We present a unique open-ended coaxial probe (OECP) that can accurately measure the permittivity of heterogeneous materials due to its large aperture. The probe works at frequencies ranging from 0.5 to 18 GHz, which is an important range for microwave remote sensing applications, and more precisely the Terrestrial Snow Mass Mission (TSMM). TSMM aims at launching a new satellite equipped with a dual Ku-band radar (13.5 and 17.2 GHz) for snow monitoring. At Ku-band frequencies, the backscattered radar signal contains information not only about the snow but also about the soil underneath. Knowing the soil permittivity will allow models to better account for the soil contribution to the total signal and to obtain more accurate snow data. To demonstrate the accuracy of the probe and the repeatability of the measures, calibration solutions of known permittivity were measured, and the results were compared to their theoretical values. Then, we conducted tests for approximating the penetration depth of the probe signal with dry and wet papers. Afterward, a protocol to use the probe to compute the permittivity of soil samples in freeze/thaw cycles was developed. The permittivities of commercial sand and organic soil from Iqaluktuuttiaq (Cambridge Bay, Nunavut, Canada) are presented for different relevant frequencies for microwave remote sensing applications and different temperatures (−20 °C to 20 °C) according to the new protocol. This article aims to present the probe and its potential use in microwave remote sensing applications, especially for the active Ku-band, where little research exists.

Land surface temperature (LST) is a crucial geophysical parameter for understanding cryospheric processes such as snow accumulation, freeze-thaw cycles, and the energy budget. However, most existing passive microwave LST retrieval algorithms are not optimized for Land Snow Surface Temperature (LSST) estimation, presenting a significant limitation for remote sensing observation of the cryosphere. To address this, this study developed a robust, all-weather, near real-time, standalone passive microwave-based LSST retrieval algorithm optimized for snow-covered conditions in the Northern Hemisphere. In this study, LSST is defined as the air temperature close to the snow surface. Due to limited in-situ observations in Arctic regions and complex microwave radiative transfer over snow-covered landscapes, the Multi-Layer Perceptron (MLP) model, which is referred to in this study as the MLP_Model, was developed. Using 2-meter air temperature as a proxy to present the air temperature close to the snow surface, this study employed Multi-Task Learning (MTL) to integrate data from in-situ Automatic Weather Stations (AWS), the ECMWF Reanalysis v5 dataset (ERA5), and simulations from the Microwave Emission Model of Layered Snowpacks (MEMLS) for model training. This integration method balanced the information from multiple data sources, thereby mitigating potential uncertainties associated with training empirical models on limited, single-source datasets while ensuring that the trained model remains broadly consistent with the currently available data and established physical principles. Data from MEMLS simulations act as a physical constraint in the training process, ensuring the model’s estimates adhere to physical model expectations. The MLP_Model estimated LSST was compared with LSST from the AWS network and the ERA5 in the Northern Hemisphere for evaluation. The Mean Absolute Error (MAE) values were 3.74 and 4.38 °C, while 75th Percentile Absolute Error (Q3AE) were 5.61 °C and 5.71 °C, respectively. Compared with the passive microwave LSST estimation algorithm developed by Kelly 2003 (Kelly_2003), which has been used in the Japan Aerospace Exploration Agency (JAXA) operational snow retrieval algorithm, the MLP_Model demonstrated a reduction in the MAE by 1.5 °C and the Q3AE by 2.1 °C. These results indicate that this newly developed model can provide daily, reliable, and consistent near real-time LSST estimates based solely on passive microwave remote sensing observations. The model-estimated LSST could serve as a reliable and independent reference for cryospheric climate studies, offering valuable input for data assimilation and reanalysis efforts. Furthermore, it could support other near real-time operational passive microwave-based retrievals of cryospheric geophysical parameters such as snow depth or freeze-thaw states.

