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.

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

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

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

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.

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

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

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

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

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.

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

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

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.

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.

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

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

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 %.

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

In this study we evaluate five different retrieval algorithms, applied on MetOp-A ASCAT backscatter data, in their ability to retrieve soil moisture on a global scale. Correlation and triple collocation analysis are performed using in situ and land surface model data as a reference. Results do not clearly identify one best algorithm. We therefore conclude that future work should focus on the exploitation of the strengths and weaknesses of different modelling approaches in a synergetic way rather than trying to find one model that suits every possible situation.

Northern Hemisphere fractional snow cover (FSC) mapping based on NPP Suomi/VIIRS data is introduced. In ESA DUE-project GlobSnow, VIIRS-based snow mapping relies on SCAmod method particularly designed to provide accurate FSC estimates also in boreal forests. Three 8-day composites from year 2013 are compared with MOD10C2 products in order to catch their possible differences in the hemispheric scale. The results indicate that in general, GlobSnow VIIRS-based snow maps are characterized by the transitional (fractional snow) zone wider than that provided by MOD10C2 products. Moreover, fractional snow cover estimates for forested areas are generally higher for GlobSnow VIIRS-based products than for MOD10C2, suggesting that SCAmod method is very capable of detecting fractional snow in forests.

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

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

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

The CoReH2O mission design is mature; there are two viable technical configurations, and the breadboards of key hardware components, such as the dual-polarized antenna array feeds and high power amplifiers have been built and tested. The baseline retrieval algorithm has been intensively tested using simulated and experimental data. The tests confirm that the threshold performance for SWE products can be met under a wide range of snow conditions. Data from further field campaigns by the airborne SnowSAR acquired Austria, Canada, and Alaska from November 2012 through April 2013 are being analysed to investigate retrieval performance over alpine, glacier and tundra terrains.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission was selected for detailed scientific and technical studies within the Earth Explorer Programme of ESA. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations The mission will deliver spatially distributed snow and ice observations to improve the representation of the croysphere in hydrological and climate models. Primary parameters are the extent and water equivalent (SWE) of the snow pack and snow accumulation on glaciers. Scientific preparations of the mission include the development and testing of algorithms for retrieval of snow parameters, studies on synergy of CoReH2O-type snow products with passive microwave measurements, the assimilation of satellite snow data in process models, and field experiments. Performance of retrievals for snow extent and SWE was tested with simulated and experimental data, including Ku- and X-band SAR images of the airborne SnowSAR system.

Snow extent and water equivalent (SWE) on land and snow accumulation on glaciers are the main parameters to be delivered by the Cold Regions Hydrology High-resolution Observatory (CoReH2O) satellite. Detailed scientific and technical studies for the mission are going on within the Earth Explorer Programme of ESA. The CoReH2O sensor is a dual frequency SAR, operating at 17.2 and 9.6 GHz, VV and VH polarizations. A main task for mission preparation is the development and validation of algorithms for retrieval of snow parameters. A constrained minimization approach is proposed for SWE retrieval, matching backscatter computed with a radiative transfer model and measurements in the four SAR channels by iterating for SWE and snow grain size. The algorithm was validated with simulated and measured backscatter data. The tests confirm the feasibility of the retrieval approach and help to quantify the requirements for statistical background information which is needed for constraining the solution.

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

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

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

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

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

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission has been selected for detailed scientific and technical studies within the ESA Living Planet Programme. The mission addresses the need for distributed snow and ice observations to improve the representation of the cryosphere in climate models and the prediction of the water cycle. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving snow mass (SWE) and other physical properties of snow and ice. A major task for mission preparation is the development and testing of algorithms for SWE retrieval. The baseline algorithm applies a constrained minimization approach, matching forward computed and measured backscatter by iterating for SWE and effective grain size of the snow volume. Experimental campaigns on Kuand X-band backscatter of snow deliver important information for retrieval development and validation.

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.

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

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

The attenuation of spheroidal melting hydrometeors is simulated in C-, Ku- and Ka-band utilizing a microphysical melting layer model. The scattering properties are obtained with Mie scattering solution. In C-band the polarimetric radar parameters are computed utilizing a method based on volume integral equation. Polarization difference is detectable, but reflectivity values are regularly smaller than those calculated with Mie solution. This is dependent on the process of formatting the particle structure according to the change in liquid water mass fraction.

A feasible method for estimating the areal fraction of snow cover for boreal forest and tundra belt from optical data is presented. The method SCAmod by the Finnish Environment Institute is based on a semi-empirical model where fractional snow cover is expressed as a function of at-satellite observed reflectance. The apparent forest transmissivity and reflectance of three major contributors (wet snow, forest canopy and snow-free ground) serve as model parameters. The forest transmissivity describes the visibility of the ground through forest canopy from above, and was previously determined from MODIS reflectance data with a great effort. Here we present a new method for transmissivity generation using global land cover map. Validation of gained FSC estimates as well as of NASA MOD10_L2 fractional snow product against Finnish ground truth data is presented.

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 ESA project GlobSnow develops products and services for snow extent and snow water equivalent. The time series of Snow Extent (SE) products will cover the whole seasonally snow-covered Earth for the years 1995-2010 based on the optical sensors ERS-2 ATSR-2 and Envisat AATSR data. A laboratory processing chain has been developed for testing and improving algorithms in an iterative process. The final version of the laboratory processing chain will function as a reference system for the implementation of an operational system for production of the full time series of products as well as near-real-time products produced on a daily basis. The first version of the SE product set spanning 15 years of the Northern Hemisphere is expected to be ready by the end of 2010 and will be made freely available.

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

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH2O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

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.

As spaceborne C-band synthetic aperture radar (SAR) observations are used for monitoring the snow cover during the spring melt period, temporal changes in backscatter from forest cover disturb the mapping of snow cover. This paper presents an analysis of snow backscattering properties in eight test areas situated around weather stations. Test areas represent open and forested landscapes in Northern Finland. Analyses are carried out using an extensive multitemporal ERS-2 C-band SAR data set from the snow melt period. We validate the following topics: 1) forest backscattering model for forest compensation; 2) Helsinki University of Technology (TKK) fractional snow-covered area (SCA) method within situobservations; and 3) inversion of a combined forest/snow/ground backscattering model in an application to yield estimates of the relative changes of snow wetness during full snow cover conditions. The results show that the semiempirical TKK backscattering model describes the average C-band backscattering properties of all test regions well as a function of forest stem volume. Comparison of SCA estimation results with available ground-truth data also shows a good performance. The retrieved relative snow wetness values agree well with temperature observations.

