Remote Sensing Technology and Application, Volume. 40, Issue 4, 802(2025)

A Synthesis on the Vegetation Spectral Invariant Theory

FANG Hongliang1,2,3
Author Affiliations
  • 1LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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    References(89)

    [1] [1] ROSS J, The radiation regime and architecture of plant stands[M]. The Hague, Netherlands: Dr. W. Junk Publishers. 1981.

    [2] [2] MYNENI R B, ROSS J, ASRAR G. A review on the theory of photon transport in leaf canopies[J]. Agricultural and Forest Meteorology, 1989, 45(1/2): 1-153. DOI: 10.1016/0168-1923(89)90002-6

    [3] [3] LIANG S, Quantitative remote sensing of land surfaces[M].New York: John Wiley and Sons. 2004.

    [4] [4] MYNENI R B, ASRAR G, KANEMASU E T. Light scattering in plant canopies: The method of Successive Orders of Scattering Approximations(SOSA)[J]. Agricultural and Forest Meteorology, 1987, 39(1): 1-12. DOI: 10.1016/0168-1923(87)90011-6

    [5] [5] MYNENI R B, ASRAR G, KANEMASU E T. Reflectance of a soybean canopy using the method of Successive Orders of Scattering Approximations (SOSA)[J]. Agricultural and Forest Meteorology, 1987, 40(1): 71-87. DOI: 10.1016/0168-1923(87)90056-6

    [6] [6] KNYAZIKHIN Y, MARSHAK A. Fundamental equations of radiative transfer in leaf canopies, and iterative methods for their solution[A].Photon-Vegetation Interactions[M].Berlin, Heidelberg: Springer Berlin Heidelberg, 1991: 9-43. DOI: 10.1007/978-3-642-75389-3_2

    [7] [7] SOBOLEV V V, Treatise on Radiative Transfer[M]. Princeton, N.J.: Van Nostrand. 1963.

    [8] [8] KNYAZIKHIN Y, MARTONCHIK J V, MYNENI R B,et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data[J]. Journal of Geophysical Research: Atmospheres, 1998, 103(D24): 32257-32275. DOI: 10.1029/98JD02462

    [9] [9] PANFEROV O, KNYAZIKHIN Y, MYNENI R B,et al. The role of canopy structure in the spectral variation of transmission and absorption of solar radiation in vegetation canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(2): 241-253. DOI: 10.1109/36.905232

    [10] [10] LEWIS P, DISNEY M. Spectral invariants and scattering across multiple scales from within-leaf to canopy[J]. Remote Sensing of Environment, 2007, 109(2): 196-206. DOI: 10.1016/j.rse.2006.12.015

    [11] [11] HUANG D, KNYAZIKHIN Y, DICKINSON R E,et al. Canopy spectral invariants for remote sensing and model applications[J]. Remote Sensing of Environment, 2007, 106(1): 106-122. DOI: 10.1016/j.rse.2006.08.001

    [12] [12] DISNEY M, LEWIS P, SAICH P. 3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains[J]. Remote Sensing of Environment, 2006, 100(1): 114-132. DOI: 10.1016/j.rse.2005.10.003

    [13] [13] SMOLANDER S, STENBERG P. Simple parameterizations of the radiation budget of uniform broadleaved and coniferous canopies[J]. Remote Sensing of Environment, 2005, 94(3): 355-363. DOI: 10.1016/j.rse.2004.10.010

    [14] [14] STENBERG P. Simple analytical formula for calculating average photon recollision probability in vegetation canopies[J].Remote Sensing of Environment, 2007, 109(2): 221-224. DOI: 10.1016/j.rse.2006.12.014

    [15] [15] STENBERG P, MTTUS M, RAUTIAINEN M. Photon recollision probability in modelling the radiation regime of canopies—A review[J]. Remote Sensing of Environment, 2016, 183: 98-108. DOI: 10.1016/j.rse.2016.05.013