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

Snow is a critical contributor to the global water-energy budget with impacts on springtime flooding and water resource management practices. Laser altimetry [light detection and ranging (LiDAR)] is a remote-sensing technique that has demonstrated skill in monitoring snow depth, but the expense of purchasing and transporting traditional LiDAR equipment limits their operational use. In this work, we demonstrate that the LiDAR sensor installed on the Apple iPhone 12 Pro consumer smartphone is a real-time, handheld measurement instrument for accurately observing changes in snow depth. Two independent field experiments in Southern Ontario, Canada, found that the iPhone LiDAR was able to accurately capture daily changes in snow depth when compared toin situsnow ruler measurements.In situand LiDAR comparisons of xs$n=75$days at measurement site A exhibit a correlation of$r > 0.99$, mean absolute bias less than 1 mm, and a root mean squared error (RMSE) of approximately 6 mm. A similar positive agreement was also noted at the second field study site for$n=16$measurements over the same period. The high accuracy of the LiDAR sensor suggests that a mobile application could be developed which allows users to quickly scan a snow-covered area before and after a snowfall event and consequently use this data to aid in filling current observational gaps through a citizen-science-based approach to measuring changes in snow depth.

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

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (Tb) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne Tbs, the inversion was also applied using the synthetic observations, where Tbs were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne Tbs are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne Tbs are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

Forecasted population growth and a changing climate will intensify the challenge of securing water for agriculture. While rainwater harvesting (RWH) reservoirs constitute a promising alternative and complimentary source to groundwater withdrawal for irrigation and have supported small-holder agriculture across South India for millennia, their hydrological role remains not well understood. Synonymous to how Earth Observation (EO) gravitational studies have opened the public and scientific discourse on groundwater sustainability, new EO developments further our understanding of RWH reservoir hydrology and their role to further future sustainable groundwater management. Using a reservoir-dominated region in South India as an illustrative case, three areas are discussed where remote sensing will accelerate our understanding of water resources sustainability for agriculture in the rural farming landscape of South India. These include: 1) advances in temporal and spatial remote sensing; ii) a perception shift, where data-driven remote sensing can support or compliment physical process understanding; and iii) the critical need for increased communication to understand end-user needs.

Moderate to high spatial resolution (<; 1 km) regional to global snow water equivalent (SWE) observation approaches are not yet available and so the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach for estimating snow depth (SD) and snow water equivalent (SWE) is presented called the Satellite-based Microwave Snow Algorithm (SMSA). Using the Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations the approach leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval. The SD and SWE retrieval approach minimizes the difference between Dense Media Radiative Transfer model estimates (Tsang et al ., 2000; Picard et al., 2012) and AMSR2 Tb observations. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2018-19 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

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

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

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

Radar measurements of accumulated snow using a Ku and X-band scatterometer instrument system were conducted at Grand Mesa, Colorado during the SnowEx 2017 field experiment in February 2017. Coincident snowpack measurements to the radar observations were made to characterize snow stratigraphy, snow density, snow grain structure, and snow thermodynamic properties. Eight sites were observed with the UWScat systems including two sites from a platform positioned 9 m above the ground and adjacent to a woodlot. The 6 non-wooded sites show consistent behavior of Ku and X-band power response (backscatter) compared with previous field studies. The first of these 6 sites was characterized by a wet snowpack response, while the other 5 had strong volume scattering response from the dry snowpack. The forest response is complex and requires further analysis to better understand the radar response from the woody biomass. The paper demonstrates and explores several response types from the radar observations of snow, including the backscatter power response, the polarimetric responses, all of which shed light on the Ku and X-band response from the ~2m snowpack.

Tank rainwater harvesting (RWH) systems are a potential solution for reducing water stress in Southern India by providing groundwater recharge and seasonal water storage. Remote sensing (RS) observations have a potential to monitor these systems over larger spatial scales and offer a systemic approach to support scientific efforts in water management. The research objective was to develop a method for water volume retrieval in tank systems in S. India. This method used data from the Sentinel-1A (S-1A) sensor in combination with a new high-resolution global digital elevation model (DEM), the TerraSAR-X Add-On for Digital Elevation Measurement (TanDEM-X), to monitor the water volume changes within seven tank structures. This research has offered a unique contribution to the hydrologic science community by incorporating synthetic aperture radar (SAR) data and a high-resolution DEM for tank monitoring.

