Microwave Emission Models (EM) are used in retrieval algorithms to estimate geophysical state parameters such as soil Water Content ( W C ) and vegetation optical depth ( τ ), from brightness ...temperatures T B p , θ measured at nadir angles θ at Horizontal and Vertical polarizations p = { H , V } . An EM adequate for implementation in a retrieval algorithm must capture the responses of T B p , θ to the retrieval parameters, and the EM parameters must be experimentally accessible and representative of the measurement footprint. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the “Tau-Omega” (TO) EM considered as the “reference” to retrieve W C and τ from L-band T B p , θ . For sparse and low-scattering vegetation T B , E M p , θ simulated with E M = { TO , 2 S } converge. This is not the case for dense and strongly scattering vegetation. Two-Parameter (2P) retrievals 2 P R C = ( W C R C , τ R C ) are computed from elevation scans T B p , θ j = T B , TO p , θ j synthesized with TO EM and from T B p , θ j measured from a tower within a deciduous forest. Retrieval Configurations ( R C ) employ either E M = TO or E M = 2 S and assume fixed scattering albedos. W C R C achieved with the 2S RC is marginally lower ( ~ 1 m 3 m − 3 ) than if achieved with the “reference” TO RC, while τ R C is reduced considerably when using 2S EM instead of TO EM. Our study outlines a number of advantages of the 2S EM over the TO EM currently implemented in the operational SMOS and SMAP retrieval algorithms.
Understanding surface melt over the Greenland Ice Sheet (GrIS) is important for evaluating the effect of the changing global climate on humanity, and for forecasting sea level rise. We demonstrate a ...recently developed L-band passive microwave algorithm for the retrieval of snow liquid water and snow and firn density over the GrIS. The retrievals are performed using brightness temperatures from the ESA SMOS satellite. The density retrievals show potential for mapping the equilibrium line altitude of the ice sheet by determining where retrieved densities exceed typical seasonal snowpack density, thus indicating the presence of firn. We integrate snow liquid water retrievals over area and time to obtain water column maps and daily estimates of the surface liquid water present over Greenland. The integrated annual surface liquid water retrieved over the entire ice sheet compares on the same order as modeled estimates of total meltwater available in current literature.
•L-band brightness temperatures from SMOS can quantify melt over Greenland•Retrieved firn/snow density can be used to map equilibrium line altitude (ELA)•Retrieved liquid water content can be used to calculate total liquid water•First known direct measurement of a surface mass balance component
Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave ...radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.
This study reports on four consecutive winter campaigns (2016-2020) at the "Davos-Laret Remote Sensing Field Laboratory" in the Swiss Alps to gain insight into the L-band microwave emission of ground ...covered with seasonal snow. Close-range L-band Brightness temperatures <inline-formula><tex-math notation="LaTeX">T_\mathrm{{B}}^{p,\phi }(\theta)</tex-math></inline-formula> were measured over the site scanning different observation nadir angles <inline-formula><tex-math notation="LaTeX">\theta</tex-math></inline-formula> and azimuth angles <inline-formula><tex-math notation="LaTeX">\phi</tex-math></inline-formula> at horizontal and vertical polarization p = {H,V}. State parameters (SPs) of the snowpack (e.g., height, density, and snow water equivalent) and the subnivean soil (permittivity, temperature) were measured quasi-simultaneously using in-situ sensors and sampling, as well as meteorological data. In each campaign, <inline-formula><tex-math notation="LaTeX">T_\mathrm{{B}}^{p,\phi }(\theta)</tex-math></inline-formula> were measured over a "natural area" and a "reflector area" with a metal mesh reflector laid on the ground before snow accumulation. The radiometer measurements over "reflector area" allowed to retrieve the time-series of Snow liquid Water-content <inline-formula><tex-math notation="LaTeX">W_\mathrm{{S}}</tex-math></inline-formula> and Snow liquid Water-Column <inline-formula><tex-math notation="LaTeX">{WC}_\mathrm{{S}}</tex-math></inline-formula>, which are employed as "derived measurements" to support interpretation of <inline-formula><tex-math notation="LaTeX">T_\mathrm{{B}}^{p,\phi }(\theta)</tex-math></inline-formula> measured over "natural areas" during different winter phases. The detailed approach for the estimation of <inline-formula><tex-math notation="LaTeX">W_\mathrm{{S}}</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">{WC}_\mathrm{{S}}</tex-math></inline-formula> using L-band radiometer data is presented. The data and analyses in this article address the following major points: 1) determination of the characteristic features of measured <inline-formula><tex-math notation="LaTeX">T_\mathrm{{B}}^{p,\phi }(\theta)</tex-math></inline-formula> during different periods in each of the four winter campaigns; 2) effects of dry and wet snow precipitation on L-band radiometer data compared to corresponding simulations; 3) effect of removal and compression of the snowpack on <inline-formula><tex-math notation="LaTeX">T_\mathrm{{B}}^{p,\phi }(\theta)</tex-math></inline-formula>; 4) effects of spatial heterogeneity on brightness temperatures. Finally, the study is concluded with recommendations relevant for future close-range remote sensing campaigns.
