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Variability and Uncertainty Challenges in Scaling Imaging Spectroscopy Retrievals and Validations from Leaves Up to Vegetation Canopies

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Abstract

Imaging spectroscopy of vegetation requires methods for scaling and generalizing optical signals that are reflected, transmitted and emitted in the solar wavelength domain from single leaves and observed at the level of canopies by proximal sensing, airborne and satellite spectroradiometers. The upscaling embedded in imaging spectroscopy retrievals and validations of plant biochemical and structural traits is challenged by natural variability and measurement uncertainties. Sources of the leaf-to-canopy upscaling variability and uncertainties are reviewed with respect to: (1) implementation of retrieval algorithms and (2) their parameterization and validation of quantitative products through in situ field measurements. The challenges are outlined and discussed for empirical and physical leaf and canopy radiative transfer modelling components, considering both forward and inverse modes. Discussion on optical remote sensing validation schemes includes also description of a multiscale validation concept and its advantages. Impacts of intraspecific and interspecific variability on collected field and laboratory measurements of leaf biochemical traits and optical properties are demonstrated for selected plant species, and field measurement uncertainty sources are listed and discussed specifically for foliar pigments and canopy leaf area index. The review concludes with the main findings and suggestions as how to reduce uncertainties and include variability in scaling vegetation imaging spectroscopy signals and functional traits of single leaves up to observations of whole canopies.

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References

  • Albert CH, Thuiller W, Yoccoz NG, Douzet R, Aubert S, Lavorel S (2010) A multi-trait approach reveals the structure and the relative importance of intra- vs. interspecific variability in plant traits. Funct Ecol 24:1192–1201

    Google Scholar 

  • Albrechtová J, Janáček J, Lhotáková Z, Radochová B, Kubínová L (2007) Novel efficient methods for measuring mesophyll anatomical characteristics from fresh thick sections using stereology and confocal microscopy: application on acid rain-treated Norway spruce needles. J Exp Bot 58:1451–1461

    Google Scholar 

  • Allen R, Wette F (1969) Calculation of dynamical surface properties of noble-gas crystals. I. The quasiharmonic approximation. Phys Rev 179:873

    Google Scholar 

  • Amato U, Antoniadis A, Carfora MF, Colandrea P, Cuomo V, Franzese M, Pignatti S, Serio C (2013) Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use. IEEE J Sel Top Appl Earth Observ Remote Sens 6:615–625

    Google Scholar 

  • Atherton J, Olascoaga B, Alonso L, Porcar-Castell A (2017) Spatial Variation of leaf optical properties in a boreal forest is influenced by species and light environment. Front Plant Sci 8:309

    Google Scholar 

  • Atzberger C, Richter K (2012) Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery. Remote Sens Environ 120:208–218

    Google Scholar 

  • Auger S, Shipley B (2013) Inter-specific and intra-specific trait variation along short enviromental gradients in an old-growth temperate forest. J Veg Sci 24:419–428

    Google Scholar 

  • Bachmann M, Adar S, Ben-Dor E, Biesemans J, Briottet X, Grant M, Hanus J, Holzwarth S, Hueni A, Kneubuehler M et al (2011) Towards agreed data quality layers for airborne hyperspectral imagery. In: Proceedings of the 7th EARSeL-SIG-IS, Edinburgh, UK, 11–13 April 2011

  • Bachmann M, Makarau A, Segl K, Richter R (2015) Estimating the influence of spectral and radiometric calibration uncertainties on EnMAP data products—examples for ground reflectance retrieval and vegetation indices. Remote Sens-Basel 7:10689–10714

    Google Scholar 

  • Bacour C, Baret F, Béal D, Weiss M, Pavageau K (2006) Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: principles and validation. Remote Sens Environ 105:313–325

    Google Scholar 

  • Baldocchi DD, Falge E, Gu L, Olson R, Hollinger DY, Running SW, Anthoni P, Bernhofer C, Davis KJ, Evans R, Fuentes J et al (2001) FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull Am Meteorol Soc 82:2415–2434