    [16] [16] HOVI A, FORSSTRM P, GHIELMETTI G,et al. Empirical validation of photon recollision probability in single crowns of tree seedlings[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169: 57-72. DOI: 10.1016/j.isprsjprs.2020.08.027

    [17] [17] KNYAZIKHIN Y, SCHULL M A, XU L,et al. Canopy spectral invariants. part 1: A new concept in remote sensing of vegetation[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011, 112(4): 727-735. DOI: 10.1016/j.jqsrt.2010.06.014

    [18] [18] HOVI A, SCHRAIK D, HANU J,et al. Assessment of a photon recollision probability based forest reflectance model in European boreal and temperate forests[J]. Remote Sensing of Environment, 2022, 269: 112804. DOI: 10.1016/j.rse. 2021.112804

    [19] [19] FANG H L. Photon recollision probability and the spectral invariant theory: Principles, methods, and applications[J]. Remote Sensing of Environment, 2023, 299: 113859. DOI: 10.1016/j.rse.2023.113859

    [20] [20] FANG H L, BARET F, PLUMMER S,et al. An overview of global Leaf Area Index (LAI): Methods, products, validation, and applications[J]. Reviews of Geophysics, 2019, 57(3): 739-799. DOI: 10.1029/2018RG000608

    [21] [21] FANG H L. Canopy clumping index(CI): A review of methods, characteristics, and applications[J]. Agricultural and Forest Meteorology, 2021, 303: 108374. DOI: 10.1016/j.agrformet.2021.108374

    [22] [22] IDSO S B, DE WIT C T. Light relations in plant canopies[J]. Applied Optics, 1970, 9(1): 177. DOI: 10.1364/ao.9.000177

    [23] [23] SCHULL M A, GANGULY S, SAMANTA A,et al. Physical interpretation of the correlation between multi-angle spectral data and canopy height[J]. Geophysical Research Letters, 2007, 34(18). DOI: 10.1029/2007GL031143

    [24] [24] SMOLANDER S, STENBERG P. A method to account for shoot scale clumping in coniferous canopy reflectance models[J]. Remote Sensing of Environment, 2003, 88(4): 363-373. DOI: 10.1016/j.rse.2003.06.003

    [25] [25] MTTUS M, RAUTIAINEN M. Scaling PRI between coniferous canopy structures[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 708-714. DOI: 10.1109/JSTARS.2013.2253307

    [26] [26] SCHULL M A, KNYAZIKHIN Y, XU L,et al. Canopy spectral invariants, part 2: Application to classification of forest types from hyperspectral data[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2011, 112(4): 736-750. DOI: 10.1016/j.jqsrt.2010.06.004

    [27] [27] WU S B, ZENG Y L, HAO D L,et al. Quantifying leaf optical properties with spectral invariants theory[J]. Remote Sensing of Environment, 2021, 253: 112131. DOI: 10.1016/j.rse.2020.112131

    [28] [28] LU W D, ZENG Y L, VILFAN N,et al. Characterizing leaf-scale fluorescence with spectral invariants[J]. Remote Sensing of Environment, 2025, 322: 114704. DOI: 10.1016/j.rse.2025.114704

    [29] [29] OKER-BLOM P, SMOLANDER H. The ratio of shoot silhouette area to total needle area in Scots pine[J]. Forest Science, 1988, 34(4): 894-906. DOI: 10.1093/forestscience/34.4.894

    [30] [30] STENBERG P, LINDER S, SMOLANDER H,et al. Performance of the LAI-2000 plant canopy analyzer in estimating Leaf Area Index of some Scots pine stands[J].Tree Physiology, 1994, 14(7-9): 981-995. DOI: 10.1093/treephys/14.7-8-9.981

    [31] [31] STENBERG P, MTTUS M, RAUTIAINEN M,et al. Quantitative characterization of clumping in Scots pine crowns[J]. Annals of Botany, 2014, 114(4): 689-694. DOI: 10.1093/aob/mct310