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.

As space-borne C-band SAR observations are used for monitoring the snow cover during the spring melt period, temporal changes in backscatter from forest cover disturb the mapping of snow cover. This paper presents an analysis of snow backscattering properties in eight test areas situated around weather stations. Test areas represent open and forested landscapes in Northern Finland. Analyses are carried out using an extensive multitemporal ERS-2 C-band SAR data set from the snow melt period. We validate the (1) forest backscattering model for forest compensation, (2) TKK fractional snow-covered area (SCA) method within situobservations and (3) inversion of a combined forest/snow/ground backscattering model in an application to yield estimates of the relative changes of snow wetness during full snow cover conditions. The results show that the semi-empirical TKK backscattering model describes the average C-band backscattering properties of all test regions well as a function of forest stem volume. Comparison of SCA estimation results with available ground truth data also shows a good performance. The retrieved relative snow wetness values agree well with temperature observations.

In the frame of EUMETSAT Satellite Application Faculty on Hydrology and Water Management (H-SAF) project, two different approaches have been developed for snow products. One is focused on flat/forested areas and has been developed by Finnish Meteorological Institute (FMI) (originally for EUMETSAT Land-SAF), and the other one by Turkish State Meteorological Service (TSMS) for mountainous areas. Snow cover over mountainous areas and over flat/forest areas show completely different physical properties, thus usage of two separate algorithms makes it possible to get better results. On the other hand, for users it would be easier to have only one unified product. This paper presents the method used in the merging of the two Snow Recognition products.

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.

Spaceborne Synthetic Aperture Radar (SAR) data have been utilized for regional scale snow-covered area (SCA) monitoring for several years. Different methods have been developed and demonstrated for different geographical regions. A method utilizing a single reference image for SCA estimation has been shown to function well on mountainous and non-forested regions. For the boreal forest zone a method using two reference images and a forest compensation procedure has been previously utilized. The single reference image method is evaluated here for the boreal forest zone and its performance is compared with the Helsinki University of Technology (TKK) SCA method that is specifically developed for boreal forest regions. The SCA evaluations are carried out using Radarsat-1 data for the snow-melt seasons of 2004–2007. The SCA estimation accuracies for the radar-based methods are determined using optical satellite based SCA data as reference. The results show that SCA estimation using a single reference image is usable for the boreal forest zone, although the accuracy is significantly weaker than that of the TKK-developed, boreal forest specific SCA method. The best accuracy obtained shows a root-mean-square error (RMSE) of 0.176 for the single reference image method and an RMSE of 0.123 for the TKK SCA method.

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.

Spaceborne synthetic aperture radar data have been utilized for regional-scale snow-covered area (SCA) monitoring for several years. Different methods have been developed and demonstrated for different geographical regions. A method utilizing a single reference image for SCA estimation has been shown to function well on mountainous and nonforested regions. For the boreal forest zone, a method using two reference images and a forest compensation procedure has been previously utilized. The single-reference-image method is evaluated here for the boreal forest zone, and its performance is compared with the Helsinki University of Technology (TKK) SCA method that is specifically developed for boreal forest regions. The SCA evaluations are carried out using Radarsat-1 data for the snow-melt seasons of 2004–2007. The SCA estimation accuracies for the radar-based methods are determined using optical satellite-based SCA data as reference. The results show that SCA estimation using a single reference image is usable for the boreal forest zone, although the accuracy is significantly weaker than that of the TKK-developed boreal forest-specific SCA method. The best accuracy obtained shows a root-mean-square error (rmse) of 0.176 for the single-reference-image method and an rmse of 0.123 for the TKK SCA method.

In this letter, we study scattering from a scots pine tree by using a realistic cylinder model of the tree and the coherent electric field scattering model. We study the directional variability of bistatic and monostatic scattering at L-band and show that, due to large variations in results, some averaging technique should be used to describe and interpret the model output efficiently. We propose that the needed averaging can be done by multilooking and that multilook data could be easily generated by rotating the tree model randomly around its vertical axis. We show that the resulting scattering data obey generally the multidimensional Gaussian distribution or the more general K-distribution, in a way similar to synthetic aperture radar (SAR) image pixels, and can therefore be represented by a single averaged covariance matrix. The trunk-ground reflection to the backscattering direction adds to the model output non-Gaussian behavior, which can be treated as texture. Covariance matrix formalism allows us to use descriptors which are commonly used to analyze SAR images, like target entropy and alpha angle. The method helps interpretation and comparison between the model output and SAR image.

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.

In this paper, an enhanced method for fractional snow-covered area (SCA) estimation for the boreal forest zone is presented. The new approach, based on utilizing weather station data alongside with spaceborne synthetic aperture radar (SAR) imagery, leads to a significantly improved estimation accuracy. While the Helsinki University of Technology (TKK) SAR-based SCA estimation method serves as a basic tool in the SCA estimation, the ground-based weather station observations are employed to still strengthen its performance at the nearly melt-off or totally melt-off conditions. The method is still improved by a new reference image selection process, leading to more accurate results and an easier adaptivity to new areas. The SCA estimation accuracy of the new enhanced method is compared with optical satellite-based SCA data. Evaluation of the method is carried out using Radarsat wide-swath data for the snow-melt seasons of 2004-2006. The results show a significant increase in accuracy when the enhanced SCA method is applied. Correlation between the radar-based and optical comparison data increases from 0.914 to 0.947 and root-mean-square error improves from 0.151 to 0.123 with the new method. Traditionally, the TKK method has provided SCA estimates for Finnish third-order subdrainage basins. In this paper, the method is adapted to produce SCA estimates also in 5 times 5 km spatial resolution. The analyses for the 5 times 5 km method indicate poorer estimation accuracy than the nominal drainage-basin-based method.

Determining the date of snowmelt clearance is an important issue for hydrological and climate research. Spaceborne radiometers are ideally suited for global snowmelt monitoring. In this paper, four different algorithms are used to determine the snowmelt date from Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager data for a nearly 30-year period. Algorithms are based on thresholding channel differences, on applying neural networks, and on time series analysis. The results are compared with ground-based observations of snow depth and snowmelt status available through the Russian INTAS-SSCONE observation database. Analysis based on Moderate Resolution Imaging Spectroradiometer data indicates that these pointwise observations are applicable as reference data. The obtained error estimates indicate that the algorithm based on time series analysis has the highest performance. Using this algorithm, a time series of the snowmelt from 1979 to 2007 is calculated for the whole Eurasia showing a trend of an earlier snow clearance. The trend is statistically significant. The results agree with earlier research. The novelty here is the demonstration and validation of estimates for a large continental scale (for areas dominated by boreal forests) using extensive reference data sets.

