Norway spruce has a wide natural distribution range, harboring substantial physiological and genetic variation. There are three altitudinal ecotypes described in this species. Each ecotype has been shaped by natural selection and retains morphological and physiological characteristics. Foliar spectral reflectance is readily used in evaluating the physiological status of crops and forest ecosystems. However, underlying genetics of foliar spectral reflectance and pigment content in forest trees has rarely been investigated. We assessed the reflectance in a clonal bank comprising three ecotypes in two dates covering different vegetation season conditions. Significant seasonal differences in spectral reflectance among Norway spruce ecotypes were manifested in a wide-ranging reflectance spectrum. We estimated significant heritable variation and uncovered phenotypic and genetic correlations among growth and physiological traits through bivariate linear models utilizing spatial corrections. We confirmed the relative importance of the red edge within the context of the study site's ecotypic variation. When interpreting these findings, growth traits such as height, diameter, crown length, and crown height allowed us to estimate variable correlations across the reflectance spectrum, peaking in most cases in wavelengths connected to water content in plant tissues. Finally, significant differences among ecotypes in reflectance and other correlated traits were detected.

Department of Experimental Plant Biology Faculty of Science Charles University Prague Prague Czechia

Department of Genetics and Physiology of Forest Trees Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Prague Czechia

VSN International Hemel Hempstead United Kingdom

Zobrazit více v PubMed

Anderegg W. R., Hicke J. A., Fisher R. A., Allen C. D., Aukema J., Bentz B., et al. . (2015). Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674–683. doi: 10.1111/nph.13477 PubMed DOI

Androsiuk P., Shimono A., Westin J., Lindgren D., Fries A., Wang X. R. (2013). Genetic status of Norway spruce (Picea abies) breeding populations for northern Sweden. Silvae Genet. 62, 127–136. doi: 10.1515/sg-2013-0017 DOI

Apan A., Held A., Phinn S., Markley J. (2004). Detecting sugarcane 'orange rust'disease using EO-1 Hyperion hyperspectral imagery. Int. J. Remote Sens. 25, 489–498. doi: 10.1080/01431160310001618031 DOI

Berger K., Verrelst J., Féret J.-B., Wang Z., Wocher M., Strathmann M., et al. . (2020). Crop nitrogen monitoring: recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sens. Environ. 242:111758. doi: 10.1016/j.rse.2020.111758 PubMed DOI PMC

Bernier P. Y., Raulier F., Stenberg P., Ung C.-H. (2001). Importance of needle age and shoot structure on canopy net photosynthesis of balsam fir (Abies balsamea): a spatially inexplicit modeling analysis. Tree Physiol. 21, 815–830. doi: 10.1093/treephys/21.12-13.815, PMID: PubMed DOI

Butler D. G., Cullis B. R., Gilmour A. R., Gogel B. J., Thompson R. (2017). ASReml-R Reference Manual Version 4. Hemel Hempstead, UK: VSN International Ltd.

Campbell P. E., Rock B. N., Martin M. E., Neefus C. D., Irons J. R., Middleton E. M., et al. . (2004). Detection of initial damage in Norway spruce canopies using hyperspectral airborne data. Int. J. Remote Sens. 25, 5557–5584. doi: 10.1080/01431160410001726058 DOI

Carter G. A. (1993). Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80, 239–243. doi: 10.1002/j.1537-2197.1993.tb13796.x DOI

Cavender-Bares J., Meireles J. E., Couture J. J., Kaproth M. A., Kingdon C. C., Singh A., et al. . (2016). Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity. Remote Sens. 8:221. doi: 10.3390/rs8030221 DOI

Čepl J., Holá D., Stejskal J., Korecký J., Kočová M., Lhotáková Z., et al. . (2016). Genetic variability and heritability of chlorophyll a fluorescence parameters in scots pine (Pinus sylvestris L.). Tree Physiol. 36, 883–895. doi: 10.1093/treephys/tpw028, PMID: PubMed DOI