    [32] [32] PISEK J, BUDDENBAUM H, CAMACHO F,et al. Data synergy between Leaf Area Index and clumping index earth observation products using photon recollision probability theory[J]. Remote Sensing of Environment, 2018, 215: 1-6. DOI: 10.1016/j.rse.2018.05.026

    [33] [33] WANG D, SCHRAIK D, HOVI A,et al. Direct estimation of photon recollision probability using terrestrial laser scanning[J]. Remote Sensing of Environment, 2020, 247: 111932. DOI: 10.1016/j.rse.2020.111932

    [34] [34] HE S Y, QI J B, WANG D,et al. Estimation of canopy photon recollision probability from airborne laser scanning[J]. Remote Sensing of Environment, 2024, 311: 114264. DOI: 10.1016/j.rse.2024.114264

    [35] [35] YAN K, ZHANG Y M, TONG Y Y,et al. Modeling the radiation regime of a discontinuous canopy based on the stochastic radiative transport theory: Modification, evaluation and validation[J]. Remote Sensing of Environment, 2021, 267: 112728. DOI: 10.1016/j.rse.2021.112728

    [36] [36] RAUTIAINEN M, STENBERG P. Application of photon recollision probability in coniferous canopy reflectance simulations[J]. Remote Sensing of Environment, 2005, 96(1): 98-107. DOI: 10.1016/j.rse.2005.02.009

    [37] [37] STENBERG P, LUKE P, RAUTIAINEN M,et al. A new approach for simulating forest albedo based on spectral invariants[J]. Remote Sensing of Environment, 2013, 137: 12-16. DOI: 10.1016/j.rse.2013.05.030

    [38] [38] ZENG Y L, XU B D, YIN G F,et al. Spectral invariant provides a practical modeling approach for future biophysical variable estimations[J]. Remote Sensing, 2018, 10(10): 1508. DOI: 10.3390/rs10101508

    [39] [39] HADI, PFEIFER M, KORHONEN L,et al. Forest canopy structure and reflectance in humid tropical Borneo: A physically-based interpretation using spectral invariants[J]. Remote Sensing of Environment, 2017, 201: 314-330. DOI: 10.1016/j.rse.2017.09.018

    [40] [40] YAN K, PARK T, CHEN C,et al. Generating global products of LAI and FPAR from SNPP-VIIRS data: Theoretical background and implementation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2119-2137. DOI: 10.1109/TGRS.2017.2775247

    [41] [41] GANGULY S, SAMANTA A, SCHULL M A,et al. Generating vegetation Leaf Area Index earth System data record from multiple sensors. Part 2: Implementation, analysis and validation[J]. Remote Sensing of Environment, 2008, 112(12): 4318-4332. DOI: 10.1016/j.rse.2008.07.013

    [42] [42] GANGULY S, SCHULL M A, SAMANTA A,et al. Generating vegetation Leaf Area Index earth system data record from multiple sensors. Part 1: Theory[J]. Remote Sensing of Environment, 2008, 112(12): 4333-4343. DOI: 10.1016/j.rse.2008.07.014

    [43] [43] GANGULY S, LI S, NEMANI R,et al. Leaf Area Index retrieval from next generation geostationary GOES-R satellite[C]// 2017 International Symposium on Recent Advances in Quantitative Remote Sensing. Valencia, Spain.2017.

