Microwave remote sensing is a widely used and effective method for observing and understanding various land, ocean, and atmospheric processes. Information on the geophysical variables associated with these processes is typically retrieved through inversion of forward models, which describe the remotely sensed observations as functions of the geophysical variables of the scene. Inversion of the forward model is often an ill-posed problem due to the limited information content of microwave measurements and the complexity of the observed scene. The Bayesian approach provides means to incorporate prior information to reduce the ill-posedness of the problem. Considering temporal information is particularly useful in this context, as the temporal characteristics of geophysical variables can be used as prior information to reduce the ill-posedness. Furthermore, considering the temporal domain is natural due to the sequential nature of remote sensing observations. To incorporate such prior information, we introduce a Bayesian inversion of a time series of geophysical variables from a time series of remote sensing observations. The method is formulated in a general form and is therefore applicable to different remote sensing problems. To demonstrate, we applied the method to Soil Moisture and Ocean Salinity (SMOS) L band brightness temperature measurements to simultaneously retrieve, for the first time, ground permittivity, surface roughness, vegetation optical depth, and scattering albedo over a one-year period at a northern boreal forest site. We compared the retrieved geophysical variables with the ground reference: retrieved and measured ground permittivity agreed with a correlation of 0.91, and retrieved and measured vegetation optical depth agreed with a correlation of 0.68 and the standard deviation of the difference of 0.11. We demonstrated that using the time series method, we could retrieve ground surface roughness and vegetation scattering albedo. Although reference measurements were not available for these variables, the retrieved values were consistent with the ground permittivity and vegetation optical depth. In general, ground surface roughness and vegetation scattering albedo are not retrieved in the nominal case, where data from different satellite overpasses are treated separately. This demonstrates that the time series method enables retrieval of additional information compared to the usual approach.

Lake ice thickness (LIT) significantly impacts water, climate, and socio-economic activities. Due to lack of observations, estimating the spatiotemporal variations in LIT is still a challenging task. Brightness temperature (TB) measured by microwave radiometers correlates well with LIT at certain frequencies, which potentially enables global and daily estimations of LIT by spaceborne passive microwave (PMW) remote sensing over decades. In this study, a generalized empirical linear regression algorithm to estimate LIT using PMW is proposed using TB as the sole data source. The method is based on statistical analysis of TB over 18 lakes. The analysis revealed a large variability of linear coefficients relating TB to LIT; however, the coefficients could be tied to TB at the Freeze-up End (FUE). When compared with LITs derived from in-situ measurements, radar altimetry, and published LIT data, the root mean square errors (RMSE) were 0.19 m, 0.16 m, and 0.15 m, respectively. This method was then used to estimate the LITs for 96 lakes in the Northern Hemisphere from 2002 to 2011, which was proven to be applicable for long-term LIT mapping and monitoring for large lakes, demonstrating improved generalizability.

Previous studies have investigated the synergies between snow process models and passive microwave (PMW) remote sensing to fill the lack of information in the PMW observations to retrieve the snow properties, especially the snow mass. We present a case study that expands the previous work, performed in a tower setting, to a satellite case. We use a state-of-the-art snow process model with high spatial resolution, to produce a time series of snow states over a study area in the northern Finland, and we simulate the vertically polarized brightness temperature (BT) time series at the frequencies 18.7GHz and 36.5GHz. We compare the modeled BTs against the satellite observations. We identify factors, on one hand, in the snow process modeling, and on the other hand, in the radiative transfer modeling, that are responsible for the errors. We also analyze the effect of spatial heterogeneity of the modeled snow properties on the coarse resolution satellite observed BT and conclude that it does not play a significant role, possibly due to the relatively homogeneous snow properties in our study area.

Snow, a crucial component of the cryosphere, significantly impacts global climate monitoring and has an important role in freshwater supply, hydropower energy and tourism. Advances in remote sensing technologies, particularly Interferometric Synthetic Aperture Radar (InSAR), enhance our understanding of snow processes. This study investigates the potential use of interferometric observables (phase, coherence, and phase closure) from a ground-based L-band SAR sensor to analyze snow dynamics. Time series data expanding one winter and one summer season are assessed with in-situ snow depth, snow water equivalent and air temperature observations. We found that by exploiting all three interferometric observables the identification of the start and the end of each snow season (wet snow, dry snow, no snow) can be performed more accurately. This study highlights the potential of L-band interferometric observables that are relevant for future L-band SAR missions.

In recent decades, the Earth Observation (EO) wave interaction modelling domain has witnessed a proliferation of both forward and inverse models. These models are developed by the scientific community to understand the relationship between electromagnetic waves and natural surfaces, and to support methodologies for extracting bio-geophysical variables from remotely sensed data. However, the current landscape exposes certain limitations such as the absence of systematic implementation, validation on limited datasets, and a scarce integration with emerging Artificial Intelligence (AI)-based inversion techniques. This manuscript introduces the Wave Interaction Models Exploitation Framework (WIMEX), developed to address these challenges in the frame of an ESA-funded project. Leveraging EO data available today, and exploiting Graphical Processing Unit and parallel computing, the framework proposes a systematic approach to create, validate, and disseminate forward and inverse models. WIMEX aims to offer a flexible development environment supporting the evolving needs of the scientific community.

Interferometric SAR (InSAR) is a promising tool for monitoring seasonal snow and for retrieving of Snow Water Equivalent (SWE) as the interferometric phase can be related to changes in SWE (ΔSWE). The boreal forest is a challenging landscape for the InSAR retrieval of SWE since it contributes to the signal by adding an undesired component originating from the vegetation. Although the technique has been validated extensively, most of these works are limited to discrete points. For comparison, we used snowpack simulations from the SnowModel, a high-resolution spatially distributed snow evolution model. This enables a better understanding of the limitations of L-band InSAR for SWE retrieval since it allows to evaluate its performance under different conditions. We analyzed the impact on coherence of snow melt between acquisitions and analyzed pairs with wet snow presence. The interferometric phase was inverted and compared to the simulated ΔSWEfrom the SnowModel distributions for three interferometric pairs. The results indicate a good spatial match between SnowModel and InSAR estimations. However, an increased difference was observed over densely forested areas when the air temperature was close to zero in at least one of the interferometric pairs. We hypothesize that the increase in permittivity of the forest for close to zero temperatures also increases the contribution from the canopy, consequently inducing errors in the retrieval. Both ALOS2 and SnowModel ΔSWEestimates were compared with in-situ data including a snow scale, snow depth from an Automatic Weather Station (AWS), a snow pit, and manual courses.

Seasonal snow cover is an important environmental component, as its characteristics affect energy and gas exchange between ground and the atmosphere. The mass of seasonal snow cover, or Snow Water Equivalent (SWE), describes the available freshwater stored in snow. When present, snow cover affects the microwave signature of the scene and should therefore be considered in any microwave based remote sensing model and retrieval algorithm. Microwave remote sensing of snow and its properties has a long history, and in particular SWE has traditionally been retrieved by using passive microwave observations at 19 and 37 GHz [1] , [2] .

With the passive microwave remote sensing of snow, the high frequency (80-100 GHz) has the advantages of high resolution and fresh shallow snow detection, however, the application of high frequency for snow observation is often limited by atmosphere influence. Until now, few studies have quantified the atmospheric influence of high frequency in snow observations. The scattering and emission of the water vapor and cloud liquid water in the atmosphere cause the increment in Brightness Temperature (TB). To explore the influence of the atmosphere on snow cover observations, we utilize Nordic Snow Radar Experiment (NoSREx) data and the Microwave Emission Model of Layered Snowpacks (MEMLS) to quantify the influence of the atmospheric downward radiance in snow observation by using 90 GHz of the ground-based radiometer. The results show that, in Sodankylä of Finland, atmospheric contribution to ground-based radiometer at 90GHz 50-degree is up to 95.76K (H-pol) and 95.02K (V-pol), with an average of 25.36K (V-pol) and 25.95K (H-pol). The further calculated root mean square errors (RMSEs) of simulated TB with Tdown and observed TB are 41.40K (V-pol)/36.85(H-pol), and of simulated TB without Tdown are 41.25K (V-pol) and 36.72K (H-pol), respectively, the MEMLS slightly increased the simulation accuracy quantitatively of the snow emission without the influence of the atmosphere.