    Google Scholar 

  • Bao Y, Ni W, Wang D, Yue C, He H, Verbeeck H (2018) Effects of tree trunks on estimation of clumping index and LAI from HemiView and terrestrial LiDAR. Forests 9:144

    Google Scholar 

  • Baraloto C, Paine TCE, Patiño S, Bonal D, Hérault B, Chave J (2010) Functional trait variation and sampling strategies in species-rich plant communities. Funct Ecol 24:208–216

    Google Scholar 

  • Baret F, Weiss M, Allard D, Garrigues S, Leroy M, Jeanjean H et al (2005) VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products. Remote Sens Environ 76:36–39

    Google Scholar 

  • Baret F, Morissette J, Fernandes R, Champeaux JL, Myneni R, Chen J, Plummer S, Weiss M, Bacour C, Garrigue S et al (2006) Evaluation of the representativeness of networks of sites for the global validation and inter-comparison of land biophysical products. Proposition of the CEOS-BELMANIP. IEEE T Geosci Remote 44:1794–1803

    Google Scholar 

  • Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Nino F, Weiss M, Samain O et al (2007) LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: part 1: principles of the algorithm. Remote Sens Environ 110:275–286

    Google Scholar 

  • Barry K, Newnham G, Stone C (2009) Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT. Agric For Meteorol 149:1209–1213

    Google Scholar 

  • Bréda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54:2403–2417

    Google Scholar 

  • Breece H, Holmes R (1971) Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo. Appl Opt 10:119–127

    Google Scholar 

  • Buddenbaum H, Stern O, Stellmes M, Stoffels J, Pueschel P, Hill J, Werner W (2012) Field imaging spectroscopy of beech seedlings under dryness stress. Remote Sens-Basel 4:3721–3740

    Google Scholar 

  • Buddenbaum H, Rock G, Hill J, Werner W (2015a) Measuring stress reactions of beech seedlings with PRI, fluorescence, temperatures and emissivity from VNIR and thermal field imaging spectroscopy. Eur J Remote Sens 48:263–282

    Google Scholar 

  • Buddenbaum H, Stern O, Paschmionka B, Hass E, Gattung T, Stoffels J, Hill J, Werner W et al (2015b) Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings. Int J Remote Sens 36:4590–4605

    Google Scholar 

  • Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile V, Disney M, Armston J, Kaasalainen M (2015) Nondestructive estimates of above- ground biomass using terrestrial laser scanning. Methods Ecol Evol 6:198–208

    Google Scholar 

  • Camps-Valls G, Verrelst J, Muoz-Mar J, Laparra V, Mateo-Jimenez F, Gomez-Dans J (2016) A survey on Gaussian processes for earth observation data analysis. IEEE Geosci Remote Sens Mag 4:58–78

    Google Scholar 

  • Cerovic ZG, Masdoumier G, Ghozlen NB, Latouche G (2012) A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol Plant 146:251–260

    Google Scholar 

  • Chen JM, Rich PM, Gower ST, Norman JM, Plummer SE (1997) Leaf area index of boreal forests: theory, techniques and measurements. J Geophys Res 102:29429–429443

    Google Scholar 

  • Claverie M, Vermote EF, Franch B, Masek JG (2015) Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products. Remote Sens Environ 109:390–403

    Google Scholar 

  • Cohen W, Justice C (1999) Validating MODIS terrestrial ecology products: linking in situ and satellite measurements. Remote Sens Environ 70:1–4

    Google Scholar 

  • Collis DG, Harris JWE (1973) Line-throwing gun and cutter for obtaining branches from tree crowns. Can J For Res 3:149–154

    Google Scholar 

  • Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y, Wang L (2002) Retrieval of canopy biophysical variables from bidirectional reflectance. Using prior information to solve the ill-posed inverse problem. Remote Sens Environ 84:1–15

    Google Scholar 

  • Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309–347

    Google Scholar 

  • Corbari C, Sobrino JA, Mancini M, Hidalgo V (2013) Mass and energy flux estimates at different spatial resolutions in a heterogeneous area through a distributed energy-water balance model and remote-sensing data. Int J Remote Sens 34:3208–3230