    [44] [44] YANG B, KNYAZIKHIN Y, MTTUS M,et al. Estimation of Leaf Area Index and its sunlit portion from DSCOVR EPIC data: Theoretical basis[J]. Remote Sensing of Environment, 2017, 198: 69-84. DOI: 10.1016/j.rse.2017.05.033

    [45] [45] LIU X J, GUANTER L, LIU L Y,et al. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model[J]. Remote Sensing of Environment, 2019, 231: 110772. DOI: 10.1016/j.rse.2018.05.035

    [46] [46] ZHANG Z Y, CHEN J M, GUANTER L,et al. From canopy-leaving to total canopy far-red fluorescence emission for remote sensing of photosynthesis: First results from TROPOMI[J]. Geophysical Research Letters, 2019, 46(21): 12030-12040. DOI: 10.1029/2019GL084832

    [47] [47] ZENG Y L, BADGLEY G, CHEN M,et al. A radiative transfer model for solar induced fluorescence using spectral invariants theory[J]. Remote Sensing of Environment, 2020, 240: 111678. DOI: 10.1016/j.rse.2020.111678

    [48] [48] ZHANG Z Y, ZHANG Y, ZHANG Y G. Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application[J]. Remote Sensing of Environment, 2023, 295: 113699. DOI: 10.1016/j.rse.2023.113699

    [49] [49] YANG P Q, VAN DER TOL C. Linking canopy scattering of far-red Sun-induced chlorophyll fluorescence with reflectance[J]. Remote Sensing of Environment, 2018, 209: 456-467. DOI: 10.1016/j.rse.2018.02.029

    [50] [50] YANG P Q, VAN DER TOL C, CAMPBELL P K E,et al. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red Sun-induced chlorophyll fluorescence[J].Remote Sensing of Environment, 2020, 240: 111676. DOI: 10.1016/j.rse.2020.111676

    [51] [51] LU X L, LIU Z Q, ZHAO F,et al. Comparison of Total Emitted Solar-Induced Chlorophyll Fluorescence (SIF) and Top-Of-Canopy (TOC) SIF in estimating photosynthesis[J]. Remote Sensing of Environment, 2020, 251: 112083. DOI: 10.1016/j.rse.2020.112083

    [52] [52] ZENG Y L, BADGLEY G, DECHANT B,et al. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence[J]. Remote Sensing of Environment, 2019, 232: 111209. DOI: 10.1016/j.rse. 2019.05.028

    [53] [53] LIU X J, LIU L Y, HU J C,et al. Improving the potential of red SIF for estimating GPP by downscaling from the canopy level to the photosystem level[J]. Agricultural and Forest Meteorology, 2020, 281: 107846. DOI: 10.1016/j.agrformet.2019.107846

    [54] [54] HAO D L, ZENG Y L, QIU H,et al. Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry[J]. Remote Sensing of Environment, 2021, 255: 112171. DOI: 10.1016/j.rse.2020.112171

    [55] [55] ZHANG Z Y, ZHANG Y G, PORCAR-CASTELL A,et al. Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence[J]. Remote Sensing of Environment, 2020, 240: 111722. DOI: 10.1016/j.rse.2020.111722

    [56] [56] QI M J, LIU X J, DU S S,et al. Improving the estimation of canopy fluorescence escape probability in the near-infrared band by accounting for soil reflectance[J]. Remote Sensing, 2023, 15(18): 4361. DOI: 10.3390/rs15184361

    [57] [57] LI W J, FANG H L. Estimation of direct, diffuse, and total FPARs from Landsat surface reflectance data and ground-based estimates over six FLUXNET sites[J]. Journal of Geophysical Research: Biogeosciences, 2015, 120(1): 96-112. DOI: 10.1002/2014JG002754

    [58] [58] LI W J, FANG H L, WEI S S,et al. Critical analysis of methods to estimate the fraction of absorbed or intercepted photosynthetically active radiation from ground measurements: Application to rice crops[J]. Agricultural and Forest Meteorology, 2021, 297: 108273. DOI: 10.1016/j.agrformet.2020.108273

    [59] [59] ZHANG Y H, FANG H L, WANG Y,et al. Variation of intra-daily instantaneous FAPAR estimated from the geostationary Himawari-8 AHI data[J]. Agricultural and Forest Meteorology, 2021, 307: 108535. DOI: 10.1016/j.agrformet. 2021.108535

