International audience

Microwave brightness temperatures observed in Antarctica at 89 GHz from the advanced microwave sounding unit B (AMSU-B) point out an exceptional decrease of 57 K at Dome Fuji ($77.31^{\circ} \text{S}$,$39.70^{\circ} \text{E}$) during the 2019–2020 summer. The grain size index (GSI) based on 89 and 150 GHz from AMSU-B and independent observations at 89 GHz from the advanced microwave scanning radiometer 2 (AMSR-2) also show concurrent unusual values. To explain such event, a theoretical analysis was carried out by means of a radiative transfer model. We explore the sensitivity of brightness temperature to surface snow properties focusing on December, just before the decrease, and April, at its end. Results confirm that this variation is mainly related to an increase in snow grain size. A decrease in snow density is also involved as suggested by the increase in brightness temperature at 1.4 GHz from soil moisture and ocean salinity (SMOS) and in the polarization ratio at 36 GHz from AMSR-2. Extreme values observed at multiple frequencies, as well as peculiar atmospheric conditions explored in a previous study, confirm the uniqueness of this event at least on decennial scale.

The Arctic snowpack, characterized mainly by a dense wind slab (WS) layer overlaying less dense and porous depth hoar (DH), generates large uncertainties in microwave radiative transfer models (RTM) used to interpret satellite observations. In this work, we tested two improvements recently implemented in the snow microwave radiative transfer (SMRT) model. First, an improvement of the snow microstructure parametrizations introduces a polydispersity geometrical parameter (K) related to the grain shape and microstructural arrangement. Second, the new electromagnetic model (EM) based on the strong contrast expansion (SCE) allows a continuous formulation of the scattering coefficient as a function of the density between low-density snow and hard and icy snow. The SCE model was compared with in situ observations to the commonly used improve Born approximation (IBA) EM. Results show improved brightness temperature simulations at 19, 37, and 89 GHz compared to surface-based and satellite microwave radiometric measurements using polydispersity values ($K_{\text {WS}} = 0.80$and$K_{\text {DH}} = 1.33$) for scaling the measured optical grain size of the different snow layers with IBA. The finding is that the polydispersity values found in this study are of general applicability for Polar-layered snowpack. The SCE model yields results similar to IBA for snow densities up to$500 \; \text {kg}\,\text {m}^{-3}$but no analysis was investigated for the range of 500–700 kgm3 (firn). These improvements in snow microstructure and the new SCE RTM allow better simulations of Polar snow and therefore better Polar snowpack monitoring by satellite.

International audience

Recent investigations using polarimetric decomposition and numerical models have helped to improve understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer models provide one method of improving this understanding. These are the first published experiments using the Snow Microwave Radiative Transfer (SMRT) model to investigate the response of different imaging SAR frequencies (L, C, and X-band) at HH and VV polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice-water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. Analysis of the backscatter response to different properties indicate that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited for other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing RMS height at the ice-water interface, which supports surface scattering at the ice-water interface as being the dominant scattering mechanism. These results demonstrate that SMRT is a valuable tool for understanding the response of SAR data to changes in freshwater lake ice properties and could be used in the development of inversion models.

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.

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

The Snow Microwave Radiative Transfer (SMRT) model was initially developed to explore the sensitivity of microwave scattering to snow microstructure for active and passive remote sensing applications. Here, we discuss the modular design of SMRT that has enabled its rapid extension by the community. SMRT can now represent a layered medium consisting of snow, land ice, lake ice and/or sea ice overlying a substrate of soil, water or parameterized by reflectivity. A time-dependent radiative transfer solution method has also been added to allow for low resolution mode altimetry applications. We illustrate the use of SMRT to simulate brightness temperature for snow on lake ice, backscatter for snow on soil and altimeter waveforms for snow on sea ice.

