The fourth radiation transfer model intercomparison (RAMI-IV): Proficiency testing of canopy reflectance models with ISO-13528

The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These mode...

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Vydáno v:	Journal of geophysical research. Atmospheres Ročník 118; číslo 13; s. 6869 - 6890
Hlavní autoři:	Widlowski, J.-L., Pinty, B., Lopatka, M., Atzberger, C., Buzica, D., Chelle, M., Disney, M., Gastellu-Etchegorry, J-P., Gerboles, M., Gobron, N., Grau, E., Huang, H., Kallel, A., Kobayashi, H., Lewis, P. E., Qin, W., Schlerf, M., Stuckens, J., Xie, D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, NJ Blackwell Publishing Ltd 16.07.2013 John Wiley & Sons American Geophysical Union
Témata:	Agricultural sciences BRDF Canopies Climate change Computer simulation Criteria Earth, ocean, space Exact sciences and technology External geophysics Geophysics ISO standards Life Sciences Mathematical analysis Mathematical models Meteorology model comparison Radiation Radiative transfer radiative transfer models Statistics Tolerances validation vegetation canopies models radiative transfer Space remote sensing Satellite observation digital simulation performances Comparative study Retrieval algorithm Canopy(vegetation) Reflectance CALIBRATION model comparison ISO standards LIGHT VALIDATION SIMULATION INVERSION BRDF ATMOSPHERE radiative transfer models VEGETATION CANOPIES CSAR MODEL SCALE
ISSN:	2169-897X, 2169-8996
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Abstract	The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI‐IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO‐13528 was developed “to determine the performance of individual laboratories for specific tests or measurements.” More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO‐13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, “truth” must be replaced by a reliable “conventional reference value” to enable absolute performance tests. This contribution will show, for the first time, how the ISO‐13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre‐screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI‐IV “ canopy” scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted. Key Points ISO‐13528 can be applied to the verification of computer simulation models. Model comparisons require detailed definitions on acceptable bias levels. Operator choices/errors are likely cause for most observed biases in RAMI‐IV.
AbstractList	The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI‐IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO‐13528 was developed “to determine the performance of individual laboratories for specific tests or measurements.” More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO‐13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, “truth” must be replaced by a reliable “conventional reference value” to enable absolute performance tests. This contribution will show, for the first time, how the ISO‐13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre‐screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI‐IV “ canopy” scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted. Key Points ISO‐13528 can be applied to the verification of computer simulation models. Model comparisons require detailed definitions on acceptable bias levels. Operator choices/errors are likely cause for most observed biases in RAMI‐IV. The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics-based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI-IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO-13528 was developed "to determine the performance of individual laboratories for specific tests or measurements." More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO-13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, "truth" must be replaced by a reliable "conventional reference value" to enable absolute performance tests. This contribution will show, for the first time, how the ISO-13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre-screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI-IV "abstract canopy" scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted. Key Points ISO-13528 can be applied to the verification of computer simulation models. Model comparisons require detailed definitions on acceptable bias levels. Operator choices/errors are likely cause for most observed biases in RAMI-IV. The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics-based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI-IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO-13528 was developed "to determine the performance of individual laboratories for specific tests or measurements." More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO-13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, "truth" must be replaced by a reliable "conventional reference value" to enable absolute performance tests. This contribution will show, for the first time, how the ISO-13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre-screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI-IV "abstract canopy" scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted. The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI‐IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO‐13528 was developed “to determine the performance of individual laboratories for specific tests or measurements.” More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO‐13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, “truth” must be replaced by a reliable “conventional reference value” to enable absolute performance tests. This contribution will show, for the first time, how the ISO‐13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre‐screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI‐IV “abstract canopy” scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted. ISO‐13528 can be applied to the verification of computer simulation models. Model comparisons require detailed definitions on acceptable bias levels. Operator choices/errors are likely cause for most observed biases in RAMI‐IV.
Author	Pinty, B. Gerboles, M. Xie, D. Qin, W. Kallel, A. Buzica, D. Widlowski, J.-L. Atzberger, C. Lewis, P. E. Schlerf, M. Stuckens, J. Chelle, M. Disney, M. Kobayashi, H. Gastellu-Etchegorry, J-P. Grau, E. Gobron, N. Huang, H. Lopatka, M.
