Anterior|Siguiente

Recent advances in the radiative transfer models of sun-induced chlorophyll fluorescence

  • role： First author第一作者
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

  • Email：zhanchunhui@smail.nju.edu.cn
  • Introduction：詹春晖，1995年生，女，硕士研究生，研究方向为植被定量遥感、叶绿素荧光遥感。E-mail：zhanchunhui@smail.nju.edu.cn
ZHAN Chunhui12,  
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

ZHANG Zhaoying12,  
  • role： Corresponding author通信作者
  • Affiliation：

    International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

    Jiangsu Center for Collaborative Innovation in Novel Software Technology and Industrialization, Nanjing 210023, China

  • Email：yongguang_zhang@nju.edu.cn
  • Introduction：张永光，1980年生，男，教授，研究方向为植被叶绿素荧光遥感和全球碳循环。E-mail：yongguang_zhang@nju.edu.cn
ZHANG Yongguang134*

resumen

Sun-Induced chlorophyll Fluorescence (SIF) has been recently used as a novel indicator of photosynthesis of vegetationdue toits direct relation to vegetation photosynthesis. The mechanism behind the SIF signal is rather complicated. Thus, the physical process and the interaction between SIF and vegetation structureshould be understoodfor better interpretation of SIF data. In this respect, the development of relevant SIF models is the key to improve the understanding and use of SIF signals. In this review, we introduce various SIF models in different scales by clarifying the mechanism behind each model andpropose the prospects in the future work.(1) At the leaf level, fluorescence models are generally based on leaf optical properties models and focus on the simulation of leaf reflectance and transmittance. Several theories canexplain the propagation of light in a turbid medium or a simplified blade. The simplest one assumes that light decays exponentially within the blade according to Beer’s law.The Kubelka–Munk differential equations arealsoused to solve radiation propagating in a turbid medium, which isfollowed by the Plate model fordiving the blade into several homogenous layers. The most popular leaf fluorescence model called Fluspect is based on the PROSPECT model, which follows Plate’s theory. The key objective is to simulate the re-absorption effect accurately due to the band overlap between SIF emission and chlorophyll absorption.(2) At the canopy level, fluorescence models incorporate the canopy radiative transfer and leaf fluorescence models, which can be characterized as 1D and 3D models. 1D models, such as FLSAIL, FluorSAIL,andSCOPE, incorporate SAIL model with a leaf fluorescence model and assume the canopy as several horizontally homogenous layers. 3D models, such as DART, FluorWPS, andFluorFLIGHT, simulate the canopy fluorescence in a realistic scene using ray tracing method. They are suitable to heterogeneous vegetation canopies.(3) At the ecosystem level, these fluorescence models help reduce the uncertainties in simulating carbon cycle and predicting ecological system response to global change by incorporating them with land surface models, such as NCAR CLM4, BEPS, and BETHY.Despite the advances in the SIF models across multiple scales, further studies are still needed with respect to model development, validation, and inversion. For example, accurate tree positions and canopy structure parameters can be derived from light detection and ranging data, which enables thereconstruction of 3D scenes based on the real landscape. With the development of insitu SIF measurement techniques, SIF models can be validated with the measurements except cross-validation through various models. SIF model inversion is a prospective research area to derive vegetation structure and biochemical parameters through spectral data. The novel machine learning approaches may also provide new opportunities to be incorporated with SIF models to solve inversion problems.

palabra clave

sun-induced chlorophyll fluorescence;SIF model;radiative transfer model;vegetation structure;multiple scattering