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.

Development of an enhanced method for fractional snow-covered area (SCA) estimation for the boreal forest zone is presented. The TKK snow-covered area estimation method was developed and demonstrated for northern boreal forest zone using ERS, Envisat ASAR and Radarsat C-band synthetic aperture radar (SAR) data. The new enhanced approach for SCA estimation, presented here, merges the proven TKK SCA estimation method with several new innovations, enhancing its reliability and accuracy in operational use. The enhancements that form the new method include the weather station assimilation method presented earlier and a new reference data selection process. The SCA estimates acquired with the enhanced method are compared with optical satellite data-based (MODIS) SCA data. The results show a significant increase in accuracy when the enhanced SCA method is applied. Correlation between the radar-based and optical comparison data increases from 0.914 to 0.947 and RMS-error improves from 0.151 to 0.123 when the new method is employed. Additionally the SCA estimation method is adapted to produce SCA estimates in 5 km times 5 km spatial resolution. The analyses for the 5 km times 5 km method indicate poorer estimation accuracy than the nominal drainage-basin-based method.

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).

The COld REgions Hydrology High-resolution Observatory (CoRe-H2O) satellite mission has been selected for scientific and technical studies within the ESA Earth Explorer Programme. The mission addresses the need for spatially detailed snow and ice observations in order to improve the representation of the cryosphere in climate models and to improve the knowledge and prediction of water cycle variability and changes. CoRe-H2O will observe the extent, water equivalent and melting state of the snow cover, accumulation and diagenetic facies of glaciers, and properties of sea ice and lake ice. The sensor is a dual frequency SAR, operating at 17 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving physical properties of snow and ice. Scientific preparation activities include experimental field campaigns, improvement of radar backscatter models, and the development of inversion algorithms.

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.

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

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.

Finnish meteorological institute (FMI) has initiated together with Finnish Environment Institute (SYKE) and Helsinki University of Technology (TKK) the development of operational European wide snow monitoring system that will employ satellite data, models and in situ observations. This will be developed in framework of two international projects: (1) HydroSAF supported by Eumetsat and (2) Polarview GMES service element sponsored by ESA. The goal is to provide following snow services operationally: (1) Snow recognition (SR), (2) Fractional snow covered area (SCA), (3) Snow cover status (ST) and (4) Snow water equivalence (SWE). The first two products are designed to cover Europe and the last two will cover also the Northern Eurasia.

An enhanced method for snow-covered area (SCA) estimation for boreal forest zone is presented. The method combines TKK developed spaceborne radar-based SCA estimation with ground-based weather station observations. The purpose is to improve the reliability of SCA estimates near and after the end of snow-melt season. The SCA estimates acquired with the enhanced method are compared with optical satellite data-based (MODIS) SCA data. Investigations were carried out for snow-melt seasons of 2004-2006. The results show a significant increase in accuracy when the enhanced SCA method is applied. Correlation between the radar-based and optical reference data increases from 0.919 to 0.937 and RMS-error improves from 0.151 to 0.140 when the new method is employed.

Taking into account the needs for improved, spatially detailed observations of snow and ice in climate research, hydrology, and glaciology, the satellite mission COld REgions Hydrology High-resolution Observatory, CoRe-H2O, was proposed to ESA. As payload a co- and cross-polarized Ku-band (17.2 GHz) and X-band (9.6 GHz) SAR was selected, because of its sensitivity to dry snow, thin sea ice, and the metamorphic state of snow, firn and ice on glaciers and ice caps. A cost-effective ScanSAR scheme with parabolic reflectors (each with multiple beams) is proposed fulfilling the requirements for swath width, spatial resolution and radiometry. The mission has been selected by ESA for further scientific and technical studies in the frame of the Earth Explorer Satellite Programme.

Reflectance spectroscopy is a well established field of research, evolved from simple laboratory instruments into a satellite-oriented global environment research tool. However, due to the effect of the atmosphere and the large area coverage of satellite images, reflectances extracted from satellite data or ground based spectra are not correlating properly. In order to improve the comparability of satellite-borne and ground-based reflectance observations, we have constructed a reflectance spectrometer measurement assembled to a 30 meter mast. With this construction we are able to average reflectance spectra from up to 400 m2area in the ground and up to 180 m2area in the level of tree tops, respectively. A primary function of our measurement system is the correction of satellite images from atmospheric effects. This paper presents the measurement system for validating satellite images, constructed to Arctic Research Centre in Sodankylauml. The operative measurements in the system started at the summer of 2006.

Knowing the onset of snow melt is an important factor in climatological and weather forecasting models. The carbon cycle in the atmosphere is directly related to melting of snow and thus is a key information understanding global climate change. Microwave radiometers are sensitive to liquid water and thus well suited for melt detection. The rather coarse resolution is ideal for monitoring the snow melt globally. However, many snow melt detection algorithms are applicable only on arctic tundra or snow covered glaciers. The authors of this paper have earlier developed melt detection algorithms for boreal forest zone using SSM/I-data. In this paper AMSR-E data is used and the algorithm is slightly modified to operate without using additional data such as ground based measurements. The algorithm is applied over the whole Northern Eurasia and the results obtained are reliable and valuable for further development.

Satellite radar-based remote sensing of snow cover during the snow-melt season has been widely studied for different geographical regions, such as mountainous, open, and forested areas. However, a single method has not been found to function well on all regions. The investigations on boreal forest zone have allowed the Helsinki University of Technology (TKK) to develop a snow-covered area (SCA) method that is feasible using spatially limited European Remote Sensing-1/2 Satellite data. This paper investigates the use of wide-swath radar data for boreal forest SCA estimation for the first time. The TKK SCA method is adapted here for HH-polarization Radarsat data. The predominant aspect originated by the use of wide-swath synthetic aperture radar (SAR) data is the large variation in the radar incidence angle. The effect of incidence angle variation on SCA estimation is characterized in this paper. The foundation for operational implementation of the TKK SCA method is also established by an error propagation analysis presented in this paper. The error propagation analysis is compared with accuracy characteristics acquired between SAR and optical SCA evaluation. The performance of forest compensation, which is a key element of the TKK method, was analyzed for the wide-swath radar data. Furthermore, the correlation between the topography and the SCA estimation accuracy was examined in this paper. This paper lays the foundation for operational SCA estimation on boreal forest zone using wide-swath SAR data

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.

