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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 using in situ observations 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., This work was supported in part by the European Space Agency (ESA) Scout Mission under Grant ESA CN: 4000129140/19/NL/CT and in part by the Academy of Finland under Grant 325397. The GNSS-R installation was funded by the Spanish Grant RTI2018-099008-B-C22/AEI/10.13039/501100011033

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.

The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing. Existing co...

The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00c76fcb347330884.

Snow water equivalent (SWE) is an important variable in describing global seasonal snow cover. Traditionally, SWE has been measured manually at snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant snow density used in modelling emission of snow. In this paper, three versions of spatially and temporally varying snow density fields were implemented using snow transect data from Eurasia and Canada and automated snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. 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 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.

We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of ∼4.4 and ∼7.3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987–2018. The mean unbiased RMSE and bias were 7.1 and −0.05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and −1.20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (>20 cm), with biases of −10.4, −8.9, and −34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai–Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of −0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC.

Forest transmissivity is a key parameter for understanding spaceborne and airborne observations of the Earth's land surface because not only does it influences the proportion of subcanopy upwelling microwave emission penetrating through the forest canopy, it also controls the forest thermal emission. In frozen winter conditions, our previous study has indicated that observed microwave transmissivity of trees is also strongly influenced by temperature. This follow-on study uses ground-based radiometer observations, advanced microwave scanning radiometer 2 (AMSR2) observations, and model simulation to evaluate how this temperature-transmissivity relationship affects spaceborne passive microwave (PM) snow observations. A model is developed to simulate the relationship between tree transmissivity and air temperature. The R2 of the model is 0.85 (0.81) with a root mean square error (RMSE) of 0.03 (0.03) at the 18.7 (36.5) GHz channels, respectively. We find that this temperature-transmissivity relationship has a significant influence on the Tb of the tree, which influences both ground-based radiometer and spaceborne AMSR2 observations. The influence of the temperature-transmissivity relationship on upwelling ground emission is frequency dependent, demonstrating that the findings have implications for frequency difference based approaches for PM snow depth and snow water equivalent retrievals which observe snow-covered landscapes, including boreal forests, at air temperatures below freezing. This effect should therefore be accounted for in frequency difference or ratio approaches of snow accumulation retrievals.

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships., Key Points Simulated snow properties from SnowModel are used in microwave‐based snow water equivalent (SWE) retrievals by MEMLS3&aBiases in physically modeled SWE can induce larger biases in microwave‐based SWE retrievalsThe challenges can be mitigated when microwave algorithms account for the physical relationship of snow properties

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

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.

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.

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 (Sodankyla 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.

We describe the Northern Hemisphere terrestrial snow water equivalent (SWE) time series covering 1979–2018, containing daily, monthly and monthly bias-corrected SWE estimates. The GlobSnow v3.0 SWE dataset combines satellite-based passive microwave radiometer data (Nimbus-7 SMMR, DMSP SSM/I and DMSP SSMIS) with ground based synoptic snow depth observations using bayesian data assimilation, incorporating the HUT Snow Emission model. The original GlobSnow SWE retrieval methodology has been further developed and is presented in its current form in this publication. The described GlobSnow v3.0 monthly bias-corrected dataset was applied to provide continental scale estimates on the annual maximum snow mass and its trend during the period 1980 to 2018., Measurement(s) snow • snow mass • snow water equivalent Technology Type(s) satellite imaging Sample Characteristic - Environment cryosphere Sample Characteristic - Location Northern Hemisphere Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14336612

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.

The Tibetan Plateau houses numerous lakes, the phenology and duration of lake ice in this region are sensitive to regional and global climate change, and as such are used as key indicators in climate change research, particularly in environment change comparison studies for the Earth three poles. However, due to its harsh natural environment and sparse population, there is a lack of conventional in situ measurement on lake ice phenology. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Snow Index (NDSI) data, which can be traced back 20 years with a 500 m spatial resolution, were used to monitor lake ice for filling the observation gaps. Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm, and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps. Through time series analysis of every single lake with size greater than 3 km2 in size, 308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset, including 216 lakes that can be further retrieved with four determinable lake ice parameters: Freeze-up Start (FUS), Freeze-up End (FUE), Break-up Start (BUS), and Break-up End (BUE), and 92 lakes with two parameters, FUS and BUE. Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing. The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13. The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations. The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.

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.

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 ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 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 of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites.