References

  1. 1.
    Allen W A, Gausman H W and Richardson A J. 1970. Mean effective optical constants of cotton leaves. Journal of the Optical Society of America, 60(4): 542-547
  2. 2.
    Allen W A, Gausman H W, Richardson A J and Thomas J R. 1969. Interaction of isotropic light with a compact plant leaf. Journal of the Optical Society of America, 59(10): 1376-1379
  3. 3.
    Baret F, Andrieu B and Guyot G. 1988. A simple model for leaf optical properties in visible and near-infrared: application to the analysis of spectral shifts determinism//Lichtenthaler H K, ed. Applications of Chlorophyll Fluorescence in Photosynthesis Research, Stress Physiology, Hydrobiology and Remote Sensing: An introduction to the Various Fields of Applications of the in vivo Chlorophyll Fluorescence Also Including the Proceedings of the First International Chlorophyll Fluorescence Symposium Held in the Physikzentrum. Dordrecht: Springer: 345-351
  4. 4.
    Berk A, Anderson G P, Bernstein L S, Acharya P K, Dothe H, Matthew M W, Adler-Golden S M, Chetwynd Jr J H, Richtsmeier S C, Pukall B, Allred C L, Jeong L S and Hoke M L. 1999. MODTRAN4 radiative transfer modeling for atmospheric correction//Proceedings of the SPIE 3756, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research III. Denver, CO, United States: SPIE
  5. 5.
    Bonham J S. 1986. Fluorescence and kubelka-munk theory. Color Research and Application, 11(3): 223-230
  6. 6.
    Bye I J, North P R J, Los S O, Kljun N, Rosette J A B, Hopkinson C, Chasmer L and Mahoney C. 2017. Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model. Remote Sensing of Environment, 188: 177-189
  7. 7.
    Cendrero-Mateo M P, Carmo-Silva A E, Porcar-Castell A, Hamerlynck E P, Papuga S A and Moran M S. 2015. Dynamic response of plant chlorophyll fluorescence to light, water and nutrient availability. Functional Plant Biology, 42(8): 746-757
  8. 8.
    Chen J M, Liu J, Cihlar J and Goulden M L. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecological Modelling 124(2): 99-119.[DOI: 10.1016/S0304-3800(99)00156-8].
  9. 9.
    Disney M I, Lewis P and North P R J. 2000. Monte Carlo ray tracing in optical canopy reflectance modelling. Remote Sensing Reviews, 18(2/4): 163-196
  10. 10.
    Feret J B, François C, Asner G P, Gitelson A A, Martin R E, Bidel L P R, Ustin S L, le Maire G and Jacquemoud S. 2008. PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6): 3030-3043
  11. 11.
    Franck F, Juneau P and Popovic R. 2002. Resolution of the Photosystem I and Photosystem II contributions to chlorophyll fluorescence of intact leaves at room temperature. Biochimica et Biophysica Acta (BBA) – Bioenergetics, 1556(2/3): 239-246
  12. 12.
    Frankenberg C, Fisher J B, Worden J, Badgley G, Saatchi S S, Lee J E, Toon G C, Butz A, Jung M, Kuze A and Yokota T. 2011. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophysical Research Letters, 38(17): L17706
  13. 13.
    Frankenberg C, Fisher J B, Worden J, Badgley G, Saatchi S S, Lee J E, Toon G C, Butz A, Jung M, Kuze A and Yokota T. 2011. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophysical Research Letters, 38(17): L17706
  14. 14.
    Frankenberg C, O'Dell C, Berry J, Guanter L, Joiner J, Köhler P, Pollock R and Taylor T E. 2014. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sensing of Environment, 147: 1-12
  15. 15.
    Fukshansky L and Kazarinova N. 1980. Extension of the Kubelka–Munk theory of light propagation in intensely scattering materials to fluorescent media. Journal of the Optical Society of America, 70(9): 1101-1111
  16. 16.
    Gastellu-Etchegorry J, Lauret N, Yin T G, Landier L, Kallel A, Malenovský Z, Bitar A A, Aval J, Benhmida S, Qi J B, Medjdoub G, Guilleux J, Chavanon E, Cook B, Morton D, Chrysoulakis N and Mitraka Z. 2017. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6): 2640-2649
  17. 17.
    Gastellu-Etchegorry J P, Demarez V, Pinel V and Zagolski F. 1996. Modeling radiative transfer in heterogeneous 3-D vegetation canopies. Remote Sensing of Environment, 58(2): 131-156
  18. 18.
