TY - JOUR
T1 - A statistical framework for the sensitivity analysis of radiative transfer models
AU - Morris, Robin D.
AU - Kottas, Athanasios
AU - Taddy, Matthew
AU - Furfaro, Roberto
AU - Ganapol, Barry D.
N1 - Funding Information:
Manuscript received June 15, 2007; revised December 10, 2007 and March 11, 2008. Current version published November 26, 2008. This work was supported by the NASA AISR Program under Grants NNG06G177G and NNX07AV69G.
PY - 2008/12
Y1 - 2008/12
N2 - Process models are widely used tools, both for studying fundamental processes themselves and as elements of larger system studies. A radiative transfer model (RTM) simulates the interaction of light with a medium. We are interested in RTMs that model light reflected from a vegetated region. Such an RTM takes as input various biospheric and illumination parameters and computes the upwelling radiation at the top of the canopy. The question we address is as follows: Which of the inputs to the RTM has the greatest impact on the computed observation? We study the leaf canopy model (LCM) RTM, which was designed to study the feasibility of observing leaf chemistry remotely. Its inputs are leaf chemistry variables (chlorophyll, water, lignin, and cellulose) and canopy structural parameters (leaf area index, leaf angle distribution, soil reflectance, and sun angle). We present a statistical approach to the sensitivity analysis of RTMs to answer the question previously posed. The focus is on global sensitivity analysis, studying how the RTM output changes as the inputs vary continuously according to a probability distribution over the input space. The influence of each input variable is captured through the "main effects" and "sensitivity indices." Direct computation requires extensive computationally expensive runs of the RTM. We develop a Gaussian process approximation to the RTM output to enable efficient computation. We illustrate how the approach can effectively determine the inputs that are vital for accurate prediction. The methods are applied to the LCM with seven inputs and output obtained at eight wavelengths associated with Moderate-resolution Imaging Spectroradiometer bands that are sensitive to vegetation.
AB - Process models are widely used tools, both for studying fundamental processes themselves and as elements of larger system studies. A radiative transfer model (RTM) simulates the interaction of light with a medium. We are interested in RTMs that model light reflected from a vegetated region. Such an RTM takes as input various biospheric and illumination parameters and computes the upwelling radiation at the top of the canopy. The question we address is as follows: Which of the inputs to the RTM has the greatest impact on the computed observation? We study the leaf canopy model (LCM) RTM, which was designed to study the feasibility of observing leaf chemistry remotely. Its inputs are leaf chemistry variables (chlorophyll, water, lignin, and cellulose) and canopy structural parameters (leaf area index, leaf angle distribution, soil reflectance, and sun angle). We present a statistical approach to the sensitivity analysis of RTMs to answer the question previously posed. The focus is on global sensitivity analysis, studying how the RTM output changes as the inputs vary continuously according to a probability distribution over the input space. The influence of each input variable is captured through the "main effects" and "sensitivity indices." Direct computation requires extensive computationally expensive runs of the RTM. We develop a Gaussian process approximation to the RTM output to enable efficient computation. We illustrate how the approach can effectively determine the inputs that are vital for accurate prediction. The methods are applied to the LCM with seven inputs and output obtained at eight wavelengths associated with Moderate-resolution Imaging Spectroradiometer bands that are sensitive to vegetation.
KW - Gaussian process (GP)
KW - Main effects
KW - Moderate resolution Imaging Spectroradiometer (MODIS)
KW - Radiative transfer model (RTM)
KW - Sensitivity analysis
KW - Sensitivity index
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U2 - 10.1109/TGRS.2008.2002026
DO - 10.1109/TGRS.2008.2002026
M3 - Article
AN - SCOPUS:79956091964
SN - 0196-2892
VL - 46
SP - 4062
EP - 4074
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 12
M1 - 4683350
ER -