Forecasting the likely future prevalence of climate sensitive infectous diseases (CSIs) in the Arctic requires prediction of how environmental conditions, both aquatic and on the land, will change under a changing climate, together with knowledge of how these changes relate to the environmental conditionals for viability of CSI host organisms. This requires the use of land surface and hydro-climatic models that have been tested against past data and can be driven by climate projections provided by Global Circulation Models for a range of climate scenarios (Representative Concentration Pathways). Uncertainties in the climate projections combine with uncertainties in the environmental models, and this combined uncertainty propagates through into subsequent CSI occurrence modelling. This chapter will describe the available environmental models, together with the data needed to drive and test them, and how we can address the uncertainty within these models, in the context of Arctic CSI prediction.

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 km² of 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 ESA Snow CCI project was initiated in 2018 to improve methodologies for snow cover extent (SE) and snow water equivalent (SWE) retrieval [1] using satellite data and construct long term data records of terrestrial snow cover for climate research purposes.The first new long term SWE data record from the ESA Snow CCI project, spanning 1979 to 2018 has been constructed and assessed in terms of retrieval performance, homogeneity and temporal stability. The initial results show that the new SWE dataset is more robust, more accurate and more consistent over the 40-year time series, compared to the earlier ESA GlobSnow SWE v1.0 and v2.0 data records [1].The improved SWE retrieval methodology incorporates a new emission model (within the retrieval scheme), an improved synoptic weather station snow depth data record (applied to support SWE retrieval), extension of the SWE retrieval to cover the whole Northern Hemisphere.The new Snow CCI SWE data record has been used to assess changes in the long term hemispherical snow conditions and climatological trends in Northern Hemisphere, Eurasia and North America. The general finding is that the peak hemispherical snow mass during the satellite era has not yet decreased significantly but has remained relatively stable, with changes to lower and higher SWE conditions in different geographical regions. References:[1] Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. Kärnä, J. Koskinen, B. Bojkov. 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 12, 3517-3529, doi:10.1016/j.rse.2011.08.014.

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover1–3. These changes in snow cover affect Earth’s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere4–6. In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking7–9. Here we use the new GlobSnow 3.0 dataset to show that the 1980–2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 ± 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40° N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433–3,380 gigatonnes (mean 2,867) to 2,846–3,062 gigatonnes (mean 2,938)—a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia; both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth’s energy, water and carbon budgets. Applying a bias correction to a state-of-the-art dataset covering non-alpine regions of the Northern Hemisphere and to three other datasets yields a more constrained quantification of snow mass in March from 1980 to 2018.

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.

Land surface freeze/thaw (F/T) dynamics impact the surface energy balance, carbon fluxes, and hydrologic processes. Recent and on-going L-Band (≈ 1.4 GHz) spaceborne missions have the potential to provide enhanced information on F/T state over large geographic regions with rapid revisit time. However, the low spatial resolution of these spaceborne observations (≈ 45 km) makes it difficult to isolate the primary contributions to the F/T signal, including the soil, snow, and vegetation states. A ground-based L-Band radiometer measurement campaign was conducted in Saskatchewan, Canada during the winter of 2014–2015 to evaluate brightness temperature sensitivity to F/T processes, snow, liquid water in snow and assess theoretical retrievals of soil permittivity (eG), and snow density from experimental data. The ground-based radiometer was run in multiple configurations. First, temporally continuous measurements were conducted through the winter over an agricultural field, with a comprehensive network of reference snow and soil observations characterizing the F/T state of the soils within or adjacent to the radiometer footprint. Secondly, weekly multi-angular L-Band measurements were made at an undisturbed site of naturally accumulating snow cover, over a site that was kept snow free, and a site with artificially compacted snow. Results from the assessment of the land surface F/T retrieval algorithm showed that L-Band measurements are sensitive to the near surface F/T state of the soil, with the highest level of agreement found between the near surface (2.5 cm) F/T reference measurements of soil temperature and eG (accuracies of 91.1% and 92.9%, respectively). Several mid-winter melt events with air temperatures (Tair) above 0°, and soil temperatures below 0 °C, illustrated that liquid water within the snow dramatically increase the TB, resulting in false retrievals of soil thaw events using existing L-Band F/T retrieval algorithms. However, Tair was also shown to have a high commission errors compared to radiometer observations in detecting snow melt, because of the delay between Tair > 0 °C and the onset of melt resulting in a measurable wet snow signal at L-Band. The retrieval of snow density (ρs), of the bottom 10 cm of the snowpack tended to underestimate high ρs (> 400 kg m− 3), and agreed well for lower ρs (

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 km² of 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.

