The airborne cryospheric synthetic aperture radar (SAR) system, called CryoSAR, has been developed to advance our scientific expertise in estimating snow accumulation on land, lakes and sea ice, characterizing freshwater ice and sea ice properties, and monitoring the freeze-thaw state of soils. Currently, the dual frequency Ku (13.5 GHz) and L-band (1.3 GHz) instrument is focused on making backscatter and phase measurements of snow and ice on land and lakes to estimate total snow accumulation. This paper describes the core system characteristics, the instrument operation, and SAR data processing that is conducted to achieve science ready data. We illustrate the system’s capability to produce science-ready data using examples from a field experiment conducted in Ontario during the winters of 2022-23 and 2023-24. CryoSAR flights were made over a farm field site and a lake site in Ontario with field measurements. Calibrated Ku and L-band polarimetric data are presented and illustrate the sensitivity of the signals to surface and backscatter processes on the ground.

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

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

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

In this paper we present processing and analysis results of an airborne Ku-band InSAR, constructed at the University of Massachusetts, and flown on a Cessna 208 Caravan over the Trail Valley Creek region in Canada's Northwest Territories during the 2018–19 snow season. In this paper, we describe the Ku-band InSAR, provide some intermediate results and discuss on how these data can be used for furthering the science in the remote sensing of snow.

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

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

An ensemble of land surface models and forcing data was developed to assess variability in SWE estimation over North America. In this study, the ensemble output was used to assess how SWE uncertainty impacts streamflow estimation. The analysis was conducted by major basins of North America over the 2009-2017 time period.

Permanently frozen and seasonally frozen soils occur over a large portion of the Earth's land surface. Changes in the freeze/thaw state of the land surface reflects major changes in thermal and hydraulic properties as well as acting as a “switch” for many ecological processes. In short, soil freeze/thaw state is a fundamental land surface variable in the water and energy cycles, and it connects to the carbon cycle. Surface freeze/thaw state is observable by passive and active microwave sensors. For example, NASA's Soil Moisture Active Passive (SMAP) mission includes a freeze/thaw data product. Such satellite sensing offers routine all-season and all-weather global observations of soil freeze/thaw state with the application of suitable algorithms. We describe early finding from the SLAPex Freeze/Thaw campaign, believed to be the first airborne campaign of its type, focusing on soil freeze/thaw.

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

We describe the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001-2016 re-gridded to 0.1 ° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals. These independent datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. Our results show that the melt-off day derived from optical springtime FSC time series provides the best correlation with the snow melt-off day as indicated by in situ data. The obtained bias is 0.9 days, and RMSE is 13.1 days. For 85 % of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate, as the applied method performs the best for areas with sustained seasonal snow cover. Based on the time series 1980-2016 for MWR-based melt-off day, separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forests was observed.

Two L-Band radiometers were deployed at a boreal forest site (one above the forest canopy and one below the overstory canopy), to characterize the microwave contributions of the ground and forest canopy on the FreezelThaw (F/T) microwave signal observed by spaceborne L-Band sensors. Preliminary results show that the F/T processes of the ground rather than the overstory canopy primarily contribute to the F/T signal at L-band. However, the radiometer above the canopy did show some sensitivity to F/T of the canopy (5 K), but this sensitivity was much lower compared to the signal induced by the ground F/T (12 K).

The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It's important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.

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

Recent advancements to the understanding of snow-microwave interaction have helped to identify the considerable potential for radar-based retrieval of terrestrial snow mass. If applied to space-borne platforms, such retrievals could provide much needed improvements to the spatial and temporal availability of snow mass estimates. To further understanding of these interactions in tundra environments, this study evaluates an extensive set of coincident in situ snow measurements and airborne dual-frequency (17.2 and 9.6 GHz) radar observations near Inuvik, Northwest Territories, Canada. Given known uncertainties related to the role of microstructure in radar-based retrievals, an enhanced snow pit protocol was introduced to objectively characterize specific surface area (SSA) with a shortwave infrared integrating sphere (IRIS) system. Snow pit and bulk snow measurements including SSA are used to parameterize the Microwave Emission Model of Layered Snowpacks Adapted to Include Backscattering (MEMLS3&a) and evaluate observed spatial diversity in the airborne radar signal.

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.

Landscape freeze/thaw (FT) state is a key variable in Earth's carbon cycle. NASA's Soil Moisture Active Passive (SMAP) satellite mission, launched in January 2015, provides global retrievals of FT state every two to three days. Validating SMAP FT observations with in-situ observations is difficult due to the substantial scale mismatch between a point estimate and a satellite footprint, inducing “representativeness errors” in the in-situ observations. Triple collocation (TC) is a validation technique that addresses this problem by combining estimates from in-situ, model and spaceborne estimates to obtain error estimates for all three products, without assuming that any product is error-free. Unfortunately, it fails when applied to binary or categorical variables, such as landscape FT state. In this study, we use a new variant of TC - categorical triple collocation (CTC) - that can be applied to binary variables, to validate the SMAP FT product across northern land regions (>45N).