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

Prosopis juliflora, a drought-tolerant fast-growing tree species, has invaded thousands of storage tanks in India: systems installed decades ago for capturing rainfall during the monsoon period. In this study, we applied Google Earth Engine (GEE) to detect and map P. juliflora invasion for a region of Tamil Nadu, India to determine the change in P. juliflora over two and a half decades. Both the Landsat legacy data and the new Sentinel-2 (S2) data were used with different setups with three classifiers - classification and regression tree (CART), random forest (RF), and support vector machine (SVM). The SVM classifier using Landsat-8 (L8) data outperformed the RF and CART classifiers, reaching overall accuracies of 90 %. When comparing S2 and L8 data for P. juliflora mapping, the use of S2 resulted in higher classification accuracies and the ability to identify dense patches of the species instead of only P. juliflora presence or absence. Over the full Gundar river basin, P. juliflora was found to invade new areas at an average rate of 27 km2/annum over the period 1993-2015. P. juliflora was detected mainly along rivers and water bodies, as well as in urban areas. Expansion occurred heavily in the tank systems throughout the basin while abandoned farmland was primarily invaded in the lower basin.

Modeling the physical state of a snowpack is widely recognized as a challenging aspect in the snowpack physical properties retrieval using passive microwave remote sensing. The Advanced Microwave Scanning Radiometer 2 (AMSR2) launched on JAXAs Global Change Observation Mission Water in 2012 with 10-15 years mission, continues observation record of Earth from space. The SWE product for AMSR2 is being developed as a satellite-based retrieval system that relies on static ancillary datasets to parameterize land surface properties that initialize retrievals. In this research, Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack, a physically based numerical model, is employed [1]. The model is based on the Dense Media Radiative Transfer (DMRT) theory for snow scattering and extinction coefficients computation and uses Discrete Ordinate Method to numerically solve the radiative transfer equation. Using DMRT-ML assumptions, the application of the DMRT-ML model to the February 2013 snowstorm in southern Ontario to the Eastern seaboard of USA as well as Canada wide stations in December 2013 and January 2014 are explored. To supply DMRT input variables, Canadian Meteorological Center (CMC) daily snow depth, analysis snow depth product, and AMSR2 brightness temperature have been used. AMSR2 data has been utilized for surface physical temperature estimation. Using forward DMRT simulation for one layer snowpack, model sensitivity to snowpack grain size via AMSR2 observations is studied. This provides insight into the inversion look-up table matrix that is being developed using DMRT-ML for AMSR2 SWE retrievals.

This study assesses the potential of RADARSAT-2 quad polarimetric and compact polarimetric (CP) parameters to monitor soil moisture and freeze/thaw state. The paper presents a preliminary analysis of the synthetic aperture radar (SAR) parameters derived from 14 RADARSAT-2 fine quad-pol images acquired from October 2013 to May 2014 over an agricultural field in Waterloo-Guelph, Ontario. Results indicate sensitivity of CP parameters to soil moisture and their potential usefulness of discerning frozen/unfrozen soils. This suggests that the CP mode can be an alternative data source for large scale soil moisture studies. Further investigation of using CP parameters in soil moisture applications is required to better support the Canadian RADARSAT Constellation Mission (RCM).

The University of Waterloo scatterometer, which is a system developed for observation of snow and ice properties, is described. The system is composed of two frequency-modulated continuous-wave radars operating at center frequencies of 17.2 and 9.6 GHz. A field-deployable platform allows a rapid setup and observation at remote sites under harsh environmental conditions. A two-axis positioning system moves the radar beam across a user-programmable range of azimuth (±180°) and elevation angles (15°-105°). Typical azimuth scans of 60° angular width generate between 21 and 586 independent samples, depending on the wavelength and the elevation angle. The backscatter response of terrestrial snow in the Canadian Subarctic is demonstrated with two experiments conducted in Churchill, MB, Canada, between 2009 and 2011.

Retrieval of snow volume properties at high spatial resolutions has become a priority in hydrological and climatological research. Recent studies have identified the 8 to 18 GHz range as particularly sensitive to snow water equivalent. In this study we present snow target observations using a dual frequency (17.2 GHz and 9.6 GHz) scatterometer system. The observations were collected in a unique tundra environment providing a novel dataset for comparison with in situ snow survey data. Moreover, the scatterometer was sled mounted allowing observations to be collected over a substantial spatial domain.