    Google Scholar 

  • Croft H, Chen J, Wang R, Mo G, Luo S, Luo X, He L, Gonsamo A, Arabian J, Zhang Y, Simic-Milas A et al The global distribution of leaf chlorophyll content. Remote Sens Environ (in review)

  • Dawson TP, Curran PJ, Plummer SE (1998) LIBERTY—modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sens Environ 65:50–60

    Google Scholar 

  • de Bello F, Lavorel S, Albert CH, Thuiller W, Grigulis K, Dolezal J, Janeček S, Lepš J (2011) Quantifying the relevance of intraspecific trait variability for functional diversity. Methods Ecol Evol 2(2):163–174

    Google Scholar 

  • Delegido J, Alonso L, González G, Moreno J (2010) Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). Int J Appl Earth Obs 12:165–174

    Google Scholar 

  • Dungan JL (2001) Scaling up and scaling down: the relevance of the support effect on remote sensing of vegetation. In: Tate NJ, Atkinson PM (eds) Modelling scale in geographic information science. Wiley, Chichester, p 277

    Google Scholar 

  • Eckrich CA, Flaherty EA, Ben-David M (2013) Estimating leaf area index in Southeast Alaska: a comparison of two techniques. PLoS ONE 8:e77642

    Google Scholar 

  • Feng M, Huang Ch, Channan S, Vermote EF, Masek JG, Townshend JR (2012) Quality assessment of Landsat surface reflectance products using MODIS data. Comput Geosci 38:9–22

    Google Scholar 

  • Féret J-B, Asner GP (2011) Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sens Environ 115:2415–2422

    Google Scholar 

  • Féret J-B, Francois C, Asner GP, Gitelson AA, Martin RE, Bidel LPR, Ustin SL, le Maire G, Jacquemoud S (2008) PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens Environ 112:3030–3043

    Google Scholar 

  • Féret J-B, Gitelson AA, Noble SD, Jacquemoud S (2017) PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ 193:204–215

    Google Scholar 

  • Fernandes R, Plummer S, Nightingale J, Baret F, Camacho F, Fang H, Garrigues S, Gobron N, Lang M, Lacaze R, Leblanc S, Meroni M, Martinez B, Nilson T, Pinty B, Pisek J, Sonnentag O, Verger A, Welles J, Weiss M, Widlowski JL (2014) Global leaf area index product validation good practices. Version 2.0. In: Schaepman-Strub G, Román M, Nickeson J (eds) Best practice for satellite-derived land product validation. Land Product Validation Subgroup (WGCV/CEOS), p 76

  • Gamon JA, Rahman AF, Dungan JL, Schildhauer M, Huemmrich KF (2006) Spectral Network (SpecNet): what is it and why do we need it? Remote Sens Environ 103:227–235

    Google Scholar 

  • Gamon JA, Somers B, Malenovský Z, Middleton EM, Rascher U, Schaepman ME (2019) Assessing vegetation function with imaging spectroscopy. Surv Geophys. https://doi.org/10.1007/s10712-019-09511-5

  • Garrigues S, Lacaze R, Baret F, Morisette J, Weiss M, Nickeson J, Fernandes R, Plummer S, Shabanov NV, Myneni R et al (2008a) Validation and intercomparison of global leaf area index products derived from remote sensing data. J Geophys Res 113:G02028

    Google Scholar 

  • Garrigues S, Shabanov NV, Swanson K, Morisette JT, Baret F, Myneni RB (2008b) Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands. Agric For Meteorol 148:1193–1209

    Google Scholar 

  • Gascon F, Bouzinac C, Thépaut O, Jung M, Francesconi B, Louis J, Lonjou V, Lafrance B et al (2017) Copernicus Sentinel-2A calibration and products validation status. Remote Sens-Basel 9:584

    Google Scholar 

  • Gastellu-Etchegorry JP, Demarez V, Pinel V, Zagolski F (1996) Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sens Environ 58:131–156

    Google Scholar 

  • Gastellu-Etchegorry JP, Lauret N, Yin T, Landier L, Kallel A, Malenovský Z et al (2017) DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE J Sel Top Appl 10:2640–2649