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

The baseline science objective of the NASA Soil Moisture Active Passive (SMAP) mission is to produce a daily landscape freeze/thaw state for the region north of 45° N latitude with a mean spatial classification accuracy of 80% and 2-3 day average intervals separated by AM and PM overpasses [1]. Following the loss of the SMAP radar in July 2015, radiometer inputs were used to develop a standard freeze/thaw product (L3-FT-P) with relaxed spatial resolution from 3km to 36km. A 9km gridded product (L3-FT-P-E) has been developed by applying enhanced resolution radiometer inputs to the same algorithm. This paper provides an overview of the algorithm development as well as the validation and calibration using in situ observations from both selected core sites and sparse ground station networks.

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.

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

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

The 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].

The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape freeze/thaw product (L3_FT_A) at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the loss of the SMAP radar in July 2015, coarser (36-km) footprint passive microwave retrievals from the SMAP radiometer were used to derive an alternative daily freeze/thaw product (L3_FT_P). This presentation will provide an overview of the development of both L3_FT products. Validation using in situ observations from core validation sites is used to illustrate differences in the sensitivity of the 3 km radar versus the 36 km radiometer measurements to the landscape freeze/thaw state.

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 %.

An artificial neural network (ANN) is presented for the purpose of estimating passive microwave (PMW) brightness temperatures over snow covered land in North America. The NASA Catchment Land Surface Model (Catchment) is used to define snowpack properties; the Catchment-based ANN is then trained with PMW measurements acquired by the Advanced Microwave Scanning Radiometer (AMSR-E). A comparison of ANN output against AMSR-E measurements not used during training activities as well as a comparison against independent PMW measurements collected during airborne surveys demonstrates the predictive skill of the ANN. When averaged over the study domain for the 9-year study period, computed statistics (relative to AMSR-E measurements not used during training) for multiple frequencies and polarizations yielded a near-zero bias, a root mean squared error less than 10 K, and a time series anomaly correlation coefficient of approximately 0.5. The ANN demonstrates skill during the accumulation phase when the snowpack is relatively dry as well as during the ablation phase when the snowpack is ripe and relatively wet. Overall, the results suggest the ANN could serve as a computationally efficient measurement operator for data assimilation at the continental scale.

Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.

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

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

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

The sensitivity of brightness temperature (TB) at 6.9, 10.7, and 18.7 GHz from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) observations is investigated over five winter seasons (2002-2007) on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada. The TBmeasurements are compared to ice thicknesses obtained with a previously validated thermodynamic lake ice model. Lake ice thickness is found to explain much of the increase of TBat 10.7 and 18.7 GHz. TBacquired at 18.7 GHz (V-pol) and 10.7 GHz (H-pol) shows the strongest relation with simulated lake ice thickness over the period of study (R2> 0.90). A comparison of the seasonal evolution of TBfor a cold winter (2003-2004) and a warm winter (2005-2006) reveals that the relationship between TBand ice growth is stronger in the cold winter (2003-2004). Overall, this letter shows the high sensitivity of TBto ice growth and, thus, the potential of AMSR-E mid-frequency channels to estimate ice thickness on large northern lakes.

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.

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

The feasibility of two MODIS-based products for identifying fully and partly snow covered areas is investigated. MODIS daily snow maps by NASA and MODIS-derived fractional snow cover maps by Finnish Environment Institute's (SYKE) SCAmo d-algorithm are compared and validated. The study was carried out for two boreal regions, Finland in North Europe and Manitoba area in Canada, covering an area of~970 000 km2. Results indicate distinct differences in recognition of fractional snow coverage.

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

New multiscale research datasets were acquired in central Saskatchewan, Canada during February 2003 to quantify the effect of spatially heterogeneous land cover and snowpack properties on passive microwave snow water equivalent (SWE) retrievals. Microwave brightness temperature data at various spatial resolutions were acquired from tower and airborne microwave radiometers, complemented by spaceborne Special Sensor Microwave/Imager (SSM/I) data for a 25/spl times/25 km study area centered on the Old Jack Pine tower in the Boreal Ecosystem Research and Monitoring Sites (BERMS). To best address scaling issues, the airborne data were acquired over an intensively spaced grid of north-south and east-west oriented flight lines. A coincident ground sampling program characterized in situ snow cover for all representative land cover types found in the study area. A suite of micrometeorological data from seven sites within the study area was acquired to aid interpretation of the passive microwave brightness temperatures. The in situ data were used to determine variability in SWE, snow depth, and density within and between forest stands and land cover types within the 25/spl times/25 km SSM/I grid cell. Statistically significant subgrid scale SWE variability in this mixed forest environment was controlled by variations in snow depth, not density. Spaceborne passive microwave SWE retrievals derived using the Meteorological Service of Canada land cover sensitive algorithm suite were near the center of the normally distributed in situ measurements, providing a reasonable estimate of the mean grid cell SWE. A realistic level of SWE variability was captured by the high-resolution airborne data, showing that passive microwave retrievals are capable of capturing stand-to-stand SWE variability if the imaging footprint is sufficiently small.