A method for the retrieval of soil freeze/thaw (F/T) state in the boreal forest region using SAR is presented in this paper. The method utilizes Sentinel-1 data and is thus suitable for continuous near real-time monitoring. The main challenge with the C-band VV-polarization signal is the sensitivity to vegetation and especially to forest canopies. A relatively simple zeroth-order model is used for the retrieval of the ground and the canopy backscatter contributions in 1 km cell size. These backscatter components are then used to identify the F/T state of the soil by comparing them to corresponding reference values representing frozen and thawed conditions. The classification algorithm is based on threshold values applied on the Euclidian distances between the retrieved backscatter and the reference values. The method is tested for three test areas across Finland, having different forest properties: Sodankyla, Nurmes and Tampere, located in northern, central and southern Finland, respectively. We first evaluated whether the use of canopy cover (CC) or stem volume (SV) as the parameter describing the forest conditions provide better model accuracy. We then assessed the Sentinel-1 based soil F/T estimates by comparing them to automatic in situ observations and the SMOS (Soil Moisture and Ocean Salinity) based soil F/T product. The model performance was generally better when SV was used as the forest parameter. Nevertheless, for both CC and SV, the RMSE between the modeled and the observed backscatter was considerably lower than the seasonal variation of the backscatter. In Sodankyla and Nurmes, the Sentinel-1 based F/T estimates were well in line with the in situ observations and the SMOS F/T product. The Sentinel-1 retrievals measuring the top soil layer were fast to react to air temperature changes between negative and positive Celsius degrees, showing similarity of 94–99% with the air temperature measurements. In Tampere the method showed weaker results; the similarity with the air temperature observations was 64%. Overall, a correct vertical freezing pattern of the soil was demonstrated in this study, with Sentinel-1 sensitive to the top soil layer, in situ sensors measuring at 5 cm depth, and SMOS reaching to 5–15 cm soil depth. Additional assessment should be conducted in southern Finland.

Synthetic aperture radar (SAR) enables the mapping of flooding over large areas, regardless of cloud and weather conditions. Simplified classification approaches based on threshold levels of backscattering intensity are typical in operational flood monitoring. Backscattering intensity from floods over open non-forested areas is typically lower, whereas backscatter from forest floods is higher compared to non-flooded areas. However, distinction of flooded areas from non-flooded surface in semi-forested areas with low canopy closure (CC) or low tree height (TH) is expected to be difficult due to confounding effects of the different scattering mechanisms. The aim of this study was to investigate X-band SAR backscattering in flooded boreal forests with varying TH and CC, and to quantify in which cases floods would be difficult to detect with typical threshold-based classification methods. To further understand the SAR signal behavior in flooded forests, the total backscatter was modeled using the HUT (Helsinki University of Technology) semi-empirical forest backscattering model. HH-polarized Cosmo Sky-Med acquisitions from four different locations in Finland were analyzed against airborne LiDAR based forest data, ground observations and a high resolution digital elevation model. Floods were well detected in open areas and dense forests. However, as hypothesized, when TH was higher than zero but lower than 4–5 m, or when CC was higher than zero but lower than 15–20%, the detection was less successful. TH was found to have slightly more influence on the capability of X-band SAR to detect floods than CC. In our four test areas, namely Kittila, Kolari, Pudasjarvi and Evo, 85.3%, 89.1%, 82.7% and 73.9% of the total floods were detected, respectively, by a simple threshold value method. The model was able to successfully estimate the different backscattering components of the flooded forests in Kittila and Pudasjarvi, where the number of observations from all forest conditions was sufficient.