An investigation of boreal forest attenuation of a radar signal in winter is presented, applying a multifrequency (1–10 GHz) ground-based synthetic aperture radar (GB-SAR). As stable targets, corner reflectors (CRs) with known radar cross section (RCS) were used under the forest canopy. This enabled to relate changes in observed wideband backscattering from the reflectors to attenuation of the radar signal in forest vegetation, eliminating the influence of the background, such as snow and soil. We found that ambient temperature affected the observed attenuation of the radar signal in the entire 1–10-GHz frequency range. For temperatures$T < 0 ~^{\circ }\text{C}$, attenuation was found to decrease by up to 4.3 dB at the lowest observed temperatures of −36 °C, with peak attenuation occurring at$T \approx 0 ~^{\circ }\text{C}$. The overall apparent two-way attenuation increased by up to 18 dB from L- to X-band. The presence of snow on the canopy was found to increase attenuation by 1–4 dB, the effect increasing with frequency while having only negligible effects on vegetation backscatter.

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

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.

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient$R \simeq 0.77$between estimated and target SD, a root-mean-square error (RMSE)$\simeq 13$cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with$R =0.74$, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount ofin situdata required for training the algorithm and of extending the algorithm exportability to other test sites.

A tower-based global navigation satellite system reflectometry (GNSS-R) experiment is set up in an Arctic wetland environment for investigating the possibility of monitoring wetland inundation and freeze/thaw (FT) dynamics which are additionally impacted by snow on the ground. Effects of inundation, snow cover, and soil FT state on observed GNSS-R signal-to-noise ratios (SNRs) are analyzed for horizontal (H) and vertical (V) polarizations. A simple classification approach is suggested to detect the inundated, frozen, or thawed soil state. A simple forward reflectivity model is formulated to evaluate the influence of snow cover, overlying frozen, or thawed soil, on the reflected GNSS signals. Reflectivity time series are simulated in H- and V-polarizations usingin situobservations of the Arctic wetland site. The simulations are used to verify the tower-based observations, which show a significant impact of wet snow on reflectivity during melting conditions in spring. The observed SNR is strongly correlated with the Sentinel-1 backscatter coefficient. Generally, soil states detected by GNSS-R are in high agreement with ground truth soil states, especially for inundated and frozen soils. Wet snow conditions, however, complicate the correct timing estimation of soil thawing by inducing reflectivities of a similar order as thawing soil. It is recommended that GNSS-R land application models and retrieval algorithms consider snow cover effects to reduce false classification, especially in FT detection. Overall, the outcome of this study is relevant to the upcoming ESA HydroGNSS mission.

For northern countries like Canada, seasonal snow cover is a key component of the water cycle and a commodity of high importance to public safety, economic sustainability, and ecosystem function. Despite this importance, snow water equivalent (SWE - the amount of water stored by snow) information from existing surface observing networks and satellite data does not adequately address most user needs. To address this gap, a new synthetic aperture radar (SAR) mission capable of providing information on terrestrial SWE at previously unrealized spatial resolution is currently under development. The Terrestrial Snow Mass Mission (‘TSMM’) will provide moderate resolution (500m) dual frequency (13.5/17.25 GHz) Ku-band radar measurements across all northern hemisphere snow covered areas every 7 days. Data from this mission will be used at Environment and Climate Change Canada to (1) provide a new level of information on the temporal/spatial variability in SWE in support of climate services, and (2) feed into environmental prediction and analysis systems to improve weather and hydrological forecasts.

The Northern Hemisphere terrestrial snow water equivalent (SWE) time series from 1979 to 2018 is determined by fusing space-borne microwave radiometer observation with synoptic weather station observations on snow depth. The employed GlobSnow approach is based on Bayesian data assimilation. The method is further developed here including a bias-correction to overcome the problems caused by the saturation of microwave brightness temperature with the increasing SWE for deep snow packs. We show here an improved assessment of the Northern Hemisphere seasonal maximum snow mass including non-alpine regions above 40° N, and analyze continental and regional trends of the snow mass. Further, the GlobSnow data set is combined with soil frost data records to describe the daily soil and snow status starting from the year 1979.

Accurate measurement of Snow Water Equivalent (SWE) from remote sensors is still an on-going topic with many open challenges. Interferometric Synthetic Aperture Radars (InSAR) offers the possibility of retrieving SWE changes between acquisitions by exploiting the relation between the interferometric phase, the snow depth and the water contained on it. However, it is susceptible to estimation errors due to loss of coherence. Sources of decorrelation in snow have not been exhaustively investigated. Here we present the results from the SWE retrieval during both the 2019–2020 and 2020–2021 winters and an analysis of various environmental parameters on the snow coherence using SodSAR (Sodankylä SAR). SodSAR is a 1-10GHz tower-based fully polarimetric SAR with InSAR capabilities operating in northern Finland. Acquisitions were made every 12 hours for the 2019–2020 winter and every 6 hours for the 2020–2021 winter. In-situ instruments are used for validation and comparison.

In this paper, three versions of spatially and temporally varying snow density fields were implemented using snow survey data from Eurasia and Canada and automated snow observations from USA. Snow density fields were used to improve the baseline GlobSnow v.3.0 SWE retrieval approach. Decadal snow density information, i.e. fields where snow density for each day of the year was taken as the mean calculated for the corresponding day over ten years, was found to produces best results.

Variable atmospheric conditions are typically ignored in the retrieval of geophysical parameters of the Earth’s surface when using spaceborne passive microwave observations. However, high frequencies, for example, 91.7 GHz, are sensitive to variable atmospheric absorption, even in winter’s dry conditions. In this article, the influence of variable atmospheric absorption on snow cover extent (SCE) mapping was quantitatively investigated. A physical method was derived to enable atmospheric correction for variable atmospheric conditions. The total column precipitable water vapor (TPWV) from Moderate Resolution Imaging Spectroradiometer (MODIS) was parametrized into transmittances in this correction method. The corrected brightness temperature at 19 and 91.7 GHz from the Special Sensor Microwave Imager Sounder (SSMIS) was applied to the threshold algorithm for snow mapping over China. Compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) data in winter from 2012 to 2013, for Qinghai–Tibet plateau (QTP), a significant improvement after correction was obtained from February to March over ephemeral and shallow snow, where the largest daily improvement of accuracy is up to 20%. The accuracy (incl. precision, recall, and F1 index) improved on average is from 0.77 (0.60, 0.68, and 0.63) to 0.79 (0.69, 0.7, and 0.68) over the full winter time from December to March. Over forest-rich Northeast China, where snow in winter is thicker, small improvement was observed at the onset of the snow season and over snow margin area. It was evidenced that high frequency is a promising way of snow cover mapping with the proposed atmospheric correction method.

One of the key variables describing global seasonal snow cover is snow water equivalent (SWE). The GlobSnow-2 SWE product is widely used in in many research areas due to the high accuracy level and a long historical record (1979 to the present) in Globally. The satellite data used in GlobSnow-2 are mainly from the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS). However, there is no launching plan for these sensors in the future. To ensure a continuation of GlobSnow-2 product, this paper assesses the consistency of SWE estimates between the Microwave Radiation Imager (MWRI) and SSMIS in GlobSnow-2 retrieval scheme. The analysis is conducted in three regions (Finland, Russia and China) over the Northern Hemisphere. The results show that the SWE difference between MWRI and SSMIS is small and even can be negligible. This study provides a scientific basis for treating the MWRI dataset as one continuous record.

Current satellite observing systems lack the capability to derive terrestrial snow water equivalent (SWE, the amount of liquid water stored in solid form by snow) at the spatial resolution, synoptic sensitivity, global coverage, and accuracy required for operational environmental monitoring, services, and prediction. The required combination of revisit time, spatial coverage, measurement resolution, and sensitivity to the mass of snow on the ground necessitates a new spaceborne observing concept. To address this observing gap, Environment and Climate Change Canada (ECCC), the Canadian Space Agency, industrial partners at Airbus, and international scientific collaborators are developing a new dual frequency (Ku-band: 13.5 and 17.2 GHz), moderate resolution (250 m), wide swath (~500 km) radar mission concept. This paper provides an overview of the measurement concept, and ongoing science activities in support of the technical mission development.

Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km2of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability, making satellite observations the only means for providing timely and complete observations of the global snow cover.

We discussed atmospheric correction of passive microwave over China in the snow covered area. SSM/I brightness temperature, radiosonde observation database and MODIS water vapor product (MOD05) were used to estimate the atmospheric influence. The traditional gradient SWE algorithm before and after the atmospheric correction was compared, and results showed atmospheric influence was bigger over snow cover area. It is about 4-5K over the whole China. In the QTP (Qinghai-Tibet Plateau) area, the atmospheric influence was less than other areas in China; also QTP always had thin snow, so the traditional gradient SWE algorithm could not perform well. The gradient algorithm at high frequency was tested, and the results showed even under the original brightness temperature difference at 19 GHz and 85.5 GHz, the gradient (19 GHz-85.5 GHz) performed good consistency with IMS products.

This paper presents a new approach for monitoring soil frost in the northern boreal forest region using co-polarized C-band synthetic aperture radar (SAR) data. Due to the high sensitivity of the C-band signal to vegetation, estimating the soil freeze/thaw (F/T) state directly from the measured backscatter is not feasible over dense vegetation, such as boreal forests. The presented method is based on applying a simple zeroth-order model to estimate the contribution of the ground and the forest canopy on the observed total backscatter. The retrieved ground and canopy backscatter values were compared with in situ information on soil F/T state. By using a linear least sum of square errors classification algorithm, the retrieved ground and canopy backscatter values representing frozen and thawed ground were successfully separated. The method was tested for various soil types and incidence angles. For soil types with higher water holding capacities and lower infiltration rates such as fine Haplic Podzol and Umbric Gleysol, the estimation accuracy of the F/T state was over 97%, whereas for drier, well-drained soil types such as Haplic Arenosol and Coarse Haplic Podzol it was over 94%. Estimation accuracy slightly increased with higher incidence angle. The method is not feasible in rocky terrain due to very low water content, or in wet snow conditions due to lack of penetration of the C-band SAR signal through wet snow. With low ancillary data and computational requirements, the proposed method is applicable for continuous near real-time monitoring of soil F/T state.

While many microwave studies related to tree emission have been undertaken, a few have considered the effect of phenological change on the emission from coniferous trees. The permittivity of vegetation tissue is known to be influenced by water content, while the water content and phase is sensitive to temperature in particular at temperatures below freezing. In addition to temperature, canopy-intercepted snow might also modify the tree emission and transmissivity in the microwave range. In this paper, a season-long experiment was designed to quantify the effect of snow accumulation and temperature on the observed microwave transmissivity from tree. A ground-based, upward-pointing multifrequency radiometer was used to monitor the microwave emissivity of a single coniferous tree at a site in Northern Finland. Radiometer measurements were combined with measurements of the canopy-intercepted snow cover and tree skin temperature. This paper presents two important findings. First, the tree transmissivity was strongly correlated with tree skin temperature under subzero temperature conditions, but uncorrelated with skin temperature changes above freezing. Second, although the tree transmissivity was slightly affected by the snow accumulation on the tree canopy, the overall influence on tree emission was statistically insignificant in this paper.

In this study, snow slab data collected from the Arctic Snow Microstructure Experiment were used in conjunction with a six-directional flux coefficient model to calculate individual slab absorption and scattering coefficients. These coefficients formed the basis for a new semiempirical extinction coefficient model, using both frequency and optical diameter as input parameters, along with the complex dielectric constant of snow. Radiometric observations, at 18.7, 21.0, and 36.5 GHz at both horizontal polarization (H-Pol) and vertical polarization (V-Pol), and snowpit data collected as part of the Sodankylä Radiometer Experiment were used to compare and contrast the simulated brightness temperatures produced by the multi-layer Helsinki University of Technology snow emission model, utilizing both the original empirical model and the new semiempirical extinction coefficient model described here. The results show that the V-Pol RMSE and bias values decreased when using the semiempirical extinction coefficient; however, the H-Pol RMSE and bias values increased on two of the lower microwave bands tested. The unbiased RMSE was shown to decrease across all frequencies and polarizations when using the semiempirical extinction coefficient.

Single pass radar interferometry (sp-InSAR) is a well established technique for generation of digital elevation models (DEM). Differencing two DEMs acquired at different times can reveal topographic changes. However snow depth estimation by DEM differencing is still an ongoing topic in radar research: in contrast to snow free surfaces, the snow surface elevation is difficult to detect either because of microwave penetration into dry snow or because of the weak backscatter return from wet snow which significantly decorrelates the interferometric signal. In this study we demonstrate first results of wet snow depth estimation by differencing sp-InSAR DEMs acquired by the TanDEM-X satellite mission. The results show, in contrast to dry snow, a clear sensitivity to wet snow. However, additionally to a high vertical sensitivity of a few ten centimeters a very low noise-equivalent-sigma-zero (NESZ) is crucial for successful snow depth estimation.

The Finnish Meteorological Institute (FMI) operates a suite of ground-based active and passive microwave instruments at a test site representative of the Northern boreal forest zone. The instruments provide hourly to daily multifrequency observations of microwave signatures from the natural landscape throughout the year. Supported by comprehensive in situ observations of soil, vegetation, snow and atmospheric conditions, the data enable testing and formulation of new methods to retrieve geophysical parameters from remote sensing data. Complementing ground-based observations, airborne campaigns have been used to extend the spatial scale of observations to cover different types of terrain. In this paper an overview of the site and instrumentation is given, giving examples of recent campaigns and use of the collected data in the development of new Earth Observation methods. All data, including the raw data observations, are available for research purposes from FMI.

Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km2of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability. Making satellite observations the only means for providing timely and complete observations of the global snow cover.

The Finnish Meteorological Institute, together with Gamma Remote Sensing, Switzerland, has developed a global soil freeze/thaw detection algorithm using passive L-band microwave observations from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The current product gives the soil state as “frozen”, “partially frozen”, or “thawed”. Estimates for a given season are derived after each winter period. An operational product with a latency of one day is under development. Global information on soil freezing and thawing has many applications; e.g. in evaluation or as an input prior in carbon and climate models, soil carrying capacity analysis, and hydrological models.

A synergistic method for obtaining soil permittivity and soil frost was developed. The method incorporates a semiempir-ical backscattering model for forested land. Soil permittivity is retrieved from active microwave observations using least squares inversion method. Bayesian assimilation scheme can be applied to combine the active retrieval with a permittivity estimate from a passive instrument. Soil frost can be determined from permittivity estimates using a threshold method. The synergistic method was tested on boreal forest site in Northern Finland using ASAR for active and SMOS for passive observations. Satellite retrievals were compared to in situ soil permittivity, temperature and frost measurements. The results show that high resolution SAR data (e.g., ASAR, Sentinel) can be used to downscale coarse resolution SMOS estimates and that synergistic method reduces variability and biases of the ASAR retrieval.

In this paper, we develop a radar snow water equivalent (SWE) retrieval algorithm based on a parameterized forward model of bicontinuous dense media radiative transfer (Bic-DMRT). The algorithm is based on retrieving the absorption loss of the snowpack which is directly proportional to the SWE. In the algorithm, Bic-DMRT is first applied to generate a lookup table (LUT) of snowpack backscattering at X- and Ku-band. Regression training is applied to the LUT to transform the dual-frequency backscatter into functions of two parameters: the scattering albedo at X-band and SWE. The background scattering is subtracted from the SnowSAR data to give the volume scattering of snow. Classification of SnowSAR data is applied to provide a priori information. Based on the obtained volume scattering and the priori information, a cost function is established to find SWE. Performance of the retrieval algorithm was tested using three sets of airborne SnowSAR data acquired over mixed areas in Finland and open tundra landscape in Canada. It is shown that the retrieval algorithm has a root-mean-square error below 30 mm of SWE and a correlation coefficient above 0.64.

