scholarly journals Uncertainty quantification of GEOS-5 L-band radiative transfer model parameters using Bayesian inference and SMOS observations

2014 ◽  
Vol 148 ◽  
pp. 146-157 ◽  
Author(s):  
Gabriëlle J.M. De Lannoy ◽  
Rolf H. Reichle ◽  
Jasper A. Vrugt
Keyword(s):  
Bayesian Inference ◽  
Radiative Transfer ◽  
Uncertainty Quantification ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
L Band

scholarly journals Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment

2018 ◽  
Vol 10 (9) ◽  
pp. 1451 ◽  
Author(s):  
Alexandre Roy ◽  
Marion Leduc-Leballeur ◽  
Ghislain Picard ◽  
Alain Royer ◽  
Peter Toose ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Brightness Temperature ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Emission Model ◽  
Canadian Prairie ◽  
Seasonal Snow ◽  
L Band

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

scholarly journals Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

2014 ◽  
Vol 18 (2) ◽  
pp. 5-9 ◽  
Author(s):  
Anna M. Jarocińska
Keyword(s):  
Radiative Transfer ◽  
Field Measurements ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Biophysical Parameters ◽  
Input Parameters ◽  
Vegetation Characteristics ◽  
Field Reflectance ◽  
Lower Water Content

Abstract Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

scholarly journals Seeking edge-on galaxies with substantial extraplanar dust using a radiative transfer model: determination of the model parameter uncertainties for EON_10.477_41.954 (FGC 79)

2019 ◽  
Vol 489 (4) ◽  
pp. 4690-4704 ◽  
Author(s):  
Jong-Ho Shinn
Keyword(s):  
Radiative Transfer ◽  
Confidence Intervals ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Target Classification ◽  
Parameter Uncertainties ◽  
Model Parameter ◽  
Galaxy Model

ABSTRACT We have revisited the target EON_10.477_41.954 in order to determine more accurately the uncertainties in the model parameters that are important for target classification (i.e. galaxies with or without substantial extraplanar dust). We performed a Markov chain Monte Carlo (MCMC) analysis for the 15 parameters of the three-dimensional radiative-transfer galaxy model we used previously for target classification. To investigate the convergence of the MCMC sampling – which is usually neglected in the literature but should not be – we monitored the integrated autocorrelation time (τint), and we achieved effective sample sizes >5650 for all the model parameters. The confidence intervals are unstable at the beginning of the iterations where the values of τint are increasing, but they become stable in later iterations where those values are almost constant. The final confidence intervals are ∼5–100 times larger than the nominal uncertainties used in our previous study (the standard deviation of three best-fitting results). Thus, those nominal uncertainties are not good proxies for the model-parameter uncertainties. Although the position of EON_10.477_41.954 in the target-classification plot (the scale height to diameter ratio of dust versus that of light source) decreases by about 20–30 per cent when compared to our previous study, its membership in the ‘high-group’ – i.e. among galaxies with substantial extraplanar dust – nevertheless remains unchanged.

A First-Order Radiative Transfer Model for Microwave Radiometry of Forest Canopies at L-Band

2011 ◽  
Vol 49 (9) ◽  
pp. 3167-3179 ◽  
Author(s):  
Mehmet Kurum ◽  
Roger H. Lang ◽  
Peggy E. O'Neill ◽  
Alicia T. Joseph ◽  
Thomas J. Jackson ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Microwave Radiometry ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Forest Canopies ◽  
First Order ◽  
L Band

scholarly journals Global Calibration of the GEOS-5 L-Band Microwave Radiative Transfer Model over Nonfrozen Land Using SMOS Observations

2013 ◽  
Vol 14 (3) ◽  
pp. 765-785 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Land Surface ◽  
Radiative Transfer Model ◽  
Absolute Difference ◽  
Surface Model ◽  
Transfer Model ◽  
Global Average ◽  
L Band ◽  
Parameter Values

Abstract A zero-order (tau-omega) microwave radiative transfer model (RTM) is coupled to the Goddard Earth Observing System, version 5 (GEOS-5) catchment land surface model in preparation for the future assimilation of global brightness temperatures (Tb) from the L-band (1.4 GHz) Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions. Simulations using literature values for the RTM parameters result in Tb biases of 10–50 K against SMOS observations. Multiangular SMOS observations during nonfrozen conditions from 1 July 2011 to 1 July 2012 are used to calibrate parameters related to the microwave roughness h, vegetation opacity τ and/or scattering albedo ω separately for each observed 36-km land grid cell. A particle swarm optimization is used to minimize differences in the long-term (climatological) mean values and standard deviations between SMOS observations and simulations, without attempting to reduce the shorter-term (seasonal to daily) errors. After calibration, global Tb simulations for the validation year (1 July 2010 to 1 July 2011) are largely unbiased for multiple incidence angles and both H and V polarization [e.g., the global average absolute difference is 2.7 K for TbH(42.5°), i.e., at 42.5° incidence angle]. The calibrated parameter values depend to some extent on the specific land surface conditions simulated by the GEOS-5 system and on the scale of the SMOS observations, but they also show realistic spatial distributions. Aggregating the calibrated parameter values by vegetation class prior to using them in the RTM maintains low global biases but increases local biases [e.g., the global average absolute difference is 7.1 K for TbH(42.5°)].

scholarly journals Inverse Modeling of Radiative Transfer by Two-Stream Approximation using the Luus-Jaakola Method

2021 ◽  
Vol 22 (2) ◽  
pp. 325-340
Author(s):  
F. F. Ferreira ◽  
J. M. Krieger ◽  
G. B. Lyra ◽  
W. R. Telles ◽  
J. L. Souza ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Terrestrial Ecosystem ◽  
Early Morning ◽  
Radiative Transfer Model ◽  
Tropical Region ◽  
Model Parameters ◽  
Transfer Model ◽  
Local Conditions ◽  
Sugarcane Crop ◽  
Adequate Representation

The simulation of terrestrial ecosystem processes, using numerical biosphere-atmosphere models that can be coupled to the Atmospheric Models, assist in a better diagnosis and forecast of climate and weather. To be able to represent a particular region, biome or ecosystem, the model parameters need to be adjusted for local conditions. This work aims to assess the Luus-Jaakola (LJ) method in the optimization of the parameters in a two-stream radiative transfer model applied to a vegetation canopy. Solar radiation components (incident, S↓, and reflected, S↑) were measured above a sugarcane crop in a Tropical region from February 17 to 24, 2006. Among the combinations of internal and external iterations evaluated for Luss-Jaakola method, 60/30 (external/internal) iterations presented more precise albedo (∝ = S↑/S↓) simulated (r^2 = 0.7386) and, for the accuracy of the simulated ∝, even though the 60/40 combination had the smallest percentual error (6.40%), the 60/30 combination was 0.03% higher. The precision and accuracy of S↑ was greater with the parameters obtained by the inverse problem with the combination of 60/30 (external/internal) iterations respectively. In general, the behavior of simulated S↑ at the top of the canopy was  underestimated compared to the observed S↑, especially in the early morning. For the simulated ∝ at the top of the canopy, the model's overestimation was observed at the lowest values of albedo. When the largest albedos are observed, only at the beginning of the day the model underestimated the values.  As shown by the tests result, the parameters optimized by Luus-Jaakola method  have an adequate representation of the observed data.

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