A new method is developed to model the volume backscattering from dry snow. The model is a combination of the exact field approach and zeroth order vector radiative transfer theory. The field approach is used to define the scattering characteristics in a single almost indefinite small snow volume unit and the calculation is realized with discrete dipole approximation (DDA). The radiative transfer theory (RT) is utilized by defining a homogenous snow layer from the averaged scattering characteristics and combining different layers together forming a vertical structure of a snow pack. Because of the DDA the presented model takes into account all multiple reflections and all polarizations inside the snow volume. The scattering amplitude values calculated with the DDA method and according to Mie theory are compared to together. Some results for a homogeneous snow layer are presented for both cubical and needle shaped snow grains.

The feasibility of SAR data for the operational mapping of the fraction of Snow Covered Area (SCA) is investigated by applying Radarsat observations together with the modeling of error propagation. Additionally, the performance of SAR retrievals is compared with optical satellite data-based (MODIS) SCA estimates. The results indicate performance characteristics comparable with those of optical data retrievals even when wide swath Radarsat ScanSAR Wide A data are applied. The developed Radarsat data processing system is also implemented for the operative use to aid hydrological forecasting at the Finnish Environment Institute (SYKE).

Information on physical snow cover characteristics, such as snow water equivalent (SWE) and the areal coverage fraction of snow covered area (SCA), can be obtained from space-borne remote sensing data. The feasible instruments include optical spectrometers and microwave radars (SCA mapping), and microwave radiometers (SWE mapping). As data assimilation techniques are applied, the EO data-derived information can improve the performance of river discharge forecasting models and the knowledge on snow climatology. The results discussed here indicate that the assimilation of EO data-based SCA estimates to hydrological modeling significantly improves the accuracy of operational river discharge forecasts. The results also indicate that the employment of space-borne microwave radiometer data using the data assimilation technique improves the SWE or snow depth mapping accuracy when compared with the use of values interpolated from synoptic observations.

Employment of satellite radar-based remote sensing data for snow monitoring during the snow melt season has been widely studied by several investigators. Several methods for the estimation of snow-covered area (SCA) fraction have been developed for different types of regions. One common deficiency with the SCA estimation methods has been the lack of statistical accuracy analyses for them. In order to incorporate SCA estimates for operational use, one vital requisite is a thorough statistical analysis of the SCA estimation accuracy. This shortcoming has been addressed for boreal forest region, as an extensive statistical accuracy analysis has been carried out for the Helsinki University of Technology (TKK)-developed SCA method. The TKK SCA method was developed for boreal forest regions, and it is studied here with 24 European Remote Sensing 2 synthetic aperture radar intensity images, on a boreal-forest-dominated test area located in northern Finland. The performance of the SCA method is investigated by using reference data acquired through hydrological modeling. The accuracy analysis is carried out for several statistical variables, and the statistical interpretation is done with respect to several affecting parameters. The accuracy analysis shows a high correlation coefficient between the SCA estimates and the reference data and root mean square error values of 0.213 for open areas and 0.179 for forested areas. In addition, the TKK method employs two reference images for the SCA estimation, and the usability of multiyear reference image utilization was analyzed and proven feasible in this study.

We evaluate the feasibility of using data from the space-borne Ku-band (13.4 GHz) radar scatterometer QuikScat for retrieval of seasonal snow parameters in Finland: snowcovered area, snow water equivalent, and onset/end of snowmelt. The results are based on satellite and ground truth data covering five winters (1999-2000 through 2003-2004). Radar-derived time series of snowmelt are produced and compared with results from optical satellite data and ground-based measurements. A neural network approach for retrieval of snow water equivalent is tested with good results. Keywords-snow; snow water equivalent; snow-covered area; scatterometer; snowmelt

The feasibility of HUT snow covered area (SCA) method for operational snow melt monitoring, using large area satellite images has been determined. Previously the feasibility of the method has been proven for snow melt monitoring using spatially limited ERS-2 data. However, the spatial coverage of the data is an essential factor determining the operative usability of the method. Thus the adaptation of the method for Radarsat ScanSAR Wide (SCW) and Envisat ASAR wide swath medium resolution (WSM) data has been carried out, and the feasibility of the method using these data products has been studied. The analysis of the method was conducted by comparing the SCA estimates acquired from WSM and SCW data to reference data derived by optical remote sensing means. The analysis showed that the HUT SCA method is suitable for operational use with large area satellite radar images.

A two-phase backscattering model with nonsymmetrical inclusions is applied to calculate radar backscatter from a half-space of wet snow using strong fluctuation theory. Wet snow is assumed to consist of dry snow (host) and liquid water (inclusions). The shape and size of water inclusions are considered using an anisotropic and azimuth symmetric correlation function. The relationship between correlation lengths and snow wetness is presented by comparing strong fluctuation theory with the experimental data at 1.2, 8.6, 17, and 35.6 GHz. In the comparisons, correlation lengths are used as free fitting parameters. The effect of snow wetness on the backscattering coefficient is investigated. Numerical results of comparison between the two-phase backscattering model with nonsymmetrical inclusions and the experimental data are illustrated at 1.2, 8.6, 17, and 35.6 GHz. The effect of size and shape of water inclusions at different snow wetness values to backscatter level is shown. The comparison of angular response of backscattering coefficient (decibels) to wet snow between the model and the experimental data is presented at 2.6, 8.6, 17, and 35.6 GHz.