There is a long-stranding need of reliable space-borne observations on snow mass. Current satellite sensors and data products are largely unable to meet requirements presented in particular by numerical prediction and watershed management. Consequently, several concept studies have been initiated to address these specific needs, outlining possibilities for future space sensors focusing on retrieval of snow mass and other characteristics of the terrestrial cryosphere. The results of these of-going mission concept studies are presented and discussed. Several possible sensor options are presented, which would address diverse needs on either hemispheric or regional scales.

Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km2of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability. Making satellite observations the only means for providing timely and complete observations of the global snow cover.

We validate an absorption based radar retrieval algorithm of snow water equivalent (SWE) using X- and Ku-band backscatter with airborne SAR data. The bicontinuous dense media radiative transfer (Bic-DMRT) model is first applied to generate a look-up table of snow properties against backscattering at X- and Ku-bands. In the retrieval algorithm, the background scattering is subtracted from the total scattering giving the volume scattering of snow. With the look-up table, we generate regression equations between multiple and single scattering and correlations between the scattering albedo and optical thickness at the two bands. With these relationships and the volume scattering of the snowpack, the best solution for the radar observation is found using a priori constrained least-squares cost function. Next, the absorption loss of the snowpack is derived from the solution, which is directly proportional to the SWE. We have applied the algorithm to airborne SAR observations from Finland and Canada. The retrieval algorithm is shown to be effective, achieving root mean square error (RMSE) of ~19 mm for both SnowSAR data, which is smaller than the 20mm RMSE requirement of SCLP.

Atmospheric transmittance is an important factor for atmospheric correction in the inversion of land surface parameters. Under nonprecipitating conditions, microwave atmospheric transmittance in the X-, Ku-, and Ka-bands is mainly determined by oxygen, water vapor, and cloud liquid water content. In this letter, radiosoundings from 119 stations in China, performed twice a day from January 2011 to July 2014, were used in the Salonen-Uppala cloud detection algorithm to distinguish cloud layers from layered atmospheric profiles and to estimate the cloud liquid water content therein. The resulting atmospheric transmittances at frequencies of the Advanced Microwave Scanning Radiometer-Earth Observing System over China were estimated using Liebe's millimeter-wave propagation model and Mie theory. Atmospheric transmittance maps were obtained by interpolating the results from individual sites, driving a climatological database for over China, which may be used to correct for atmospheric influence in surface parameter retrievals. The simulated transmittances were validated through a series of on-site field experiments in the North of China. We compared simulated atmospheric brightness temperature with measurements performed in situ using a ground-based radiometer system. The correlation coefficients between the measured and simulated values were 0.97, 0.99, and 0.98, in the X-, Ku-, and Ka-bands, respectively, with a root-mean-square error of 0.6, 1.6, and 9.5 K, respectively.

The brightness temperature of snow-covered terrain was monitored from January through April 1985 using tower-based radiometers operating at 1, 16.5, and 37 GHz (vertical and horizontal polarization) in southern Finland. Ground truth data on snow, soil and weather were collected. Layered dielectric, extinction and wetness information on snow at the test site was obtained with free-space transmission systems operating at 12 and 35 GHz. In this paper we report the 37 and 16.5 GHz vertically polarized brightness temperatures (incidence angle 50 degrees off nadir) for melting and refreezing snow over a 26-hour period and compare experimental and theoretical results.

Recent theoretical and experimental studies have indicated the feasibility of passive microwave L-band observationsfor observing dry snow cover characteristics, namely snow density in the lower approx.. 10 cm of the snowpack. The sensitivity of L-band emission to snow density is based on the dual influence of refraction and impedance matching on observed brightness temperature with changing effective snow permittivity. The permittivity of pure, dry snow, on the other hand, depends largely on snow density. In this study, we expand the theoretical and experimental results of retrieving dry snow density to passive L-band satellite observations. Such retrievals could be appealing in the context of improving satellite based retrievals of e.g. Snow Water Equivalent (SWE) using other sensors. Retrievals are applied to both multi-angular observations from the ESA SMOS mission, and observations of the NASA SMAP radiometer on a single angle of observation. While in theory the multi-angular approach is preferable, improved RFI mitigation in SMAP provides more spatially and temporally more stable retrievals. The applied dual-parameter retrieval scheme produces also an estimate of ground permittivity; experimental data showed dry snow cover to have a clear influence on ground permittivity retrievals, implicating that even dry snow cover is non-negligible also in retrievals of soil moisture from L-band observations.

There is a significant difference in SWE retrieval performance between the different satellite-based products. The assessment using the Russian and Finnish snow transect data covers an extremely large and varied geographical region and spans a total of ten years (2002–2011). Additionally, the reference data are well suited for assessing coarse resolution data, as they are not point-wise measurements but distributed measurements from the snow transects or snow courses.

The seasonal snow over the Tibetan Plateau is an important parameter for the regional energy and water cycle. The snow there experience a different trend compared to that of the Northern Hemisphere in the past decades. Because of the shallow and patchy snow with a fast precipitation process and cloud occurrence, the traditional snow algorithm (18GHz and 27GHz gradient brightness temperature) experiences an insufficient accuracy there when employed the global coefficients. In this paper, the atmosphere condition was assumed to play a key role. Through the simulation, and the realistic atmospheric balloon measurement, the transmittance difference between Plateau and low land area was calculated, and the correspondent cloud influence was analyzed. As a result, we need to consider the cloud and water vapor effect, the method over the Tibetan Plateau needs to consider these two components, i.e. the existence of clouds over snow cover areas, and the atmospheric water vapor content, which typically separates high elevation regions from low lying landscapes.

The database helps to better understand and quantify the effect of F/T and snow on the L-Band signal. The information will be useful for the validation and calibration of satellite based products. The database will also be used to validate and calibrate different L-Band snow emission models [3–4–5].

Three years radiosoundings measurement at China were inputted in the Salonen-Uppala cloud detection algorithm to distinguish cloud layers, and to estimate cloud liquid water content therein, then atmospheric transmittances of China at frequencies of the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) were estimated using MPM and Mie theory. The validation expressed a good agreement with the field experimental observations. A comprehensive temporal and spatial analysis has been executed, appeared that microwave atmospheric transmittances were much dependence on water vapor distribution, and transmittances were relatively stable from October to March, but fluctuated obviously in summer. Transmittances of northwest China were higher than those of south China, and the highest transmittances were in Qinghai-Tibet Plateau region. The correlation coefficient between microwave atmospheric transmittance and MODIS total column precipitable water vapor (MYD05) in January at 18.7, 23.7, 36.5 and 89 GHz was 0.904, 0.923, 0.847 and 0.879 respectively. The relationship between monthly averaged microwave atmospheric transmittances and MYD05 could be used to estimate the high resolution microwave atmospheric transmittances products. The results prepared to ensure atmospheric effect in the applications of passive microwave remote sensing.

Reliable global and regional scale SWE maps can be calculated by the assimilation of space borne derived SWE estimates and ground based SD observations. The spatial resolution of these products is ~25 km per pixel which is good enough for climate research but for hydrology a higher resolution is often optimal. A regional SWE processing system with nominal resolution of ~ 5 km per pixel over Europe is described in this paper. In addition the validation results show that the sensitivity to SWE is on the same level as with the lower resolution products. SWE data are also assimilated with HOPS hydrological model and the results show an improvement in river discharge estimates.

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in 4π space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different TBsimulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankylä, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate TBfor deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of -1.4 K and a root-mean-square error of 11.0 K.

We have conducted airborne multi-frequency radiometer measurements over two lakes and adjacent land areas in southern Finland over a period of several winters using a frequency range of 1.4 to 36.5 GHz. Data have been collected under a variety of snow, ice, and weather conditions in order to determine the behavior of the snow-ice-water system. This paper presents an overview of the airborne campaigns and results confirming that the brightness temperature behavior of the snow/lake ice/water system is different from that of the snow/terrain system. This needs to be taken into account in algorithms for retrieval of snow characteristics from space-borne radiometer data for northern lake-rich areas. Comparisons between experimental brightness temperatures and theoretical results show that the HUT snow emission model performs well for lake ice.