Čepl J., Stejskal J., Korecký J., Hejtmánek J., Faltinová Z., Lstibůrek M., et al. . (2020). The dehydrins gene expression differs across ecotypes in Norway spruce and relates to weather fluctuations. Sci. Rep. 10, 1–9. doi: 10.1038/s41598-020-76900-x PubMed DOI PMC

Čepl J., Stejskal J., Lhotáková Z., Holá D., Korecký J., Lstibůrek M., et al. . (2018). Heritable variation in needle spectral reflectance of scots pine (Pinus sylvestris L.) peaks in red edge. Remote Sens. Environ. 219, 89–98. doi: 10.1016/j.rse.2018.10.001 DOI

Chappelle E. W., Kim M. S., McMurtrey J. E., III (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, 239–247. doi: 10.1016/0034-4257(92)90089-3 DOI

Croft H., Chen J. M., Zhang Y. (2014). The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol. Complex. 17, 119–130. doi: 10.1016/j.ecocom.2013.11.005 DOI

Curran P. J., Dungan J. L., Gholz H. L. (1990). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol. 7, 33–48. doi: 10.1093/treephys/7.1-2-3-4.33, PMID: PubMed DOI

Datt B. (1999). A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves. J. Plant Physiol. 154, 30–36. doi: 10.1016/S0176-1617(99)80314-9 DOI

Deepak M., Keski-Saari S., Fauch L., Granlund L., Oksanen E., Keinänen M. (2019). Leaf canopy layers affect spectral reflectance in silver birch. Remote Sens. 11:2884. doi: 10.3390/rs11242884 DOI

Ditmarová Ľ., Kurjak D., Palmroth S., Kmeť J., Střelcová K. (2010). Physiological responses of Norway spruce (Picea abies) seedlings to drought stress. Tree Physiol. 30, 205–213. doi: 10.1093/treephys/tpp116, PMID: PubMed DOI

Einzmann K., Atzberger C., Pinnel N., Glas C., Böck S., Seitz R., et al. . (2021). Early detection of spruce vitality loss with hyperspectral data: results of an experimental study in Bavaria, Germany. Remote Sens. Environ. 266:112676. doi: 10.1016/j.rse.2021.112676 DOI

Eitel J. U., Gessler P. E., Smith A. M., Robberecht R. (2006). Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For. Ecol. Manag. 229, 170–182. doi: 10.1016/j.foreco.2006.03.027 DOI

Eldhuset T. D., Nagy N. E., Volařík D., Børja I., Gebauer R., Yakovlev I. A., et al. . (2013). Drought affects tracheid structure, dehydrin expression, and above-and belowground growth in 5-year-old Norway spruce. Plant Soil 366, 305–320. doi: 10.1007/s11104-012-1432-z DOI

Farjon A., Filer D. (2013). An Atlas of the World's Conifers: An Analysis of Their Distribution, Biogeography, Diversity and Conservation Status. Leiden, Netherlands: Brill.

Farooq M., Wahid A., Kobayashi N., et al. (2009). Plant drought stress: effects, mechanisms and management. Agron. Sustain. Dev. 29, 185–21. doi: 10.1051/agro:2008021 DOI

Gates D. M., Keegan H. J., Schleter J. C., Weidner V. R. (1965). Spectral properties of plants. Appl. Opt. 4, 11–20. doi: 10.1364/AO.4.000011 DOI

Gitelson A. A., Chivkunova O. B., Merzlyak M. N. (2009). Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. Am. J. Bot. 96, 1861–1868. doi: 10.3732/ajb.0800395, PMID: PubMed DOI

Gitelson A. A., Gritz Y., Merzlyak M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160, 271–282. doi: 10.1078/0176-1617-00887, PMID: PubMed DOI

Gitelson A. A., Merzlyak M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 18, 2691–2697. doi: 10.1080/014311697217558 DOI

Gitelson A. A., Merzlyak M. N., Lichtenthaler H. K. (1996). Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. J. Plant Physiol. 148, 501–508. doi: 10.1016/S0176-1617(96)80285-9 DOI