Author_xml	– sequence: 1 givenname: J.-L. surname: Widlowski fullname: Widlowski, J.-L. organization: Institute for Environment and Sustainability, DG Joint Research Centre, Ispra, European Commission, Italy – sequence: 2 givenname: B. surname: Pinty fullname: Pinty, B. organization: Institute for Environment and Sustainability, DG Joint Research Centre, Ispra, European Commission, Italy – sequence: 3 givenname: M. surname: Lopatka fullname: Lopatka, M. organization: Institute for Environment and Sustainability, DG Joint Research Centre, Ispra, European Commission, Italy – sequence: 4 givenname: C. surname: Atzberger fullname: Atzberger, C. organization: Institute of Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences, Vienna, Austria – sequence: 5 givenname: D. surname: Buzica fullname: Buzica, D. organization: Industrial Emissions, Air Quality and Noise Unit, DG Environment, Brussels, European Commission, Belgium – sequence: 6 givenname: M. surname: Chelle fullname: Chelle, M. organization: Institut National de la Recherche Agronomique, Thiverval-Grignon, France – sequence: 7 givenname: M. surname: Disney fullname: Disney, M. organization: Department of Geography, University College London, UK – sequence: 8 givenname: J-P. surname: Gastellu-Etchegorry fullname: Gastellu-Etchegorry, J-P. organization: Centre d'Etudes Spatiales de la BIOsphère, Toulouse, France – sequence: 9 givenname: M. surname: Gerboles fullname: Gerboles, M. organization: Institute for Environment and Sustainability, DG Joint Research Centre, Ispra, European Commission, Italy – sequence: 10 givenname: N. surname: Gobron fullname: Gobron, N. organization: Institute for Environment and Sustainability, DG Joint Research Centre, Ispra, European Commission, Italy – sequence: 11 givenname: E. surname: Grau fullname: Grau, E. organization: Centre d'Etudes Spatiales de la BIOsphère, Toulouse, France – sequence: 12 givenname: H. surname: Huang fullname: Huang, H. organization: Beijing Forestry University, Beijing, China – sequence: 13 givenname: A. surname: Kallel fullname: Kallel, A. organization: Institut Supérieur d'Electronique et de Communication de Sfax, Tunisia – sequence: 14 givenname: H. surname: Kobayashi fullname: Kobayashi, H. organization: Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA – sequence: 15 givenname: P. E. surname: Lewis fullname: Lewis, P. E. organization: Department of Geography, University College London, UK – sequence: 16 givenname: W. surname: Qin fullname: Qin, W. organization: Science Systems and Applications, Inc., Maryland, Greenbelt, USA – sequence: 17 givenname: M. surname: Schlerf fullname: Schlerf, M. organization: Département Environnement et Agro-biotechnologie, Centre de Recherche Public - Gabriel Lippmann, Belvaux, Luxembourg – sequence: 18 givenname: J. surname: Stuckens fullname: Stuckens, J. organization: Biosystems Department, Katholieke Universiteit Leuven, Leuven, Belgium – sequence: 19 givenname: D. surname: Xie fullname: Xie, D. organization: Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing, China
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Copyright	2013. American Geophysical Union. All Rights Reserved. 2014 INIST-CNRS Distributed under a Creative Commons Attribution 4.0 International License
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Keywords	models radiative transfer Space remote sensing Satellite observation digital simulation performances Comparative study Retrieval algorithm Canopy(vegetation) Reflectance CALIBRATION model comparison ISO standards LIGHT VALIDATION SIMULATION INVERSION BRDF ATMOSPHERE radiative transfer models VEGETATION CANOPIES CSAR MODEL SCALE
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Publisher	Blackwell Publishing Ltd John Wiley & Sons American Geophysical Union