European Space Agency's SnowScat instrument is a real aperture scatterometer which was developed by Gamma Remote Sensing AG. It operates in a continuous-wave mode, covers a frequency range of 9.15–17.9 GHz in a user-defined frequency-step and has a full polarimetric capability. The measurement campaigns were started first in February 2009 at Weissfluhjoch, in Davos, Switzerland, as an initial test of the instrument over a deep alpine snowpack. Physical characterizations of the snowpack and meteorological measurements were carried out, which formed a detailed in situ dataset. SnowScat was then moved to Sodankyla in Finland in early November 2009, a site of the Finnish Meteorological Institute in Lapland. In addition to the in situ snowpack characterizations and meteorological observations, continuous passive microwave observations were also performed. During the 2012–2013 winter period, a vertical time-domain snow profiling experiment was carried out in addition for resolving the scattering contributions from the snow layers of different physical properties. This paper summarizes the results of the SnowScat observations and initial comparisons against the in situ meteorological and snowpack data. The Sodankyla campaign data evidenced the high variability of the radar backscatter behavior of snowpack from year to year, which indicates its strong dependency on changing snow microstructure. Indeed, the snow microstructure is continuously driven by snow metamorphism, which are further affected by meteorological conditions and their interannual variability. The backscattering property of snowpack in the range X - to Ku -band for all polarizations appeared to be dominated by its microstructural morphology and underlying ground conditions, and to a lesser extent by the snow depth, or its snow-water-equivalent.

The potential of retrieving the bottom layer snow density and soil permittivity under dry snow cover conditions from L-band passive microwave observations was analyzed using multi-angular brightness temperatures measured at horizontal and vertical polarization over two test sites in northern Finland. The near-continuous time series of L-band brightness temperatures covers a total of six winter seasons, over both dry mineral soil in a forest clearing, and organic soil over a wetland site. Detailed measurements of snow and soil conditions are available from both sites. Complementing a previous theoretical study, we show that dry snow cover influences the observed L-band brightness temperatures as a result of both impedance matching and changes in the refraction angle at the snow–soil interface. Exploiting these effects, we demonstrate the retrieval of the bottom layer snow density and the influence of dry snow cover on simultaneously retrieved soil permittivity — a consideration which is currently not accounted for in Soil Moisture and Ocean Salinity (SMOS) retrievals of soil permittivity in the presence of dry snow. Depending on season, the mean bias error between retrieved and in situ snow density measured in the lower snow layers was between − 6 kg m − 3 and 15 kg m − 3 for the forest clearing site, and between 37 kg m − 3 and 90 kg m − 3 for the wetland site, demonstrating the feasibility of the retrieval approach at the plot scale. In winter conditions, the ground permittivity retrieved without considering the impact of dry snow on L-band emission was, on average, 35% lower for both test sites, which indicates possible errors in current SMOS ground permittivity retrievals under dry snow conditions. The application of SMOS data to simultaneously retrieve dry snow density and ground permittivity is a complex task due to heterogeneous land cover and snow/soil conditions within SMOS pixels (≈ 45 km resolution). An approach that considers land cover variations and the spatial variability of snow cover is required to fully determine the feasibility of the methodology to aid e.g. improving estimates snow water equivalent from other sensors, and to take into account effects of dry snow in SMOS-based retrievals of ground permittivities. The results should also be applicable to other L-band sensors in space, such as the recently launched NASA Soil Moisture Active Passive (SMAP) mission.

A prototype algorithm for hemispheric scale detection of autumn soil freezing using space-borne L-band passive microwave observations is presented. The methodology is based on earlier empirical and theoretical studies of L-band emission properties of freezing and thawing soils. We expand a method originally developed for soil freeze–thaw (F/T) state detection from L-band tower based observations to satellite scale, applying observations from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The developed algorithm is based on first establishing spatially variable thresholds for L-band brightness temperatures representing frozen and thawed states of soil, and comparing these to current values of different indicators of soil freezing, calculated based on observed brightness temperature at different polarizations and incidence angles. An exponential relation between the freezing indicators and the depth of soil frost is developed based on a large amount of manual soil frost tube observations across Finland. An additional processing filter based on observed physical temperature and snow cover information is used to flag obvious F/T detection errors. The estimated soil F/T-states provided in this study are limited to the autumn freezing period, as melting snow in spring effectively prevents acquisition of information from the soil surface using microwaves for large areas in Northern latitudes. The F/T estimate is produced as daily information and provided in the equal-area scalable Earth (EASE) grid. Soil F/T-state is categorized into three discrete levels: ‘frozen’, ‘partially frozen’, and ‘thawed’, and accompanied with a quality data matrix estimating the data reliability for each freezing season separately. Comparisons to in situ data were conducted at 10 different locations in Finland, Northern America and Siberia. These comparison results indicate that the onset of autumn soil freezing can be estimated from SMOS observations to within 1 to 14days, depending on the freezing indicator applied and the in situ data used in comparison. Although the initial results are encouraging, more comprehensive assessment of SMOS based soil F/T estimates still requires further comparison to other reference sites, particularly to sites with measurements available for all locally representative land cover types, as well as other satellite-based soil freezing products.