An efficient algorithm for electromagnetic wave scattering from rough dielectric surfaces is being developed for the simulation of bistatic scattering and emission from ocean surface. The algorithm is bused on a single magnetic field integral equation (SMFIE) and the surface is discretized using Rao-Wilton-Glisson (RWG) triangular basis function. The new feature of the algorithm is the application of the adaptive integral method (AIM) with SMFIE for speeding up the calculation. The new method will enable accurate simulations over large ocean surfaces, which have been until recently prohibited by the lack of computer power and method efficiency. Although the solver is applicable to a wide range of practical problems, the main goal of this work is to simulate the bistatic scattering and emission from a large area, with respect to wavelength, of ocean surface, size of which is made possible by the efficiency of the new method. The surface is illuminated with a Gaussian beam so that the edges of the surface do not contribute to the results significantly. In this paper we present a solution to the rough surface scattering using SMFIE with AIM, so that the computation can he speeded up with fast Fourier transform (FFT)

This study examines and discusses the usability of airborne spectrometer in investigation of the effect of variable viewing angle in snow cover monitoring. The focus is on the effect of the observation angle to the detected snow spectra and forest transmissivity. Reflectance determination is sensitive to snow anisotropical reflectance properties and, in forested areas, spectra can be very different, observed from variable sensor view angles. The results of this study are used to investigate the anisotropical scattering of snow-covered areas and they are also compared with Envisat MERIS and Terra MODIS observations. With the airborne spectrometer - derived land cover multiangular spectral features the data obtained at wide viewing angles can be corrected. The results suggest that the airborne data are feasible for the modelling and accuracy assessment of satellite data-based snow cover area (SCA) estimation algorithms.

We study the feasibility of using space borne scatterometer (QuikScat onboard SeaWinds) data for retrieval of snow parameters in Finland: onset of snow melt, end of snow melt, and fraction of snow-covered area during the seasonal snow melting period. The results are based on satellite and ground truth data for 21 test sites in Finland covering the winters of 1999-2000, 2000-2001 and 2001-2002. Radar-derived time series of snowmelt are produced and compared with corresponding snow products based on optical MODIS spectrometer data.

The statistical accuracy of HUT Snow Covered Area (SCA) estimation method is assessed. The HUT SCA method is a two step procedure for estimating Snow Covered Area for boreal forest regions. The analysis of the method is conducted with 24 ERS-2 SAR intensity images for 14 boreal forest dominated sub-drainage basins in Northern Finland. The accuracy analysis is carried out for several statistical variables and the statistical interpretation is done with respect to several affecting parameters. The accuracy analysis shows a high correlation coefficient between the SCA estimates and the reference data and RMSE values of 0.213 for open areas and 0.179 for forested areas.

In this work we demonstrate the feasibility of imaging spectrometer for the detection of oil spills on sea ice. We show that optical spectrometer images can be used as an alternative for oil spill mapping in winter when SAR-based detection algorithms fail due to ice. By comparing high-resolution airborne spectrometer image to satellite images, we evaluate the usability of MODIS and Landsat images for oil pollution detection on ice and discuss the limitations, set by image resolution and spectral band availability. We evaluate here several spectral indices and discuss the results. We propose simple algorithms for oil detection on ice. Our study strongly suggests that an imaging spectrometer suits very well to oil detection on sea ice. However usability of satellite instruments like MODIS have serious limitations set by the image resolution and band selection. Landsat ETM has significantly better resolution and it is therefore more suitable for most typical, small-scale pollution detection, but its imaging frequency does not meet the monitoring demands

The response of ERS-2 SAR to changes in soil and forest canopy moisture is investigated at a boreal forest test region in Finland. An inversion approach to estimate moisture characteristics from SAR data is applied. The method requires that a priori information on forest biomass (stem volume) and soil type distribution is available. The inversion technique provides estimates that are here, in addition to backscattering signatures, directly compared with daily in situ moisture values. The results indicate that time-series of C-band radar observations can be used for the monitoring of boreal forest moisture variations. Especially, the detection of the driest and the wettest conditions on mineral soil sites appears to be a feasible application even for single channel radar. The obtained SAR-based soil moisture estimates showed an RMSE level of 6% units against the in situ data for pine-dominated mineral soil sites.

Estimation of snow moisture (total liquid water content) and the fraction of snow covered area (SCA) are investigated by applying multi-year ERS-2 SAR and Envisat ASAR data sets. An inversion approach for the moisture retrieval is introduced. The results suggest that C-band radar is operationally feasible for both applications.

The performance of interferometric synthetic aperture radar (INSAR)-based boreal forest stem volume retrieval is strongly affected by weather conditions around the time of the SAR image acquisitions. Since weather conditions cannot be controlled, the suitability of a particular interferometric pair for stem volume retrieval can only be assessed afterward. In this letter, four objective measures based on observed forest coherence were compared in assessing the suitability of interferometric pairs for stem volume retrieval. These suitability measures can be used to identify the best and worst pairs, i.e., the ones with the most and least favorable weather conditions. Stem volume retrievals were performed using single European Remote Sensing (ERS-1/2) Tandem interferometric pairs by inverting a backscattering-coherence model for boreal forests. A total of 14 ERS Tandem image pairs acquired in varying weather conditions were studied, and the stem volume retrieval performance was assessed against ground-based stem volume estimates on 134 boreal forest stands. Stem volume retrieval performance as measured by R/sup 2/-values between INSAR-estimated stem volumes and ground truth was found to be directly proportional to boreal forest coherence. The interferometric coherence-contrast (ICC), i.e., the difference in coherence between sparsest and densest boreal forest stands was found to be the best of the four studied suitability measures. The ICC could be used as a suitability parameter in the selection of the best interferometric pairs for operational boreal forest stem volume retrieval.

The traditional method used in the water quality classification of Finnish lakes includes the collection of water samples from lakes and their analysis in laboratory conditions. The classification is based on statistical analysis of water quality parameter values and on expert opinion. It is possible to acquire similar information by using radiance values measured with the Earth Observing System Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). In this letter, the classification accuracy with MODIS data is about 80%. Only about 0.2% of the 20 391 pixels were misclassified by two or more classes, as a four-class classification system is used.

Relationship between correlation lengths and snow wetness is investigated by fitting to experimental data. The strong fluctuation theory is applied to calculate the backscattering from a half space of wet snow. The effective permittivity of wet snow is calculated using the two-phase model with non-symmetrical inclusions. In the two-phase model, wet snow is assumed to consist of dry snow (host) and liquid water (inclusions). The shape and size of water inclusion are considered using an anisotropic and azimuth symmetric correlation function. Numerical results for the backscattering coefficients of wet snow versus snow wetness are illustrated at 17 Ghz.

A radar-based method for producing both land-cover classification and stem volume estimates for the forested areas is demonstrated. The method utilizes multi-temporal INSAR data that is segmented into quasi-homogenous segments, and a semi- empirical backscattering-coherence model that is inverted to produce stem volume estimates for the forest segments. Forest stands with known stem volumes are required as training areas for determining the values of the model parameters. The performance of the method was studied by estimating the stem- volumes of 4000+ forest segments and comparing the results with stem volume estimates produced by ground-based estimates and the National Forest Inventory (NFI) of Finland. The method is suitable for operational use and the performance in stem volume estimation is comparable with optical methods.