Goetz A. F. H. (2009). Three decades of hyperspectral remote sensing of the earth: a personal view. Remote Sens. Environ. 113, S5–S16. doi: 10.1016/j.rse.2007.12.014 DOI

Golhani K., Balasundram S. K., Vadamalai G., Pradhan B. (2019). Selection of a spectral index for detection of orange spotting disease in oil palm (Elaeis guineensis Jacq.) using red edge and neural network techniques. J. Indian Soc. Remote Sens. 47, 639–646. doi: 10.1007/s12524-018-0926-4 DOI

Harb A., Krishnan A., Ambavaram M. M., Pereira A. (2010). Molecular and physiological analysis of drought stress in Arabidopsis reveals early responses leading to acclimation in plant growth. Plant Physiol. 154, 1254–1271. doi: 10.1104/pp.110.161752, PMID: PubMed DOI PMC

Hart S. J., Veblen T. T., Eisenhart K. S., Jarvis D., Kulakowski D. (2014). Drought induces spruce beetle (Dendroctonus rufipennis) outbreaks across northwestern Colorado. Ecology 95, 930–939. doi: 10.1890/13-0230.1, PMID: PubMed DOI

Hernández-Clemente R., Navarro-Cerrillo R. M., Zarco-Tejada P. J. (2012). Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+ DART simulations. Remote Sens. Environ. 127, 298–315. doi: 10.1016/j.rse.2012.09.014 DOI

Hovi A., Raitio P., Rautiainen M. (2017). A spectral analysis of 25 boreal tree species. Silva Fenn. 51:7753. doi: 10.14214/sf.7753 DOI

Hunt E. R., Rock B. N. (1989). Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens. Environ. 30, 43–54. doi: 10.1016/0034-4257(89)90046-1 DOI

Isik F., Holland J., Maltecca C. (2017). Genetic Data Analysis for Plant and Animal Breeding (Vol. 400). Cham, Switzerland: Springer International Publishing.

Jactel H., Petit J., Desprez-Loustau M. L., Delzon S., Piou D., Battisti A., et al. . (2012). Drought effects on damage by forest insects and pathogens: a meta-analysis. Glob. Chang. Biol. 18, 267–276. doi: 10.1111/j.1365-2486.2011.02512.x DOI

Jaleel C. A., Manivannan P., Wahid A., Farooq M., Al-Juburi H. J., Somasundaram R., et al. . (2009). Drought stress in plants: a review on morphological characteristics and pigments composition. Int. J. Agric. Biol. 11, 100–105.

Jansson G., Danusevičius D., Grotehusman H., Kowalczyk J., Krajmerova D., Skrøppa T., et al. . (2013). “Norway spruce (Picea abies (L.) H. Karst.),” in Forest Tree Breeding in Europe. ed. Paques L. E. (Dordrecht: Springer; ), 123–176.

Jensen A. M., Warren J. M., Hanson P. J., Childs J., Wullschleger S. D. (2015). Needle age and season influence photosynthetic temperature response and total annual carbon uptake in mature Picea mariana trees. Ann. Bot. 116, 821–832. doi: 10.1093/aob/mcv115, PMID: PubMed DOI PMC

Jiang J., Comar A., Burger P., Bancal P., Weiss M., Baret F. (2018). Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model. Plant Methods 14, 1–16. doi: 10.1186/s13007-018-0291-x PubMed DOI PMC

Junker L. V., Ensminger I. (2016). Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves. Tree Physiol. 36, 694–711. doi: 10.1093/treephys/tpv148, PMID: PubMed DOI

Karlsson P. E., Medin E. L., Wallin G., Selldén G., Skärby L. (1997). Effects of ozone and drought stress on the physiology and growth of two clones of Norway spruce (Picea abies). New Phytol. 136, 265–275. doi: 10.1046/j.1469-8137.1997.00735.x DOI

Kokaly R. F., Asner G. P., Ollinger S. V., Martin M. E., Wessman C. A. (2009). Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 113, S78–S91. doi: 10.1016/j.rse.2008.10.018 DOI

