日光诱导叶绿素荧光辐射传输模型研究进展
Recent advances in the radiative transfer models of sun-induced chlorophyll fluorescence
- 2020年24卷第8期 页码:945-957
纸质出版日期: 2020-08-07
DOI: 10.11834/jrs.20209379
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2020-08-07 ,
扫 描 看 全 文
詹春晖,章钊颖,张永光.2020.日光诱导叶绿素荧光辐射传输模型研究进展.遥感学报,24(8): 945-957
Zhan C H,Zhang Z Y and Zhang Y G. 2020. Recent advances in the radiative transfer models of sun-induced chlorophyll fluorescence. Journal of Remote Sensing(Chinese),24(8): 945-957[DOI:10.11834/jrs.20209379]
日光诱导叶绿素荧光(SIF)是指示植被光合作用过程的无损探针,在不同时空尺度上对植被进行SIF的观测可以反映植被的实际光合作用及生理状态。然而在观测、分析和利用SIF的过程中,仍存在很多不确定因素。SIF的发生具有较为复杂的机理,从机理出发理解SIF与植被结构的相互作用,并分析影响SIF激发的主要因素将有助于更好地理解SIF与光合作用以及生物量的内在联系。因此,植被SIF辐射传输模型在解释和利用SIF遥感信号方面具有重要的作用。植被SIF信号相对较弱,且受环境、植被和生理等多种因子的影响,需要定量化描述,这为SIF辐射传输模型的构建带来挑战。近年来,大量学者已经发展一系列SIF辐射传输模型,为SIF遥感的发展提供了坚实的理论基础。本文回顾了叶片、冠层和生态系统尺度的SIF模型,从建模机理出发,对比模型优劣势,并对未来SIF模型的发展前景进行了展望。
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.
日光诱导叶绿素荧光叶绿素荧光模型辐射传输模型植被结构多次散射
sun-induced chlorophyll fluorescenceSIF modelradiative transfer modelvegetation structuremultiple scattering
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 [DOI: 10.1364/JOSA.60.000542http://dx.doi.org/10.1364/JOSA.60.000542]
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 [DOI: 10.1364/JOSA.59.001376http://dx.doi.org/10.1364/JOSA.59.001376]
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 [DOI: 10.1007/978-94-009-2823-7_43http://dx.doi.org/10.1007/978-94-009-2823-7_43]
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 [DOI: 10.1117/12.366388http://dx.doi.org/10.1117/12.366388]
Bonham J S. 1986. Fluorescence and kubelka-munk theory. Color Research and Application, 11(3): 223-230 [DOI: 10.1002/col.5080110310http://dx.doi.org/10.1002/col.5080110310]
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 [DOI: 10.1016/j.rse.2016.10.048http://dx.doi.org/10.1016/j.rse.2016.10.048]
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 [DOI: 10.1071/FP15002http://dx.doi.org/10.1071/FP15002]
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].
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 [DOI: 10.1080/02757250009532389http://dx.doi.org/10.1080/02757250009532389]
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 [DOI: 10.1016/j.rse.2008.02.012http://dx.doi.org/10.1016/j.rse.2008.02.012]
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 [DOI: 10.1016/s0005-2728(02)00366-3http://dx.doi.org/10.1016/s0005-2728(02)00366-3]
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 [DOI: 10.1029/2011gl048738http://dx.doi.org/10.1029/2011gl048738]
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 [DOI: 10.1029/2011gl048738http://dx.doi.org/10.1029/2011gl048738]
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 [DOI: 10.1016/j.rse.2014.02.007http://dx.doi.org/10.1016/j.rse.2014.02.007]
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 [DOI: 10.1364/josa.70.001101http://dx.doi.org/10.1364/josa.70.001101]
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 [DOI: 10.1109/JSTARS.2017.2685528http://dx.doi.org/10.1109/JSTARS.2017.2685528]
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 [DOI: 10.1016/0034-4257(95)00253-7http://dx.doi.org/10.1016/0034-4257(95)00253-7]
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 [DOI: 10.1111/j.1365-2486.2007.01352.xhttp://dx.doi.org/10.1111/j.1365-2486.2007.01352.x]
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 [DOI: 10.1073/pnas.1320008111http://dx.doi.org/10.1073/pnas.1320008111]
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 [DOI: 10.1016/j.rse.2017.02.012http://dx.doi.org/10.1016/j.rse.2017.02.012]
Jacquemoud S and Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75-91 [DOI: 10.1016/0034-4257(90)90100-Zhttp://dx.doi.org/10.1016/0034-4257(90)90100-Z]
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 [DOI: 10.5194/bgd-7-8281-2010http://dx.doi.org/10.5194/bgd-7-8281-2010]
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 [DOI: 10.5194/bg-8-637-2011http://dx.doi.org/10.5194/bg-8-637-2011]
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 [DOI: 10.5194/amt-6-2803-2013http://dx.doi.org/10.5194/amt-6-2803-2013]
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 [DOI: 10.1046/j.1365-2699.2000.00159.xhttp://dx.doi.org/10.1046/j.1365-2699.2000.00159.x]
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 [DOI: 10.5194/bgd-12-707-2015http://dx.doi.org/10.5194/bgd-12-707-2015]
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 [DOI: 10.1029/2018GL079031http://dx.doi.org/10.1029/2018GL079031]
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 [DOI: 10.1146/annurev.pp.42.060191.001525http://dx.doi.org/10.1146/annurev.pp.42.060191.001525]
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):
GB1015 [DOI: 10.1029/2003GB002199http://dx.doi.org/10.1029/2003GB002199]
Kubelka P F. Munk, Ein Beitrag zur Optik von Farbanstrichen, Zeitschrift für technische Physik, 1931, 593-601
Kuusk A. 1994. A multispectral canopy reflectance model. Remote Sensing of Environment, 50(2): 75-82 [DOI: 10.1016/0034-4257(94)90035-3http://dx.doi.org/10.1016/0034-4257(94)90035-3]
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 [DOI: 10.1029/2011MS00045http://dx.doi.org/10.1029/2011MS00045]