In this study the usability of optical airborne spectrometer data in snow cover monitoring is examined and discussed. Snow-covered area (SCA) estimation, specifically during the spring melt period, is important both for hydrological forecasting and climatological studies. The results of this study are used in accuracy assessment and further development of the Finnish Environment Institute's satellite data-based SCA-algorithm. The algorithm applies an empirical reflectance model that describes the reflectance from target area as a function of forest, snow and bare ground reflectance, SCA and average forest transmissivity.

We examined the use of data from active (QuikScat on SeaWinds) and passive (SSM/I on DMSP) space-borne microwave sensors for monitoring key snow parameters in Finland. The feasibility of these data for the task was determined both separately and using a combined data set. The results are based on satellite and ground truth data for 21 test sites in Finland covering the winters of 1999-2000, 2000-2001, and 2001-2002. We show that a Ku-band scatterometer with a fixed incidence angle provides reasonable accuracy for retrieval of dry snow water equivalent (SWE). Using the combined active/passive data set retrieval accuracy is better than with SSM/I data. QuikScat data can also be used for determining onset of snow melting and snow-covered area (SCA) in spring.

In this work a coherent backscattering model for cylinders has been employed to simulate L-band and C-band polarimetric backscattering form a pine forest. Scattering covariance matrix, entropy, alpha angle, polarimetric coherence and temporal coherence are calculated and compared with SAR measurements. The results show that direct backscattering from tree crowns is an important scattering mechanism. Realistic ground reflection modelling was shown to be very important. By simulating the tree growth, general agreement between the known biomass and backscattering parameters was achieved.

In this paper an empirical algorithm for the estimation of the time of increased free liquid water content in snowpack has been assembled and tested using SSM/I data. The algorithm is based on employment of the microwave radiometer channel differences together with the hydrological model-based estimated temperature of the target. The obtained results show that the progress of snow melt can be monitored using space-scale radiometer observations when ground is covered by a thick snowpack.

Dry snow temperature, snow water equivalent (SWE) and snow depth have been retrieved by using the 19 and 37 GHz SSM/I brightness temperatures and artificial neural networks (ANNs). The results obtained have been compared with those obtained using other approaches such as the spectral polarization difference, the HUT model-based iterative inversion, the Chang algorithm and linear regressions. In general, it has been noted that the ANN based technique gives better results than the other approaches, which tend to underestimate the unknown parameters.

This letter presents a synthetic L-band (1.4 GHz) multiangular brightness temperature dataset over land surfaces that was simulated at a half-degree resolution and at the global scale. The microwave emission of various land-covers (herbaceous and woody vegetation, frozen and unfrozen bare soil, snow, etc.) was computed using a simple model [L-band Microwave Emission of the Biosphere (L-MEB)] based on radiative transfer equations. The soil and vegetation characteristics needed to initialize the L-MEB model were derived from existing land-cover maps. Continuous simulations from a land-surface scheme for 1987 and 1988 provided time series of the main variables driving the L-MEB model: soil temperature at the surface and at depth, surface soil moisture, proportion of frozen surface soil moisture, and snow cover characteristics. The obtained global maps constitute a useful dataset for a first evaluation of the sensitivity of future satellite-based L-band radiometry data to soil moisture.

This paper presents the applicability of combined Landsat Thematic Mapper and European Remote Sensing 2 synthetic aperture radar (SAR) data to turbidity, Secchi disk depth, and suspended sediment concentration retrievals in the Gulf of Finland. The results show that the estimated accuracy of these water quality variables using a neural network is much higher than the accuracy using simple and multivariate regression approaches. The results also demonstrate that SAR is only a marginally helpful to improve the estimation of these three variables for the practical use in the study area. However, the method still needs to be refined in the area under study.

In this study, the strong fluctuation theory is applied to calculate the scattering from a half space of wet snow. The first and second moments of the fields are calculated, respectively, by using the bilocal and the distorted Born approximations, and the low frequency limit is taken. The singularity of the dyadic Green's function is taken into account. The effective permittivity of wet snow is calculated by the two-phase model with non-symmetrical inclusions. In the two-phase model, wet snow is assumed to consist of dry snow (host) and liquid water (inclusions). Numerical results for the backscattering coefficients of wet snow are illustrated for random media with isotropic and anisotropic correlation functions. The three-phase strong fluctuation theory model with symmetrical inclusions is also presented for theoretical comparison. In the three-phase model, wet snow is assumed to consist of air (host), ice (inclusions) and water (inclusions) and the shape of the inclusions are spherical.

The response of the SSM/I microwave radiometer to snow-covered terrain in Finland is investigated using data for the winters of 1996-97, 1997-98, and 1998-99. The effect of temperature to the brightness temperature of dry snow is examined and a method is proposed to reduce snow/soil temperature-caused fluctuations in the brightness temperature. The feasibility of the method is verified.

Assimilation of ERS-2 SAR data to the operational hydrological runoff model using a nonlinear Bayesian techniques is demonstrated and tested. The developed assimilation technique combines SAR observations to runoff model by applying a constrained iteration procedure and forward modeling of SAR observations. The aim is to improve river discharge forecasts. The test site is located in the Northern Finland, in river Kemijoki drainage area. The satellite data consists of ERS-2 SAR images from four springs. The results show that inclusion of the satellite data can improve the performance of the discharge forecasting model during the snow melt period.

The applicability of INSAR coherence observations for stem volume (biomass) retrieval is investigated by applying coherence data from 14 ERS-1 and ERS-2 C-band SAR image pairs. A novel technique for stem volume retrieval is developed based on the inversion of a non-linear empirical forest coherence model. The seasonal behavior of interferometric coherence, the accuracy of coherence modeling as well as the performance of stem volume retrieval is tested. The data set enables the study of stem volume retrieval performance under varying conditions and as a function of the number of images. The results indicate that the applicability of winter images with snow-covered terrain is superior to that of images obtained under summer conditions. The highest correlation coefficient between the estimated stem volume and the ground truth stem volume shows values as high as r=0.89 (obtained with two optimum images using separate training and testing data sets) and a percentage RMSE level of 48%.