Korecký J., Čepl J., Stejskal J., Faltinová Z., Dvořák J., Lstibůrek M., et al. . (2021). Genetic diversity of Norway spruce ecotypes assessed by GBS-derived SNPs. Sci. Rep. 11, 1–12. doi: 10.1038/s41598-021-02545-z PubMed DOI PMC

Krutovskii K. V., Bergmann F. (1995). Introgressive hybridization and phylogenetic relationships between Norway, Picea abies (L.) Karst., and Siberian, P. obovata Ledeb., spruce species studied by isozyme loci. Heredity 74, 464–480. doi: 10.1038/hdy.1995.67 DOI

Lehnert L. W., Meyer H., Obermeier W. A., Silva B., Regeling B., Thies B. (2019). Hyperspectral Data Analysis in R: The hsdar Package. J. Stat. Softw. 89, 1–23. doi: 10.18637/jss.v089.i12 DOI

Lhotáková Z., Kopačková-Strnadová V., Oulehle F., Homolová L., Neuwirthová E., Švik M., et al. . (2021). Foliage biophysical trait prediction from laboratory spectra in Norway spruce is more affected by needle age Than by site soil conditions. Remote Sens. 13:391. doi: 10.3390/rs13030391 DOI

Lichtenthaler H. K. (1987). Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. Methods Enzymol. 148, 350–382. doi: 10.1016/0076-6879(87)48036-1 DOI

Masaitis G., Mozgeris G., Augustaitis A. (2013). Spectral reflectance properties of healthy and stressed coniferous trees. Iforest 6, 30–36. doi: 10.3832/ifor0709-006 DOI

McMurtrey J. E., Chappelle E. W., Kim M. S., Meisinger J. J., Corp L. A. (1994). Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sens. Environ. 47, 36–44. doi: 10.1016/0034-4257(94)90125-2 DOI

Mišurec J., Kopačková V., Lhotáková Z., Hanuš J., Weyermann J., Entcheva-Campbell P., et al. . (2012). Utilization of hyperspectral image optical indices to assess the Norway spruce forest health status. J. Appl. Remote. Sens. 6, 063545–063551. doi: 10.1117/1.JRS.6.063545 DOI

Moorthy I., Miller J. R., Noland T. L. (2008). Estimating chlorophyll concentration in conifer needles with hyperspectral data: an assessment at the needle and canopy level. Remote Sens. Environ. 112, 2824–2838. doi: 10.1016/j.rse.2008.01.013 DOI

Morgenstern E. K. (1996). Geographic Variation in Forest Trees: Genetic Basis and Application of Knowledge in Silviculture. UBC Press: Vancouver, 109–115.

Neuwirthová E., Kuusk A., Lhotáková Z., Kuusk J., Albrechtová J., Hallik L. (2021). Leaf age matters in remote sensing: taking ground truth for spectroscopic studies in hemiboreal deciduous trees with continuous leaf formation. Remote Sens. 13:1353. doi: 10.3390/rs13071353 DOI

Oleksyn J., Modrzýnski J., Tjoelker M. G., Zytkowiak R., Reich P. B., Karolewski P. (1998). Growth and physiology of Picea abies populations from elevational transects: Common garden evidence for altitudinal ecotypes and cold adaptation. Funct. Ecol. 12, 573–590. doi: 10.1046/j.1365-2435.1998.00236.x DOI

Peñuelas J., Fillela I., Biel C., Serrano L., Savé R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 14, 1887–1905. doi: 10.1080/01431169308954010 DOI

Peñuelas J., Inoue Y. (1999). Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica 36, 355–360. doi: 10.1023/A:1007033503276 DOI

Persson A., Holmgren J., Soderman U. (2002). Detecting and measuring individual trees using an airborne laser scanner. Photogramm. Eng. Remote. Sens. 68, 925–932.