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References	Bruegge, C. J., N. L. Chrien, R. R. Ando, D. J. Diner, W. A. Abdou, M. C. Helmlinger, S. H. Pilorz, and K. J. Thome (2002), Early validation of the multi-angle imaging spectroradiometer (MISR) radiometric scale, IEEE Trans. Geosci. Remote Sens., 40, 1477-1492. ISO 5725-2 (1994), Accuracy (trueness and precision) of measurement methods and results-Part 2:, International Standard, ISO 5725-2:1994 (E), 42 pp., International Organization for Standardization, ISO/TC69-SC6, Geneva, Switzerland. Rahman, H., B. Pinty, and M. M. Verstraete (1993a), Coupled surface-atmosphere reflectance (CSAR) model. 2. Semiempirical surface model usable with NOAA Advanced Very High Resolution Radiometer data, J. Geophys. Res., 98, 20791-20801. Gobron, N., B. Pinty, M. M. Verstraete, and Y. Govaerts (1997), A semi-discrete model for the scattering of light by vegetation, J. Geophys. Res., 102, 9431-9446. Goel, N. (1988), Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data, Remote Sens. Rev., 4, 1-212. Thome, K. J., K. Arai, S. Tsuchida, and S. F. Biggar (2008), Vicarious calibration of ASTER via the reflectance-based approach, IEEE Trans. Geosci. Remote Sens., 46, 3285-3295. Huang, H., M. Chen, and Q. Liu (2009), A realistic structure model for large-scale surface leaving radiance simulation of forest canopy and accuracy assessment, Int. J. Remote Sens., 30(20), 5421-5439. Widlowski, J.-L., et al. (2007b), The third radiation transfer model intercomparison (RAMI) exercise: Documenting progress in canopy reflectance modelling, J. Geophys. Res., 112, D09111, doi:10.1029/2006JD007821. Hund, E., D. L. Massart, and J. Smeyers-Verbeke (2000), Inter-laboratory studies in analytical chemistry, Anal. Chim. Acta, 423, 145-165. Rahman, H., M. M. Verstraete, and B. Pinty (1993b), Coupled surface-atmosphere reflectance (CSAR) model. 1. Model description and inversion on synthetic data, J. Geophys. Res., 98, 20779-20789. Lewis, P. (1999), Three-dimensional plant modelling for remote sensing simulation studies using the botanical plant modelling system, Agron. Agric. Environ., 19, 185-210. Wang, Y., J. Czapla-Myers, A. Lyapustin, K. Thome, and E. Dutton (2011), Aeronet-based surface reflectance validation network (ASRVN) data evaluation: Case study for railroad valley calibration site, Remote Sens. Environ., 115, 2710-2717, doi:10.1016/j.rse.2011.06.011. Chen, J. M., and J. Cihlar (1995), Plant canopy gap size analysis theory for improving optical measurements of leaf area index, Appl. Opt., 34, 6211-6222. Widlowski, J. -L., M. Robustelli, M. Disney, J. -P. Gastellu-Etchegorry, T. Lavergne, P. Lewis, P. J. R. North, B. Pinty, R. Thompson, and M. M. Verstraete (2007a), The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models, Remote Sens. Environ., 112(3), 1144-1150, doi:10.1016/j.rse.2007.07.016. Kallel, A. (2012), Extension of virtual flux decomposition model to the case of two vegetation layers: FDM-2, J. Quant. Spectrosc. Radiat. Transfer, 113, 440-460. Schlerf, M., and M. Atzberger (2006), Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data, Remote Sens. Environ., 100, 281-294. Goel, N. S., and D. E. Strebel (1984), Simple beta distribution representation of leaf orientation in vegetation canopies, Agron. J., 76, 800-803. Kallel, A. (2010), Vegetation radiative transfer modeling based on virtual flux decomposition, J. Quant. Spectrosc. Radiat. Transfer, 111, 1389-1405. Gerboles, M., et al. (2011), Interlaboratory comparison exercise for the determination of As, Cd, Ni and PM10 in Europe, Atmos. Environ., 45, 3488-3499. Disney, M. I., P. Lewis, M. Bouvet, A. Prieto-Blanco, and S. Hancock (2009), Quantifying surface reflectivity for spaceborne lidar via two independent methods, IEEE Trans. Geosci. Remote Sens., 47(10), 3262-3271, doi:10.1109/TGRS.2009.2019268. Thome, K. J. (2001), Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method, Remote Sens. Environ., 78, 27-38. Disney, M. I., P. Lewis, and P. R. J. North (2000), Monte Carlo raytracing in optical canopy reflectance modelling, Remote Sens. Rev., 18, 163-196. Kobayashi, H., and H. Iwabuchi (2008), Acoupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape, Remote Sens. Environ., 112, 173-185. Liu, Q. H. H. H. G., W. Qin, K. Fu, and X. Li (2007), Anextended 3-D radiosity graphics combined model for studying thermal-emission directionality of crop canopy, IEEE Trans. Geosci. Remote Sens., 45, 2900-2918. Pharr, M., and G. Humphreys (2010), Physically Based Rendering: From Theory to Implementation, 1167 pp., Morgan Kaufmann, San Fransisco. Widlowski, J.