    Google Scholar 

  • GCOS (2011) Systematic observation requirements for satellite-based products for climate, 2011 Update, Supplemental Details to the Satellite-Based Component of the Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC. GCOS-154: 138

  • Gorroño J, Fomferra N, Peters M, Gascon F, Underwood CI, Fox NP, Kirches G, Brockmann C (2017) A radiometric uncertainty tool for the Sentinel 2 mission. Remote Sens-Basel 9:178

    Google Scholar 

  • Govaerts Y, Verstraete MM (1998) Raytran: a Monte Carlo ray-tracing model to compute light scattering in three-dimensional heterogeneous media. IEEE Trans Geosci Remote 36:493–505

    Google Scholar 

  • Guanter L, Kaufmann H, Segl K, Foerster S, Rogass C, Chabrillat S, Kuester T, Hollstein A, Rossner G, Chlebek C, Straif C et al (2015) The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sens-Basel 7:8830–8857

    Google Scholar 

  • Homolová L, Malenovský Z, Clevers JGPW, García-Santos G, Schaepman ME (2013) Review of optical-based remote sensing for plant trait mapping. Ecol Complex 15:1–16

    Google Scholar 

  • Hovi A, Raitio P, Rautiainen M (2017) A spectral analysis of 25 boreal tree species. Silva Fenn 51:7753

    Google Scholar 

  • Hueni A, Nieke J, Schopfer J, Kneubühler M, Itten K (2009) The spectral database SPECCHIO for improved long term usability and data sharing. Comput Geosci 37:861–873

    Google Scholar 

  • Hueni A, Lenhard K, Baumgartner A, Schaepman ME (2013) The APEX (airborne prism experiment-imaging spectrometer) calibration information system. IEEE Trans Geosci Remote Sens 51(11):5169–5180

    Google Scholar 

  • Hueni A, Damm A, Kneubuehler M, Schläpfer D, Schaepman ME (2017) Field and airborne spectroscopy cross validation—some considerations. IEEE J Sel Top Appl 10:1117–1135

    Google Scholar 

  • Humplík JF, Lazár D, Husičková A, Spíchal L (2015) Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses—review. Plant Methods 11:29

    Google Scholar 

  • Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34:75–91

    Google Scholar 

  • Janoutová R, Homolová L, Malenovský Z, Hanuš J, Lauret N, Gastellu-Etchegorry JP (2019) Influence of 3D spruce tree representation on accuracy of airborne and satellite forest reflectance simulated in DART. Forests 10:292

    Google Scholar 

  • Jay S, Bendoula R, Hadoux X, Féret B, Gorretta N (2016) A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy. Remote Sens Environ 177:220–236

    Google Scholar 

  • Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F (2004) Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agric For Meteorol 121:19–35

    Google Scholar 

  • Justice C, Belward A, Morisette J, Lewis P, Privette J, Baret F (2000) Developments in the ‘validation’ of satellite sensor products for the study of the land surface. Int J Remote Sens 21(17):3383–3390

    Google Scholar 

  • Juszak I, Iturrate-Garcia M, Gastellu-Etchegorry JP, Schaepman ME, Maximov TC, Schaepman-Strub G (2017) Drivers of shortwave radiation fluxes in Arctic tundra across scales. Remote Sens Environ 193:86–102

    Google Scholar 

  • Kanning M, Kühling I, Trautz D, Jarmer T (2018) High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sens 10:2000

    Google Scholar 

  • Kattge J, Díaz S, Lavorel S, Prentice IC, Leadley P et al (2011) TRY—a global database of plant traits. Glob Change Biol 17:2905–2935

    Google Scholar 

  • Kimes D, Knyazhikhin Y, Privette J, Abuelgasim A, Gao F (2000) Inversion methods for physically-based models. Remote Sens Rev 18:381–439

    Google Scholar 

  • Knipling E (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159

    Google Scholar 

  • Knyazikhin Y, Schull MA, Stenberg P, Mõttus M, Rautiainen M, Yang Y, Marshak A, Carmona PL, Kaufmann RK, Lewis P, Disney MI et al (2013) Hyperspectral remote sensing of foliar nitrogen content. Proc Natl Acad Sci USA 110:E185–E192

