scholarly journals MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

2015 ◽  
Vol 8 (8) ◽  
pp. 2611-2626 ◽  
Author(s):  
M. Proksch ◽  
C. Mätzler ◽  
A. Wiesmann ◽  
J. Lemmetyinen ◽  
M. Schwank ◽  
...  
Keyword(s):  
Microwave Remote Sensing ◽  
Horizontal Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Splitting Parameter ◽  
Set Up ◽  
Passive Measurements ◽  
Active Microwave Remote Sensing

Abstract. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like- and cross-polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoid fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx (Nordic Snow Radar Experiment) campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in Matlab and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

scholarly journals MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

2015 ◽  
Vol 8 (3) ◽  
pp. 2605-2652 ◽  
Author(s):  
M. Proksch ◽  
C. Mätzler ◽  
A. Wiesmann ◽  
J. Lemmetyinen ◽  
M. Schwank ◽  
...  
Keyword(s):  
Microwave Remote Sensing ◽  
Horizontal Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Input Parameters ◽  
Fitting Procedures ◽  
Splitting Parameter ◽  
Set Up ◽  
Passive Measurements ◽  
Active Microwave Remote Sensing

Abstract. The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave emissions of snowpacks in the frequency range 5–100 GHz. It is based on six-flux theory to describe radiative transfer in snow including absorption, multiple volume scattering, radiation trapping due to internal reflection and a combination of coherent and incoherent superposition of reflections between horizontal layer interfaces. Here we introduce MEMLS3&a, an extension of MEMLS, which includes a backscatter model for active microwave remote sensing of snow. The reflectivity is decomposed into diffuse and specular components. Slight undulations of the snow surface are taken into account. The treatment of like and cross polarization is accomplished by an empirical splitting parameter q. MEMLS3&a (as well as MEMLS) is set up in a way that snow input parameters can be derived by objective measurement methods which avoids fitting procedures of the scattering efficiency of snow, required by several other models. For the validation of the model we have used a combination of active and passive measurements from the NoSREx campaign in Sodankylä, Finland. We find a reasonable agreement between the measurements and simulations, subject to uncertainties in hitherto unmeasured input parameters of the backscatter model. The model is written in MATLAB and the code is publicly available for download through the following website: http://www.iapmw.unibe.ch/research/projects/snowtools/memls.html.

Characterization of Errors in a Coupled Snow Hydrology–Microwave Emission Model

2008 ◽  
Vol 9 (1) ◽  
pp. 149-164 ◽  
Author(s):  
Konstantinos M. Andreadis ◽  
Ding Liang ◽  
Leung Tsang ◽  
Dennis P. Lettenmaier ◽  
Edward G. Josberger
Keyword(s):  
Forest Cover ◽  
Microwave Radiometer ◽  
Coupled Model ◽  
Microwave Remote Sensing ◽  
Quantitative Information ◽  
Microwave Emission ◽  
Prediction Errors ◽  
Emission Model ◽  
Snow Hydrology ◽  
Infiltration Capacity

Abstract Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology [Variable Infiltration Capacity (VIC)] and microwave emission [Dense Media Radiative Transfer (DMRT)] model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), aircraft [Polarimetric Scanning Radiometer (PSR)], and satellite [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)] TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions.

scholarly journals Microwave emission from density-stratified Antarctic firn at 6 cm wavelength

1996 ◽  
Vol 42 (140) ◽  
pp. 63-76 ◽  
Author(s):  
Richard D. West ◽  
Dale P. Winebrenner ◽  
Leung Tsang ◽  
Helmut Rott
Keyword(s):  
Brightness Temperature ◽  
Depth Resolution ◽  
Density Fluctuations ◽  
Microwave Emission ◽  
Estimation Methods ◽  
Emission Model ◽  
Model Predictions ◽  
Input Parameters ◽  
Limited Length ◽  
Model Tuning