    [60] [60] PICKETT-HEAPS C A, CANADELL J G, BRIGGS P R,et al. Evaluation of six satellite-derived Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) products across the Australian continent[J]. Remote Sensing of Environment, 2014, 140: 241-256. DOI: 10.1016/j.rse.2013.08.037

    [61] [61] XIAO Z Q, LIANG S L, SUN R,et al. Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf Area Index Product[J]. Remote Sensing of Environment, 2015, 171: 105-117. DOI: 10.1016/j.rse.2015.10.016

    [62] [62] FANG H L, LI S J, ZHANG Y H,et al. New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and Leaf Area Index[J]. Science of Remote Sensing, 2021, 4: 100027. DOI: 10.1016/j.srs.2021.100027

    [63] [63] CHEN J M, LIU J, CIHLAR J,et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications[J]. Ecological Modelling, 1999, 124(2/3): 99-119. DOI: 10.1016/S0304-3800(99)00156-8

    [64] [64] Bonan G B. Ecological Climatology[M]. New York: Cambridge University Press, 2002: 678.

    [65] [65] MTTUS M, TAKALA T L H, STENBERG P,et al. Diffuse sky radiation influences the relationship between canopy PRI and shadow fraction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 54-60. DOI: 10.1016/j.isprsjprs.2015.03.012

    [66] [66] HERNNDEZ-CLEMENTE R, KOLARI P, PORCAR-CASTELL A,et al. Tracking the seasonal dynamics of boreal forest photosynthesis using EO-1 Hyperion reflectance: Sensitivity to structural and illumination effects[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(9): 5105-5116. DOI: 10.1109/TGRS.2016.2554466

    [67] [67] MONSI M, SAEKI T. Uber den lichtfaktor in den pflanzegeesellschaften und seine bedeutung fur die stoffproduktion[J]. Japanese Journal of Botany, 1953. 14: 22-52.

    [68] [68] MONSI M. On the factor light in plant communities and its importance for matter production[J]. Annals of Botany, 2004, 95(3): 549-567. DOI: 10.1093/aob/mci052

    [69] [69] NILSON T. A theoretical analysis of the frequency of gaps in plant stands[J]. Agricultural Meteorology, 1971, 8: 25-38. DOI: 10.1016/0002-1571(71)90092-6

    [70] [70] KNYAZIKHIN Y, SCHULL M A, STENBERG P,et al. Hyperspectral remote sensing of foliar nitrogen content[J].Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(3): E185-E192. DOI: 10.1073/pnas.1210196109

    [71] [71] TUCKER C, BRANDT M, HIERNAUX P,et al. Sub-continental-scale carbon stocks of individual trees in African drylands[J]. Nature, 2023, 615(7950): 80-86. DOI: 10.1038/s41586-022-05653-6

    [72] [72] WANG Y J, BUERMANN W, STENBERG P,et al. A new parameterization of canopy spectral response to incident solar radiation: Case study with hyperspectral data from pine dominant forest[J]. Remote Sensing of Environment, 2003, 85(3): 304-315. DOI: 10.1016/S0034-4257(03)00009-9

    [73] [73] TIAN Y H, WANG Y J, ZHANG Y,et al. Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions[J]. Remote Sensing of Environment, 2003, 84(1): 143-159. DOI: 10.1016/S0034-4257(02)00102-5

    [74] [74] YAN K, PARK T, YAN G J,et al. Evaluation of MODIS LAI/FPAR product collection 6. part 1: Consistency and improvements[J]. Remote Sensing, 2016, 8(5): 359. DOI: 10.3390/rs8050359

    [75] [75] YANG B, KNYAZIKHIN Y, LIN Y,et al. Analyses of impact of needle surface properties on estimation of needle absorption spectrum: Case study with coniferous needle and shoot samples[J]. Remote Sensing, 2016, 8(7): 563. DOI: 10.3390/rs8070563