    Grace J, Nichol C, Disney M, Lewis P, Quaife T and Bowyer P. 2007. Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence? Global Change Biology, 13(7): 1484-1497
  19. 19.
    Guanter L, Zhang Y G, Jung M, Joanna J, Voigt M, Berry J A, Frankenberg C, Huete A R, Zarco-Tejada P, Lee J E, Moran M S, Ponce-Campos G, Beer C, Camps-Valls G, Buchmann N, Gianelle D, Klumpp K, Cescatti A, Baker J M and Griffis T J. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America, 111(14): E1327-E1333
  20. 20.
    Hernández-Clemente R, North P R J, Hornero A and Zarco-Tejada P J. 2017. Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193: 165-179
  21. 21.
    Jacquemoud S and Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91
  22. 22.
    Joiner J, Yoshida Y, Vasilkov A P, Yoshida Y, Corp L and Middleton E M. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8(3): 637–651
  23. 23.
    Joiner J, Yoshida Y, Vasilkov A P, Yoshida Y, Corp L A and Middleton E M. 2011. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences, 8(3): 637-651
  24. 24.
    Joiner J, Guanter L, Lindstrot R, Voigt M, Vasilkov A P, Middleton E M, Huemmrich K F, Yoshida Y and Frankenberg C. 2013. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodo-logy, simulations, and application to GOME-2. Atmospheric Measurement Techniques, 6(10): 2803–2823
  25. 25.
    Knorr W. 2000. Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties. Global Ecology and Biogeography, 9(3): 225-252
  26. 26.
    Koffi E N, Rayner P J, Norton A J, Frankenberg C and Scholze M. 2015. Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system. Biogeosciences, 12(13): 4067-4084
  27. 27.
    Köhler P, Frankenberg C, Magney T S, Guanter L, Joanna J and Landgraf J. 2018. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophysical Research Letters, 45(19): 10456-10463
  28. 28.
    Krause G H and Weis E. 1991. Chlorophyll fluorescence and photosynthesis: the basics. Annual Review of Plant Physiology and Plant Molecular Biology, 42: 313-349
  29. 29.
    Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Friedlingstein P, Ciais P, Sitch S and Prentice I C. 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19(1):
  30. 30.
    Kubelka P F. Munk, Ein Beitrag zur Optik von Farbanstrichen, Zeitschrift für technische Physik, 1931, 593-601
  31. 31.
    Kuusk A. 1994. A multispectral canopy reflectance model. Remote Sensing of Environment, 50(2): 75-82
  32. 32.
    Lawrence D M, Oleson K W, Flanner M G, Thornton P E, Swenson S C, Lawrence P J, Zeng X B, Yang Z L, Levis S, Sakaguchi K, Bonan G B and Slater A G. 2011. Parameterization improvements and functional and structural advances in version 4 of the community land model. Journal of Advances in Modeling Earth Systems, 3(1): M03001
  33. 33.
    Lee J E, Berry J A, van der Tol C, Yang X, Guanter L, Damm A, Baker I and Frankenberg C. 2015. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Global Change Biology, 21(9): 3469-3477
  34. 34.
    Lichtenthaler H K and Rinderle U. 1988. Chlorophyll fluorescence spectra of leaves as induced by blue light and red laser light//Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing. Aussois: ESA Publications Division: 251
  35. 35.
    Liu J, Chen J M, Cihlar J and Park W M. 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sensing of Environment, 62(2): 158-175
  36. 36.
    Liu W W, Atherton J, Mõttus M, Gastellu-Etchegorry J P, Malenovský Z, Raumonen P, Åkerblom M, Mäkipää R and Porcar-Castell A. 2019. Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sensing of Environment, 232: 111274
  37. 37.
    Macbean N, Maignan F, Bacour C, Lewis P, Peylin P, Guanter L, Köhler P, Gómez-Dans J and Disney M. 2018. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. Scientific Reports, 8(1): 1973
  38. 38.
    Malenovský Z, Mishra K B, Zemek F, Rascher U and Nedbal L. 2009. Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence. Journal of Experimental Botany, 60(11): 2987-3004
  39. 39.