    Google Scholar 

  • Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA (2009) Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens Environ 113:S78–S91

    Google Scholar 

  • Kükenbrink D, Hueni A, Schneider FD, Damm A, Gastellu-Etchegorry JP, Schaepman ME, Morsdorf F (2019) Mapping the irradiance field of a single tree: quantifying vegetation induced adjacency effects. IEEE Trans Geosci Remote. https://doi.org/10.1109/tgrs.2019.2895211

    Google Scholar 

  • Kuusk A, Kuusk J, Lang J (2014) Modeling directional forest reflectance with the hybrid type forest reflectance model FRT. Remote Sens Environ 49:196–204

    Google Scholar 

  • Laurent VCE, Verhoef W, Damm A, Schaepman ME, Clevers JGPW (2013) A Bayesian object-based approach for estimating vegetation biophysical and biochemical variables from APEX at-sensor radiance data. Remote Sens Environ 139:6–17

    Google Scholar 

  • Laurent VCE, Schaepman ME, Verhoef W, Weyermann J, Chavez RO (2014) Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image. Remote Sens Environ 140:318–329

    Google Scholar 

  • Lewis P (1999) Three-dimensional plant modelling for remote sensing simulation studies using the botanical plant modelling system. Agron Agric Environ 19:185–210

    Google Scholar 

  • Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, Hoboken. ISBN 0-471-28166-2

    Google Scholar 

  • Lichtenthaler HK, Ač A, Marek MV, Kalina J, Urban O (2007) Differences in pigment composition, photosynthetic rates and chlorophyll fluorescence images of sun and shade leaves of four tree species. Plant Physiol Biochem 45:577–588

    Google Scholar 

  • Liu Z, Chen JM, Jin G, Qi Y (2015) Estimating seasonal variations of leaf area index using litterfall collection and optical methods in four mixed evergreen-deciduous forests. Agric For Meteorol 209–210:36–48

    Google Scholar 

  • Lucieer A, Malenovský Z, Veness T, Wallace L (2014) HyperUAS—imaging spectroscopy from a multi-rotor unmanned aircraft system. J Field Robot 31:571–590

    Google Scholar 

  • Lukeš P, Stenberg P, Rautiainen M, Mottus M, Vanhatalo K (2013) Optical properties of leaves and needles for boreal tree species in Europe. Remote Sens Lett 4:667–676

    Google Scholar 

  • Lukeš P, Homolová L, Navrátil M, Hanuš J (2017) Assessing the consistency of optical properties measured in four integrating spheres. Int J Remote Sens 38:3817–3830

    Google Scholar 

  • Lyapustin A, Wang Y, Xiong X, Meister G, Platnick S, Levy R, Franz B, Korkin S, Hilker T, Tucker J, Hall F, Sellers P, Wu A, Angal A (2014) Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos Meas Tech 7:4353–4365

    Google Scholar 

  • Macfarlane C, Arndt SK, Livesley SJ, Edgar AC, White DA, Adams MA, Eamus D (2007) Estimation of leaf area index in eucalypt forest with vertical foliage, using cover and fullframe fisheye photography. For Ecol Manag 242:756–763

    Google Scholar 

  • Malenovský Z, Albrechtová J, Lhotáková Z, Zurita-Milla R, Clevers JGPW, Schaepman ME, Cudlín P (2006) Applicability of the PROSPECT model for Norway spruce needles. Int J Remote Sens 27:5315–5340

    Google Scholar 

  • Malenovský Z, Bartholomeus HM, Acerbi-Junior FW, Schopfer JT, Painter TH, Epema GF, Bregt AK (2007) Scaling dimensions in spectroscopy of soil and vegetation. Int J Appl Earth Obs 9:137–164

    Google Scholar 

  • Malenovský Z, Homolová L, Zurita-Milla R, Lukeš P, Kaplan V, Hanuš J, Gastellu-Etchegorry JP, Schaepman ME (2013) Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer. Remote Sens Environ 131:85–102

    Google Scholar 

  • Malenovský Z, Turnbull J, Lucieer A, Robinson SA (2015) Antarctic moss stress assessment based on chlorophyll, water content, and leaf density retrieved from imaging spectroscopy data. New Phytol 208:608–624

