Methods of sensitivity analysis in remote sensing: implications for canopy reflectance model inversion

ABSTRACTPROSAIL 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.

Parameter estimation is a major challenge for mathematical modelling of biological systems. Given the uncertainties associated with model parameters, it is important to understand how sensitive the model output is to variations in parameter values. A local sensitivity analysis determines the model sensitivity to parameter variations over a localised region around the nominal parameter values, whereas a global sensitivity analysis (GSA) investigates the sensitivity over the entire parameter space. Using a T-cell receptor-activated Erk-MAPK signalling pathway model as an example, the authors present a comparative study of a variety of different sensitivity analysis techniques. These techniques include: local sensitivity analysis, existing GSA methods of partial rank correlation coefficient, Sobol's, extended Fourier amplitude sensitivity test, as well as a weighted average of local sensitivities and a new GSA method to extract global parameter sensitivities from a parameter identification routine. Results of this study revealed critical reactions in the signalling pathway and their impact on the signalling dynamics and provided insights into embedded regulatory mechanisms such as feedback loops in the pathway. From this study, a recommendation emerges for a general sensitivity analysis strategy to efficiently and reliably infer quantitative, dynamic as well as topological properties from systems biology models.

To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.

Identifying sensitive sources and key control handles for the reduction of greenhouse gas emissions from wastewater treatment

Determination of an optimal measurement configuration in optical scatterometry using global sensitivity analysis

Copula-based methods for global sensitivity analysis with correlated random variables and stochastic processes under incomplete probability information

Nonlinear creep-damage constitutive model of surrounding rock in salt cavern reservoir

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning

Simulation of fate and transport mechanisms in the porous systems from contaminant transport models are affected by uncertainty associated with input model parameters. Sensitivity analysis (SA) provides tools to quantify the sources of uncertainty to the variations in the model output metrics. To quantify the uncertainty associated with physical non-equilibrium contaminant transport model, global SA was conducted for the problem mimicking reactive transport in the saturated soil column conditions. Five global SA methods, namely Morris, RSA (Regionalized Sensitivity Analysis), Sobol, FAST (Fourier Amplitude Sensitivity Test), and PAWN, were tested based on the temporal moments of contaminant concentrations for two output metrics: zeroth temporal moment (ZTM) and mean residence time (MRT). The ranking order of ten input model parameters from global SA methods for two output metrics was compared. Morris SA implied that the ZTM at outlet of the soil column is highly sensitive to sorption distribution coefficient in the mobile and immobile regions and less sensitive to dispersion coefficient and degradation rate constant in the immobile region. The mass-transfer coefficient showed highest non-linear interactions with other flow and transport parameters based on the highest value of standard deviation of elementary effects (EEs) for ZTM output metric. The sorption distribution coefficient in the mobile region and mass-transfer coefficient showed highest sensitivity and non-linear effect toward MRT based on the Morris method. The comparison of global SA methods revealed that the top two sensitive parameters affecting ZTM and MRT were the same from all the considered methods. However, large difference in the ranking order of bottom three sensitive parameters was observed. The sorption distribution coefficient of mobile region and mass-transfer coefficient were observed as the most sensitive parameters affecting ZTM and MRT based on the comparison of all five global SA methods. Overall results suggest that the non-linear contaminant transport model should be examined based on multiple sensitivity output metrics via a multi-model approach. The multi-model global SA approach implemented in this study highlighted its significance in quantifying the interplay of non-linear model parameters.

Global sensitivity analysis and uncertainty quantification of soot formation in an n-dodecane spray flame

Optimization of sensitivity analysis in best estimate plus uncertainty and the application to large break LOCA of a three-loop pressurized water reactor

Intelligent sensitivity analysis framework based on machine learning for spacecraft thermal design

Global Sensitivity Analysis of Arrhythmic Risk Biomarkers in Cardiac Single Cell Models

This paper presents an application of the Global Sensitivity Analysis (GSA) method in evaluating performance of impedance measurement algorithms implemented in modern Intelligent Electronic Devices (IEDs) when affected by uncertainty. To be specific, the GSA is applied in testing an algorithm for distance protection of a single transmission line in a system with two lines on the same towers. Factors contributing to uncertainty are discussed in the paper. The GSA method decomposes variance of the estimation error according to the factors and their interactions, and in this way identifies the factors that have the highest impact on the estimation error. Computation of the GSA sensitivity indices relies on the Quasi-Monte Carlo (QMC) technique with Sobol's quasi-random sampling. This is time-consuming for large number of factors therefore, fast pre-screening process based on the Morris method is implemented to remove non-influential parameters and reduce the factor space before running the QMC. The methodology is implemented using fault simulation software DIgSILENT PowerFactory to calculate fault impedance as a function of uncertain parameters (i.e. factors) as well as voltage and current signals that are inputs to an IED under test. In this simulation, factor space is sampled using Sobol's quasi-random sequence generated with the standard GSA software tool called SIMLAB. All simulations of the transmission line faults for various parameter values and calculation of the performance indices (i.e. sensitivities) is automated using the program developed in DIgSILENT Programming Language (DPL). This testing technique is applied to study the fault impedance measurement implemented in SEL-421. (6 pages)