Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution-emission model

To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution-emission model can be used to simulate the brightness temperature (T_B) of the snowpack.During a dedicated campaign in the winter season, from November to April, of 2007-2008 two surface-based radiometers operating at 19 GHz and 37 GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of T_B over the whole season, with an hourly step, using a coupled multi-layer snow evolution-emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict T_B at 19 GHz and 37 GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of T_B predicted by the Crocus-MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers.The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of T_B. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus-MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux.When the snowpack was completely dry, the global trend of T_B during the season was characterized by a decrease of T_B due to growth in the snow grain size. During most of the season, Crocus-MEMLS correctly predicted the evolution of T_B resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8 K for the 19 GHz vertical polarization (19V) and 6.9 K for the 37 GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of T_B were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed T_B and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model.     

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature (T_B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz-19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T_B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T_B and SWE.The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T_B observed from satellite measurements. AMSR-E brightness temperatures (2002/03-2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37 GHz T_B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r² = 0.77 with monthly averages; r² = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r² values calculated for January through April 2003-2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).     

Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Immediately before an April 2007 snow survey and passive microwave radiometer field campaign in the Northwest Territories, Canada, a rain-on-snow event deposited a thin (~ 3 mm) continuous layer of ice on the surface of the snowpack. At eight sites the brightness temperature (T_b) of the undisturbed snow pack was measured with a multi-frequency dual polarization (6.9, 19, 37, and 89 GHz) ground based radiometer system. The ice lens was then carefully removed and the T_bs were measured again. The individual V-pol channels and the 37 V − 19 V difference were largely unaffected by the presence of the ice lens, exhibiting a systematic shift of about 3 K. In comparison, the ice lens had a considerable effect on the H-pol T_b at all frequencies, with a mean difference (ice lens present − ice lens removed) of − 9 K (± 5.3 K) at 6.9 GHz, − 40 K (± 11.3 K) at 19 GHz, − 33 K (± 7.6 K) at 37 GHz, and − 19 K (± 8.0 K) at 89 GHz. The effect of the ice lens on H-pol measurements was also observed with spaceborne data from the Advanced Microwave Scanning Radiometer (AMSR-E) satellite data.Simulations of T_b were produced for each site using a new two layer formulation of the Helsinki University of Technology (HUT) snow emission model. The ice lens was used as the top layer and the underlying snowpack considered as a homogenous second layer. The agreement between observations and simulations was variable, with agreement strongest at 19 GHz. A comparison with simulations produced using the Microwave Emission Model of Layered Snowpacks (MEMLS) suggests HUT model uncertainty is related not to the ice lens, but to difficulties in simulating emission from deep snow. Overall, the observations and simulations suggest H-pol measurements are capable of detecting new ice layers across the tundra snowpack, while V-pol measurements are more appropriate for snow water equivalent (SWE) retrievals due to their relative insensitivity to ice layers.     

Evaluation of the HUT modified snow emission model over lake ice using airborne passive microwave measurements

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80 × 100 m were used as ground-truth to evaluate the simulations.The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of −8.4 K and −8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (−5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R = −0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (−4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.     

Detection of snow and frost in southern China in January 2008 using AMSR-E scattering and polarization indexes

All-weather, all-season microwave remote sensing is one of the most powerful technologies for monitoring natural disasters. Multichannel brightness temperature (T _b) measurements from satellite-borne passive microwave remote sensing has played an important role in retrieving quantitative physical information regarding global and regional weather and climate, atmospheric precipitation, land hydrology, and oceanic surface winds. However, in January 2008, during severe weather conditions with heavy snow and frost in the usually warm south of China, the operational algorithm for snow detection using multichannel T _b data failed to detect snow. In this article, based on the simulation of vector radiative transfer of a snowpack model of dense and sticky Mie ice particles, characteristic indexes of scattering and polarization differences, average indexes in the previous year under normal situation, and changes in antecedent indexes are newly defined and analysed. A new detection flowchart is designed to effectively detect the regional snow and frost disaster in 2008 in southern China.     

Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

The key variable describing global seasonal snow cover is snow water equivalent (SWE). However, reliable information on the hemispheric scale variability of SWE is lacking because traditional methods such as interpolation of ground-based measurements and stand-alone algorithms applied to space-borne observations are highly uncertain with respect to the spatial distribution of snow mass and its evolution. In this paper, an algorithm assimilating synoptic weather station data on snow depth with satellite passive microwave radiometer data is applied to produce a 30-year-long time-series of seasonal SWE for the northern hemisphere. This data set is validated using independent SWE reference data from Russia, the former Soviet Union, Finland and Canada. The validation of SWE time-series indicates overall strong retrieval performance with root mean square errors below 40 mm for cases when SWE < 150 mm. Retrieval uncertainty increases when SWE is above this threshold. The SWE estimates are also compared with results obtained by a typical stand-alone satellite passive microwave algorithm. This comparison demonstrates the benefits of the newly developed assimilation approach. Additionally, the trends and inter-annual variability of northern hemisphere snow mass during the era of satellite passive microwave measurements are shown.     

Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

积雪属性非均匀性对被动微波遥感积雪的影响

积雪属性的非均匀性在水平方向上表现为像元内积雪未完全覆盖和雪深分布的不均匀,在垂直方向上表现为积雪剖面上粒径和密度的不一致导致的积雪分层现象。这些积雪属性的非均匀性对被动微波遥感反演雪深或雪水当量带来很大的不确定性,并且给反演结果的验证带来不确定性。通过野外积雪的微波辐射特性观测、遥感积雪产品对比分析、积雪辐射传输模型模拟对这些问题进行阐述和探讨,为今后积雪微波遥感反演算法发展和结果验证提供参考。     

Identification of snow cover regimes through spatial and temporal clustering of satellite microwave brightness temperatures

Areas of similar ecology are often delineated based on homogenous topography, temperature, and land cover. Once delineated, these zones become the basis for a wide variety of scientific research and management activities. For instance, in Canada, ecozones are commonly utilized ecological management units delineated using geographic, topographic, and climatic information aided by spring and summer vegetation conditions. Snow cover has an influence on local and regional hydrological conditions and climate, as well as on animal habitats. As such, we posit that inclusion of winter conditions, incorporating spatial- and temporal-variation in snow cover is an additional element for consideration when delineating areas with homogenous conditions. In our analysis we use satellite passive microwave brightness temperatures from 19 years of Special Sensor Microwave/Imager (SSM/I) measurements to produce a daily time-series on snow cover, and demonstrate how these data can be used to delineate areas of similar winter conditions. We use splines and curve fitting to generalize the dense time-series (of over 6900 days) to a set of metrics, and select three for use in cluster-based generalization of snow cover regimes: annual maximum difference between 37 and 19 GHz SSM/I measurements (with differences in magnitudes indicative of snow accumulation), variation of 37-19 GHz brightness temperatures (indicative of snow cover variability), and variation in the rate of brightness temperature change during the snow melt season (indicative of seasonal change). Our results indicate that these metrics produce spatial units that are unique, and not captured by conventional ecological management units, while also producing spatial units that cohere to those generated from summer conditions. Spatial units that are found to have spatial cohesion between summer and winter data sources are located in regions where the amount of snow tends to be low, and snow cover variability minimal. We propose that snow cover regimes may be used to augment typical vegetation-based ecological zonations or to provide insights on hydrology and animal habitat conditions. Inclusion of winter conditions is especially important when areal delineations are used to monitor impacts of climate change, and as a baseline for monitoring changes in snow cover amount, extent, and/or distribution.     