-L., et al. (2011), RAMI4PILPS: An intercomparison of formulations for the partitioning of solar radiation in land surface models, J. Geophys. Res., 116, G02019, doi:10.1029/2010JG001511. Kneubühler, M., M. Schaepman, K. Thome, F. Baret, and A. Müller (2002), Calibration and validation of Envisat MERIS. Part 1: Vicarious calibration at Rail Road valley Playa (NV), Proceedings of MERIS level 2 validation Workshop, ESRIN. Frascati, Italy, December 9-13. Pinty, B., et al. (2001), The radiation transfer model intercomparison (RAMI) exercise, J. Geophys. Res., 106, 11937-11956. Gastellu-Etchegorry, J. -P., V. Demarez, V. Pinel, and F. Zagolski (1996), Modeling radiative transfer in heterogeneous 3-D vegetation canopies, Remote Sens. Environ., 58, 131-156. ISO 5725-3 (1994), Accuracy (trueness and precision) of measurement methods and results-Part 3:, International Standard, ISO 5725-3:1994 (E), 25 pp., International Organization for Standardization, ISO/TC69-SC6, Geneva, Switzerland. Gastellu-Etchegorry, J. -P., E. Martin, and F. Gascon (2004), Dart: A 3D model for simulating satellite images and studying surface radiation budget, Int. J. Remote Sens., 25, 73-96. Qin, W., and S. A. W. Gerstl (2000), 3-D scene modeling of semi-desert vegetation cover and its radiation regime, Remote Sens. Environ., 74, 145-162. Chelle, M. (2006), Could plant leaves be treated as Lambertian surfaces in dense crop canopies to estimate light absorption?Ecol. Modell., 198, 219-228. ISO 13528 (2005), Statistical methods for use in proficiency testing by interlaboratory comparisons, International Standard, ISO 13528:2005(E), 66 pp., International Organization for Standardization, ISO/TC6-SC6, Geneva, Switzerland. Pinty, B., et al. (2004), The radiation transfer model intercomparison (RAMI) exercise: Results from the second phase, J. Geophys. Res., 109, D06210, doi:10.1029/2004JD004252. 2011; 116 2011; 115 2007a; 112 2009; 47 1993b; 98 2012 2011 2010 2004; 25 1995; 34 1997 2008 2006; 198 1995 2006 2005 1994 2002 1996; 58 2004; 109 2001; 106 1997; 102 1988; 4 2000; 18 2009; 30 2012; 113 2000 2000; 423 1999; 19 2002; 40 1984; 76 2000; 74 2010; 111 2008; 46 2011; 45 2007b; 112 2008; 112 2001; 78 2007; 45 1993a; 98 2006; 100 e_1_2_9_31_1 ISO 5725‐2 (e_1_2_9_20_1) 1994 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 Pharr M. (e_1_2_9_30_1) 2010 e_1_2_9_36_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_42_1 Atzberger C. (e_1_2_9_2_1) 2000 Kneubühler M. (e_1_2_9_26_1) 2002 e_1_2_9_40_1 e_1_2_9_22_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 ISO 13528 (e_1_2_9_19_1) 2005 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_28_1 ISO 5725‐3 (e_1_2_9_21_1) 1994 e_1_2_9_27_1 e_1_2_9_29_1
References_xml	– reference: Qin, W., and S. A. W. Gerstl (2000), 3-D scene modeling of semi-desert vegetation cover and its radiation regime, Remote Sens. Environ., 74, 145-162. – reference: Pinty, B., et al. (2001), The radiation transfer model intercomparison (RAMI) exercise, J. Geophys. Res., 106, 11937-11956. – reference: Widlowski, J.-L., et al. (2011), RAMI4PILPS: An intercomparison of formulations for the partitioning of solar radiation in land surface models, J. Geophys. Res., 116, G02019, doi:10.1029/2010JG001511. – reference: Chen, J. M., and J. Cihlar (1995), Plant canopy gap size analysis theory for improving optical measurements of leaf area index, Appl. Opt., 34, 6211-6222. – reference: Hund, E., D. L. Massart, and J. Smeyers-Verbeke (2000), Inter-laboratory studies in analytical chemistry, Anal. Chim. Acta, 423, 145-165. – reference: Rahman, H., B. Pinty, and M. M. Verstraete (1993a), Coupled surface-atmosphere reflectance (CSAR) model. 