R Core Team (2022). A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/

Rathod P. H., Brackhage C., Müller I., Van der Meer F. D., Noomen M. F. (2018). Assessing metal-induced changes in the visible and near-infrared spectral reflectance of leaves: a pot study with sunflower (Helianthus annuus L.). J. Indian Soc. Remote Sens. 46, 1925–1937. doi: 10.1007/s12524-018-0846-3 DOI

Rock B. N., Hoshizaki T., Miller J. R. (1988). Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sens. Environ. 24, 109–127. doi: 10.1016/0034-4257(88)90008-9 DOI

Rock B. N., Williams D. L., Moss D. M., Lauten G. N., Kim M. (1994). High-spectral resolution field and laboratory optical reflectance measurements of red spruce and eastern hemlock needles and branches. Remote Sens. Environ. 47, 176–189. doi: 10.1016/0034-4257(94)90154-6 DOI

Sánchez M. T., De la Haba M. J., Benítez-López M., Fernández-Novales J., Garrido-Varo A., Pérez-Marín D. (2012). Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. J. Food Eng. 110, 102–108. doi: 10.1016/j.jfoodeng.2011.12.003 DOI

Sankey T., Donager J., McVay J., Sankey J. B. (2017). UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens. Environ. 195, 30–43. doi: 10.1016/j.rse.2017.04.007 DOI

Schaepman-Strub G., Schaepman M. E., Painter T. H., Dangel S., Martonchik J. V. (2006). Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sens. Environ. 103, 27–42. doi: 10.1016/j.rse.2006.03.002 DOI

Schiop S. T., Al Hassan M., Sestras A. F., Boscaiu M., Sestras R. E., Vicente O. (2017). Biochemical responses to drought, at the seedling stage, of several Romanian Carpathian populations of Norway spruce (Picea abies L. Karst). Trees 31, 1479–1490. doi: 10.1007/s00468-017-1563-1 DOI

Schlerf M., Atzberger C., Hill J., Buddenbaum H., Werner W., Schüler G. (2010). Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. Int. J. Appl. Earth Obs. Geoinf. 12, 17–26. doi: 10.1016/j.jag.2009.08.006 DOI

Sena C. M., Leandro A., Azul L., Seiça R., Perry G. (2018). Vascular oxidative stress: impact and therapeutic approaches. Front. Physiol. 9:1668. doi: 10.3389/fphys.2018.01668, PMID: PubMed DOI PMC

Serrano L., Peñuelas J., Ustin S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens. Environ. 81, 355–364. doi: 10.1016/S0034-4257(02)00011-1 DOI

Sims D. A., 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, 337–354. doi: 10.1016/S0034-4257(02)00010-X DOI

Šindelář J. (1975). Klonové archivy smrku ztepilého Picea abies Karst. na PLO Zbraslav-Strnady – polesí Jíloviště. Jíloviště-Strnady. VÚLHM.

Slaton M. R., Hunt E. R., Smith W. K. (2001). Estimating near-infrared leaf reflectance from leaf structural characteristics. Am. J. Bot. 88, 278–284. doi: 10.2307/2657019, PMID: PubMed DOI

Smith R. C. G., Adams J., Stephens D. J., Hick P. T. (1995). Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Aust. J. Agric. Res. 46, 113–125. doi: 10.1071/AR9950113 DOI

Solovchenko A. E., Chivkunova O. B., Gitelson A. A., Merzlyak M. N. (2010). Non-destructive Estimation Pigment Content Ripening Quality and Damage in Apple Fruit With Spectral Reflectance in the Visible Range. Fresh Produce. 4.

Sonobe R., Wang Q. (2017). Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests. Eco. Inform. 37, 1–9. doi: 10.1016/j.ecoinf.2016.11.007 DOI

Tao X., Li Y., Yan W., Wang M., Tan Z., Jiang J., et al. . (2021). Heritable variation in tree growth and needle vegetation indices of slash pine (Pinus elliottii) using unmanned aerial vehicles (UAVs). Ind. Crop. Prod. 173:114073. doi: 10.1016/j.indcrop.2021.114073 DOI

Tomášková I., Pastierovič F., Krejzková A., Čepl J., Hradecký J. (2021). Norway spruce ecotypes distinguished by chlorophyll a fluorescence kinetics. Acta Physiol. Plant. 43, 1–6. doi: 10.1007/s11738-020-03190-1 DOI