Analysis of the Microwave Brightness Temperature Simulations of Snow-coverd Forest Areas in Northeast China

Retrieving accurate quantitative snow parameters in forested regions is still a difficult problem for decades.The key to solve this problem is to improve the understanding of the physical mechanisms of the microwave radiation transfer process of forest-snow system.Forward simulation of microwave radiation brightness temperature is one of the crucial steps in retrieving snow parameters from radiative transfer model.To further comprehend the physical process of microwave radiation transfer of forest-snow system,ground-based remote sensing observation experiment 14 subregions (10 km×10 km) of the typical snow-covered forest areas in Daxing’anling and Xiaoxing’anling regions were carried out.The forest microwave transmissivity as animportant input parameter were acquired by two different methods,one is using ground-based microwave radiometer to observe(i.e.radiometer-simulation method) and the other is through an empirical regression formula to calculate with tree volume sampled data(i.e.volume-simulation method).And the detected brightness temperature of spaceborne microwave radiometer is simulated by HUT radiative transfer model(i.e.T simu B).The correlation analysis of the simulated result shows that there is a volume scattering effect(correlation coefficient R²≤0.37)in the forest under the K-band horizontal polarization condition,while under the Ka-band dual polarization and K\|band vertical polarization conditions,the forest volume scattering effect almost does not exist(correlation coefficient R²≥0.53).On this basis,the difference between the simulated brightness temperature T simu B and that detected by FY3C MWRI is compared.based on the calibration accuracy( of MWRI,the deviation |Δ|≤3 K is proposed as the consensus criterion to evaluation model simulation results.In radiometer-simulationprocess,the consistency of horizontal polarization (H) and vertical polarization (V) in K-band is 79%and 82%,respectively;and that of H and V in Ka-band is 43% and 50%,respectively.In volume-simulation process,the consistency of H and V in K-band is 57% and 86%,respectively,and that of H and V in Ka-band is both 64%.These results show that the uncertainty caused by snow cover scattering is greater than that caused by forest scattering when the HUT model is used to simulate the brightness temperature of microwave radiation in forest-snow system.based on the above analysis,the applicability of HUT model and the selection principlesofground-truthvalidationsiteforsnowcovered forest areas in Northeast China are put forward.     

Synthesis of passive microwave and radar altimeter data for estimating accumulation rates of dry polar snow

In this paper, we compare dry-snow extinction coefficients derived from satellite radar altimeter data with brightness temperature data from passive microwave measurements over a portion of the East Antarctic plateau. The comparison between the extinction coefficients and the brightness temperatures shows a strong negative correlation, where the correlation coefficients ranged from –0·87 to –0·95. The large-scale trend shows that the extinction coefficient of the dry polar snow decreases with increasing surface elevation, while the average brightness temperature increases with surface elevation. Our analysis shows that the observed trends are related to geographical variations in scattering coefficient of snow, which, in turn, are controlled by variations in surface temperature and snow accumulation rate. By combining informa.tion present in the extinction coefficient and brightness temperature datasets, we develop a simple semi-empirical model that can be used to obtain accumulation rate estimates of dry polar snow.     

A comparative study of Chang and HUT models for UK snow depth retrieval

For the development of passive microwave remote sensing techniques, brightness temperature information on the medium covering the Earth's surface under different conditions is required. An emission model is a useful tool for the estimation of the brightness temperature of the medium. If the medium is a snow pack, the microwave radiation emitted will depend on the physical temperature, crystal characteristics, stratification and density of the snow. The parameters of microwave emission models available for the retrieval of snow characteristics are highly dependent on local environmental and climatological conditions. The aim of this study was to compare the empirical Chang model with the semiempirical radiative transfer model of snow developed at Helsinki University of Technology (HUT) for snow depth (SD) retrieval for UK snow packs. In the first step we used the HUT model. The root mean square error (RMSE) was used to validate the accuracy of model estimates. The snow events from different days in 1995, 1996 and 1997 were used in this study. In the second step a revised form of the Chang model, which was originally calibrated for global snow monitoring, was applied to estimate the SD. It is evident from this study that the Chang model underestimates the SD whereas the HUT model both underestimates and overestimates the SD for UK snow cover. This study also demonstrates that the application of algorithms for snow pack monitoring requires local calibration for effective and reasonable results.     

Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model

Snow Water Equivalent (SWE) is a crucial parameter in the study of climatology and hydrology. Active microwave remote sensing is one of the most promising techniques for estimating the distribution of SWE at high spatial resolutions in large areas. Development of reliable and accurate inversion techniques to recover SWE is one of the most important tasks in current microwave researches. However, a number of snow pack properties, including snow density, particle size, crystal shape, stratification, ground surface roughness and soil moisture, affect the microwave scattering signals and need to be properly modeled and exploited. In this paper, we developed a multi-layer, multi-scattering model for dry snow based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from NASA Cold Land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3) and SARALPS-2007 field experiment supported by ESA. The results indicated that model predictions were in good agreement with field observations. With the confirmed confidence, the analyses on multiple scattering, scatterer shape, and snow stratification effects were further made based on the model simulations. Furthermore, a parameterized snow backscattering model with a simple form and high computational efficiency was developed using a database generated by the multiple-scattering model. For a wide range of snow and soil properties, this parameterized model agrees well with the multiple-scattering model, with the root mean square error 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively. This simplified model can be useful for the development of SWE retrieval algorithm and for fast simulations of radar signals over snow cover in land data assimilation systems.     

AMSR-E数据散射与极化指数检测2008年1月中国南方冰雪灾害

全天候全天时微波遥感是监测自然灾害的重要手段。星载被动微波遥感测量的多通道辐射亮度温度对全球和区域性天气与气候、大气降水、陆地水文、海面风场等物理信息的获取已发挥了重要的作用。2008年初中国通常温暖的南方发生暴雪和冰冻灾害,用业务运行的多通道辐射亮度温度算法产品却无法识别这一冰雪灾害。通过对积雪层的矢量辐射传输模拟,根据多通道微波扫描辐射计AMSR-E数据的散射与极化特征指数、往年同时期同地区正常条件下的平均特征指数以及先时特征指数的变化,构成新的判据流程,能有效地识别中国南方区域性冰雪自然灾害。     

A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface-atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150 km² study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.     

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995.The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.     

Review of: “Glaciers-Ocean-Atmosphere Interactions”. Edited by V. M. KOTLYAKOV,A. USHAKOV and A. GLAZOVSKY,Proceedings of the International Symposium,St Petersburg, 24-29 September 1990 (Wallingford,UK: IAHS Press, 1991.) [Pp. 549.] Price US$60.

Data gathered during the NASA sponsored Multisensor Aircraft Campaign Hydrology (MACHYDRO) experiment in central Pennsylvania (U.S.A.) in July, 1990 have been analysed to study the combined use of active and passive microwave sensors for estimating soil moisture from vegetated areas. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR), and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed, which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. Various multi-sensor techniques are currently under investigation to improve the accuracy of remote sensing estimates of the soil moisture in the presence of vegetation and surface roughness conditions using these data sets. One such algorithm involving combination of active and passive microwave sensors is presented here, and is applied to representative corn fields in the Mahantango watershed that was the focus of study during the MACHYDRO experiment. In this algorithm, a simple emission model is inverted to obtain Fresnel reflectivity in terms of ground and vegetation parameters. Since Fresnel reflectivity depends on soil dielectric constant, soil moisture is determined from reflectivity using dielectric-soil moisture relations. The algorithm requires brightness temperature, vegetation and ground parameters as the input parameters. The former is measured by a passive microwave technique and the later two are estimated by using active microwave techniques. The soil moisture estimates obtained by this combined use of active and passive microwave remote sensing techniques, show an excellent agreement with the in situ soil moisture measurements made during the MACHYDRO experiment.     

Review of snow water equivalent retrieval methods using spaceborne passive microwave radiometry

ABSTRACT

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework.     

Identification of clay pans from AMSR-E passive microwave observations

A new methodology to derive the spatial distribution of clay pans from satellite microwave data is presented. Soil moisture has a different temporal signature in clay pans compared with other soils, which is directly reflected in the satellite-observed brightness temperatures. Three years of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) 6.9 GHz microwave observations were compiled and analysed over continental Australia to identify clay pans. This led to the development of a brightness temperature variance index (BTVI), which shows a strong spatial correspondence to an existing soil texture map and the ability to map clay pans for semi-arid regions. This simple method emphasizes the potential use of passive microwave remote sensing for soil type mapping.