The Microwave Emission Model of Layered Snowpacks (MEMLS) was originally developed for microwave
emissions of snowpacks in the frequency range 5–100
Empirical observations reveal a wide range of different microwave signatures
in active or passive remote sensing over snow covered areas as shown e.g., by
As an advantage of IBA and the characterization of snow in terms of
correlation functions, the most relevant snow input parameters of MEMLS,
correlation length and density, can be measured directly and objectively by
various methods. Other models may require e.g., a conversion of measured
parameters to model-effective ones
In recent years, there was an increasing interest of the snow remote sensing
community in active microwave measurements, which was mainly driven by the
Cold Regions Hydrology High-Resolution Observatory CoReH
To cope with recent requirements in active microwave remote sensing, while
relying on an established, physical model of intermediate complexity, it is
the aim of the present paper to extend MEMLS and develop a first version of
MEMLS3&a. Thereby, we can build on the description of the microstructure in
terms of the exponential correlation length as a single, objective parameter
which can be derived from in situ field measurements. For the backscattering
model, we shall extend the description of the snowpack in MEMLS to account
for a slightly undulated snow surface as shown in Fig.
Snowpack (blue) with slightly undulated snow surface and layers.
Waves incident at nadir angle
The paper is organized as follows: in Sect.
In MEMLS the snow cover is considered as a stack of
At any given frequency and polarization of electromagnetic radiation
with incident direction
Unspecified in Eq. (
For both v and h polarization the total reflectivity is the sum of the
diffuse and the specular component:
Apart from the physical temperatures of all snow layers including the ground
temperature, the downwelling sky brightness temperature
According to Fig.
Geometry of the
Finally, we briefly recap how specular and diffuse components from the
previous section are practically reassembled in MEMLS3&a for the computation
of the total backscatter.
The total backscatter The specular component The diffuse component of the backscatter
Thus, the model accounts for multiple scattering at the undulated layer interfaces. The diffuse
scattered radiation is assumed to be Lambertian, which allows estimating the fraction scattered in
the backscatter direction. More complex processes such as coherent backscatter enhancement recently
presented by
Primary input parameters used in MEMLS3&a, with snow input parameters for each snow layer (upper part) and general model parameters (lower part). In addition, the value and unit of the parameter, as well as a typical way of determination, are indicated.
For a simulation run at a given frequency
We used snow input data generated from three different snow measurement
methods to run model simulations which are compared to backscatter
measurements from ESA's SnowScat scatterometer for validation
In the NoSREx campaign, the SnowScat scatterometer and SodRad radiometers were installed on two platforms overlooking a forest clearing. For the NoSREx measurements, SnowScat was set to measure several incidence angles over a wide sector. For the purpose of the present work both SnowScat and SodRad were turned in azimuth to point towards the same location on the snowpack, where a destructive snow-pit measurement was made after the microwave measurements were completed.
The soil composition under the snowpack is dominantly mineral soil, with
a thin vegetation layer on the surface (ca. 5
The validation data were measured with ESA's SnowScat instrument
The SodRad (Sodankylä Radiometer) system was mounted on
a 4.1
The most crucial snow input parameters required to drive MEMLS3&a are
density and correlation length. We derived these parameters from three
different snow measurement methods in order to illustrate different ways of
acquisition (Fig.
Left: snow-pit overview with the locations of the SnowMicroPen (SMP) measurements (arrows) surrounding the profile wall (black rectangular). Right: close-up of the profile wall, with locations of near infrared photography (NIP), computed tomography (CT) and density cutter measurements.
As NIP does not provide the snow density, it was measured using a standard
100
Density profile derived by SMP (green),
Besides the snow input parameters, the snow–ground reflectivity
To account for the correct incidence angle at the snow–ground interface, the
following auxiliary procedure is carried out for each model run. First,
MEMLS3&a is run with
The model of
The soil temperature was measured to be
Correlation length profile derived by SMP (green),
A further input to the model is the downwelling brightness temperature
We choose the scattering option of the improved Born approximation
The dependence on the incidence angle at 10.2 and 16.7
In this section, the sensitivity of MEMLS3&a to
The specular snow–ground reflectivity
The empirical cross-polarization ratio
A larger value of
To prove the concept of the MEMLS architecture, which is the fundament for
MEMLS3&a, we compare our active simulations with passive simulations using
the same input data (Sect.
To run MEMLS, 15 SMP measurements inside the main test site in
Sodankylä were used in order to capture the spatial variability of the
snowpack. For each SMP measurement one MEMLS simulation was conducted.
Figure
The agreement between model and observation generally decreased towards
higher frequencies. At 36
As shown in Sect.
The specular part of the snow–ground reflectivity
The cross-polarization in MEMLS3&a is solely determined empirically via the parameter
Another parameter chosen empirically is the mean slope of surface undulations
View of about 1
The individual magnitudes of the specular and diffuse contributions are shown
in Fig.
In contrast to MEMLS3&a, MEMLS does not require free empirical parameters.
In this regard, we attribute the fact that MEMLS3&a matches the SnowScat
observation better than MEMLS the SodRad observations to the additional free
parameters in MEMLS3&a, foremost
In contrast, the mismatch between model and measurements was largest at
36
Ratio of the simulated diffuse (
We further tried to assess the influence of the spatial variability of the
snowpack. The standard deviation obtained from the 15 MEMLS runs is
8
We also found that the higher values measured by SodRad throughout the whole
frequency range at h-pol for an azimuth angle of 140
The degree of complexity of existing models simulating microwave
backscattering from snow range from single-layer approaches
Presently, models differ not only in the representation of snow
microstructure but also in the solution of the radiative transfer or the
type of interfaces between the layers, which makes it difficult to attribute
the discrepancies in model performance to a particular part of the model.
A comparison by
We adapted the MEMLS to include
backscattering and presented a detailed description of the relevant
parameters and their derivation. The reflectivity was decomposed into diffuse
and specular components, and the snowpack was allowed to be slightly
undulated. This procedure could be applied to other passive microwave models
as well. Model simulations were in reasonable agreement with scatterometer
observations, if the specular snow–ground reflectivity
MEMLS3&a is integrated in the standard release of MEMLS as a separate sub-routine. Both versions, active and passive are built on the same set of core functions.
The purpose of the appendix is to derive the specular part of the reflectivity of a layered snowpack, in order to separate it from the diffuse part by subtraction from the total reflectivity using MEMLS. It is assumed here that all layer interfaces are smooth and parallel to the surface in order to produce specular reflection. Separation between diffuse and specular reflection is required in bistatic scattering and in backscatter models.
We consider a plane-parallel snowpack used in MEMLS as shown in
Fig.
The aim of the following procedure is to derive an expression for the total
specular reflectivity,
In order to solve these equations for
The parameters of a selected layer
The described procedure is applied for horizontal and vertical polarization,
separately. For v polarization we call
The model is written in Matlab and available to the public through the
following website:
The validation data were acquired during Nordic Snow and Radar Experiment NoSREx III in Sodankylä, Finland, ESA ESTEC contract no. 22761/09/NL/JA. Proksch further acknowledges support from ESA's Networking/Partnering Initiative NPI no. 235-2012. In particular we want to acknowledge FMI staff for help and support during the field campaigns. A first version of this model is based on ESA ESTEC contract no. 4200020716/07/NL/EL CCN2. Edited by: N. Kirchner