    Google Scholar 

  • Malenovský Z, Lucieer A, King D, Turnbull J, Robinson SA (2017) Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods Ecol Evol 8:1842–1857

    Google Scholar 

  • Marceau DJ, Hay GJ (1999) Remote sensing contributions to the scale issue. Can J Remote Sens 25:357–366

    Google Scholar 

  • McGloin R, Šigut L, Havránková K, Dušek J, Pavelka M, Sedlák P (2018) Energy balance closure at a variety of ecosystems in Central Europe with contrasting topographies. Agric For Meteorol 248:418–431

    Google Scholar 

  • Moreno-Martínez Á, Camps-Valls G, Kattge J, Robinson N, Reichstein M et al (2018) A methodology to derive global maps of leaf traits using remote sensing and climate data. Remote Sens Environ 218:69–88

    Google Scholar 

  • Morisette JT, Baret F, Privette JL, Myneni RB, Nickeson JE, Garrigues S, Shabanov NV, Weiss M, Fernandes RA, Leblanc SG et al (2006) Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Trans Geosci Remote 44:1804–1817

    Google Scholar 

  • Mussche S, Samson R, Nachtergale L, De Schrijver A, Lemeur R, Lust N (2001) A comparison of optical and direct methods for monitoring the seasonal dynamics of leaf area index in deciduous forests. Silva Fenn 35:373–384

    Google Scholar 

  • Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR et al (2002) Global products of vegetation leaf area and absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231

    Google Scholar 

  • Niinemets Ü (2010) A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol Res 25:693–714

    Google Scholar 

  • Noda HM, Motohka T, Murakamii K, Muraoka H, Nasahara KN (2013) Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer. Plant Cell Environ 36:1903–1909

    Google Scholar 

  • North PRJ (1996) Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans Geosci Remote 34:946–956

    Google Scholar 

  • Parry C, Blonquist JM, Bugbee B (2014) In situ measurement of leaf chlorophyll concentration: analysis of the optical/absolute relationship. Plant Cell Environ 37:2508–2520

    Google Scholar 

  • Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, Bret-Harte MS et al (2013) New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot 61:167–234

    Google Scholar 

  • QA4EO Task Team (2009) A quality assurance framework for Earth Observation: Principles. http://qa4eo.org/docs/QA4EO_Principles_v4.0.pdf. Accessed 20 Sept 2017

  • Rahman H, Verstraete M, Pinty B (1993) Coupled surface-atmosphere reflectance (CSAR) model 1. Model description and inversion on synthetic data. J Geophys Res 98:779–789

    Google Scholar 

  • Rautiainen M, Stenberg P (2005) Application of photon recollision probability in coniferous canopy reflectance simulations. Remote Sens Environ 96:98–107

    Google Scholar 

  • Rivera JP, Verrelst J, Leoneko G, Moreno J (2013) Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model. Remote Sens 5:3280–3304

    Google Scholar 

  • Schaaf C, Gao F, Strahler A, Lucht W, Li X, Tsang T, Strugnell N, Zhang X, Jin Y et al (2002) First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens Environ 83:135–148

    Google Scholar 

  • Schaepman ME, Ustin SL, Plaza AJ, Painter TH, Verrelst J, Liang S (2009) Earth system science related imaging spectroscopy—an assessment. Remote Sens Environ 113:S123–S137

    Google Scholar 

  • Schaepman ME, Jehle M, Hueni A, D’Odorico P, Damm A, Weyerman J, Schneider F, Laurent V, Popp C, Seidel F et al (2015) Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX). Remote Sens Environ 158:207–219

    Google Scholar 

  • Schlerf M, Atzberger C (2006) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens Environ 100:281–294

    Google Scholar 

  • 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 12:17–26

    Google Scholar 

  • Schneider FD, Leiterer R, Morsdorf F, Gastellu-Etchegorry JP, Lauret N, Pfeifer N, Schaepman ME (2014) Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sens Environ 152:235–250