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

The Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched in 2002 aboard NASA's Aqua satellite, has improved spatial resolution capabilities compared with previous passive microwave instruments. Snow depth retrievals from AMSR-E are tested with GEWEX (Global Energy and Water Experiment) Asian Monsoon Experiment (GAME) and Coordinated Enhanced Observing Period (CEOP) data. Level 1B AMSR-E brightness temperatures are used in the study. Seven acoustic snow gauge sites recorded daily snow depth within a 100 km times 100 km domain located near Yakutsk in Siberia. These point snow depth measurements span a period between October 2002 and February 2003 and are used to test AMSR-E ascending and descending retrievals. The paper describes the accuracy of the snow depth estimates compared with the GAME/CEOP Siberia sites. It also assesses how World Meteorological Organization/Global Telecommunication System meteorological snow data from a station at Yakutsk compare with the GAME/CEOP site snow depth data and AMSR-E estimates

Summary only given. Passive microwave observations from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2975000 km/sup 2/). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

We demonstrate the "baseline" global snow water equivalent retrieval (SWE) algorithm using Advanced Microwave Scanning Radiometer EOS (AMSR-E). Daily, pentad and monthly records for March 2003 of AMSR-E SWE estimates are generated and gridded to the 25 km EASE-grid projection. The estimates are tested using ground measurements from the World Meteorological Organization Global Telecommunications Network. Preliminary bias characteristics are evaluated. A fraction of the error is related to uncertainties about the grain size changes throughout the winter season that directly affect the parameterization of the snow depth estimation in algorithm. The algorithm includes the need for a correction of forest cover and this effect is clearly observed in the retrieval. AMSR-E has twice the spatial resolution of the Special Sensor Microwave Imager and is able to characterize snow variability at the local scale. Development of the algorithm is focused on a dynamic parameterization of the snow grain size and density via a dense media radiative transfer model plus the inclusion of a high quality forest correction data set.

The mapping of estimated snow depth (SD) or snow water equivalent (SWE) from spaceborne passive microwave imagery is usually achieved by detecting a snow scattering signal from the land surface and then by calibrating the magnitude of the scattering with snow depth or snow water equivalent. Scattering is estimated from the brightness temperature difference between 19 GHz and 37 GHz vertical polarization channels of a microwave radiometer (or frequencies not too dissimilar to these). If a snow scattering signal is present, it is generally assumed that snow covers the entire area of a coarse spatial resolution passive microwave pixel (or footprint). For seasonal snowpacks, this is a reasonable assumption because at high latitudes in general, snow cover is often spatially continuous over wide areas during the winter season. However, for spatially discontinuous snowpacks (such as early winter snow, ephemeral snow covers or perhaps regions marginal to mid-winter continental snow covers), snow might be detected in a microwave pixel but it may be inaccurate to assume that snow covers the entire pixel; snow might be spatially localized but dominant enough radiometrically to trigger a snow scattering signal. In this paper we investigate the effect of sub-pixel scale fractional snow extent on the microwave detection of snow. Under cloud-free conditions determined by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10/spl I.bar/L2 product, we compare the snow scattering signal for various DMSP Special Sensor Microwave Imager pixels with the MODIS MOD10/spl I.bar/L2 snow product (500 m/spl times/500 m spatial resolution). Using the 25 km/spl times/25 km EASE grid projection for the passive microwave imagery, comparisons are made between the microwave scattering signal and the percentage of MODIS pixels classed as 100% snow within the 25 km/spl times/25 km. We also compare microwave snow scattering signals with the degree of MODIS snow pixel clustering/dispersion within 25 km/spl times/25 km pixels. This research has important implications for the errors of passive microwave mapping of SD or SWE in discontinuous snow covered regions.

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.

For accurate estimation of global snow depth or snow water equivalent on the Earth's surface using passive microwave instruments, knowledge of the snow pack's physical properties is important. It is known that the bulk snow grain size distribution exerts an important control over the microwave response from snow between 3 mm and 300 mm wavelengths. In the absence of high quality snowpack data at a global scale, we show how the grain size distribution can be estimated using a general empirical model of grain growth. This information is used to parameterize a dense media radiative transfer model (DMRT) to estimate the radiometric response from a snow pack as a function of changing grain size distribution. The DMRT equations are based on the quasi-crystalline approximation (QCA) for densely distributed moderate sized particles in a medium such as a snow pack. The model snow depth estimates from the DMRT are used to calibrate a Special Sensor Microwave Imager (SSM/I) snow depth retrieval algorithm which is based on the brightness temperature difference between 19 and 37 GHz with the SWE. The method is tested using meteorological data from the WMO global network. Results show that using the DMRT model coupled with a grain size model improved estimates of snow depth are obtained.