A key variable describing the mass of seasonal snow cover is snow water equivalent (SWE), which plays an important role in hydrological applications, weather forecasting and land surface process simulations. In this paper, the accuracy of an SWE product, GlobSnow-2, which combines microwave satellite data and in situ measurements, is assessed using three reference evaluation datasets north of 35°N in China. The GlobSnow-2 estimates are also compared with stand-alone satellite products (AMSR2, Chang and FY-3D SWE). The overall unbiased root mean square error (RMSE) and bias of the GlobSnow-2 SWE product validated with three reference datasets are 17.4 mm and 11.2 mm, respectively, which outperforms the AMSR2 SWE (39.3 mm and 37.3 mm, respectively) and Chang SWE (57.5 mm and 46.2 mm, respectively) products. The FY-3D SWE product performs better than the GlobSnow-2 estimate for shallow snow (SWE

Lake ice is a sensitive factor for the Earth’s environment and climate change research. In the comparative study on climate change to the Earth three-pole (Antarctic, Arctic, and Tibetan Plateau), the lake ice phenology can be used to represent seasonal climate changes. In this paper, the lake ice phenology and the corresponding daily temperature for 48 lakes in Northern Europe, Tibetan Plateau and Mongolia Plateau were used as the typical case for comparative research. Based on passive microwave remote sensing data. The results showed that the average lake ice cover duration experienced a consistent decreasing for the three regions from 1978-2018. However, in the northern Tibetan Plateau, though the lake ice cover duration showed a shortening trend from 1978 to 2000, it displayed a prolonged trend from 2000 to 2018. Moreover, the air temperature changes of the lake area had a significant correlation with the changes of ice cover duration in each region, and the change of 0 °C isotherm in the Northern Europe lakes are more sensitive than the Mongolian Plateau and the Tibetan Plateau. The air temperature played a major role in the change of lake ice phenology, but there were still other factors that affected the lake ice phenology changes, especially in northern Tibetan Plateau. By comparing the analysis of the ice cover duration in the three regions and the effect of air temperature on the lake ice cover duration change, it provides more evidences for the study on climate change research in different regions.

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 km2 of 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 (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.

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.

Current methods for retrieving SWE (snow water equivalent) from space rely on passive microwave sensors. Observations are limited by poor spatial resolution, ambiguities related to separation of snow microstructural properties from the total snow mass, and signal saturation when snow is deep (similar to>80 cm). The use of SAR (Synthetic Aperture Radar) at suitable frequencies has been suggested as a potential observation method to overcome the coarse resolution of passive microwave sensors. Nevertheless, suitable sensors operating from space are, up to now, unavailable. Active microwave retrievals suffer, however, from the same difficulties as the passive case in separating impacts of scattering efficiency from those of snow mass. In this study, we explore the potential of applying active (radar) and passive (radiometer) microwave observations in tandem, by using a dataset of co-incident tower-based active and passive microwave observations and detailed in situ data from a test site in Northern Finland. The dataset spans four winter seasons with daily coverage. In order to quantify the temporal variability of snow microstructure, we derive an effective correlation length for the snowpack (treated as a single layer), which matches the simulated microwave response of a semi-empirical radiative transfer model to observations. This effective parameter is derived from radiometer and radar observations at different frequencies and frequency combinations (10.2, 13.3 and 16.7 GHz for radar; 10.65, 18.7 and 37 GHz for radiometer). Under dry snow conditions, correlations are found between the effective correlation length retrieved from active and passive measurements. Consequently, the derived effective correlation length from passive microwave observations is applied to parameterize the retrieval of SWE using radar, improving retrieval skill compared to a case with no prior knowledge of snow-scattering efficiency. The same concept can be applied to future radar satellite mission concepts focused on retrieving SWE, exploiting existing methods for retrieval of snow microstructural parameters, as employed within the ESA (European Space Agency) GlobSnow SWE product. Using radar alone, a seasonally optimized value of effective correlation length to parameterize retrievals of SWE was sufficient to provide an accuracy of