    [76] [76] WANG W L, NEMANI R, HASHIMOTO H,et al. An interplay between photons, canopy structure, and recollision probability: A review of the spectral invariants theory of 3D canopy radiative transfer processes[J]. Remote Sensing, 2018, 10(11): 1805. DOI: 10.3390/rs10111805

    [77] [77] FAN W L, CHEN J M, JU W M,et al. Hybrid geometric optical-radiative transfer model suitable for forests on slopes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9): 5579-5586. DOI: 10.1109/TGRS.2013.2290590

    [78] [78] FAN W J, LIU Y, XU X R,et al. A new FAPAR analytical model based on the law of energy conservation: A case study in China[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(9): 3945-3955.

    [80] [80] LI W H, YAN G J, GENG J,et al. A model based on spectral invariant theory for correcting topographic effects on vegetation canopy reflectance[J]. Remote Sensing of Environment, 2025, 322: 114695. DOI: 10.1016/j.rse.2025.114695

    [81] [81] CAO B, GUO M Z, FAN W J,et al. A new directional canopy emissivity model based on spectral invariants[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(12): 6911-6926. DOI: 10.1109/TGRS.2018.2845678

    [82] [82] GUO M Z, CAO B, FAN W J,et al. Scattering effect contributions to the directional canopy emissivity and brightness temperature based on CE-P and CBT-P models[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6): 957-961. DOI: 10.1109/LGRS.2018.2886606

    [83] [83] YANG P Q, VERHOEF W, FANG H L,et al. Linking Kubelka-Munk and recollision probability theories for radiative transfer simulations in turbid canopy[J]. Remote Sensing of Environment, 2025, 321: 114680. DOI: 10.1016/j.rse.2025.114680

    [84] [84] SONG W J, KNYAZIKHIN Y, WEN G Y,et al. Implications of whole-disc DSCOVR EPIC spectral observations for estimating earth's spectral reflectivity based on low-earth-orbiting and geostationary observations[J]. Remote Sensing, 2018, 10(10): 1594. DOI: 10.3390/rs10101594

    [85] [85] SUN Y H, KNYAZIKHIN Y, SHE X J,et al. Seasonal and long-term variations in leaf area of Congolese rainforest[J].Remote Sensing of Environment, 2022, 268: 112762. DOI: 10.1016/j.rse.2021.112762

    [86] [86] LIN Y, LIU S Y, YAN L,et al. Improving the estimation of canopy structure using spectral invariants: Theoretical basis and validation[J]. Remote Sensing of Environment, 2023, 284: 113368. DOI: 10.1016/j.rse.2022.113368

    [87] [87] GU C P, LI J, LIU Q H,et al. Deriving leaf-scale chlorophyll index(CIleaf) from canopy reflectance by correcting for the canopy multiple scattering based on spectral invariant theory[J]. Remote Sensing of Environment, 2025, 322: 114692. DOI: 10.1016/j.rse.2025.114692

    [88] [88] TAO Y Z, PENG N J, FAN W J,et al. High spatiotemporal resolution vegetation FAPAR estimation from Sentinel-2 based on the spectral invariant theory[J]. Science of Remote Sensing, 2025, 11: 100207. DOI: 10.1016/j.srs.2025.100207

    [89] [89] HE Y C, ZENG Y L, HAO D L,et al. Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy[J]. Remote Sensing of Environment, 2025, 323: 114716. DOI: 10.1016/j.rse.2025.114716

    [90] [90] IHALAINEN O, MTTUS M. Spectral invariants in ultra-high spatial resolution hyperspectral images[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2022, 288: 108265. DOI: 10.1016/j.jqsrt.2022.108265

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    Paper Information

    Received: Mar. 4, 2024

    Accepted: Aug. 26, 2025

    Published Online: Aug. 26, 2025

    The Author Email:

    DOI:10.11873/j.issn.1004-0323.2025.4.0802

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