Time series analysis of the spaceborne passive microwave data record (1978-present) has identified an interannually consistent zone of high snow water equivalent (SWE) retrievals across the northern boreal forest of Western Canada. Because of potentially significant hydrological and climatological implications, and the sparse conventional observing network across this region, a dedicated field sampling campaign was conducted to verify the existence of this pattern. Snow measurements were made along an approximately 500 km transect across Northern Manitoba, Canada, during late November 2003 and early March 2004. Both snow surveys revealed strong agreement between Special Sensor Microwave/Imager (SSM/I) derived passive microwave SWE retrievals (using the Meteorological Service of Canada algorithm suite) and in situ measurements across the northern boreal forest: a gradient of increasing SWE between Thompson and Gillam was evident, as was a well defined zone of high SWE values to the north and east of Gillam. Further to the north, SSM/I derived SWE retrievals over the open tundra were anomalously low. These findings suggest that development of a tundra-specific SWE retrieval algorithm is necessary, given the unique snow pack properties, and high fraction of surface water (frozen lakes)

When Special Sensor Microwave/Imager (SSM/I; in operation 1987 to present) and Scanning Multichannel Microwave Radiometer (SMMR; 1978-1987) data are combined, the time series of dual polarized, multichannel, spaceborne passive microwave brightness temperatures extends from 1978 to the present. The Meteorological Service of Canada (MSC) has developed operational snow water equivalent (SWE) retrieval algorithms for western North America that can be applied to both SMMR and SSM/I data. Evaluation of this cross-platform time series shows that SWE estimates derived during SMMR winter seasons are systematically and significantly lower than retrievals during SSM/I seasons if no brightness temperature adjustments are employed. An examination of co-located brightness temperatures over terrestrial surfaces of central North America during the SMMR and SSM/I overlap period of August 1987 show that SSM/I brightness temperatures systematically exceed SMMR measurements, with the magnitude of this difference dependant on overpass time and brightness temperature magnitude. Regression relationships were determined using data from the overlap period, and utilized to adjust midlatitude terrestrial SMMR data to an SSM/I F-8 baseline. A SWE time series was subsequently reprocessed using these adjusted SMMR brightness temperatures. The resulting SWE dataset produces a time series of acceptable cross-platform homogeneity when assessed with in situ SWE measurements and remotely sensed snow extent data. Climatological research questions that demand a time series of significant length can now be confidently addressed with this passive microwave derived dataset.

Vertically polarized 18-, 19-, and 37-GHz brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) are examined for the August 2-20, 1987 period when data from both sensors are available in the Equal-Area Scalable Earth Grid (EASE-Grid) projection. Colocated measurements over terrestrial surfaces of central North America are compared because of a previously observed inconsistency in derived winter season snow water equivalent across the cross-platform passive microwave time series. The results of this comparison show that SSM/I brightness temperatures systematically exceed SMMR measurements, with the magnitude of this difference dependant on overpass time and brightness temperature magnitude. Regression relationships are determined for adjusting EASE-Grid SMMR data to an SSM/I F8 baseline and are compared to the results of a previous study that examined daily averaged data for the polar regions. These results suggest that adjustment factors are not globally applicable; rather the region and application must be considered.

When Special Sensor Microwave/Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) data are combined, the time series of spaceborne passive microwave brightness temperatures extends from 1978 to the present. The Meteorological Service of Canada (MSC) has developed a series of operational snow water equivalent (SWE) retrieval algorithms for western Canada that can be applied to both SMMR and SSM/I data. Before research issues can be addressed with a cross-platform time series, however, attention must be given to the impact of spatial, temporal, and radiometric differences between the SMMR and SSM/I data on time series continuity and consistency. In this study, we illustrate that passive microwave SWE retrievals with the MSC algorithms during the nine SMMR winter seasons (1978 - 1987) are consistently lower than the SWE estimates produced for the following eight SSM/I winter seasons (1987 - 1995). A snow extent anomaly time series produced by the National Oceanic and Atmospheric Administration (NOAA) from optical satellite data shows that the SMMR seasons of 1978/79 through 1986/87 are not characterized by deficit snow cover when compared to the SSM/I seasons of 1987/88 through 1994/95, indicating a consistency problem in the cross-platform passive microwave time series. Previously derived empirical brightness temperature corrections are examined, but appear to be unsuitable for use with the MSC algorithms.