A synthetic study was performed to determine the potential to retrieve dry-snow density and ground permittivity from multiangular L-band brightness temperatures. The thereto employed emission model was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS) coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. The restriction to L-band made it possible to avoid scattering and absorption in the snow volume, leading to a rather simple formulation of our emission model. Parametric model studies revealed L-band signatures related to the mass density of the bottom layer of a dry snowpack. This gave rise to the presented analysis of corresponding retrieval performances based on measurements synthesized with the developed emission model. The question regarding the extent to which random noise translates into retrieval uncertainties was investigated. It was found that several classes of snow densities could be distinguished by retrievals based on L-band brightness temperatures with soil moisture and ocean salinity (SMOS)-typical data quality. Further synthetic retrievals demonstrated that propagation effects must be taken into account in dry snow even at L-band when retrieving permittivity of the underlying ground surface. Accordingly, current SMOS-based retrievals seam to underestimate actual ground permittivity by typically 30% as dry snow is wrongly considered as “invisible.” Although experimental validation has not yet been performed, the proposed retrieval approach is seen as a promising step toward the full exploitation of L-band brightness temperatures available from current and future satellite Earth observation missions, especially over the cold regions of the Northern Hemisphere.

Four satellite-based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500-m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat-5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley & Sons, Ltd.

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.

The Helsinki University of Technology (HUT) snow emission model forms the basis of the European Space Agency's GlobSnow snow water equivalent (SWE) product (Takala et al., 2011). The model applies a semi-empirical radiative transfer calculation to account for the interaction of the snow medium with microwaves; separate components are applied to account for vegetation, the atmosphere, and emission from the ground surface. For the retrieval of SWE, an innovative method is used to account for spatial and temporal variability in snow conditions by retrieving an effective parameter describing the scattering behavior of microwaves in the snow (a proxy indicator of the microwave effective snow grain size). In this study, the influence of differing snow conditions, as well as varying land cover, on the retrieved microwave effective snow grain size was analyzed. Passive microwave measurements were acquired using tower-based, mobile sled-based and airborne radiometers in a mixed forest environment near Sodankyla, Finland. Forward simulations at the tower site over an entire winter period showed that the use of an empirical relation to modify the classical in situ measured grain size produced HUT model bias errors less than 6 K on average at 19 and 37 GHz from a ~ 20–80 cm deep boreal snowpack. Model simulations for airborne and sled-based observations showed that using a simplified 2-layer representation of the snowpack improves simulation biases and RMSE, although modification of the measured grain size was again necessary to achieve these results, regardless of the layering configuration. The microwave effective grain size retrieved from HUT predictions was closely related to a simple average grain size measured in situ, both in terms of magnitude and temporal trend. This is an important finding as the retrieval scheme of Takala et al. (2011) relies on the microwave effective grain size to retain a degree of physical basis for values to be generally applicable over larger areas, which is challenging because this free parameter also accounts for errors unrelated to the effective snow grain size (i.e. vegetation, soil conditions, scene heterogeneity, etc.).

Passive microwave measurements from space are known to be sensitive to the freeze/thaw (F/T) state of the land surface. These measurements are at a coarse spatial resolution (~15–50 km) and the spatial variability of the microwave emissions within a pixel can have important effects on the interpretation of the signal. An L-band ground-based microwave radiometer campaign was conducted in the Canadian Prairies during winter 2014–2015 to examine the spatial variability of surface emissions during frozen and thawed periods. Seven different sites within the Kenaston soil monitoring network were sampled five times between October 2014 and April 2015 with a mobile ground-based L-band radiometer system at approximately monthly intervals. The radiometer measurements showed that in a seemingly homogenous prairie landscape, the spatial variability of brightness temperature (TB) is non-negligible during both frozen and unfrozen soil conditions. Under frozen soil conditions, TB was negatively correlated with soil permittivity (εG). This correlation was related to soil moisture conditions before the main freezing event, showing that the soil ice volumetric content at least partly affects TB. However, because of the effect of snow on L-Band emission, the correlation between TB and εG decreased with snow accumulation. When compared to satellite measurements, the average TB of the seven plots were well correlated with the Soil Moisture Ocean Salinity (SMOS) TB with a root mean square difference of 8.1 K and consistent representation of the strong F/T signal (i.e., TB increases and decreases when soil freezing and thawing, respectively). This study allows better quantitative understanding of the spatial variability in L-Band emissions related to landscape F/T, and will help the calibration and validation of satellite-based F/T retrieval algorithms.

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.

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 km2 of 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.