The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An ...improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”.
Assessing the Surface Mass Balance (SMB) of the Antarctic Ice Sheet is crucial for understanding its response to climate change. Synthetic Aperture Radar observations from Sentinel-1 provide the ...potential to monitor the variability of SMB processes through changes in the scattering response of near-surface and internal snow layers. However, the interplay between several factors, such as accumulation, wind erosion, deposition, and melt, complicates the interpretation of changes in the scattering of the microwave signal. Additionally, lack of reliable ground truth measurements of the snow surface limits our capability to associate the SMB processes with dominant scattering mechanism. In this study, we aim to quantify the dominant scattering in Sentinel-1 signal and evaluate the changes in scattering in drifting snow-dominated regions of East Antarctica. We introduce a scattering indicator, <inline-formula><tex-math notation="LaTeX">\alpha _{scat,\varepsilon }</tex-math></inline-formula>, derived from scattering-type and entropy descriptors. This provides a measure of the dominant scattering between volume and pure scattering. By relating the field measurements to <inline-formula><tex-math notation="LaTeX">\alpha _{scat,\varepsilon }</tex-math></inline-formula>, we establish that the evolution of dominant scattering in the presence of snowdrift is complex. First, <inline-formula><tex-math notation="LaTeX">\alpha _{scat,\varepsilon }</tex-math></inline-formula> strongly correlates with surface roughness (<inline-formula><tex-math notation="LaTeX">R^{2}=0.92</tex-math></inline-formula>, RMSE <inline-formula><tex-math notation="LaTeX">=2^\circ</tex-math></inline-formula>). Spatially variable erosion patterns significantly increase the roughness and result in a strong affinity towards pure scattering despite net accumulation. Second, high surface densities also tend to influence pure scattering; however, the effect is dependent on the accumulation rate. With more accumulation, we observe an increasing dominance of volume scattering from internal snow layers. Long-term trends in <inline-formula><tex-math notation="LaTeX">\alpha _{scat,\varepsilon }</tex-math></inline-formula> (2017/2023) further suggest that it is challenging to address the causes behind the scattering source based on a single snow surface process. We thus demonstrate the potential and limitations of <inline-formula><tex-math notation="LaTeX">\alpha _{scat,\varepsilon }</tex-math></inline-formula> to infer the variability in dominant scattering from changes in surface processes.
The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness ...temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between −6kgm−3 and 15kgm−3 for the forest clearing site, and between 37kgm−3 and 90kgm−3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈45km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.
•Observed L-band signatures are shown to be sensitive to dry snow.•Effect is due to refraction and impedance matching caused by snow cover.•Experimental L-band data are used to retrieve dry snow density and soil permittivity.•Retrievals agree well with in situ data for test site over mineral soil.•Application to satellite data requires addressing more complex land cover issues.
The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well ...suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner-Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was ...to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals.
A thermal microwave emission model of layered snowpacks (MEMLS) was developed for the frequency range 5–100 GHz. It is based on radiative transfer, using six-flux theory to describe multiple volume ...scattering and absorption, including radiation trapping due to total reflection and a combination of coherent and incoherent superpositions of reflections between layer interfaces. The scattering coefficient is determined empirically from measured snow samples, whereas the absorption coefficient, the effective permittivity, refraction, and reflection at layer interfaces are based on physical models and on measured ice dielectric properties. The number of layers is only limited by computer time and memory. A limitation of the empirical fits and thus of MEMLS is in the range of observed frequencies and correlation lengths (a measure of grain size). First model validation for dry winter snow was successful. An extension to larger grains is given in a companion article
(Mätzler and Wiesmann, 1999). The objective of the present article is to describe and illustrate the model and to pave the way for further improvements. MEMLS has been coded in MATLAB. It forms part of a combined land-surface-atmosphere microwave emission model for radiometry from satellites
(Pulliainen et al., 1998).
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