    Meroni M, Busetto L, Colombo R, Guanter L, Moreno J and Verhoef W. 2010. Performance of Spectral Fitting Methods for vegetation fluorescence quantification. Remote Sensing of Environment, 114(2): 363-374
  40. 40.
    Miller J R, Berger M, Goulas Y, Jacquemoud S, Louis J, Mohammed G, Moise N, Moreno J, Moya I, Pedrós R, Verhoef W and Zarco-Tejada P. 2005. Development of a vegetation fluorescence canopy model. ESTEC contract No.16365/02/NL/FF. Noordwijk, The Netherlands: European Space Researchand Technology Centre (ESTEC)
  41. 41.
    Mohammed G H, Colombo R, Middleton E M, Rascher U, van der Tol C, Nedbal L, Goulas Y, Pérez-Priego O, Damm A, Meroni M, Joiner J, Cogliati S, Verhoef W, Malenovský Z, Gastellu-Etchegorry J P, Miller J R, Guanter L, Moreno J, Moya I, Berry J A, Frankenberg C and Zarco-Tejada P J. 2019. Remote sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in vegetation: 50  years of progress. Remote Sensing of Environment, 231: 111177
  42. 42.
    Moya I, Camenen L, Evain S, Goulas Y, Cerovic Z G, Latouche G, Flexas J and Ounis A. 2004. A new instrument for passive remote sensing: 1. Measurements of sunlight-induced chlorophyll fluorescence. Remote Sensing of Environment, 91(2): 186-197
  43. 43.
    North P R J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4): 946-956
  44. 44.
    Norton A J, Rayner P J, Koffi E N and Scholze M. 2018. Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE: model description and information content. Geoscientific Model Development Discussions: 1-26
  45. 45.
    Olioso A, Méthy M and Lacaze B. 1992. Simulation of canopy fluorescence as a function of canopy structure and leaf fluorescence. Remote Sensing of Environment, 41(2/3): 239-247
  46. 46.
    Ounis A, Cerovic Z G, Briantais J M and Moya I. 2001. Dual-excitation FLIDAR for the estimation of epidermal UV absorption in leaves and canopies. Remote Sensing of Environment, 76(1): 33-48
  47. 47.
    Pedrós R, Goulas Y, Jacquemoud S, Louis J and Moya I. 2015. FluorMODleaf: a new leaf fluorescence emission model based on the PROSPECT model. Remote Sensing of Environment, 114(1): 155-167
  48. 48.
    Pinto F, Müller-Linow M, Schickling A, Cendrero-Mateo M P, Ballvora A and Rascher U. 2017. Multiangular observation of canopy sun-induced chlorophyll fluorescence by combining imaging spectroscopy and stereoscopy. Remote Sensing, 9(5): 415
  49. 49.
    Porcar-Castell A, Tyystjärvi E, Atherton J, van der Tol C, Flexas J, Pfündel E E, Moreno J, Frankenberg C and Berry J A. 2014. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65(15): 4065-4095
  50. 50.
    Qiu B, Chen J M, Ju W M, Zhang Q and Zhang Y G. 2019. Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures. Remote Sensing of Environment, 233: 111373
  51. 51.
    Rascher U, Agati G, Alonso L, Cecchi G, Champagne S, Colombo R, Damm A, Daumard F, de Miguel E, Fernandez G, Franch B, Franke J, Gerbig C, Gioli B, Gómez J A, Goulas Y, Guanter L, Gutiérrez-de-la-Cámara Ó, Hamdi K, Hostert P, Jiménez M, Kosvancova M, Lognoli D, Meroni M, Miglietta F, Moersch A, Moreno J, Moya I, Neininger B, Okujeni A, Ounis A, Palombi L, Raimondi V, Schickling A, Sobrino J A, Stellmes M, Toci G, Toscano P, Udelhoven T, van der Linden S and Zaldei A. 2009. CEFLES2: the remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the oxygen absorption bands. Biogeosciences, 6(7): 1181-1198
  52. 52.
    Rayner P J, Scholze M, Knorr W, Kaminski T, Giering R and Widmann H. 2005. Two decades of terrestrial Carbon fluxes from a Carbon Cycle Data Assimilation System (CCDAS). Global Biogeochemical Cycles, 19(2):
  53. 53.
    Rosema A, Verhoef W, Noorbergen H and Borgesius J J. 1992. A new forest light interaction model in support of forest monitoring. Remote Sensing of Environment, 42(1): 23-41
  54. 54.
    Rosema A, Verhoef W, Schroote J and Snel J F H. 1991. Simulating fluorescence light-canopy interaction in support of laser-induced fluorescence measurements. Remote Sensing of Environment, 37(2): 117-130
  55. 55.
    Sušila P and Nauš J. 2007. A Monte Carlo study of the chlorophyll fluorescence emission and its effect on the leaf spectral reflectance and transmittance under various conditions. Photochemical and Photobiological Sciences, 6(8): 894-902
