Quantifying the uncertainty in passive microwave snow water equivalent observations

Passive microwave sensors (PM) onboard satellites have the capability to provide global snow observations which are not affected by cloudiness and night condition (except when precipitating events are occurring). Furthermore, they provide information on snow mass, i.e., snow water equivalent (SWE), which is critically important for hydrological modeling and water resource management. However, the errors associated with the passive microwave measurements of SWE are well known but have not been adequately quantified thus far. Understanding these errors is important for correct interpretation of remotely sensed SWE and successful assimilation of such observations into numerical models.This study uses a novel approach to quantify these errors by taking into account various factors that impact passive microwave responses from snow in various climatic/geographic regions. Among these factors are vegetation cover (particularly forest cover), snow morphology (crystal size), and errors related to brightness temperature calibration. A time-evolving retrieval algorithm that considers the evolution of snow crystals is formulated. An error model is developed based on the standard error estimation theory. This new algorithm and error estimation method is applied to the passive microwave data from Special Sensor Microwave/Imager (SSM/I) during the 1990-1991 snow season to produce annotated error maps for North America. The algorithm has been validated for seven snow seasons (from 1988 to 1995) in taiga, tundra, alpine, prairie, and maritime regions of Canada using in situ SWE data from the Meteorological Service of Canada (MSC) and satellite passive microwave observations. An ongoing study is applying this methodology to passive microwave measurements from Scanning Multichannel Microwave Radiometer (SMMR); future study will further refine and extend the analysis globally, and produce an improved SWE dataset of more than 25 years in length by combining SSMR and SSM/I measurements.     

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.     

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.     

基于积雪调查数据的东北地区被动微波积雪遥感产品精度验证与分析

积雪遥感数据产品可以提供积雪的时空分布信息，是积雪监测的重要数据源。对现有的不同遥感产品进行精度验证和对比分析，明确其适用范围，有利于积雪数据产品的进一步发展和应用。为验证积雪产品在东北地区的适用性，以中国积雪特性及分布调查项目为依托，精心设计野外实验，观测了东北地区25 km典型样方和积雪线路调查数据，验证了在阔叶林和农田两种下垫面下，FY-3B雪深产品、AMSR-2雪深产品、GlobSnow雪水当量产品在东北地区的反演精度。结果表明：GlobSnow雪水当量产品精度最高，不区分下垫面的情况下，最大偏差和均方根误差分别为10.87 cm和12.53 cm。考虑下垫面的影响，GlobSnow雪水当量产品和FY-3B雪深产品在两种下垫面下的雪深反演精度差别很小，偏差和均方根误差的差值小于2.11 cm和3.46 cm，AMSR-2积雪产品在两种下垫面下反演精度差别很大，两种下垫面下偏差和均方根误差的差值大于9.94 cm和7.19 cm。对于3种积雪产品，下垫面为农田的雪深反演精度均高于下垫面为阔叶林的反演精度。     

基于积雪调查数据的东北地区被动微波积雪遥感产品精度验证与分析

积雪遥感数据产品可以提供积雪的时空分布信息,是积雪监测的重要数据源。对现有的不同遥感产品进行精度验证和对比分析,明确其适用范围,有利于积雪数据产品的进一步发展和应用。为验证积雪产品在东北地区的适用性,以中国积雪特性及分布调查项目为依托,精心设计野外实验,观测了东北地区25 km典型样方和积雪线路调查数据,验证了在阔叶林和农田两种下垫面下,FY-3B雪深产品、AMSR-2雪深产品、GlobSnow雪水当量产品在东北地区的反演精度。结果表明：GlobSnow雪水当量产品精度最高,不区分下垫面的情况下,最大偏差和均方根误差分别为10.87 cm和12.53 cm。考虑下垫面的影响,GlobSnow雪水当量产品和FY-3B雪深产品在两种下垫面下的雪深反演精度差别很小,偏差和均方根误差的差值小于2.11 cm和3.46 cm,AMSR-2积雪产品在两种下垫面下反演精度差别很大,两种下垫面下偏差和均方根误差的差值大于9.94 cm和7.19 cm。对于3种积雪产品,下垫面为农田的雪深反演精度均高于下垫面为阔叶林的反演精度。     

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.     

A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g. floods, droughts). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western USA. However, the ability to produce accurate forecasts and analyses is dependent upon the quality of these predictors. Run-off and stream volume analysis in the region is currently based upon in situ telemetry snow data products. Recent satellite deployments offer an alternative data source of regional snowpack. The proposed research investigates and compares remotely sensed snow water equivalent (SWE) data sets in western US watersheds in which snowpack is the primary driver of streamflow. Watersheds investigated include the North Platte, Upper Green and Upper Colorado. SWE data sets incorporated are in situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer-earth observing system (AMSR-E) aboard NASA's Aqua satellite. The time period analysed is 2003-2008, coincident with the deployment of the NASA Aqua satellite. Bivariate techniques between data sets are performed to provide valuable information on the time series of the snow products. Multivariate techniques including principal component analysis (PCA) and singular value decomposition (SVD) are also applied to determine similarities and differences between the data sets and investigate regional snowpack behaviours. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine whether remotely sensed SWE data provide a comparable option to in situ data sets. Correlation analysis resulted in only 11 of the 84 SNOTEL sites investigated being significant at 90% or greater with a corresponding AMSR-E cell. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Two distinct snow regions were found to behave similarly between both data sets using a rotated PCA approach. Additionally, SVD linked both data products with streamflow in the region and found similar behaviour among data sets. However, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent bias in the absolute magnitude (SWE) of the data sets. The streamflow forecasting results conclude regions that have few (or zero) land-based weather stations can incorporate the AMSR-E SWE product into a streamflow forecast model and obtain accurate values.     

Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande,New Mexico

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day^?1, respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day^?1, with a mean of 5.5 mm day^?1 and standard deviation of 1.85 mm day^?1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.     

冰沟流域积雪面积比例与雪水当量关系初探

像元尺度上积雪面积比例与雪水当量的关系是将积雪遥感面积数据引入水文模型的有效手段。以冰沟流域为例,利用合成孔径雷达ENVISAT-ASAR数据反演得到积雪面积、雪水当量信息,分析了500m像元尺度上积雪面积比例与雪水当量的关系。结果表明:1在积雪面积比例未达到全覆盖饱和状态,雪水当量和积雪面积比例呈正相关关系,积雪面积比例控制着雪水当量的最大值,但由于受到地形的影响,关系不显著;2当考虑地形因子影响,即将坡度、坡向、海拔、积雪面积比例与雪水当量进行多元线性回归,回归系数的显著性水平均小于0.05,相关系数(r)达到0.841。因此,在高分辨率地形因子已知的情况下,结合遥感积雪数据,可建立良好的积雪面积比例和雪水当量之间的关系,有利于高分辨率积雪面积比例数据在寒区分布式水文模型中的应用。     

Remote sensing of snow – a review of available methods

The use of satellite remote sensing for the mapping of snow-cover characteristics has a long-lasting history reaching back until the 1960s. Because snow cover plays an important role in the Earth's climate system, it is necessary to map snow-cover extent and snow mass in both high temporal and high spatial resolutions. This task can only be achieved by the use of remotely sensed data. Many different sensors have been used in the past decades with various algorithms and respective accuracies. This article provides an overview of the most common methods. The limitations, advantages and drawbacks will be illustrated while error sources and strategies on how to ease their impact will be reviewed. Beginning with a short summary of the physical and spectral properties of snow, methods to map snow extent from the reflective part of the spectrum, algorithms to estimate snow water equivalent (SWE) from passive microwave (PM) data and the combination of both spectra will be delineated. At the end, the reader should have an overarching overview of what is currently possible and the difficulties that can occur in the context of snow-cover mapping from the reflective and microwave parts of the spectrum.     

Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼ daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from − 0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction.     

Estimation of snow depth in the UK using the HUT snow emission model

Snow cover characteristics have significant effects on upwelling naturally emitted microwave radiation through processes of forward scattering. This study simulates numerically the electromagnetic responses from snow in the UK using the radiative transfer‐based semiempirical model developed at the Helsinki University of Technology (HUT), which takes into account the influence of soil surface, forest canopy and atmosphere on space‐borne observed brightness temperature by using empirical and semiempirical formulas. A sensitivity analysis of the HUT model was conducted to determine the most sensitive parameter affecting upwelling radiation from snow in the UK. The model‐based results were compared with observed Special Sensor Microwave Imager (SSM/I) brightness temperatures to better understand the SSM/I response to snow. The available ensemble of data required for input to the HUT model comprise surface physical temperature, ground level pressure and water vapour content, forest stem volume and land cover water fraction. Based on the sensitivity analyses, numerical parameters representing physical snow pack quantities (e.g. snow grain size, snow moisture and snow depth (SD)) were varied and the method of root mean square error (RMSE) minimization was used to invert the SD. The HUT model was applied to different days in 3 months (23–31 January, 1–5 and 26–27 February and 1–7 March 1995) of records of daily SD and SSM/I observations. The results show that the HUT model both underestimates and overestimates SD prediction. For the month of January 1995, the HUT model underestimated SD with a bias of ?0.59 cm, whereas for February and March 1995 the HUT model overestimated the SD with a bias of 1.89 cm and 1.64 cm, respectively. This study demonstrates that microwave remote sensing of snow can be used successfully in the UK, where most research on snow cover is conducted by using a visible and infrared radiometer. It is also evident from this work that application of algorithms to snow pack monitoring needs local calibration for effective and reasonable results.     

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semi-empirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sun-synchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R² = 0.88; root mean square (rms) error = 2.21 °C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed – a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various land-cover types. The standard error of this coefficient estimate was 0.002 mm h^?1 K^?1 for both time periods. A strong correlation (R² > 0.87; rms. error <0.17 mm h^?1) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.     