AbstractPrevious observations have shown spatial covariances between microwave emission from Antarctic firn at 6 cm wavelength, physical firn temperature and firn-density stratification. Such observations motivate us to understand the physics underlying such covariances and, based on that understanding, to develop estimation methods for firn in which density, and therefore dielectric permittivity, varies randomly in discrete layers with mean thicknesses on the order of centimeters. The model accounts for depth profiles of the physical temperature, mean density and variance of random density fluctuations from layer to layer. We also present a procedure to estimate emission-model input parameters objectively from in situ density-profile observations, as well as uncertainties in the input parameters and corresponding uncertainties in theoretical brightness-temperature predictions. We compare emission-model predictions with ground-based observations at four diverse sites in Antarctica which span a range of accumulation rates and other parameters. We use coincident characterization data to estimate model inputs. At two sites, layered-medium emission-model predictions based on the most probable input parameters (i.e. with no model tuning) agree with observations to within 3.5% for incidence angles≤50°. Corresponding figures for the other two sites are 7.5% and 10%. However, uncertainties in the input parameters are substantial due to the limited length and depth resolution of the characterization data. Uncertainties in brightness-temperature predictions are correspondingly substantial. Thus brightness-temperature predictions for the last-mentioned sites based on only slightly less probable input parameters are also in close agreement with observations. The significance of agreements and discrepancies could be clarified using characterization measurements with finer depth resolution.

Retrieval of basic soil physical properties by assimilating radiometry observations in the community land surface model

2020 ◽  
Author(s):  
Hong Zhao ◽  
Yijian Zeng ◽  
Bob Su ◽  
Xujun Han
Keyword(s):  
Soil Moisture ◽  
Soil Properties ◽  
Land Surface ◽  
Land Surface Model ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Surface Model ◽  
Emission Model ◽  
The Impact ◽  
Parameter Retrieval

<p>Accurate basic soil properties information is fundamental for obtaining reliable soil moisture using land surface models. In view of the passive microwave remote sensing, basic soil properties have an impact on soil dielectric constant, together with soil moisture and temperature. The common link enables to use coupled land surface model with microwave emission model for retrieving basic soil properties in space, especially in remote areas such as the third pole region. The Maqu site in the eastern Tibetan Plateau, including ELBARA-III radiometry observations, was taken as the case. This paper employed an improved observation operator— a discrete scattering-emission model of L-band radiometry with an air-to-soil transition model embedded in, which considers both geometric and dielectric roughness impacts from heterogeneous topsoil structure on surface emission. Community Land Model 4.5 together with Local Ensemble Transform Kalman Filter algorithm were used by mean of the Open Source Multivariate Land Data Assimilation Framework. The retrieved basic soil properties were compared to in situ measurements, as well as the update soil moisture and temperature and energy fluxes. The impacts from surface roughness consideration and polarization configuration on parameter retrieval were also evaluated. To gain an insight on the impact from time interval of observations on parameter retrieval, results using observations at SMAP descending and ascending time were discussed.</p>

scholarly journals Radar backscattering measurement of bare soil and vegetation covered soil using X-band and full polarization

2014 ◽  
Vol XL-8 ◽  
pp. 733-736 ◽  
Author(s):  
B. Goswami ◽  
M. Kalita
Keyword(s):  
Land Cover ◽  
Monsoon Season ◽  
Microwave Remote Sensing ◽  
Bare Soil ◽  
Horn Antennas ◽  
X Band ◽  
Suitable Combination ◽  
Set Up ◽  
The Look ◽  
Active Microwave Remote Sensing

The objective of the study is to measure backscattered power of bare soil and vegetation covered soil using X-band scatterometer system with full polarization and various angles during monsoon season and relate backscattered power to the density of vegetation over soil. The measurement was conducted at an experimental field located in the campus of Assam Engineering College, Guwahati, India. The soil sample consists of Silt and Clay in higher proportions as compared to Sand. The scatterometer system consists of dual-polarimetric square horn antennas, Power meter, Klystron, coaxial cables, isolator and waveguide detector. The polarization of the horn antennas as well as the look angle can be changed in the set-up. The backscattering coefficients were calculated by applying a radar equation for the measured values at incident angles between 30° and 60° for full polarization (HH, VV, HV, VH), respectively, and compared with vegetation cover over soil for each scatterometer measurement simultaneously. The VH polarization and 60° look angle are found to be the most suitable combination of configuration of an X-band scatterometer for distinguishing the land cover targets such as bare soil and vegetation covered soil. From the analysis of the results, polarimetric scatterometer data appear to be promising to distinguish the land cover types such as bare soil and soil completely covered by vegetation. The results of this study will help the scientists working in the field of active microwave remote sensing.