    Google Scholar 

  • Schneider FD, Morsdorf F, Schmid B, Petchey OL, Hueni A, Schimel DS, Schaepman ME (2017) Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat Commun 8:1441

    Google Scholar 

  • Schneider FD, Kükenbrink D, Schaepman ME, Schimel DS, Morsdorf F (2019) Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR. Agric For Meteorol 268:249–257

    Google Scholar 

  • Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA (2015) Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecol Appl 25:2180–2197

    Google Scholar 

  • Sobrino JA, Franch B, Mattar C, Jiménez-Muñoz JC, Corbari C (2012) A method to estimate soil moisture from Airborne Hyperspectral Scanner (AHS) and ASTER data: application to SEN2FLEX and SEN3EXP campaigns. Remote Sens Environ 117:415–428

    Google Scholar 

  • Stuckens J, Verstraeten W, Delalieux S, Swennen R, Coppin P (2009) A dorsiventral leaf radiative transfer model: development, validation and improved model inversion techniques. Remote Sens Environ 113:2560–2573

    Google Scholar 

  • Thimonier A, Sedivy I, Schleppi P (2010) Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. Eur J For Res 129:543–562

    Google Scholar 

  • Turner D, Ritts W, Zhao M, Kurc S, Dunn A, Wofsy S, Small E, Running S (2006) Assessing interannual variation in MODIS-based estimates of gross primary production. IEEE Trans Geosci Remote 44:1899–1907

    Google Scholar 

  • Ustin SL, Gitelson AA, Jacquemoud S, Schaepman ME, Asner GP, Gamon JA, Zarco-Tejada P (2009) Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens Environ 113:S67–S77

    Google Scholar 

  • Van Der Tol C, Verhoef W, Timmermans J, Verhoef A, Su Z (2009) An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences 6(12):3109–3129

    Google Scholar 

  • Verhoef W (1984) Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens Environ 16:125–141

    Google Scholar 

  • Vermote EF, Saleous NZ, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens Environ 83:97–111

    Google Scholar 

  • Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ 185:46–56

    Google Scholar 

  • Verrelst J, Rivera-Caicedo J (2017) A global sensitivity analysis toolbox to quantify drivers of vegetation radiative transfer models. In: Petropoulos G, Srivastava PK (eds) sensitivity analysis in earth observation modelling. Elsevier, Amsterdam, pp 319–339

    Google Scholar 

  • Verrelst J, Alonso L, Camps-Valls G, Delegido J, Moreno J (2012) Retrieval of vegetation biophysical parameters using Gaussian process techniques. IEEE Trans Geosci Remote Sens 50:1832–1843

    Google Scholar 

  • Verrelst J, Alonso L, Rivera-Caicedo J, Moreno J, Camps-Valls G (2013) Gaussian process retrieval of chlorophyll content from imaging spectroscopy data. IEEE J Sel Top Appl Earth Observ Remote Sens 6:867–874

    Google Scholar 

  • Verrelst J, Malenovský Z, van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP, Lewis P, North P, Moreno J (2019) Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surv Geophys. https://doi.org/10.1007/s10712-018-9478-y

  • Vilfan N, van der Tol C, Muller O, Rascher O, Verhoef W (2016) Fluspect-B: a model for leaf fluorescence, reflectance and transmittance spectra. Remote Sens Environ 186:596–615

    Google Scholar 

  • Vilfan N, van der Tol C, Yang P, Wyber R, Malenovský Z, Robinson SA, Verhoef W (2018) Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics. Remote Sens Environ 211:345–356

    Google Scholar 

  • Wallace L, Lucieer A, Malenovský Z, Turner D, Vopěnka P (2016) Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests 7:62

    Google Scholar 

  • Wang Z, Wang T, Darvishzadeh R, Skidmore AK, Jones S, Suarez L, Woodgate W, Heiden U, Heurich M, Hearne J (2016) Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest. Remote Sens-Basel 8:491

    Google Scholar 

  • Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P (2004) Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agric For Meteorol 121:37–53

    Google Scholar 

  • Weiss M, Baret F, Block T, Koetz B, Burini A, Scholze B, Lecharpentier P, Brockmann C, Fernandes R et al (2014) On Line Validation Exercise (OLIVE): a web based service for the validation of medium resolution land products. Application to FAPAR products. Remote Sens-Basel 6:4190–4216