In recent years, special attention has been paid, in microwave remote sensing from space, to extend observations to low frequencies (i.e lower than L band). Active mission at P-band (i.e. the ESA's Biomass mission based on SAR) will be launched in 2022 and missions based on low frequency microwave radiometer are proposed to both ESA and NASA. Because of the high penetration depth in ice, these sensors are particularly suitable for investigating parameters of the cryosphere. Nevertheless, in order to properly derive information on such geophysical parameters, there is the need to improve our knowledge of the interaction of electromagnetic waves and the different media. In this paper we present and discuss a first series of results obtained during a propagation measurement campaign carried out at the French–Italian Concordia research station during the summer campaign 2019-2020 in Antarctica in order to investigate on the electromagnetic properties of the Antarctic's firn in the frequency range 0.4-2 GHz. Specific instrumentation has been designed and assembled, essentially consisting of a transmitting unit and a receiving one, to be let down into two boreholes in the firn. Preliminary measurement results are presented and discussed.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

In Antarctica, the coastal and ice-shelves areas are affected by snowmelt during the austral summer. The length and extend of this melting period are key parameters in the study of climate and its interannual variations in these regions. Melting events have a significant impact on the microwave emissivity of the surface. Thus, satellite microwave observations can be used in order to provide useful information over the whole Antarctic coast and ice-shelves. Several studies exploited the 19 and 37 GHz long time series to retrieve snowmelt events. In this study, these algorithms previously developed have been used to detect melt from the Soil Moisture and Ocean Salinity (SMOS) satellite observations at 1.4 GHz. Snowmelt dataset was obtained from April 2010 and March 2017 with SMOS observations. Finally, the potential of combined low and high frequencies to provide a synergetic description of surface melting events in Antarctica have been highlighted.

Earth's cold regions are key elements of the planet's climate system: they have strong feedbacks with global change and they have a direct impact on human activities. Despite their importance, at present they are not adequately monitored by state-of-the-art instruments. In order to fill this gap, a dedicated spaceborne mission called Cryorad has been proposed in the framework of the ESA Earth Explorer 10 call. The mission would comprise a 0.4-2 GHz nadir-looking radiometer installed on a polar-orbit satellite. Scientific and technical studies are underway, as well as experimental campaigns in Greenland and Antarctica.

The Snow Microwave Radiative Transfer (SMRT) model computes the thermal emission and backscatter model of snopwpack. Compared to similar existing models, it was developed to unify and inter-compare different descriptions of the snow microstructure found in different microwave models. For that, SMRT offer the capability of switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. The current version of SMRT includes the Dense Media Radiative Transfer theory (DMRT), the Improved Born Approximation (IBA) and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of snow layers. Under IBA, SMRT was used to compare sticky hard sphere and exponential microstructure representation and to identify that several former studies conducting simulations with in-situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. The model is available as open source software.

In Antarctica, the coastal and ice-shelves areas are affected by snowmelt during the austral summer. The length and extend of this melting period are key parameters in the study of climate and its interannual variations in these regions. Melting events have a significant impact on the microwave emissivity of the surface. Thus, satellite microwave observations can be use in order to provide useful information over the whole Antarctic coast and ice-shelves. Several studies exploited the 19- and 37-GHz long time series to retrieve snowmelt events. Due to the large penetration depth at L-band in regard of higher microwave frequencies, SMOS observations (1.4-GHz) could give additional information. In this study, the algorithms developed in these previous works are used to detect melt from the SMOS brightness temperature at horizontal polarization. Snowmelt product is obtained from July 2010 and June 2015 with SMOS observations.

The internal ice sheet temperature is a key parameter for the understanding of the ice sheet dynamics which, at present, is available only from glaciological models or in the few boreholes where temperature has been measured. From the analysis of space-borne L-band data from SMOS, collected over Antarctica, it was proved that they are sensitive to the ice sheet temperature profile. In this paper it is demonstrated that starting from satellite data and using a combination of glaciological and microwave emission model it is possible to retrieve this important geophysical parameter at continental scale.

