Development of Microwave Emission Model for Frozen Soil with Considering the Volume Scattering Effect

2020 ◽  
Author(s):  
Jian Wang ◽  
Lingmei Jiang ◽  
Xiaojing Liu ◽  
Jianwei Yang
Keyword(s):  
Frozen Soil ◽  
Microwave Emission ◽  
Emission Model ◽  
Volume Scattering ◽  
Scattering Effect

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 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 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 Microstructure representation of snow in coupled snowpack and microwave emission models

2016 ◽  
Author(s):  
Melody Sandells ◽  
Richard Essery ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Leena Leppänen ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Microstructure Evolution ◽  
Microwave Emission ◽  
Model Systems ◽  
Research Centre ◽  
Emission Model ◽  
Mean Error ◽  
Scale Factors ◽  
Wide Range ◽  
Emission Models

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES, and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS or DMRT-ML) provided a consistently good fit across all frequencies and polarizations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarization ranged from 0.9 to 7.2 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM-Empirical MEMLS model combination (2011–2012), and 5.0 or greater when considering non-sticky particles in DMRT-ML in conjunction with MOSES or SNICAR microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K when the JIM ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

scholarly journals Microwave snow emission modeling uncertainties in boreal and subarctic environments

2015 ◽  
Vol 9 (5) ◽  
pp. 5719-5773
Author(s):  
A. Roy ◽  
A. Royer ◽  
O. St-Jean-Rondeau ◽  
B. Montpetit ◽  
G. Picard ◽  
...  
Keyword(s):  
Grain Size ◽  
Spatial Variability ◽  
Boreal Forest ◽  
Frozen Soil ◽  
Microwave Emission ◽  
Small Scale ◽  
Radiometric Measurements ◽  
Snow Properties ◽  
Modeling Uncertainties

Abstract. This study aims to better understand and quantify the uncertainties in microwave snow emission models using the Dense Media Radiative Theory-Multilayer model (DMRT-ML) with in situ measurements of snow properties. We use surface-based radiometric measurements at 10.67, 19 and 37 GHz in boreal forest and subarctic environments and a new in situ dataset of measurements of snow properties (profiles of density, snow grain size and temperature, soil characterization and ice lens detection) acquired in the James Bay and Umijuaq regions of Northern Québec, Canada. A snow excavation experiment – where snow was removed from the ground to measure the microwave emission of bare frozen ground – shows that small-scale spatial variability in the emission of frozen soil is small. Hence, variability in the emission of frozen soil has a small effect on snow-covered brightness temperature (TB). Grain size and density measurement errors can explain the errors at 37 GHz, while the sensitivity of TB at 19 GHz to snow increases during the winter because of the snow grain growth that leads to scattering. Furthermore, the inclusion of observed ice lenses in DMRT-ML leads to significant improvements in the simulations at horizontal polarization (H-pol) for the three frequencies (up to 20 K of root mean square error). However, the representation of the spatial variability of TB remains poor at 10.67 and 19 GHz at H-pol given the spatial variability of ice lens characteristics and the difficulty in simulating snowpack stratigraphy related to the snow crust. The results also show that for ground-based radiometric measurements, forest emission reflected by the surface leads to TB underestimation of up to 40 K if neglected. We perform a comprehensive analysis of the components that contribute to the snow-covered microwave signal, which will help to develop DMRT-ML and to improve the required field measurements. The analysis shows that a better consideration of ice lenses and snow crusts is essential to improve TB simulations in boreal forest and subarctic environments.

scholarly journals Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

2015 ◽  
Vol 9 (6) ◽  
pp. 2101-2117 ◽  
Author(s):  
H. Löwe ◽  
G. Picard
Keyword(s):  
Correlation Function ◽  
Volume Fraction ◽  
Hard Spheres ◽  
Microwave Emission ◽  
Scattering Coefficient ◽  
Equivalent Diameter ◽  
Emission Model ◽  
Data Set ◽  
Point Correlation ◽  
Point Correlation Function

Abstract. The description of snow microstructure in microwave models is often simplified to facilitate electromagnetic calculations. Within dense media radiative transfer (DMRT), the microstructure is commonly described by sticky hard spheres (SHS). An objective mapping of real snow onto SHS is however missing which prevents measured input parameters from being used for DMRT. In contrast, the microwave emission model of layered snowpacks (MEMLS) employs a conceptually different approach, based on the two-point correlation function which is accessible by tomography. Here we show the equivalence of both electromagnetic approaches by reformulating their microstructural models in a common framework. Using analytical results for the two-point correlation function of hard spheres, we show that the scattering coefficient in both models only differs by a factor which is close to unity, weakly dependent on ice volume fraction and independent of other microstructural details. Additionally, our analysis provides an objective retrieval method for the SHS parameters (diameter and stickiness) from tomography images. For a comprehensive data set we demonstrate the variability of stickiness and compare the SHS diameter to the optical equivalent diameter. Our results confirm the necessity of a large grain-size scaling when relating both diameters in the non-sticky case, as previously suggested by several authors.

scholarly journals Variations in snowpack melt on the Greenland ice sheet based on passive-microwave measurements

1995 ◽  
Vol 41 (137) ◽  
pp. 51-60 ◽  
Author(s):  
Thomas L. Mote ◽  
Mark R. Anderson
Keyword(s):  
Climate Change ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ablation Rate ◽  
Passive Microwave ◽  
Microwave Emission ◽  
The Arctic ◽  
Emission Model ◽  
The Impact ◽  
Microwave Data

AbstractA simple microwave-emission model is used to simulate 37 GHz brightness temperatures associated with snowpack-melt conditions for locations across the Greenland ice sheet. The simulated values are utilized as threshold values and compared to daily, gridded SMMR and SSM/I passive-microwave data, in order to reveal regions experiencing melt. The spatial extent of the area classified as melting is examined on a daily, monthly and seasonal (May-August) basis for 1979–91. The typical seasonal cycle of melt coverage shows melt beginning in late April, a rapid increase in the melting area from mid-May to mid-July, a rapid decrease in melt extent from late July through mid-August, and cessation of melt in late September. Seasonal averages of the daily melt extents demonstrate an apparent increase in melt coverage over the 13 year period of approximately 3.8% annually (significant at the 95% confidence interval). This increase is dominated by statistically significant positive trends in melt coverage during July and August in the west and southwest of the ice sheet. We find that a linear correlation between microwave-derived melt extent and a surface measure of ablation rate is significant in June and July but not August, so caution must be exercised in using the microwave-derived melt extents in August. Nevertheless, knowledge of the variability of snowpack melt on the Greenland ice sheet as derived from microwave data should prove useful in detecting climate change in the Arctic and examining the impact of climate change on the ice sheet.

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

2009 ◽  
Vol 10 (1) ◽  
pp. 213-226 ◽  
Author(s):  
Matthias Drusch ◽  
Thomas Holmes ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo
Keyword(s):  
Soil Moisture ◽  
Dynamic Range ◽  
Microwave Radiometer ◽  
Space Station ◽  
Microwave Emission ◽  
Emission Model ◽  
Soil Database ◽  
Brightness Temperatures ◽  
L Band ◽  
Rms Errors

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.

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