scholarly journals Microwave emission from density-stratified Antarctic firn at 6 cm wavelength

1996 ◽  
Vol 42 (140) ◽  
pp. 63-76 ◽  
Author(s):  
Richard D. West ◽  
Dale P. Winebrenner ◽  
Leung Tsang ◽  
Helmut Rott
Keyword(s):  
Brightness Temperature ◽  
Depth Resolution ◽  
Density Fluctuations ◽  
Microwave Emission ◽  
Estimation Methods ◽  
Emission Model ◽  
Model Predictions ◽  
Input Parameters ◽  
Limited Length ◽  
Model Tuning

AbstractPrevious observations have shown spatial covariances between microwave emission from Antarctic firn at 6 cm wavelength, physical firn temperature and firn-density stratification. Such observations motivate us to understand the physics underlying such covariances and, based on that understanding, to develop estimation methods for firn in which density, and therefore dielectric permittivity, varies randomly in discrete layers with mean thicknesses on the order of centimeters. The model accounts for depth profiles of the physical temperature, mean density and variance of random density fluctuations from layer to layer. We also present a procedure to estimate emission-model input parameters objectively from in situ density-profile observations, as well as uncertainties in the input parameters and corresponding uncertainties in theoretical brightness-temperature predictions. We compare emission-model predictions with ground-based observations at four diverse sites in Antarctica which span a range of accumulation rates and other parameters. We use coincident characterization data to estimate model inputs. At two sites, layered-medium emission-model predictions based on the most probable input parameters (i.e. with no model tuning) agree with observations to within 3.5% for incidence angles≤50°. Corresponding figures for the other two sites are 7.5% and 10%. However, uncertainties in the input parameters are substantial due to the limited length and depth resolution of the characterization data. Uncertainties in brightness-temperature predictions are correspondingly substantial. Thus brightness-temperature predictions for the last-mentioned sites based on only slightly less probable input parameters are also in close agreement with observations. The significance of agreements and discrepancies could be clarified using characterization measurements with finer depth resolution.

scholarly journals Simulation of microwave emission from physically modeled snowpacks

2000 ◽  
Vol 31 ◽  
pp. 397-405 ◽  
Author(s):  
Andreas Wiesmann ◽  
Charles Fierz ◽  
Christian Mätzler
Keyword(s):  
Microwave Radiation ◽  
Meteorological Data ◽  
High Sensitivity ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Detailed Knowledge ◽  
Emission Model ◽  
Snowpack Properties ◽  
Good Agreement

AbstractDetailed knowledge of snowpack properties is crucial for the interpretation and modeling of thermal microwave radiation. Here we use two well-known snow models, Crocus and SNTHERM, to obtain snow profiles from meteorological data. These profiles are compared with pit profiles and used as input to the Microwave Emission Model of Layered Snowpacks (MEMLS) for the simulation of microwave radiation. The snow-profile data can be applied almost directly. Adaptation is needed only in the conversion of the grain-size used in the snow models to the correlation length used in the emission model; it is based on empirical fits. The resulting emissivities are compared with in situ microwave measurements. The computed snow depths are in good agreement with observations. Comparison of selected profiles shows that Crocus is in good agreement with the pit profile, but the density of simulated melt-freeze crusts is underestimated. The SNTHERM profiles show no such crusts, and the density deviates from the pit profiles. The computed temporal behavior of the snowpack emissivity is reasonable. Comparison of selected situations with in situ measurements indicates good agreement. However, the polarization difference tends to be underestimated because of inaccuracies in the simulation of density profiles. The results show the potential of combined snow-physical and microwave-emission models for understanding snow signatures and for developing snow algorithms for microwave remote sensing. Based on the frequency-selective penetration and on the high sensitivity to snow texture, density and wetness, microwave radiometry can offer a new dimension to snow physics. Potential applications are described.

scholarly journals Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

2021 ◽  
Vol 13 (10) ◽  
pp. 2012
Author(s):  
Yue Yu ◽  
Jinmei Pan ◽  
Jiancheng Shi
Keyword(s):  
Process Model ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Single Layer ◽  
Microwave Emission ◽  
Emission Model ◽  
Snow Layer ◽  
Optimal Method ◽  
Microstructure Parameter ◽  
Multiple Layer

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

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.

Sign in / Sign up

Export Citation Format

Share Document