Snow microwave radiance assimilation (RA) or brightness temperature data assimilation (DA) has shown promise for improving snow water equivalent (SWE) estimation. A successful RA study requires, however, an analysis of the error characteristics of coupled land surface-radiative transfer models (LSM/RTMs). This paper focuses on the Community Land Model version 4 (CLM4) as the land-surface model and on the microwave emission model for layered snowpacks (MEMLS) and the dense media radiative transfer multilayer (DMRT-ML) model as RTMs. Using the National Aeronautics and Space Administration Cold Land Processes Field Experiment (CLPX) data sets and through synthetic experiments, the errors of the coupled CLM4/DMRT-ML and CLM4/MEMLS are characterized by: 1) evaluating the CLM4 snowpack state simulations; 2) assessing the performance of RTMs in simulating the brightness temperature (TB); and 3) analyzing the correlations between the SWE error (ε_SWE) and the TBerror (ε_TB) from the RA perspective. The results using the CLPX data sets show that, given a large error of the snow grain radius (ε_re) under dry snowpack conditions (along with a small error of the snow temperature (ε_Tsnow)), the correlations between ε_SWE and ε_TBare mainly determined by the relationship between ε_reand the snow depth error (ε_dsnow) or the snow density error (ε_ρsnow). The synthetic experiments were carried out for the CLPX region (shallow snowpack conditions) and the Rocky Mountains (deep snowpack conditions) using the atmospheric ensemble reanalysis produced by the coupled DA Research Testbed/Community Atmospheric Model (CAM4). The synthetic experiments support the results from the CLPX data sets and show that the errors of soil (the water content and the temperature), snow wetness, and snow temperature mostly result in positive correlations between ε_SWE and ε_TB. CLM4/DMRT-ML and CLM4/MEMLS tend to produce varying RA performance, with more positive and negative correlations between ε_SWE and ε_TB, respectively. These results suggest the necessity of using multiple snowpack RTMs in RA to improve the SWE estimation at the continental scale. The results in this paper also show that the magnitude of ε_reand its relationship to ε_SWE are important for the RA performance. Most of the SWE estimations in RA are improved when ε_SWE and ε_reshow a high positive correlation (greater than 0.5).

Two electromagnetic models were used to simulate snow emission at L-band from in situ measurements of snow properties collected at Dome C in Antarctica. Two different approaches were used: one based on the radiative transfer theory and the other on the wave approach. The soil moisture ocean salinity (SMOS) satellite observations performed at 1.4 GHz (21 cm) were used to check the validity of these models. Model results based on the wave approach were in good agreement with SMOS observations, particularly for incidence angles lower than 55°. Comparisons suggest that the wave approach is more suitable to simulate brightness temperature at L-band than the transfer radiative theory, because interference between the layers of the snowpack is better taken into account. The model based on the wave approach was then used to investigate several L-band characteristics at Dome C. The emission e-folding depth, i.e., 67% of the signal, was estimated at 250 m, and 99% of the signal emanated from the top 900 m. L-band brightness temperature is only slightly affected by seasonal variations in surface temperature, confirming the high temporal stability of snow emission at low frequency. Sensitivity tests showed that good knowledge of density variability in the snowpack is essential for accurate simulations in L-band.

The Antarctic Plateau presents ideal characteristics to study the relationship between microwave observations and snow/ice properties. It is also a promising target for radiometer calibration and sensor intercalibration, which are critical for applications requiring subkelvin accuracy, such as sea surface salinity retrievals. This paper presents the spaceborne Aquarius L-band radiometric observations collected since August 2011 over the Antarctic Plateau, and it focuses on their temporal evolutions at Dome C (75.1° S, 123.35° E). Aquarius operates three radiometers with a sensitivity of 0.15 K (over the oceans), allowing us to analyze small variations in brightness temperature (TB) and changes with incidence angles. Over the Antarctic Plateau, Aquarius TBs have a relatively low annual standard deviation (0.2-0.9 K) where melting never occurs. However, the analysis of the TB time series at Dome C revealed significant variations (up to 2.5 K) in summer. First, these variations are compared with a remote sensing grain index (GI) based on high-frequency (89 and 150 GHz) shallow-penetration TB channels. Variations in the ratio of TBs observed at horizontal and vertical polarizations are synchronous with GI changes. Second, Aquarius TB variations are compared with the presence of hoar crystals on the surface identified using surface-based near-infrared photographs. The largest and longest changes in TBs correspond to periods with hoar crystals on the surface. Therefore, in spite of the deep penetration of the L-band radiation, evolutions of the snow properties near the surface, which usually change rapidly and irregularly, do influence L-band observations. Collection of accurate snow surface measurements and thorough analyses of the L-band observations are thus needed to use the Antarctic Plateau as a calibration/inter-calibration target.

