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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.

Early flowering together with small size would be useful for various biotechnical or genetic studies on trees. We report here the selection and micropropagation of early flowering birch (Betula pendula) clones (BPM1–12) obtained from seeds of birches bred elsewhere for early flowering. Under conditions that accelerate flowering (a high CO2 level, strong and continuous illumination), the first male inflorescences emerged in 3–5 months, the trees then being 20–80 cm high. Transgenic lines (CaMV 35S-GUS INT) were produced through Agrobacterium-mediated gene transfer from BPM2, BPM5 and JR1/4 (a normally flowering birch). β-Glucuronidase (GUS) activities in the different lines, assayed 1–1.5 years after transformation, varied greatly. During further in vitro culture for 10 months, the activities decreased to 0.3–7% of the original values. GUS activities were detected in all organs studied, including the developing male inflorescences; the highest activity was in the roots.

B-function genes determine the identity of petals and stamens in the flowers of model plants such as Arabidopsis and Antirrhinum. Here, we show that a putative B-function gene BpMADS2, a birch homolog for PISTILLATA, is expressed in stamens and carpels of birch inflorescences. We also present a novel birch gene BpMADS8, a homolog for APETALA3/DEFICIENS, which is expressed in stamens. Promoter-GUS analysis revealed that BpMADS2 promoter is active in the receptacle of Arabidopsis flower buds while BpMADS8 promoter is highly specific in mature stamens. BpMADS2 promoter::BARNASE construct prevented floral organ development in Arabidopsis and tobacco. In birch, inflorescences with degenerated stamens and carpels were obtained. BpMADS8::BARNASE resulted in degeneration of stamens in Arabidopsis and birch causing male sterility. In tobacco, only sepals were developed instead of normal flowers. The results show that the BpMADS2::BARNASE construct can be used to specifically disrupt floral organ development in phylogenetically distant plant species. The stamen-specific promoter of BpMADS8 is a promising tool for biotechnological applications in inducing male sterility or targeting gene expression in the late stamen development.

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.

During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL–VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL–VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL–VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.

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 $4\pi$ 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 $T_{B}$ simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyla, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate $T_{B}$ for 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.

The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

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.

An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011–2012 is presented (ESA Earth Observation Campaigns data 2000–2016). The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow–vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.

Datasets derived from measurements at Sodankylä, Finland, for driving and evaluating snow models are presented. This is the first time that such complete datasets have been made available for a site in the Arctic. The continuous October 2007–September 2014 driving data comprise all of the meteorological variables required as inputs for physically based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees is found to extend the duration of both modelled and observed snow cover on the ground by several days a year.

Detailed angular ground-based L-band brightness temperature (TB) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the TB simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured TB at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed TB at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature.

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.

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.

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

In this paper, we incorporate the cyclical terms in dense media radiative transfer (DMRT) theory to model combined active and passive microwave remote sensing of snow over the same scene. The inclusion of cyclical terms is crucial if the DMRT is used to model both the active and passive contributions with the same model parameters. This is a necessity when setting out on a joint active/passive retrieval. Previously, the DMRT model has been applied to active and passive separately, and in each case with a separate set of model parameters. The traditional DMRT theory only includes the ladder terms of the Feynman diagrams. The cyclical terms are important in multiple volume scattering and volume–surface interactions. This leads to backscattering enhancement which represents itself as a narrow peak centered at backward direction. This effect is of less significance in passive remote sensing since emissivity is relating to the angular integral of bistatic scattering coefficients. The inclusion of cyclical terms in first-order radiative transfer (RT) accounts for the enhancement of the double bounce contribution and makes the results the same as that of distorted Born approximation in volume–surface interactions. In this paper, we develop the methodology of cyclical corrections within the framework of DMRT beyond first order to all orders of multiple scattering. The active DMRT equation is solved using a numerical iterative approach followed by cyclical corrections. Both quasi-crystalline approximation (QCA)–Mie theory with sticky spheres and bicontinuous media scattering model are used to illustrate the results. The cyclical correlation introduces around 1 dB increase in backscatter with a moderate snowpack optical thickness of ${\sim}\text{0.2}$ . The bicontinuous/DMRT model is next applied to compare with data acquired in the Nordic Snow Radar Experiment (NoSREx) campaign in the snow season of 2010–2011. The model results are validated against coincidental active and passive measurements using the same set of physical parameters of snow in all frequency and polarization channels. Results show good agreement in multiple active and passive channels.

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.

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.).

Knowledge of snow microstructure is relevant for modelling the physical properties of snow cover and for simulating the propagation of electromagnetic waves in remote-sensing applications. Characterization of the microstructure in field conditions is, however, a challenging task due to the complex, sintered and variable nature of natural snow cover. A traditional measure applied as a proxy of snow microstructure, which can also be determined in field conditions, is the visually estimated snow grain size. Developing techniques also allow measurement, for example, of the specific surface area (SSA) of snow, from which the optical-equivalent grain size can be derived. The physical snow model SNOWPACK simulates evolution of snow parameters from meteorological forcing data. In this study we compare an extensive experimental dataset of measurements of traditional grain size and SSA-derived optical grain size with SNOWPACK simulations of grain-size parameters. On average, a scaling factor of 1.2 is required to match traditional grain-size observations with the corresponding SNOWPACK simulation; a scaling factor of 2.1 was required for the optical equivalent grain size. Standard deviations of scaling factors for the winters of 2011/12 and 2012/13 were 0.36 and 0.42, respectively. The largest scaling factor was needed in early winter and under melting conditions.