  56. 56.
    Thompson R L and Goel N S. 1998. Two models for rapidly calculating bidirectional reflectance of complex vegetation scenes: Photon Spread (PS) model and Statistical Photon Spread (SPS) model. Remote Sensing Reviews, 16(3): 157-207
  57. 57.
    van der Tol C, Berry J A, Campbell P K E and Rascher U. 2014. Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence. Journal of Geophysical Research: Biogeosciences, 119(12): 2312-2327
  58. 58.
    van der Tol C, Verhoef W, Timmermans J, Verhoef A and Su Z. 2009. An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6(12): 3109-3129
  59. 59.
    van der Tol C, Vilfan N, Dauwe D, Cendrero-Mateo M P and Yang P Q. 2019. The scattering and re-absorption of red and near-infrared chlorophyll fluorescence in the models Fluspect and SCOPE. Remote Sensing of Environment, 232: 111292
  60. 60.
    Van Wittenberghe S, Alonso L, Verrelst J, Hermans I, Delegido J, Veroustraete F, Valcke R, Moreno J and Samson R. 2013. Upward and downward solar-induced chlorophyll fluorescence yield indices of four tree species as indicators of traffic pollution in Valencia. Environmental Pollution, 173: 29-37
  61. 61.
    Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16(2): 125-141
  62. 62.
    Verhoef W. 1985. Earth observation modeling based on layer scattering matrices. Remote Sensing of Environment, 17(2): 165-178
  63. 63.
    Vilfan N, van der Tol C, Muller O, Rascher U and Verhoef W. 2016. Fluspect-B: a model for leaf fluorescence, reflectance and transmittancespectra. Remote Sensing of Environment, 186: 596-615
  64. 64.
    Widlowski J L, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R, Côté J F, Eenmäe A, Essery R, Gastellu-Etchegorry J P, Gobron N, Grau E, Haverd V, Homolová L, Huang H G, Hunt L, Kobayashi H, Koetz B, Kuusk A, Kuusk J, Lang M, Lewis P E, Lovell J L, Malenovský Z, Meroni M, Morsdorf F, Mõttus M, Ni-Meister W, Pinty B, Rautiainen M, Schlerf M, Somers B, Stuckens J, Verstraete M M, Yang W Z, Zhao F and Zenone T. 2015. The fourth phase of the Radiative Transfer Model Intercomparison (RAMI) exercise: actual canopy scenarios and conformity testing. Remote Sensing of Environment, 169: 418-437
  65. 65.
    Wolanin A, Rozanov V V, Dinter T, Noël S, Vountas M, Burrows J P and Bracher A. 2015. Global retrieval of marine and terrestrial chlorophyll fluorescence at its red peak using hyperspectral top of atmosphere radiance measurements: feasibility study and first results. Remote Sensing of Environment, 166: 243-261
  66. 66.
    Yang P Q, Verhoef W and van der Tol C. 2017. The mSCOPE model: a simple adaptation to the SCOPE model to describe reflectance, fluorescence and photosynthesis of vertically heterogeneous canopies. Remote Sensing of Environment, 201: 1-11
  67. 67.
    Zarco-Tejada P J, Catalina A, González M R and Martín P. 2013. Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery. Remote Sensing of Environment, 136: 247-258
  68. 68.
    Zhang L F, Wang S H and Huang C P. 2018. Top-of-atmosphere hyperspectral remote sensing of solar-induced chlorophyll fluorescence: a review of methods. Journal of Remote Sensing, 22(1): 1-12
  69. 69.
    Zhang Z Y, Wang S H, Qiu B, Song L and Zhang Y G. 2019. Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application. Journal of Remote Sensing, 23(1): 37-52
  70. 70.
    Zhao F, Guo Y Q, Huang Y B, Verhoef W, Van der Tol C, Dai B, Liu L Y, Zhao H J and Liu G. 2015a. Quantitative estimation of fluorescence parameters for crop leaves with bayesian inversion. Remote Sensing, 7(10): 14179-14199
  71. 71.
    Zhao F, Dai X, Verhoef W, Guo Y Q, van der Tol C, Li YG and Huang Y B. 2016. FluorWPS: a monte carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment, 187: 385-399
  72. 72.
    Zhao F and Ni Q. 2018. A model to simulate the radiative transfer of fluorescence in a leaf. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3: 2347-2351

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website