Dry snow can be considered as a transparent but refractive medium which causes a phase delay in the reflected signal of active radar remote sensing systems. Here, we analyze the phase delay to estimate Snow Water Equivalent (SWE), the depth of fresh snow and the anisotropic orientation of ice grains in the snow volume. SWE is determined from the integrated phase shift measured by differential interferometry. The temporal evolution of the snow anisotropy could be observed because different microwave polarizations show different propagation speeds in anisotropic snow. The depth of fresh snow as well as snow metamorphosis is discussed with respect to characteristic phase-shifts in the co-polar phase difference. Ground based radar observations from the Snow-scat instrument installed at a test site near Sodankylä, Finland, form the data basis for this paper.

Dry snow is conventionally considered as having minimal effect on microwave radiation at long wavelengths (such as L-band). However, dry snow affects observed microwave signatures even at these wavelengths through changes in impedance matching between soil and the overlying media, as well as through changes in the refraction angle at the soil interface. Exploiting these effects, the multi-angular, dual-polarized L-band observations of e.g. the European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission have the potential to derive snow properties, such as the density of the lowest layers of the snowpack in contact with the ground. This in turn, would have the potential to inform retrieval schemes of snow cover based on EO-data from other sensors. In addition, the theoretical studies demonstrate that the effect of dry snow on retrieval of other geophysical variables, such as soil moisture, is not negligible. In this study, we demonstrate the simultaneous retrieval of snow density and ground permittivity in dry snow conditions, using a multi-year dataset of tower-based L-band observations. We show that following predictions of the theoretical studies, the retrieved snow density matches that of the density measured for the lowest snow layers; dry snow cover is also shown to affect retrievals of ground permittivity by up to 40 %.

Different researches were addressed to the assessment of the boreal forest environment using active microwave remote sensing. Some of these activities were also devoted to estimate the ground parameters under the forest (i.e. soil moisture, snow mass) and, in order to understand the complex mechanisms which govern the radar backscattering, different electromagnetic models were developed for simulating the boreal scenario. An improvement of these models, for better characterizing the sparse forests, also considered the effect of shadow induced by the trees. Besides the computation of the attenuation caused by shadow on ground backscattering, the first needed parameter was the quantification of the percentage of the pixel affected by shadow. The estimation of this parameter can be obtained from high resolution optical images which are not always available at global scale. An alternative semi- empirical method based on 3D CAD modelling and ancillary information, which allow to quantify the amount of the shaded area is presented in the paper. Different forest profiles, height and densities and different geometry of observation were considered, and semi-empirical relationships between shadow area extension and these parameters were founded. The effect of electromagnetic shadowing was also quantified by performing model simulations. Finally a validation of the method was achieved by using high-resolution data collected from optical sensors in a forested area of Finland.

Recent studies, which were carried out within the framework of ESA's CoReH2O Phase-A mission, demonstrated that multi-frequency SAR data are able to quantify the amount of snow mass on land or glaciers (SWE). On the other hand the presence of forest has a significant impact on the propagation of the radar signal, depending on its structure, biomass, water content and cover fraction. In particular for dense forest, scattering of vegetation strongly hides the signal from snow and, consequently, compromises the sensitivity to snow parameters. A method for the compensation of the vegetation effect for the SWE retrieval in boreal forests is presented in this research. The procedure is based on the possibility to separate the scattering contribution from soil and vegetation from the data itself using ancillary information. The compensation follow through the estimation of the direct scattering from vegetation by using a minimization technique. Moreover, using a time series of backscatter images, this method is able to monitor the evolution of soil and vegetation status changes along the season, and improve the quality of the SWE retrieval algorithm.

Satellite-based snow-cover monitoring is performed using optical, synthetic aperture radar (SAR), and passivemicrowave sensors. Effects of forest canopy on the observed signal need to be considered with all of these sensor types. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high computational and ancillary data requirements. However, for retrieval purposes, simple models are preferred. This work aims at increasing the understanding of the effect of forest canopy on remote sensing observations of snow-covered terrain for both microwave and optical regimes and at quantifying the capability of simple zeroth-order models in simulating these effects. To achieve these goals, a spatial analysis of optical, SAR, and passive-microwave remote sensing data in the northern boreal forest region was performed. Model parameters for vegetation transmissivity as well as the properties of the underlying surface were optimized by utilizing lidar-ranging- and Landsat-based simplified proxy parameters describing forest canopy closure and stem volume. The results demonstrated that despite using these relatively simple proxies, a zeroth-order model can accurately estimate the extinction of electromagnetic signals in a forest, particularly for passive microwave and optical data. The SAR model successfully estimated the median of the observations, but larger scatter of the observations was reflected by a higher root mean square error and lower correlation between models and observations. Due to both good estimation accuracy and simplicity, the presented models can be considered to be applicable in existing snow retrieval algorithms.

Using the brightness temperature data measured by an Advanced Microwave Scanning Radiometer (AMSR-E) and the in-situ measurement, this paper compared the accuracy and applicability of five snow-depth retrieval algorithms (Chang algorithm, GSFC 96 algorithm, AMSR-E SWE algorithm, the improved algorithm for Qinghai-Tibet Plateau and atmosphere-correction algorithm) in the regions of Xinjiang, Qinghai-Tibet Plateau, Inner Mongolia, Northeast China, Northwest China and the North China Plain. Generally, over the whole China area, the results of the study show that, the improved algorithm for Qinghai-Tibet Plateau shows a relatively higher accuracy, with a root-mean-square error (RMSE) of 9.16, 9.96 and 9.63cm and a mean relative error (MRE) of 59.77%, 52.79% and 48.47%. According to regional verification respectively, the accuracy algorithm for Xinjiang region is the GSFC 96 algorithm, the RMSE of which is 6.85cm~7.48cm; For Inner Mongolia, it is the QTP algorithm, the RMSE of which is 5.9, 6.11 and 5.46cm; and For Northeast China, it is also the Qinghai-Tibet Plateau algorithm, the RMSE of which is from 6.21cm to 7.83cm. None of the five algorithms is in a good quality for Northwest China and the North China plain regions. For the Qinghai-Tibetan plateau region, the author is not able to obtain the results of statistical verification due to a lack of actually measured data.

Snow water equivalent is an important parameter for natural science studies. One promising sensor configuration for quantitative snow water equivalent remote sensing is the X and Ku band dual-polarization SAR, such as the CoreH2O mission. The retrieval algorithm of terrestrial snow water equivalent from this sensor configuration suffers two major problems which are the decomposition of volume and surface backscattering, and the calibration of snow grain size effect. In previous study, we proposed a preliminary algorithm for snow water equivalent retrieval using X and Ku band dual-polarization radar. In the meantime, in the past years there has been progress in both ground experiment techniques and electromagnetic scattering modeling of snow cover. This enables us to make a further refinement and improvement of the retrieval algorithm based on the new experimental data and newly developed electromagnetic scattering models. In this study, we propose a refined version snow water equivalent inversion algorithm based on X and Ku band dual-polarization radar. The volume backscattering of snow is decomposed using the depolarization ratio, and the single scattering albedo and the optical depth is retrieved by the dual-frequency volume backscattering signal. Then the snow water equivalent is retrieved using the absorption part of the optical depth. The retrieval algorithm is tested using the Nosrex experiment carried out in Finland.

Passive microwave remote sensing observations have been widely used for long-term global monitoring of the Earth. Passive microwave data can be utilized to obtain important parameters (e.g. precipitation, snow cover, sea ice and soil moisture) of the Earth system with relatively high temporal resolution and regardless of lighting and cloud conditions. However, due to the limited lifetime of individual satellite sensors it is necessary to examine and cross-calibrate the brightness temperature data of different instruments when establishing long-term time series of observations. In this paper, brightness temperature data from SSMIS, AMSR-E and MWRI were compared over the Greenland ice sheet. In addition, the brightness temperature data from these instruments were also compared against tower-based brightness temperature observations over a test site in the boreal forest zone. A simple Snow Water Equivalent (SWE) retrieval algorithm was applied to the three satellite data sources to investigate the effect of observational biases to a typical satellite product on snow cover.