Passive L-band (1–2 GHz) observables are sensitive to surface soil moisture and ocean salinity, which is the core of the “soil moisture and ocean salinity” (SMOS) mission of the European Space Agency (ESA). This work investigates microwave emission processes that are important to link L-band brightness temperatures with soil freeze/thaw states and the presence and the state of snow. To this end, a ground snow radiative transfer (GS RT) model has been developed on the basis of the “Microwave Emission Model of Layered Snowpacks” (MEMLS). Our model sensitivity study revealed that L-band emission of a freezing ground can be affected significantly by dry snow, which has been mostly disregarded in previous studies. Simulations suggest that even dry snow with mostly negligible absorption at the L-band can impact L-band emission of winter landscapes significantly. We found that impedance matching and refraction caused by a dry snowpack can increase or decrease L-band emission depending on the polarization and the observation angle. Based on the performed sensitivity study, these RT processes can be compensatory at vertical polarization and the observation angle of 50°. This suggests the use of vertical polarized brightness temperatures measured at around 50° in order to achieve segregated information on soil-frost. Furthermore, our simulations demonstrate a significant sensitivity of L-band emission at horizontal polarization with respect to the density of the lowest snow layer as the result of refraction and impedance matching by the snowpack.

Two unique observational data sets are used to evaluate the ability of multi-layer snow emission models to simulate passive microwave brightness temperatures (TB) in high latitude, observation sparse, snow-covered environments. Data were utilized from a coordinated series of 18 sites measured across the subarctic Northwest Territories and Nunavut, Canada in April 2007 during a 1000 km segment of a 4200 km snowmobile traverse from Fairbanks, Alaska to Baker Lake, Nunavut (~64°N). In April 2011, a network of 22 high Arctic sites was sampled across a 60 × 60 km study area on the Fosheim Peninsula, Ellesmere Island (~80°N). In comparison to sites across the subarctic, high Arctic snow was more spatially variable, thinner (site averages between 15 and 25 cm versus 30 to 40 cm), colder (−25°C versus −10°C), composed of fewer layers, had a proportionally higher fraction of wind slabs (storing 57% of the snow water equivalent (SWE) versus 15%), with these slabs comparatively denser (often exceeding 450 g/cm3, compared to 350 g/cm3 in the subarctic). The physical snow measurements were used as inputs to snow emission model simulations. The radiometric difference between simulations of “typical” arctic and subarctic snow reached 30 K at 37 GHz. Sensitivity analysis showed that this TB difference could be partitioned between the effects of physical temperature (~5 K between −25°C and −10°C), wind slab density (~5 K between 0.40and 0.35 g/cm3), and vertical depth hoar fraction (~20 K between 70% and 30% vertical fraction of total snow depth). Model simulations at the satellite scale (625 km2) were produced using the observational spread for snow depth and snow stratigraphy. The range of TB from simulations with varied stratigraphy extended unrealistically far below the magnitude of satellite measured TB, illustrating that the snow depth first guess is very important for SWE retrieval schemes that are based on forward emission model simulations.

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.

We present a novel algorithm for detecting seasonal soil freezing processes using L-band microwave radiometry. L-band is the optimal choice of frequency for the monitoring of soil freezing, due to the inherent high contrast of the microwave signature between the frozen and thawed states of the soil medium. Dual-polarized observations of L-band brightness temperature at a range of observation angles were collected from a tower-based instrument, and evaluated against ancillary information on soil and snow properties over four winter seasons. During the first three winter periods the measurement site was located over mineral soil on a forest clearing, for the fourth winter the instrument was moved to a wetland site. Both sites are located in Sodankyla, Northern Finland. The test sites represent two environments typical for the northern boreal forest zone. The data were applied to derive an empirical relation between the onset and progress of soil freezing and the observed passive L-band signature. A retrieval algorithm was developed using the observations at the forest opening site. The algorithm exploits the perceived change in brightness temperature and the change in the relative difference between the signatures at horizontal and vertical polarization. With the collected experimental dataset, these features were linked optimally to the progress of soil freezing by choice of observation angle, polarization and temporal averaging. The wetland site observations provided the first opportunity for demonstrating the developed algorithm over a different soil type, giving a first estimate of the algorithm performance over larger heterogeneous targets. The future objective is to adapt the algorithm to L-band satellite observations. The present study is highly relevant for the development of freeze–thaw algorithms from current and future L-band satellite missions such as SMOS and SMAP.