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 [DOI: 10.1111/gcb.12948http://dx.doi.org/10.1111/gcb.12948]
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
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 [DOI: 10.1016/S0034-4257(97)00089-8http://dx.doi.org/10.1016/S0034-4257(97)00089-8]
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 [DOI: 10.1016/j.rse.2019.111274http://dx.doi.org/10.1016/j.rse.2019.111274]
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 [DOI: 10.1038/s41598-018-20024-whttp://dx.doi.org/10.1038/s41598-018-20024-w]
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 [DOI: 10.1093/jxb/erp156http://dx.doi.org/10.1093/jxb/erp156]
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 [DOI: 10.1016/j.rse.2009.09.010http://dx.doi.org/10.1016/j.rse.2009.09.010]
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)
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 [DOI: 10.1016/j.rse.2019.04.030http://dx.doi.org/10.1016/j.rse.2019.04.030]
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 [DOI: 10.1016/j.rse.2004.02.012http://dx.doi.org/10.1016/j.rse.2004.02.012]
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 [DOI: 10.1109/36.508411http://dx.doi.org/10.1109/36.508411]
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 [DOI: 10.5194/gmd-2017-34http://dx.doi.org/10.5194/gmd-2017-34]
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 [DOI: 10.1016/0034-4257(92)90081-Thttp://dx.doi.org/10.1016/0034-4257(92)90081-T]
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 [DOI: 10.1016/s0034-4257(00)00190-5http://dx.doi.org/10.1016/s0034-4257(00)00190-5]
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 [DOI: 10.1016/j.rse.2009.08.019http://dx.doi.org/10.1016/j.rse.2009.08.019]
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 [DOI: 10.3390/rs9050415http://dx.doi.org/10.3390/rs9050415]
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 [DOI: 10.1093/jxb/eru191http://dx.doi.org/10.1093/jxb/eru191]
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 [DOI: 10.1016/j.rse.2019.111373http://dx.doi.org/10.1016/j.rse.2019.111373]
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 [DOI: 10.5194/bg-6-1181-2009http://dx.doi.org/10.5194/bg-6-1181-2009]
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):
GB2026 [DOI: 10.1029/2004GB002254http://dx.doi.org/10.1029/2004GB002254]
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 [DOI: 10.1016/0034-4257(92)90065-rhttp://dx.doi.org/10.1016/0034-4257(92)90065-r]
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 [DOI: 10.1016/0034-4257(91)90023-yhttp://dx.doi.org/10.1016/0034-4257(91)90023-y]
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 [DOI: 10.1039/B618315Hhttp://dx.doi.org/10.1039/B618315H]
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 [DOI: 10.1080/02757259809532351http://dx.doi.org/10.1080/02757259809532351]
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 [DOI: 10.1002/2014JG002713http://dx.doi.org/10.1002/2014JG002713]
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 [DOI: 10.5194/bg-6-3109-2009http://dx.doi.org/10.5194/bg-6-3109-2009]
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 [DOI: 10.1016/j.rse.2019.111292http://dx.doi.org/10.1016/j.rse.2019.111292]
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 [DOI: 10.1016/j.envpol.2012.10.003http://dx.doi.org/10.1016/j.envpol.2012.10.003]
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 [DOI: 10.1016/0034-4257(84)90057-9http://dx.doi.org/10.1016/0034-4257(84)90057-9]
Verhoef W. 1985. Earth observation modeling based on layer scattering matrices. Remote Sensing of Environment, 17(2): 165-178 [DOI: 10.1016/0034-4257(85)90072-0http://dx.doi.org/10.1016/0034-4257(85)90072-0]
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 [DOI: 10.1016/j.rse.2016.09.017http://dx.doi.org/10.1016/j.rse.2016.09.017]
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 [DOI: 10.1016/j.rse.2015.08.016http://dx.doi.org/10.1016/j.rse.2015.08.016]
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 [DOI: 10.1016/j.rse.2015.05.018http://dx.doi.org/10.1016/j.rse.2015.05.018]
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 [DOI: 10.1016/j.rse.2017.08.029http://dx.doi.org/10.1016/j.rse.2017.08.029]
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 [DOI: 10.1016/j.rse.2013.05.011http://dx.doi.org/10.1016/j.rse.2013.05.011]
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
张立福, 王思恒, 黄长平. 2018. 太阳诱导叶绿素荧光的卫星遥感反演方法. 遥感学报, 22(1): 1-12 [DOI: 10.11834/jrs.20187211http://dx.doi.org/10.11834/jrs.20187211]
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
章钊颖, 王松寒, 邱博, 宋练, 张永光. 2019. 日光诱导叶绿素荧光遥感反演及碳循环应用进展. 遥感学报, 23(1): 37-52 [DOI: 10.11834/jrs.20197485http://dx.doi.org/10.11834/jrs.20197485]
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 [DOI: 10.3390/rs71014179http://dx.doi.org/10.3390/rs71014179]
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 [DOI: 10.1016/j.rse.2016.10.036http://dx.doi.org/10.1016/j.rse.2016.10.036]
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 [DOI: 10.5194/isprs-archives-XLII-3-2347-2018http://dx.doi.org/10.5194/isprs-archives-XLII-3-2347-2018]
相关作者
相关机构