Small-scale variability in land cover influences both the snow cover and the microwave response of a snow-covered surface. Since low microwave frequencies penetrate below the snowpack, the differing dielectric properties of soil and water have a significant effect on passive microwave observations and therefore cause errors in the interpretation of snow parameters from satellite data. Here, the brightness temperature of snow- and ice-covered lakes and wetlands is studied using airborne and spaceborne microwave radiometer observations and modeling of brightness temperature from in situ measurements. We aim at assessing the validity of the multilayer Helsinki University of Technology (HUT) snow emission model on lake- and wetland-rich areas and at examining the error from omission of water bodies in the forward modeling of brightness temperature. The results indicate that the model can estimate brightness temperatures of lakes and wetlands with rms errors of 12-28 K and 9-16 K, respectively. The inclusion of lakes in the satellite-scale simulations reduces the simulation error in 52%-100% of the simulated areas at 18.7 and 36.5 GHz. The inclusion of wetlands further improves simulations, resulting in an rms error of satellite scenes of 4-5 K at 18.7 and 36.5 GHz (5-10 K without lakes and wetlands). However, the natural variability of brightness temperature over water bodies is not entirely captured particularly at 10.65 GHz. The inclusion of lakes and wetlands can be used to reduce errors in the forward model and thus increase the accuracy of snow parameters derived from satellite data.

In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. A RTT model capable of simulating scattering from a snow-covered vegetated terrain was developed and implemented. A sensitivity analysis to snow and vegetation parameters was carried out thus a comparison with real SAR data is presented in the paper.

Airborne microwave radiometer measurements of lake ice have been performed in 2004, 2007, 2011, and 2012 over two lakes in southern Finland using radiometer systems that cover frequencies from 1.4 to 36.5 GHz. Airborne and surface data have been collected under circumstances ranging from early winter dry snow to late winter dry and wet snow conditions. Water and slush on top of ice and dry snow grain size have been determined to be the two most important parameters affecting brightness temperature.

Land Surface temperature (LST) is a critical parameter in water and energy circle supporting the global climate research. The traditional optical/thermal remote sensing estimates LST under clear-sky conditions, while the microwave remote sensing provides the all-weather LST estimation capability. In this paper, we derivate a new LST retrieval algorithm based on the emissivity parameterization using the Advanced Microwave Scanning Radiometer - Earth Observation (AMSR-E), considering the atmospheric influence, to tackle the LST retrieval under the non-scattering rain and cloud condition. The new derived LST algorithm has a physical basis and the RMSE is 1.8K with the station validation over boreal forest area at Sodänkylä, Finland.

The launch of the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) satellite mission in November 2009 opened a new era of global passive monitoring at L-band (1.4-GHz band reserved for radio astronomy). The main objective of the mission is to measure soil moisture and sea surface salinity; the sole payload is the Microwave Imaging Radiometer using Aperture Synthesis. As part of comprehensive calibration and validation activities, several ground-based L-band radiometers, so-called ETH L-Band radiometers for soil moisture research (ELBARA-II), have been deployed. In this paper, we analyze a comprehensive set of measurements from one ELBARA-II deployment site in the northern boreal forest zone. The focus of this paper is in the detection of the evolution of soil frost (a relevant topic, e.g., for the study of carbon and methane cycles at high latitudes). We investigate the effects that soil freeze/thaw processes have on the L-band signature and present a simple modeling approach to analyze the relation between frost depth and the observed brightness temperature. Airborne observations are used to expand the analysis for different land cover types. Finally, the first SMOS observations from the same period are analyzed. Results show that soil freezing and thawing processes have an observable effect on the L-band signature of soil. Furthermore, the presented emission model is able to relate the observed dynamics in brightness temperature to the increase of soil frost.

SnowCarbo project is funded by the European commission (EC) Life+ program. The project was started at the beginning of January 2009 and it will end in December 2012. Coordinating Beneficiary of SnowCarbo project is Finnish Meteorological Institute (FMI) and Associated Beneficiary(ies) are Finnish Environment Institute (SYKE), and Commissariat a l'energie atomique Laboratoire des Sciences du Climat et de l'Environnement (CEA-LSCE). The main objective of the Snowcarbo project is to implement and demonstrate a new innovative approach for the net CO2balance mapping in northern Finland and northern Eurasian region. The approach employs connected REMO regional climate and JSBACH ecosystem models [1,2,3] and is based on a combination of different information sources describing snow evolution, phenology, land cover, CO2fluxes and concentrations. The implemented method combines local in situ observations and global Earth Observation satellite data together with land cover class information in a new way. Snowcarbo aims to produce carbon dioxide balance maps over northern Finland and northern Eurasia by combining different earth observation data sources and modeling of CO2balance.

In this paper, how typical snowpack parameters with layering processes affect to the sensitivity of X- and Ku-band backscatter to the increase of SWE (Snow Water Equivalent) was analyzed. A particular motivation of this work was to contribute to the development of the geophysical algorithm of CoReH2O, a proposed ESA SAR mission currently in Phase A [1][2]. DSLDMRT forward backscatter model for microwave backscatter from snow covered terrain was used in analysis. The software is based on a second order radiative transfer model using the dense medium approach [3]. The analyses showed that the layering of snowpack changes the sensitivity of backscatter to SWE. A layer of refrozen at the bottom of snow pack (resulting from thaw-refreeze cycles at early winter) can cause a negative correlation of backscatter with the increase SWE for the beginning of the dry snow accumulation period. The positive correlation between snow grain size and SWE, typical for the temporal metamorphosis, increases the correlation between SWE and backscattering coefficient.

Airborne microwave radiometer measurements of lake ice have been performed in 2004, 2007, and 2011 in southern Finland. The HUTRAD radiometer system provided data in the 6.9 to 36.5 GHz range using an incidence angle of 50 degrees off nadir. In 2011 also the interferometric HUT-2D radiometer was used to provide 1.4 GHz imagery of lake ice.

The acquisition of Snow Water Equivalent (SWE) at spatial resolutions higher than those of the present methods relying on inversion of coarse-scale passive microwave observations is a possible application for space-borne SAR imagery. The presented experimental campaign NoSREx (Nordic Snow Radar Experiment) was initiated to contribute to the knowledge of snowpack backscattering and emission properties, in particular, to help develop methods to retrieve SWE from high-resolution two-frequency SAR observations (at X and Ku band). Another objective was to provide data for studies exploring the synergistic use of active and passive microwave observations for monitoring of snow properties. The NoSREx campaign began in November 2009, and has recently concluded a second winter period of observations.

This paper presents the evaluation of the 30-years GlobSnow SWE data record, spanning Northern Hemisphere, for climate research purposes. It includes a brief validation of the SWE data record with ground- based reference data and evaluation of the hemispherical scale SWE trends.

Over the western China, due to the influence from shallow snow, the passive microwave remote sensing algorithm show its imbecility when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this paper, we employee several ground time-series snow depth dataset and the corresponding satellite brightness temperature to evaluate the 36.5Ghz/l 8.7Ghz and high frequencies' (85Ghz or 89.0Ghz/18.7Ghz) ability for the shallow snow retrieval. From the analysis, we can get that the high frequencies has its potential for the shallow, which is suit for the snow situation over the high land, even with the atmosphere influence at high frequency. The result tell that the relative deep snow (>; 20cm) the Tbs at 36-18GHz are more reliable than that of high frequency, while over the shallow snow especially <;20cm), the pair 36-18 is insensitive, but the high frequency pair (89/85-18GHz) shows its obvious possibility.

Annually large areas of northern hemisphere freeze and are covered with snow. This paper presents results from synthetic aperture radiometer measurements of soils in unfrozen and frozen state and examines changes in their L-band emissivity.