    Google Scholar 

  • Weyermann J, Damm A, Kneubühler M, Schaepman ME (2014) Correction of reflectance anisotropy effects of vegetation on airborne spectroscopy data and derived products. IEEE Trans Geosci Remote 52:616–627

    Google Scholar 

  • Widlowski JL, Taberner M, Pinty B, Bruniquel-Pinel V, Disney M, Fernandes R, Gastellu-Etchegorry JP, Gobron N, Kuusk A, Lavergne T, Leblanc S et al (2007) The third RAdiation transfer Model Intercomparison (RAMI) exercise: documenting progress in canopy reflectance modelling. J Geophys Res 112(D09111):28

    Google Scholar 

  • Widlowski JL, Pinty B, Lopatka M, Atzberger C, Buzica D, Chelle M, Disney M, Gastellu-Etchegorry JP, Gerboles M, Gobron N, Grau E et al (2013) The fourth radiation transfer model intercomparison (RAMI-IV): proficiency testing of canopy reflectance models with ISO-13528. J Geophys Res 118(D09111):13

    Google Scholar 

  • Widlowski JL, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R et al (2015) The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: actual canopy scenarios and conformity testing. Remote Sens Environ 169:418–437

    Google Scholar 

  • Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab 58:109–130

    Google Scholar 

  • Wyber R, Malenovský Z, Ashcroft M, Osmond B, Robinson S (2017) Do daily and seasonal trends in leaf solar induced fluorescence reflect changes in photosynthesis, growth or light exposure? Remote Sens-Basel 9:604

    Google Scholar 

  • Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012) Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 117:322–337

    Google Scholar 

  • Zhang Y, Chen JM, Miller JR (2005) Determining digital hemispherical photograph exposure for leaf area index estimation. Agric For Meteorol 133(1–4):166–181

    Google Scholar 

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Acknowledgements

The contribution of ZM was supported by the Australian Research Council Future Fellowship: Bridging scales in remote sensing of vegetation stress (FT160100477). The work of LH and PL was supported by the Ministry of Education, Youth and Sports of the Czech Republic by the National Sustainability Program I (NPU I) from Grant Number LO1415. The German Aerospace Center (DLR) and the Federal Ministry of Economics and Technology supported HB within the framework of the EnMAP project (Contract No. 50 EE 1530). JV was supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (Grant Agreement 755617). The University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB) supported the contribution of MES. The authors acknowledge constructive comments provided by the reviewers that helped to improve the quality of the manuscript.

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Authors and Affiliations

  1. Surveying and Spatial Sciences Group, School of Technology, Environments and Design, University of Tasmania, Private Bag 76, Hobart, TAS, 7001, Australia

    Zbyněk Malenovský

  2. Global Change Research Institute CAS, Remote Sensing Department, Bělidla 986/4a, 603 00, Brno, Czech Republic

    Zbyněk Malenovský, Lucie Homolová & Petr Lukeš

  3. USRA/GESTAR, NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, 8800 Greenbelt Rd, Greenbelt, MD, 20771, USA

    Zbyněk Malenovský

  4. Environmental Remote Sensing and Geoinformatics, Trier University, 54286, Trier, Germany

    Henning Buddenbaum

  5. Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980, Paterna, Valencia, Spain

    Jochem Verrelst & Luis Alonso

  6. Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

    Michael E. Schaepman

  7. Centre d’Etudes Spatiales de la Biosphère - UPS, CNES, CNRS, IRD, Université de Toulouse, 31401, Toulouse Cedex 9, France

    Nicolas Lauret & Jean-Philippe Gastellu-Etchegorry

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  1. Zbyněk Malenovský
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  2. Lucie Homolová
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Malenovský, Z., Homolová, L., Lukeš, P. et al. Variability and Uncertainty Challenges in Scaling Imaging Spectroscopy Retrievals and Validations from Leaves Up to Vegetation Canopies. Surv Geophys 40, 631–656 (2019). https://doi.org/10.1007/s10712-019-09534-y

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