A new technique to retrieve snow depth (SD) information by assimilating time series of passive microwave radiometer data to SD estimates interpolated from discrete ground-based observations is presented. The technique is based on Bayesian (statistical) inversion of an analytical brightness temperature model. The data assimilation is demonstrated for the boreal forests and sub-arctic regions of Eurasia using SSM/I observations from 22 test stations around Russia, covering a time period from November 1993 to the beginning of April 1994. The obtained results indicate that the data assimilation improves the performance of regional SD estimation when compared with the case of only using the SD estimates interpolated from a discrete ground-based observation network.

Presents the detection of sea surface temperature (SST) in the Gulf of Finland using AVHRR data. AVHRR imagery is evaluated as a main data source for monitoring SST as a measure of upwelling dynamics. Sea surface effects (SSE), however, cause a temperature difference between the sea surface skin and water below the surface. Therefore, SSE are taken into account as one of the major error factors in the SST estimation.

The capability of SAR interferometry has been previously demonstrated in various applications. In particular, the use of interferometric coherence has shown promising results in forest monitoring, however, mainly in discriminating forested and nonforested areas. The authors have collected ERS-1 and ERS-2 Tandem data from two boreal forest test sites in Finland. The data have been processed into interferometric coherence and intensity images. These images have been used to a) compare the behavior of interferometric coherence and intensity for various land-use and forest classes and b) extensive analysis on the behavior of interferometric coherence in boreal forests as a function of stem volume. Based on the observations and the use of a boreal forest semi-empirical backscattering model, they have developed an empirical model that describes interferometric coherence of boreal forests using backscattering information. The results indicate that coherence is more sensitive to the stem volume than the C-band backscattering intensity. However, the intensity and coherence data contain complementary information and therefore, the use of both data sources is beneficial in the observation of boreal forest.

A set of ERS-1 SAR images along with airborne non-imaging ranging scatterometer (HUTSCAT) measurements and in situ surveys has been obtained from the Sodankyla test site (center latitude=67.41/spl deg/N, center longitude=26.58/spl deg/E) in Northern Finland. A total of five measurement campaigns were organized during 1991-1993. Nineteen test lines have been selected from the test site to represent different land-use categories. The land-use in the test area consists of open areas (agricultural fields, bogs, and clear-cut areas) and sparsely forested areas (mires, pine, and mixed forests). Microwave signatures representing the test lines have been extracted from ERS-1 SAR images and HUTSCAT measurements. The behavior of these signatures has been compared with each other and with their boreal forest semiempirical backscattering model. A set of extensive field measurements (snow depth, density, wetness, coverage, and snow water equivalent) made on the test lines are used in the various analyzes. The results indicate that the behavior of ERS-1 SAR microwave signatures is similar to that of HUTSCAT even in the presence of forest canopies. Also, the deviations of microwave signatures for various land-use classes behave similarly. This allows the authors to use the boreal forest semiempirical backscattering model based on HUTSCAT data to divide the ERS-1 backscattering signal into two contributions: 1) backscattering contribution from the top layer of vegetation canopy and 2) backscattering contribution from the canopy, ground, and ground-canopy reflections. By using the boreal forest semiempirical model, the behavior of these contributions is also observed in various soil conditions. These results explain some aspects of the boreal forest backscattering mechanism in the presence of snow cover and wet soil, which have not been experimentally investigated before.

The response of JERS-1 and ERS-1 synthetic aperture radar (SAR) to the forest stem volume (biomass) was investigated by employing a digital stem volume map and weather information. The stem volume map was produced from the National Forest Inventory sample plot data together with a LANDSAT thematic mapper (TM) image. A new indirect inversion method was developed and tested to estimate the forest blockwise stem volume from JERS-1 and/or ERS-1 SAR images. The method is based on using a semiempirical backscatter model for inversion. The model presumes that backscatter from a forest canopy is determined by the stem volume, soil moisture, and vegetation moisture. The area of interest is divided into a training and test area. In this study, the training area was 10% of the test site, while the remaining 90% was used for testing the method. The inversion algorithm is carried out in the following three steps. 1) For the training area, the soil and vegetation moisture parameters are estimated from the backscattering coefficients and stem volume (must be known for training areas) with the semiempirical backscatter model. 2) For the area of interest, the stem volume is estimated from the moisture parameters and backscattering coefficients with the semiempirical backscattering model. 3) If several SAR images are used, the stem volume estimates are combined with a multiple linear regression. The regression equation is defined using the stem volume estimates for the training area. The results for the stem volume estimation using L-band and/or C-band SAR data showed promising accuracies: the relative retrieval rms error varied from 30 to 5% as the size of the forest area varied from 5 to 30000 ha.

The derivation, testing, and employment to parameter retrieval of the Helsinki University of Technology (HUT) snow microwave emission model is presented. The radiative transfer-based semi-empirical model describes the emission behavior of a homogeneous snowpack as a function of water equivalent (SWE), effective grain size, and density of snow. Additionally, the modeling approach takes into account the influence of soil surface, forest canopy, and atmosphere to spaceborne observed brightness temperature by using empirical and semi-empirical formulas. The comparison of model predictions with independent experimental data shows good correlations, especially in terms of spectral characteristics. This enables the development of a new inversion technique for the SWE retrieval from spaceborne data based on the developed model. The test results using special sensor microwave/imager (SSM/I) data from boreal forest zone showed SWE retrieval accuracies considerably higher than those obtained with conventional algorithms. A discussion and analysis on the feasibility of the new SWE retrieval technique for operational applications is also included.

An analysis of L- and C-band boreal forest backscattering properties with respect to various temporally changing parameters is presented. The seasonal and weather dependent parameters considered include the depth of soil frost, topsoil moisture, snow water equivalent, air temperature and precipitation. The effect of these parameters on /spl sigma//spl deg/ are studied for various stem volume (biomass) classes by comparing the results against a cloud model-based semi-empirical modeling approach. Semi-empirical modeling is also used for a forest biomass retrieval experiment. The SAR data set includes 4 JERS-1 (L-band, HH-polarization) and 19 ERS-1 (C-band, VV-polarization) images for a test area in southern Finland. Additionally, a set of 2 JERS-1 and 3 ERS-1 images for another test area in northern Finland is employed. The results show that radar response to forest biomass is more sensitive to changes in temporally varying parameters at C-band than at L-band. The semi-empirical modeling approach describes well the behavior of /spl sigma//spl deg/ at both frequency bands when large forest areas are considered. Moreover, the modeling approach appears to be applicable for different conifer-dominated boreal forest types. Since the modeling approach explains satisfactorily the average backscattering behavior, the results in biomass retrieval show high accuracies (25-30% relative RMSE) when areas under investigation are large enough, i.e. about 20 ha.