2. Semiempirical surface model usable with NOAA Advanced Very High Resolution Radiometer data, J. Geophys. Res., 98, 20791-20801. – reference: ISO 5725-3 (1994), Accuracy (trueness and precision) of measurement methods and results-Part 3:, International Standard, ISO 5725-3:1994 (E), 25 pp., International Organization for Standardization, ISO/TC69-SC6, Geneva, Switzerland. – reference: Thome, K. J., K. Arai, S. Tsuchida, and S. F. Biggar (2008), Vicarious calibration of ASTER via the reflectance-based approach, IEEE Trans. Geosci. Remote Sens., 46, 3285-3295. – reference: Gastellu-Etchegorry, J. -P., V. Demarez, V. Pinel, and F. Zagolski (1996), Modeling radiative transfer in heterogeneous 3-D vegetation canopies, Remote Sens. Environ., 58, 131-156. – reference: Pharr, M., and G. Humphreys (2010), Physically Based Rendering: From Theory to Implementation, 1167 pp., Morgan Kaufmann, San Fransisco. – reference: Widlowski, J. -L., M. Robustelli, M. Disney, J. -P. Gastellu-Etchegorry, T. Lavergne, P. Lewis, P. J. R. North, B. Pinty, R. Thompson, and M. M. Verstraete (2007a), The RAMI On-line Model Checker (ROMC): A web-based benchmarking facility for canopy reflectance models, Remote Sens. Environ., 112(3), 1144-1150, doi:10.1016/j.rse.2007.07.016. – reference: Bruegge, C. J., N. L. Chrien, R. R. Ando, D. J. Diner, W. A. Abdou, M. C. Helmlinger, S. H. Pilorz, and K. J. Thome (2002), Early validation of the multi-angle imaging spectroradiometer (MISR) radiometric scale, IEEE Trans. Geosci. Remote Sens., 40, 1477-1492. – reference: Goel, N. (1988), Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data, Remote Sens. Rev., 4, 1-212. – reference: Pinty, B., et al. (2004), The radiation transfer model intercomparison (RAMI) exercise: Results from the second phase, J. Geophys. Res., 109, D06210, doi:10.1029/2004JD004252. – reference: Schlerf, M., and M. Atzberger (2006), Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data, Remote Sens. Environ., 100, 281-294. – reference: Gobron, N., B. Pinty, M. M. Verstraete, and Y. Govaerts (1997), A semi-discrete model for the scattering of light by vegetation, J. Geophys. Res., 102, 9431-9446. – reference: Huang, H., M. Chen, and Q. Liu (2009), A realistic structure model for large-scale surface leaving radiance simulation of forest canopy and accuracy assessment, Int. J. Remote Sens., 30(20), 5421-5439. – reference: Gerboles, M., et al. (2011), Interlaboratory comparison exercise for the determination of As, Cd, Ni and PM10 in Europe, Atmos. Environ., 45, 3488-3499. – reference: Liu, Q. H. H. H. G., W. Qin, K. Fu, and X. Li (2007), Anextended 3-D radiosity graphics combined model for studying thermal-emission directionality of crop canopy, IEEE Trans. Geosci. Remote Sens., 45, 2900-2918. – reference: Kobayashi, H., and H. Iwabuchi (2008), Acoupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape, Remote Sens. Environ., 112, 173-185. – reference: Kneubühler, M., M. Schaepman, K. Thome, F. Baret, and A. Müller (2002), Calibration and validation of Envisat MERIS. Part 1: Vicarious calibration at Rail Road valley Playa (NV), Proceedings of MERIS level 2 validation Workshop, ESRIN. Frascati, Italy, December 9-13. – reference: Thome, K. J. (2001), Absolute radiometric calibration of Landsat 7 ETM+ using the reflectance-based method, Remote Sens. Environ., 78, 27-38. – reference: Lewis, P. (1999), Three-dimensional plant modelling for remote sensing simulation studies using the botanical plant modelling system, Agron. Agric. Environ., 19, 185-210. – reference: Disney, M. I., P. Lewis, and P. R. J. North (2000), Monte Carlo raytracing in optical canopy reflectance modelling, Remote Sens. 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Snippet	The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics‐based radiative transfer (RT) models under controlled... The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics-based radiative transfer (RT) models under controlled...
SourceID	hal proquest pascalfrancis crossref wiley istex
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	6869
SubjectTerms	Agricultural sciences BRDF Canopies Climate change Computer simulation Criteria Earth, ocean, space Exact sciences and technology External geophysics Geophysics ISO standards Life Sciences Mathematical analysis Mathematical models Meteorology model comparison Radiation Radiative transfer radiative transfer models Statistics Tolerances validation vegetation canopies
Title	The fourth radiation transfer model intercomparison (RAMI-IV): Proficiency testing of canopy reflectance models with ISO-13528
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Volume	118
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