Trujillo-Moya C., George J. P., Fluch S., Geburek T., Grabner M., Karanitsch-Ackerl S., et al. . (2018). Drought sensitivity of Norway spruce at the species' warmest fringe: quantitative and molecular analysis reveals high genetic variation among and within provenances. G3 8, 1225–1245. doi: 10.1534/g3.117.300524 PubMed DOI PMC

Tutin T. G., Heywood V. H., Burges N. A., Valentine D. H. (eds.) (1964). Flora Europaea: Plantaginaceae to Compositae (and Rubiaceae). Vol. 4. Cambridge, UK: Cambridge University Press.

Ustin S. L., Gitelson A. A., Jacquemoud S., Schaepman M., Asner G. P., Gamon J. A., et al. . (2009). Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens. Environ. 113, S67–S77. doi: 10.1016/j.rse.2008.10.019 DOI

Valcu C.-M., Lalanne C., Plomion C., Schlink K. (2008). Heat induced changes in protein expression profiles of Norway spruce (Picea abies) ecotypes from different elevations. Proteomics 8, 4287–4302. doi: 10.1002/pmic.200700992, PMID: PubMed DOI

Van der Maaten-Theunissen M., Kahle H. P., van der Maaten E. (2013). Drought sensitivity of Norway spruce is higher than that of silver fir along an altitudinal gradient in southwestern Germany. Ann. For. Sci. 70, 185–193. doi: 10.1007/s13595-012-0241-0 DOI

Verrelst J., Malenovský Z., Van der Tol C., Camps-Valls G., Gastellu-Etchegorry J.-P., Lewis P., et al. . (2019). Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surv. Geophys. 40, 589–629. doi: 10.1007/s10712-018-9478-y PubMed DOI PMC

Wallin G., Karlsson P. E., Selldén G., Ottosson S., Medin E. L., Pleijel H., et al. . (2002). Impact of four years exposure to different levels of ozone, phosphorus and drought on chlorophyll, mineral nutrients, and stem volume of Norway spruce, Picea abies. Physiol. Plant. 114, 192–206. doi: 10.1034/j.1399-3054.2002.1140205.x, PMID: PubMed DOI

Wang C., Feng M., Yang W., Ding G., Xiao L., Li G., et al. . (2017). Extraction of sensitive bands for monitoring the winter wheat (Triticum aestivum) growth status and yields based on the spectral reflectance. PLoS One 12:e0167679. doi: 10.1371/journal.pone.0167679, PMID: PubMed DOI PMC

Weed A. S., Ayres M. P., Hicke J. A. (2013). Consequences of climate change for biotic disturbances in north American forests. Ecol. Monogr. 83, 441–470. doi: 10.1890/13-0160.1 DOI

Wu C., Niu Z., Tang Q., Huang W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric. For. Meteorol. 148, 1230–1241. doi: 10.1016/j.agrformet.2008.03.005 DOI

Yakovlev I. A., Asante D. K., Fossdal C. G., Partanen J., Junttila O., Johnsen Ø. (2008). Dehydrins expression related to timing of bud burst in Norway spruce. Planta 228, 459–472. doi: 10.1007/s00425-008-0750-0, PMID: PubMed DOI

Yang C. M., Chen R. K. (2004). Modeling rice growth with hyperspectral reflectance data. Crop Sci. 44, 1283–1290. doi: 10.2135/cropsci2004.1283 DOI

Yang X., Tang J., Mustard J. F., Wu J., Zhao K., Serbin S., et al. . (2016). Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. Remote Sens. Environ. 179, 1–12. doi: 10.1016/j.rse.2016.03.026 DOI

Zhang Y., Zheng L., Li M., Deng X., Ji R. (2015). Predicting apple sugar content based on spectral characteristics of apple tree leaf in different phenological phases. Comput. Electron. Agric. 112, 20–27. doi: 10.1016/j.compag.2015.01.006 DOI

Mind the leaf anatomy while taking ground truth with portable chlorophyll meters

Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings