Sens 82:27–40
Ranjan R, Chopra UK, Sahoo RN et al (2012) Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices. Int J Remote Sens 33:6342–6360. https://doi.org/10.1080/01431161.2012.687473
Ribeiro da Luz B (2006) Attenuated total reflectance spectroscopy of plant leaves: a tool for ecological and botanical studies. New Phytol 172:305–318. https://doi.org/10.1111/j.1469-8137.2006.01823.x
Ribeiro da Luz B, Crowley JK (2007) Spectral reflectance and emissivity features of broad leaf plants: prospects for remote sensing in the thermal infrared (8.0–14.0 μm). Remote Sens Environ 109:393–405. https://doi.org/10.1016/j.rse.2007.01.008
Rouse JW, Haas RH, Schell JA et al (1974) Monitoring the vernal advancements and retrogradation. Texas A M Univ, Texas
Salisbury JW (1986) Preliminary measurements of leaf spectral reflectance in the 8-14 μm region. Int J Remote Sens 7:1879–1886
Salisbury JW, Milton NM (1988) Thermal infrared (2.5-to 13.5-μm) directional hemispherical reflectance of leaves. Photogramm Eng Remote Sensing 54:1301–1304
Samborski SM, Tremblay N, Fallon E (2009) Strategies to make use of plant sensors-based diagnostic information for nitrogen recommendations. Agron J 101:800–816
Sepulcre-Cantó G, Zarco-Tejada PJ, Jiménez-Muñoz JC et al (2006) Detection of water stress in an olive orchard with thermal remote sensing imagery. Agric For Meteorol 136:31–44. https://doi.org/10.1016/j.agrformet.2006.01.008
Serrano L, Peñuelas J, Ustin SL (2002) Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sens Environ 81:355–364. https://doi.org/10.1016/S0034-4257(02)00011-1
Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354. https://doi.org/10.1016/S0034-4257(02)00010-X
Sinclair TR, Ludlow MM (1985) Who taught plants thermodynamics? The unfulfilled potential of plant water potential. Funct Plant Biol 12:213–217
Steele M, Gitelson AA, Rundquist D (2008) Nondestructive estimation of leaf chlorophyll content in grapes. Am J Enol Vitic 59:299–305
Stimson HC, Breshears DD, Ustin SL, Kefauver SC (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens Environ 96:108–118. https://doi.org/10.1016/j.rse.2004.12.007
Strauss SY, Zangerl AR (2002) Plant-insect interactions in terrestrial ecosystems. Plant-animal interactions: an evolutionary approach 77–106
Thomas EV, Haaland DM (1990) Comparison of multivariate calibration methods for quantitative spectral analysis. Anal Chem 62:1091–1099. https://doi.org/10.1021/ac00209a024
Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32. https://doi.org/10.1016/0034-4257(80)90096-6
Ullah S, Skidmore AK, Naeem M, Schlerf M (2012) An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis. Sci Total Environ 437:145–152. https://doi.org/10.1016/j.scitotenv.2012.08.025
Ullah S, Skidmore AK, Groen TA, Schlerf M (2013) Evaluation of three proposed indices for the retrieval of leaf water content from the mid-wave infrared (2–6μm) spectra. Agric For Meteorol 171–172:65–71. https://doi.org/10.1016/j.agrformet.2012.11.014
Ullah S, Skidmore AK, Ramoelo A et al (2014) Retrieval of leaf water content spanning the visible to thermal infrared spectra. ISPRS J Photogramm Remote Sens 93:56–64. https://doi.org/10.1016/j.isprsjprs.2014.04.005
Ustin SL, Roberts DA, Pinzón J et al (1998) Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens Environ 65:280–291. https://doi.org/10.1016/S0034-4257(98)00038-8
Vaiphasa C, Ongsomwang S, Vaiphasa T, Skidmore AK (2005) Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuar Coast Shelf Sci 65:371–379. https://doi.org/10.1016/j.ecss.2005.06.014
van Leeuwen WJD, Huete AR (1996) Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sens Environ 55:123–138
Wang L, Qu JJ (2007) NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys Res Lett 34:1–5
Wiegand CL, Richardson AJ, Escobar DE, Gerbermann AH (1991) Vegetation indices in crop assessments. Remote Sens Environ 35:105–119
Yanli L, Qiang L, Shaolan H et al (2015) Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Int J Agric Biol Eng 8:80
Zarco-Tejada PJ, Miller JR, Noland TL et al (2001) Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans Geosci Remote Sens 39:1491–1507. https://doi.org/10.1109/36.934080
Zarco-Tejada PJ, Rueda CA, Ustin SL (2003) Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens Environ 85:109–124
Zarco-Tejada PJ, Miller JR, Morales A et al (2004) Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens Environ 90:463–476
Zhang X, Liu F, He Y, Gong X (2013) Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosyst Eng 115:56–65. https://doi.org/10.1016/j.biosystemseng.2013.02.007
Zhao D, Raja Reddy K, Kakani VG et al (2003) Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant Soil 257:205–218. https://doi.org/10.1023/A:1026233732507
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Das, B., Mahajan, G.R., Singh, R. (2018). Hyperspectral Remote Sensing: Use in Detecting Abiotic Stresses in Agriculture. In: Bal, S., Mukherjee, J., Choudhury, B., Dhawan, A. (eds) Advances in Crop Environment Interaction. Springer, Singapore. https://doi.org/10.1007/978-981-13-1861-0_12
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