Snow water equivalent (SWE) is one of the key parameters describing seasonal snow cover. Traditional methods such as interpolating ground-based measurements or estimating SWE from spaceborne measurements have their shortcomings. In this paper an assimilation approach has been used to estimate a time series of SWE in hemispherical scale for 30 years. The behaviour of the algorithm is analyzed and scatterplot of validation results is presented. Results show an improvement over using traditional algorithms.

L-band radiometry is widely considered the best technique for Earth observing satellites to measure sea surface salinity (SSS). Interferometric aperture synthesis is a new technology applicable in spaceborne remote sensing at low frequencies. The challenge of the technology comes with decreased radiometric resolution and complexity in calibration compared to conventional radiometer systems. Due to these issues and the overall newness of the concept, validation of the technology for salinity retrieval purposes is desired. In this paper, we describe an intense measurement campaign carried out with the complete interferometric aperture synthesis radiometer system HUT-2-D, designed and operated by the Helsinki University of Technology. The campaign aimed at the detection of a changing salinity level in the Baltic Sea, in the coastal areas of Finland. We describe the campaign comprising details of the ground truth collection, sea surface emission modeling, and radiometric data analysis. We have a special emphasis on the assessment of the impact of the sea state on the radiometric measurements, which is considered one of the major obstacles for SSS retrieval at the L-band. For this purpose, we present a new correlation between sea roughness information collected with the Global Navigation Satellite System reflectometer and radiometric data measured by an L-band radiometer system.

This paper describes the analysis of L-band radiometric measurement data gathered with the synthetic aperture radiometer HUT-2D during several ground-based and airborne measurement campaigns. The radiometric data are analyzed from the instrument's performance point of view, aiming to verify the theoretical performance of an instrument of this kind and to assess the performance of the HUT-2D radiometer system in particular. The data sets considered for the study consist of measurements of well-known natural targets, such as cosmic background radiation, and measurements of pure water scenes, the brightness temperature of which is possible to model based on in situ measurements. We define four figures of merit, which are applicable for synthetic aperture radiometers. These are radiometric resolution, image bias, pixel-to-pixel random error, and temporal stability. Then, we use the selected data sets to assess these in the case of HUT-2D. The experimental results are discussed and compared to the theoretical values, where applicable. Also, we discuss possibilities to improve the presented performance. The main results of this paper are the consolidated performance parameters of the HUT-2D instrument. We study and discuss the properties of the error components related to the technology in a general level, and study the scalability of the errors as a function of the measured targets. In particular, the stability of the direction-dependent error component is pointed out, and a mitigation guideline is proposed.

This paper describes the development of information systems and techniques for improving hydrological forecasting by applying satellite observations, weather radars, and in situ measurements from automatic monitoring stations. In the methodology developed and demonstrated, the observation data are accompanied with a detailed soil and land cover information. The information system is concerned with the following physical characteristics relevant to river discharges and flooding: snow water equivalent (SWE), cumulative amount of precipitation, fraction of snow covered area during the melting period (FSC), soil moisture, and soil frost. Feasibility of the multi-source information system is demonstrated in a pilot experiment for Finnish Lapland, using the hydrological forecasting system of the Finnish Environment Institute (SYKE) as an example of a typical operational distributed model.

The successful launch of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission on November 2nd 2009 opened a new era of global monitoring with L-band passive microwave instruments. The main objective of the mission is to measure soil moisture and sea surface salinity globally. The sole payload of SMOS is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS). In this paper we compare the first SMOS measurements over a sub-Arctic boreal forest area with tower-based and airborne reference data. Our main interest is the evolution of the boreal soil frost, since it has a large impact on the carbon cycle in the Arctic region.

We present initial results from an experimental campaign aiming to acquire a comprehensive, full-snow season dataset of simultaneous backscatter and brightness temperature measurements of snow covered ground. The campaign is a part of Phase A activities in support of the proposed CoReH2O mission, aiming both to contribute to investigations on interpreting snow properties from active microwave observations, and to explore the possibilities for synergistic use of active measurements with existing passive microwave instruments. The campaign period covers the winter season of 2009-2010. Microwave observations are complemented by detailed in situ data of snow cover properties.

Snow extent and snow depth (SD) are critical parameters in metro-hydrological models and are sensitive to the global climate change. Over the western China, due to the influence from shallow snow, changing seasonal permafrost and the sparse observation stations, the passive microwave remote sensing algorithm show its applicability when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this work, we employ one whole-winter Tb extracted from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) L2A swath dataset and the ground measurements of snow depth (SD) to analyse the snow microwave emission and gradient algorithm ability. The time series analysis shows that the Tb differences (36.5-18.7) and (36.5-10.7) are sensitive to relatively deep snow (>20cm), while the Tb differences (89.0-18.7) are sensitive to the occurrence of the new snow, with a promising correlation with shallow snow (<;15cm) and quickly decreasing (melting) snow depth, which suggest that a high frequency Tb difference could potentially be a good snow monitoring signal for the shallow snow cover over western China.

Snow Water Equivalent (SWE) is a measure which describes the amount of snow. The authors have developed algorithm to estimate SWE assimilating spaceborne and ground based observations. The authors have also developed algorithm to detect the snow clearance date. In this work those algorithms are combined to produce a single snow product. Two series of figures are presented and the improvements discussed.

Modeling of snow emission at microwave frequencies is necessary in order to understand the complex relations between the emitted brightness temperature and snowpack characteristics such as density, grain size, moisture content, and vertical structure. Several empirical, semiempirical, and purely theoretical models for the prediction of snow emission properties have been developed in recent years. In this paper, we investigate the capability of one such model to simulate snow emission during the peak snow season-a new multilayer version of the Helsinki University of Technology (HUT) snow model. Developed with a single layer, the original HUT model was easily applied over large geographic areas for the estimation of snow cover characteristics by model inversion. A single homogenous layer, however, may not accurately allow the simulation of vertically structured natural snowpacks. The new modification to the model allows the simulation of emission from a snowpack with several snow or ice layers, with the individual component layers treated as in the original HUT model. The results of modeled snowpack emission, using both the original model and the new multilayer modification, are compared with reference measurements made using ground-based radiometers deployed in Finland and Canada. Detailedin situmeasurements of the snowpack are used to set the model inputs. We show that, in most cases, use of the multiple-layer model improves estimates for the higher frequencies tested, with up to 38% improvement in rms error. In some cases, however, the use of the multiple-layer model weakens model performance particularly at lower frequencies.

In the above titled paper (ibid., vol. 48, no. 7, pp. 2781-2794, Jul. 10), there is an error in Section II-B, which is corrected here.

Brightness temperatures of snow-covered terrain, measured at 37 GHz and 16.5 GHz over a whole season, are compared against theoretically modeled values, computed with observed snow parameters.

Interferometnc aperture synthesis is a new technology proposed for space-borne remote sensing of sea surface salinity. In the lack of availability of a complete instrument of this kind, validation of the technology for salinity retrieval purposes is desired. In this paper we describe an intense measurement campaign carried out with a complete interferometnc aperture synthesis radiometer system HUT-2D. The campaign aimed for the detection of a changing salinity level in the Baltic Sea. We have a special emphasis on the assessment of the impact of sea state on the radiometnc measurements, which is considered one of the major obstacles for sea surface salinity retrieval at L-band. For this purpose we study sea roughness information collected with Global Navigation Satellite System Reflectometer.

Modeling of snow emission properties on microwave frequencies is necessary in order to understand the complex relations between the snowpack microwave emission and its characteristics, such as density, snow grain size, moisture content and snowpack vertical structure. With a reliable model, snowpack characteristics could be derived from passive microwave observations using model inversion, potentially improving retrieval accuracy of snow parameters when compared to traditional empirical inversion algorithms. In this study, we present a summary of recent activities aiming at experimental validation of the semi-empirical HUT snow emission model. The activities consist of comparisons of modeled brightness temperatures against tower-based and airborne reference radiometer data. Model inputs are derived form intensive field measurements of snowpack characteristics. Forward modeling of snow emission on the satellite scale, and a comparison with satellite observations is presented. Furthermore, a recent update of the model, enabling simulation of multiple snow layers and special cases such as snow covered lake ice, is experimented.