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.

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.

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.

The dense media radiative transfer (DMRT) theory is applied to data analysis of recent measurements of multifrequency microwave backscatter from the snow cover on earth. Measurement includes ground-based campaign (SnowScat) and airborne mission (SnowSAR). Both the quasi-crystalline approximation (QCA) model and the bicontinuous model are used for a multilayer snow medium. Two size parameters are used for both models. Grain size and stickiness parameter are used for QCA model. The bicontinuous model has two parameters: the mean wave number 〈ζ〉 and the parameter b. The mean wave number 〈ζ〉 corresponds to the inverse of the grain size, while the b parameter controls the width of the wave number distribution and is related to the clustering property. The bicontinuous model is used to generate the microstructures of snow by computer, and Maxwell equations are solved numerically for each sample of computer-generated structure to calculate the extinction coefficient and the phase matrix. Other geometric descriptors of the bicontinuous medium include correlation functions and specific surface areas, both of which can be calculated from the parameters 〈ζ〉 and b. In making comparisons, we use ground measurements of specific surface area, grain size, densities, and layering of snow cover as input for the theoretical models. The geometric properties and the scattering properties of the bicontinuous model are also compared with past models. In making the multifrequency comparisons, we use the same physical parameters of all three frequencies: 1) X band; 2) Ku bands of 13.3 GHz; and 3) 16.7 GHz. It is emphasized that the DMRT models provide frequency, size, and angular dependence that depart from the classical model of Rayleigh scattering and are in better agreement with experimental observations.

An ice thickness retrieval algorithm utilizing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) was developed and applied to Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada, for the period 2002–2009. The temporal evolution of vertically polarized AMSR-E brightness temperature (TB) at 18.7 GHz was explored to estimate ice thickness between the late freeze-up (ice-on) and early break-up (melt-onset) periods. The sensitivity of AMSR-E TB at H and V polarizations to the seasonal evolution of ice thickness was examined statistically and with forward simulations of TB using the most recent version of the Helsinki University of Technology (HUT) model, which incorporates a freshwater ice layer. The strong relation found between TB at 18.7 GHz V-pol and ice thickness was exploited for the development of a regression-based ice thickness retrieval algorithm. Simple linear regression equations allow for the estimation of ice thickness on a monthly basis from January to April; one, the Global equation, combines TB data for GBL and GSL, and two others, the Regional equations, use TB data for each lake alone. Estimated late-winter ice thicknesses on GBL were determined to be on average 5–10 cm thicker than on GSL with the exception of ice season 2005–2006 when it was estimated to be 10 cm thicker on GSL. For both lakes the 2004–2005 and 2008–2009 ice seasons experienced the thickest end-of-winter ice thicknesses, ranging from 130 to 134 cm on GBL and GSL. The thinnest end-of-winter ice thicknesses were on average 120 cm on GBL (2005–2006) and 123 cm on GSL (2007–2008). Variability in air temperature, snowfall and subsequent redistribution by wind, and lake depth explain ice thickness variations within and between lakes over the seven winter seasons analyzed. Estimated ice thicknesses from AMSR-E compare well with coincident in situ measurements collected on GBL and GSL over a limited number of ice seasons and sites within the large passive microwave footprints (Mean Bias Error, MBE = 6 cm; Root Mean Square Error, RMSE = 19 cm).

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.

We examined whether gene expression in the young salmon (Salmo salar) gill differs in relation to the salinity of their migration habitat by comparing three salmon stocks: (1) fish that migrate from a river system to Lake Saimaa, (2) fish that migrate to the brackish waters of the Baltic Sea, and (3) fish that migrate to the full-strength salinity of the Arctic Ocean. Transcripts of the gill tissue were measured at three successive developmental stages (parr, smolt and postsmolt) using the cDNA microarray in fish reared under common conditions. The changes in gene expression were qualitatively and quantitatively similar in the three stocks irrespective of the salinity of the natural growing habitat. This suggests that the parr—smolt transformation in the gill tissue of the landlocked fresh-water salmon stock is similar to the seawater migrating salmon. The transformation of the gill to a hypoosmotic organ in the freshwater salmon has been retained in evolution, possibly due to its adaptive role as a signa...

This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolutionfunctions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error−0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightnesstemperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM–empirical MEMLS model combination (2011–2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differencesbetween microwave emission models, suggesting that more accurate simulations in coupled snowpack–microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followedby improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructureand empirical MEMLS emission model for the 2011–2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

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.

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.

Recent theoretical and experimental studies have indicated the feasibility of passive microwave L-band observations for 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.

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].

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.

We have conducted a multiyear airborne multifrequency microwave radiometer data collection program over a test site comprising two lakes and adjacent land areas in southern Finland. A variety of snow and ice conditions were encountered, covering early winter, mid-winter, and late winter conditions, and the melting season. A semi-empirical radiative transfer forward model has been used for simulations and the theoretical values have been compared with experimental brightness temperature observations.