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

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

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

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

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

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

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

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

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

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

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.

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

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

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.

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

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

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

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

The Helsinki University of Technology (HUT) snow emission model is used to calculate the time series of brightness temperature of snow-covered sparsely forested area for the winter 2006-2007. Brightness temperature simulations that apply in situ observed physical parameters as input are compared with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations. Three models for the extinction coefficient of snow and the statistical and physical atmospheric models are compared. Simulation results are presented with full in situ data set and only air temperature and snow depth (SD) as input data. The obtained results indicate that the extinction coefficient model of Hallikainen originally used with the HUT snow emission model is the best suited for the Finnish snow data set used in this paper and also on frequencies which are outside the original range of the extinction coefficient model. The simulation results obtained using only air temperature and SD input data show that the HUT snow model is quite reliable even with a minimal in situ data set. A time series of optimized grain sizes was calculated by minimizing the simulation error. The optimized grain size tended to saturate with large values, and therefore, a new model to calculate an effective grain size was developed. The simulation with the effective grain size as input has lower rms error and higher correlation with AMSR-E data than the simulation with the measured grain size.

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

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

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

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

Large discrepancies are observed between snow albedo in Numerical Weather Prediction (NWP) models and from satellite observations in the case of high vegetation. Knowledge of the Bidirectional Reflectance Distribution Function (BRDF) of snow-forest system is required to solve the problem. The 3-years SNORTEX (Snow Reflectance Transition Experiment) campaign acquires from 2008 in situ measurements of snow and forest properties in support to the development of modelling tools and to validate coarse resolution satellite products (POLDER, MODIS, MERIS, METOP). The measurement scheme and some first example results are presented from the Intensive Observing Period (IOP) of 2008, which can be decomposed into airborne and ground operations. Multi-temporal BRDF at a metric resolution were acquired from OSIRIS (airPOLDER) onboard a helicopter and from ground with FigiFiGo spectrogoniometer. The same helicopter embarked a pair of UV sensors, pyranometers and a wide-optics camera. Ground component includes exhaustive snow measurements.

Field spectroscopy is an effective means to determine the reflectance of different terrain surfaces for the development and validation of monitoring systems using Earth Observation data. The objective of this investigation is to examine the variability of snow and forest canopy reflectance in the boreal forest area in order to improve the existing snow mapping algorithms, such as the reflectance model-based snow covered area (SCA) SCAmod snow mapping method by the Finnish Environment Institute (SYKE). The field experiments were conducted by using two identical spectroradiometers. One instrument was in portable use and the other was installed to a 30-meter mast in order to obtain the forest canopy reflectances. We present here the obtained reflectance variability for dry snow and the simulated results for scene reflectance above the tree cover obtained by the comparison of mast-based spectrometer measurements with linear spectral mixing of ground-based reflectances. The results show that the field spectrometer observations are feasible for the assessment of the variability of reflectance and validation of satellite based mapping.

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

In this paper, three different models for the extinction coefficient of snow are compared by simulating the brightness temperature of snow-covered ground with HUT snow emission model. The input in-situ data set was measured in Sodankyla, Finland during winter 2006-2007. The simulation results are compared with AMSR-E measurements. All the extinction coefficient models are developed for dry snow. Thus, in addition to the whole winter time series, the dry snow periods are studied. Since all the three models calculate extinction coefficient from snow grain size, the effect of grain size is studied by minimizing the simulation error using grain size as optimization parameter.

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

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

In this work we compared the MODIS surface reflectance observations with the spectrometer measurements made from the 30 m high mast set up at Sodankyla. Combining MODIS data from wider area around the mast location with the information of land cover types found in high resolution (25 m*25 m) CORINE2000 database we were able to retrieve the reflectance of coniferous forests, which is the land cover type around the tower. As a result we found a good co-variation between the reflectance values of MODIS and the mast-based spectrometer although there was a consistent low bias in MODIS values. The effect of aerosol content in the atmosphere on the biases between two instruments was studied and a significant correlation was found between the biases and the AOD values.

For several applications, spaceborne microwave measurements are used to get large scale information of snow- covered terrain. Emission models for soil, vegetation and snow are needed in extraction of snow parameters from satellite measurements. In this paper space-observed brightness temperature of snow-covered terrain is simulated from in situ measurements using HUT snow model, rough bare soil reflectivity model and boreal forest emission model. The results are compared with AMSR-E data. Correlations of time series between simulated and measured brightness temperatures were best on the highest frequencies being better than 0.7 on frequencies above 18 GHz.

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