The feasibility of the ERS-1 Wind Scatterometer (WS) for monitoring the boreal forest zone is investigated, concentrating on soil frost and soil moisture monitoring. The ERS-1 WS measures the target area with coarse spatial resolution (about 50 km) using three separate antenna beams and a wide angular range. The investigations are concerned with the boreal forest zone using data (1) from test areas located in Finland and (2) covering the whole northern European boreal forest zone. The seasonal behavior of WS data is studied and a semiempirical forest backscattering model-based inversion method for the retrieval of soil moisture and for soil frost monitoring from WS data is developed. The developed inversion method employs nearly simultaneous three-beam measurements and a varying incidence angle. Promising results were obtained in the monitoring of soil frost, and the retrieval of soil moisture also appears to be a feasible field of application. The applicability of the instrument for forest biomass retrieval using single images was found to be limited to long-term change detection.

The application of remote sensing methods in the estimation of forest stand characteristics, especially biomass and stem volume, has been intensively investigated during the last few years. The new methods, however, have not been accurate enough for operational standwise inventory with a required accuracy typically of 15% for main stand characteristics (stem volume, basal area, and mean height). The present work demonstrates the feasibility of a nonimaging helicopter-borne ranging scatterometer for standwise forest inventory. The radar-derived stand profiles were compared with the standwise field inventory data by applying multivariate data analysis methods. The 1300 ha Teijo test site, locating 130 km west of Helsinki, was divided into 18 parallel radar flight lines with a 150 m spacing. A total of 28 radar variables, including profile information and ground and crown backscatter contributions at 5.4 and 9.8 GHz (polarizations VV, HV, and HH), were used in regression model development. The capability of a ranging radar to classify development class, land use class, bog type and fertility (site) class was demonstrated for the first time. The accuracy of the radar-derived estimates for mean height was 1.6 m (13%) meeting the requirement of operational use. The obtained stem volume accuracy of 31 m/sup 3//ha (26%) was slightly better than has been obtained by aerial photographs. The accuracy of stem volume estimation could be easily improved by decreasing the space between flight lines. However, this leads to considerable increase in flight costs, and, therefore, a scanning ranging radar capable of producing three-dimensional (3D) images of forests would be a better alternative.

ERS-1 SAR data, airborne data and in situ snow data were acquired for the Sodankyla test site in northern Finland for the winters of 1991-1992 and 1992-1993. The test area consists of sparsely forested areas (pine, mixed forests, and mires) and open areas (bogs, lakes, clear-cut areas, and urban area). A set of multitemporal ERS-1 SAR images covering the two winters have been analyzed and the results have been compared with in situ surveys and a digital land-use map. The results indicate that even in the presence of forest canopies (1) wet snow can be distinguished from other soil/snow conditions (dry snow and bare ground), and (2) snow melt maps can be derived from SAR images. Snow-melt maps indicate areas fully covered with wet snow, partly melted areas and snow-free areas.

Novel inversion methods for the retrieval of surface temperature (air temperature at ground level) in forested areas using space-borne multi-channel microwave radiometer data are presented and analyzed. The first technique is an inversion method based on the use of a constrained least squares algorithm for the inversion of a semi-empirical emission model. The other methods discussed are empirical approaches: multiple linear regression and polarization difference formulas. The validity of the inversion method, as well as the feasibility of the empirical approaches, are evaluated in the case of Finnish boreal forests employing SSM/I data. The results show that for conifer dominated boreal forests the surface temperature can be estimated reliably from SSM/I measurements during snow-free conditions. The highest test site-wise determined correlation coefficients (r) between the ground-based reference values (near-surface air temperature) and the SSM/I-based estimates are above 0.97 and the corresponding unbiased rms errors are smaller than 1.3/spl deg/C. These values were obtained using morning time overpasses of the SSM/I without any data rejection.

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images.

The backscattering properties of boreal forests are studied using empirical airborne and spaceborne radar data from Finland. Airborne measurements were carried out in the summer of 1992 by the HUTSCAT scatterometer at the Teijo test area in southern Finland. The HUTSCAT scatterometer is an eight-channel helicopter-borne profiling radar operating at the C- and X-bands. The ranging capability of the HUTSCAT scatterometer was employed in the semiempirical modeling of forest backscatter. The backscatter profile information was used in the analysis of the canopy transmissivity and the canopy backscattering coefficient by distinguishing backscattering contributions from the canopy and the ground. Additionally, ERS-1 C-band satellite SAR measurements were obtained for the Teijo test area and for the reference test area in Sodankyla in northern Finland. The radar results were compared with operational ground-based forest assessment data on forest compartments (stands) of the area. The key parameter investigated was the stem (bole) volume per hectare. The results obtained show the behavior of the canopy transmissivity and the canopy backscatter as a function of stem volume (directly related to the forest biomass). The influence of seasonal and diurnal changes on, and the effects of the changes in soil moisture to the backscattering coefficient were also investigated.>

For pt.II see ibid., vol.31, no.1, p.170-9 (1993). HUTSCAT, a helicopter-borne dual-frequency FM-CW scatterometer, is described. The HUTSCAT measures the backscattering properties of a target with a range resolution of 65 cm. The real-time ranging capability is obtained by performing the fast Fourier transform (FFT) to the received time-domain signal. The measurement is made simultaneously at eight channels (VV, HH, HV, and VH modes of polarization at 5.4 GHz and 9.8 GHz). The scatterometer measures the radar return spectrum for eight channels in 16.6 ms, which corresponds to an along-track distance of 0.33 m for the helicopter speed of 20 m/s. The radar system has been designed for remote sensing of forests, sea ice, and snow.>

The feasibility of using spaceborne microwave radiometry to retrieve geophysical parameters is described. The study concentrates on the development of inversion techniques for multichannel spaceborne radiometers, especially the statistical inversion approach. The applications of the planned MIMR (Multi-Frequency Imaging Microwave Radiometer) instrument are discussed. The inversion algorithms used are conventional algorithms for different applications and the statistical inversion approach. Comparisons between results from different inversion algorithms are presented. The statistical inversion approach has been found to give promising parameter retrieval accuracies and has the potential to improve the operational use of passive spaceborne remote sensing. An analysis of the sensitivity of the radiometer apparent temperature to different geophysical parameters and the statistical behavior of the atmospheric transmissivity are presented.>