Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

ABSTRACT PROSAIL is a combination of the leaf optical properties spectra (PROSPECT) model and the scattering by arbitrarily inclined leaves (SAIL) canopy bidirectional reflectance model. When modelling forest canopy reflectance using the PROSAIL radiative transfer model, the sensitivities of parameters can affect the modelling accuracy. Traditionally, sensitivities have been assessed using local sensitivity analysis (LSA); however, drawbacks to this approach include a lack of consideration for coupled effects between different parameters. In this study, parameter sensitivities in the PROSAIL model were calculated using two global sensitivity analysis (GSA) methods (the Extended Fourier Amplitude Sensitivity Test (EFAST) method and the Morris method), field measurements, and Landsat 5 Thematic Mapper (TM) data for a Moso bamboo forest. The results of GSA were compared with those of LSA in order to identify the key parameters impacting the Moso bamboo forest canopy reflectance, and to provide a reference for model optimization and vegetation canopy inversion improvement. The results showed that: (1) the sensitivities of six major input parameters of the PROSAIL model were generally consistent with the sorting orders of the two GSA methods, but were not in accordance with those from the LSA method, especially in the mid-infrared band; (2) coupled effects among parameters acting on reflectance simulation in visible light bands were greater than those in infrared bands; (3) the simulated canopy reflectance was evaluated using Landsat 5 TM data, and the results simulated based on LSA analysis showed higher error than those based on GSA analysis, because the LSA method ignored the influence of some parameters on canopy reflectance, e.g. leaf mesophyll structure (N), average leaf angle (ALA), leaf water content (Cw), and leaf dry matter content (Cm). However, GSA was able to fully consider the coupled effects among parameters, and thus identified the sensitive parameters impacting on reflectance more accurately.

[1]  Vincent Barra,et al.  Screening parameters in the Pasture Simulation model using the Morris method , 2013 .

[2]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[3]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[4]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[5]  A. Saltelli,et al.  The role of sensitivity analysis in ecological modelling , 2007 .

[6]  John R Fieberg,et al.  Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk , 2005 .

[7]  Yan Li,et al.  Identification of influential parameters through sensitivity analysis of the TOUGH + Hydrate model using LH-OAT sampling , 2015 .

[8]  Rahul Nigam,et al.  Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[10]  A. Kuusk,et al.  A reflectance model for the homogeneous plant canopy and its inversion , 1989 .

[11]  J. Campbell.  Introduction to remote sensing , 1987 .

[12]  S. Jacquemoud.  Inversion of the PROSPECT + SAIL Canopy Reflectance Model from AVIRIS Equivalent Spectra: Theoretical Study , 1993 .

[13]  He Wei,et al.  EFAST Method for Global Sensitivity Analysisof Remote Sensing Model’s Parameters , 2014 .

[14]  J. Bessereau,et al.  Stay and play eFAST or scoop and run eFAST? That is the question! , 2014, The American journal of emergency medicine.

[15]  A. Skidmore,et al.  Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model , 2012 .

[16]  Bo-Hui Tang,et al.  Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[18]  David Michael King,et al.  Morris method of sensitivity analysis applied to assess the importance of input variables on urban water supply yield – A case study , 2013 .

[19]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[20]  K. Sudheer,et al.  Adaptive modified function projective synchronization of multiple time-delayed chaotic Rossler system , 2011 .

[21]  Mazdak Arabi,et al.  Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation , 2012 .

[22]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[23]  Samuel Buis,et al.  Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model , 2010, Environ. Model. Softw..

[24]  Stefano Tarantola,et al.  A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output , 1999, Technometrics.

[25]  J. E. Walsh,et al.  Contributions to the Theory of Rank Order Statistics--The Two Sample Case , 1958 .

[26]  C. Ciric,et al.  Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model , 2012 .

[27]  R. Iman,et al.  A measure of top-down correlation , 1987 .

[28]  D. Shahsavani,et al.  Variance-based sensitivity analysis of model outputs using surrogate models , 2011, Environ. Model. Softw..

[29]  Guo-mo Zhou,et al.  [Retrieval of leaf area index of moso bamboo forest with Landsat Thematic Mapper image based on PROSAIL canopy radiative transfer model]. , 2013, Ying yong sheng tai xue bao = The journal of applied ecology.

[30]  Kunle Olukotun,et al.  Map-Reduce for Machine Learning on Multicore , 2006, NIPS.

[31]  Fugui Wang,et al.  Global sensitivity analysis of a modified CENTURY model for simulating impacts of harvesting fine woody biomass for bioenergy. , 2013 .

[32]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[33]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[34]  Jon C. Helton,et al.  A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling , 2005, Reliab. Eng. Syst. Saf..

[35]  S. Ustin,et al.  Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response , 2014 .

[36]  J. Privette,et al.  Inversion methods for physically‐based models , 2000 .

[37]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[38]  K.,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[39]  A. Saltelli,et al.  Sensitivity Anaysis as an Ingredient of Modeling , 2000 .

[40]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[41]  Roger David Braddock,et al.  The use of graph theory in the sensitivity analysis of the model output: a second order screening method , 1999 .

[42]  Sabrina Gaba,et al.  Combined use of local and ANOVA-based global sensitivity analyses for the investigation of a stochastic dynamic model: Application to the case study of an individual-based model of a fish population , 2006 .

[43]  Su Wei,et al.  Sensitivity analysis of CERES-Wheat model parameters based on EFAST method. , 2012 .

[44]  Stefano Tarantola,et al.  Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations, climates and methods of analysis on model sensitivity to crop parameters , 2010, Environ. Model. Softw..

[45]  Michel M. Verstraete,et al.  Potential and limitations of information extraction on the terrestrial biosphere from satellite remote sensing , 1996 .

[46]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[47]  Yu Chang,et al.  [Sensitivity analysis in ecological modeling]. , 2004, Ying yong sheng tai xue bao = The journal of applied ecology.

[48]  W. Verhoef,et al.  Global sensitivity analysis of the spectral radiance of a soil-vegetation system , 2014 .

[49]  João Roberto dos Santos,et al.  Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle , 2015, Int. J. Appl. Earth Obs. Geoinformation.