In satellite passive microwave measurements, the sky brightness temperature is a function of frequencies, sensitive to parameters such as water vapor content, liquid water (cloud and precipitation), oxygen, hydrometeors and atmospheric temperature. In order to investigate the atmospheric influence to the retrieval of snow parameters quantitatively, firstly, we combined the HUT (Helsinki University of Technology) snow emission model (except the atmosphere parameterization) and an atmosphere model to do theoretical simulation estimations. We indicate that the C and X band atmospheric influence could be ignored, while the atmosphere is a non-negligible absorber and emitter of microwave radiation at frequencies higher than 19 GHz. We also launched a 13-day experimental measurement in winter time over Sodankyla¿, Finland, with synchronous satellite (AMSR-E) and tower-based radiometer measurements, together with extensive in-situ atmospheric measurement dataset. The evaluation result indicates that the atmosphere plays a relative positive contribution (about 20K for 36.5GHz and 89.0/94.0GHz). The difference between satellite observation and point experiment comparison suggests conducting more physical model work with atmosphere contribution.

We have studied usage of our airborne L-band 2-D synthetic aperture radiometer HUT-2D for estimation of soil moisture. Measurements were conducted over three sites in Northern and Southern Finland in August 2007. Good results were achieved for bare soil and low vegetation, whereas soil moisture retrieval for forested areas requires further studies.

During the last two decades, the development of synthetic aperture radiometers for remote sensing has been studied intensively. One of the proposed methods for the calibration of such an instrument is the application of a distributed noise injection network. This paper focuses on the origin and effect of errors arising from this methodology. A generalized analytical method to calculate the accumulation of phase and amplitude errors in a distributed noise injection network is presented. This method is then applied to the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), the interferometric radiometer aboard the European Soil Moisture and Ocean Salinity satellite. The effect of the resulting errors to MIRAS' brightness temperature is analyzed. The presented method is applicable also to other interferometric radiometers, whose calibration relies on distributed noise injection.

The seasonal snowpack across the boreal forest is an important national resource in both Canada and Finland, contributing freshwater for agriculture, human consumption, and hydropower generation. In both countries, satellite passive microwave data are utilized to provide operational information on snow depth and snow water equivalent (SWE) throughout the snow cover season. Airborne passive microwave surveys conducted independently across Finland and western Canada during March and April 2005 and March 2006 provided the opportunity to assess the level of similarity in snowpack physical properties and brightness temperature response to snowpack qualities using two independent data sets. The primary objectives of these campaigns were to determine the influence of small-scale heterogeneity on satellite data, using relatively high resolution airborne measurements, and to assess the Helsinki University of Technology (HUT) snow emission model capability of predicting emitted brightness temperatures under varying snowpack and landscape conditions. Comparisons of brightness temperature emissions over different land cover types showed a clear distinction of wetlands and snow-covered ice from forested and open areas. This is reflected also as a strong relationship between 6.9-GHz measurements and fractional lake cover in both Canada and Finland, with relationships at 18 and 37 GHz being less consistent between data sets. Comparisons of experimental data versus HUT snow emission model predictions showed relatively good agreement between the simulations and airborne data, specifically for the Finnish data set.

The study focuses on the microwave emission properties of snow-covered lake ice. Lakes typically differ from their surrounding terrain regarding snowpack structure, and thus microwave emission. Ice and water layers beneath the snow also influence the result when compared to frozen ground, decreasing brightness temperatures especially on low frequencies. Estimates of snowpack properties from low-resolution microwave data, such as snow depth or snow water equivalent, are susceptible to these effects. In order to correct for the resulting underestimation, the lake fraction over the area of study as well as the emission properties of those lakes should be known. This could potentially be achieved through the assimilation of modeled estimates of snow-covered lake emissions to satellite data. In this study, a modified HUT snow emission model, including modeled influence from the ice and water layers, is applied to model emission over several lakes in Finland during two winter periods. Input parameters to the model are derived from a large quantity of available ground data. Airborne radiometer data are applied to investigate the quality of the emission estimates. Finally, emissions over several AMSR-E pixels are modeled using fractional lake coverage and available ground data.

The Moderate Resolution Imaging Spectra-radiometer (MODIS/Aqua) and the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) are two sensors aboard on satellite Aqua. Atmospheric parameters retrieved from MODIS/Aqua, such as the layered atmosphere temperature, humidity and pressure profile and land surface temperature (LST), are used to help the estimation of AMSR-E instantaneous microwave emissivity in clear sky conditions over land. As an example, a two-week (from 12-08-2006 to 25-08-2006) instantaneous emissivity over land has been calculated globally for 6.9-, 10.7-, 23.8-, 36.5-, and 89.0-GHz using both polarizations, ascending and descending orbit respectively. The calculated AMSR-E emissivities agree well with the other study [6] through comparison, and can provide more details. The frequency dependency and correlation analysis show the promising emissivity prediction with different channels. The time series analysis over different land covers reveal that the variation of emissivities do not exceed 0.05 in average and it is almost zero for polarization difference (PD) change, which all induce that the time extrapolation of emissivities could tackle cloud contamination issues (time series gap).

Ground calibration of the calibration subsystems of MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) has been performed. The MIRAS instrument is the payload of European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. The calibration subsystems are Calibration Subsystem (CAS), which is a noise distribution network, and Noise Injection Radiometer (NIR), which measures the noise levels of CAS and the average incident brightness temperature. This paper presents the used measurement approaches, related uncertainties, and the calibration results for the NIR and CAS. The results show that in spite of uncertainties, the characterization methods allow accurate ground calibration of the two subsystems. The performance of both subsystems meet the requirements.

An operational system for production of snow water equivalent (SWE) maps over the whole Eurasia is presented. The system uses synoptic weather station measurements and microwave radiometer data to determine the snow water equivalent over the area. The novel feature of the system is that it combines satellite observations of brightness temperature with ground- based data applying a non-linear Bayesian data assimilation technique. This yields accuracy characteristics better than those of only using either of the two data.

In this paper, the thermally stabilized front-end of an L-band total power receiver is presented. Applications on the L-band have become one of the most important focus points in the field of passive microwave remote sensing. Measuring environmental parameters such as ocean salinity and soil moisture remain a challenge, posing strict requirements to instrument performance. For traditional radiometers, especially the stability of front-end components is critical. The principle of the stabilization technique and the actual design are described. The design also features an on-board active cold load for calibration purposes. The presented technique aims to stabilize the front-end section PCB with heaters to sub 0.05 C level.

The Helsinki University of Technology's airborne fully polarimetric profiling radiometer at 36.5 GHz has been used for wind-vector measurements over the Gulf of Finland. The results, collected in a series of measurements over a period of two years, are presented in this paper. The Fourier coefficients of the harmonics of the first three modified Stokes parameters (in brightness temperature) have been solved, and their behavior as a function of the measurement incidence angle and the wind speed has been examined, resulting in a linear model in the measurement range. In this paper, we show a clear relationship between the incidence angle and the third modified Stokes parameter (in brightness temperature), which has been used to compensate for aircraft motion during measurements. Furthermore, the sensitivity of the wind-speed measurement to the incidence angle has been studied, and a model for wind-speed retrieval as a function of the harmonic coefficients and incidence angle was developed.

Interferometric radiometry is a novel concept in remote sensing that is also presenting particular challenges for calibration methods. In this paper, we describe the calibration subsystem (CAS) developed for the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) interferometer of the Soil Moisture and Ocean Salinity (SMOS) satellite. CAS is important for the overall performance of the payload as it calibrates out the differences between the multiple receivers of MIRAS. SMOS is in the final phase of development and is due to launch in 2008.

The seasonal snowpack across the boreal forest is an important national resource in both Canada and Finland, contributing freshwater for agriculture, human consumption, and hydropower generation. In both countries, satellite passive microwave data are utilized to provide operational information on snow depth (SD) and snow water equivalent (SWE) throughout the snow cover season. Airborne passive microwave surveys conducted independently across Finland and western Canada during March and April 2005 (and again during March 2006) have provided the opportunity to assess the level of similarity in snowpack physical properties and brightness temperature response in these two countries.