Passive microwave remote sensing is extensively used in polar regions to study the cryosphere. To better understand the measured signal above continental ice-covered areas, our objective is to estimate the microwave emission of bubbly-ice surfaces using a physically based multilayer electromagnetic model, i.e., the dense media radiative transfer-multilayer model (DMRT-ML). This model accounts for ice layers with variable amounts of bubbles. Each layer is fully described by its temperature, density, thickness, and air bubble radius. Simulations are performed using in situ data from two distinct sites: one in Antarctica on a coastal Blue Ice Area near the Cap Prud'Homme (CPH) station in Adelie Land and the other on the Barnes Ice Cap (BIC) located on Baffin Island in the Arctic. On this ice cap, superimposed ice with seasonal snow cover about 1 m thick was observed. In both cases, several ice parameters were measured or estimated, and the others were optimized. Results of the DMRT-ML simulations are compared with in situ surface-based radiometer (SBR) measurements at 11, 19, and 37 GHz at both horizontal and vertical polarizations. Results show that DMRT-ML is able to reproduce the microwave emission of different ice types with good accuracy when accounting for ice bubbles: final RMSE = 7.37 K and 8.42 K, for CPH and BIC, respectively, compared with RMSE ranging from 15 K to 40 K without bubbles. Comparisons between SBR measurements and satellite data for the BIC also show good agreement (RMSE = 4.1 K for 19 and 37 GHz, both polarizations).

Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m3·ha-1) showed that brightness temperatures (Tb) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore,Tbvalues saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snowTbfrom the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.

Assimilation of Earth Observation (EO) data into Dynamic Vegetation Models (DVMs) can either be via derived products (e.g., LAI or fAPAR) or through radiances. Successful assimilation generally requires that the distribution of errors in an observed variable is well known. Radiance measurements conform to this requirement more strongly than derived products which have undergone more complex processing and whose relation to the true value of the estimated variable is poorly understood. To enable radiances to be assimilated a DVM must be able to predict canopy leaving radiation. To do this it is necessary to couple it with a Canopy Reflectance Model (CRM). As DVMs require some concept of intercepted radiation to drive photosynthesis and ultimately plant growth, there is a common framework between DVMs and CRMs which may be exploited for this purpose. This work describes the mechanisms by which a simple radiative transfer CRM can be coupled with a DVM and discusses the disparities in the assumptions made by each concerning photon-vegetation interactions. Results of forward modelled canopy reflectances are presented and compared with EO estimates of reflectance.

Radiative transfer models have been widely used to interpret the radar backscatter from forested areas. Most of these models are based on an iterative solution of the radiative transfer equation, usually solved up to first or second order, thus taking into account single and double scattering. Although this method leads to results agreeing well with copolarized backscatter measurements, it produces less accurate estimates for horizontal-vertical (HV) polarization. This paper presents a radiative transfer backscatter model that accounts for multiple scattering by using the discrete ordinate and eigenvalue method applied to a layered medium. Using parameters derived from an architectural tree model, calculations at C- and L-band are compared with HV data acquired for a maritime pine forest in the southwest of France during the Spaceborne Imaging Radar-C missions. Good agreement is found at C-band for all values of forest biomass, and reasonable agreement at L-band for high biomass, when the soil backscatter plays a minor role. For low biomass, the L-band modeling is inadequate because of difficulties in estimating the soil backscatter. Comparison with calculations from a first-order radiative transfer model shows that multiple scattering is significant, especially at C-band.

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

This paper describes a modeling approach to interpret the C-band synthetic aperture radar (SAR) data from wheat canopies as provided by European Remote Sensing (ERS) satellites, RADARSAT, and the forthcoming Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) satellite. At a first step, the results of a first-order modeling were compared to ERS data and scatterometer data over the growing season at two different test sites. The prediction by first-order approach was in disagreement with the data from stem extension stage to soft ripening stage. The first-order approach was found to overestimate the attenuation at vertical (V) polarization, resulting in a predicted backscattering coefficient one order of magnitude lower than that observed by the SAR system. To improve the prediction, a multiple-scattering modeling based on numerical solution of multiple-scattering Foldy-Lax equation was used. The multiple-scattering modeling provides better backscatter estimates at vertical-vertical (VV) polarization for both test sites. Then, the model is used to derive the prevailing interactions mechanisms at horizontal-horizontal (HH) and VV polarizations and 23/spl deg/ and 40/spl deg/ of incidence angle. Finally, the retrieval of crop parameters from C-band SAR data is addressed.