Passive and active airborne microwave remote sensing of snow cover

The presence of snow cover affects the regional energy and water balance, thus having a significant impact on the global climate system. Temporal knowledge of the onset of snow melt and snow water equivalent (SWE) values are important variables in the prediction of flooding, as well as water resource applications such as reservoir management and agricultural activities. Microwave remote sensing techniques have been effective for monitoring snow pack parameters (snow extent, depth, water equivalent, wet/dry state). Coincident ground data, airborne polarimetric C-band (5.3 GHz) Synthetic Aperture Radar (SAR) and passive microwave radiometer data (19, 37 and 85 GHz) were collected on four dates (1 December 1997, 6 March 1998, 12 March 1998 and 9 March 1999) over two flight lines in Eastern Ontario, Canada. The multitemporal, multi-sensor data were analysed for changes in SAR polarimetric signatures and microwave brightness temperatures as a function of changing snow pack parameters. Results indicate that certain parameters such as linear polarizations and pedestal height are sensitive to changes in snow pack parameters, and respond differently to various snow conditions. SWE values derived from the passive microwave brightness temperatures compare well with ground measurements, with the exception of low snow volume and in the presence of significant ice layers.     

On the feasibility of temporally upscaling instantaneous evapotranspiration using weather forecast information

Quantification of land-surface evapotranspiration (ET) is highly significant in water resources management, climate change studies, and numerical weather prediction. The constant reference evaporative fraction method (EF_r, the ratio of the actual to reference ET), which assumes that the daily EF_r is equal to that at the satellite overpass time, is a scheme that has been widely applied to upscale remotely sensed instantaneous ET to daily ET. To overcome the difficulties encountered in the acquisition of tower-based meteorological variables, this study investigates the feasibility of using publicly available weather forecast information to estimate the daily reference ET using the constant EF_r method. A two-source energy balance model is adopted to compute the instantaneous ET using Moderate-Resolution Imaging Spectroradiometer (MODIS) remote-sensing data acquired between January 2011 and October 2012 at the Yucheng Comprehensive Experimental Station in the North China Plain. The results show that the daily maximum and minimum air temperatures from weather forecast information are consistent with the corresponding ground-based measurements, with a bias of 0.8 K and a root mean square error (RMSE) of <2.0 K. The daily global solar radiation and daily wind speed were poorly forecast when compared with the ground-based measurements. Using the meteorological variables from the daily weather forecast information produced a small bias of 0.1 mm day^–1 and an RMSE of 0.6 mm day^–1 when the estimated daily reference ET was compared with that derived using the ground-based meteorological measurements. When the remotely sensed instantaneous ET and half-hourly reference ET were as accurate as the ground-based measurements, the upscaling method produced the daily ET, using the meteorological variables from the weather forecast information, with a bias of 0.1 mm day^–1 and an RMSE of 0.7 mm day^–1.     

Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran

Snow water equivalent (SWE) is a key parameter in hydrological cycle, and information on regional SWE is required for various hydrological and meteorological applications, as well as for hydropower production and flood forecasting. This study compares the snow depth and SWE estimated by multivariate linear regression (MLR), discriminant function analysis, ordinary kriging, ordinary kriging-multivariate linear regression, ordinary kriging-discriminant function analysis, artificial neural network (ANN) and neural network-genetic algorithm (NNGA) models. The analysis was performed in the 5.2 km² area of Samsami basin, located in the southwest of Iran. Statistical criteria were used to measure the models’ performances. The results indicated that NNGA, ANN and MLR methods were able to predict SWE at the desirable level of accuracy. However, the NNGA model with the highest coefficient of determination (R ² = 0.70, P value < 0.05) and minimum root mean square error (RMSE = 0.202 cm) provided the best results among the other models. The lower SWE values were registered in the east of study area and higher SWE values appeared in the west of study area where altitude was higher.     

A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada

The Meteorological Service of Canada (MSC) has developed an operational snow water equivalent (SWE) retrieval algorithm suite for western Canada that can be applied to both Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) data. Separate algorithms derive SWE for open environments, deciduous, coniferous, and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. The combined SSM/I and SMMR time series of dual polarized, multichannel, spaceborne passive microwave brightness temperatures extends back to 1978, providing a lengthy time series for algorithm assessment. In this study, 5-day average (pentad) passive microwave-derived SWE imagery for 18 winter seasons (December, January, February 1978/79 through 1995/96) was compared to SWE estimates taken from a distributed network of surface measurements throughout western Canada.Results indicated both vegetative and snowpack controls on the performance of MSC algorithms. In regions of open and low-density forest cover, the in situ and passive microwave SWE data exhibited both strong agreement and similar levels of interannual variability. In locations where winter season SWE typically exceeded 75 mm, and/or dense vegetative cover was present, dataset agreement weakened appreciably, with little interannual variability in the passive microwave SWE retrievals. These results have important implications for extending the SWE monitoring capability of the MSC algorithm suite to northern regions such as the Mackenzie River basin.     

积雪遥感动态研究的现状及展望

简要讨论了积雪遥感研究的现状,主要包括常用传感器的物理参数及其可行性和局限性,云和雪的区分技术,雪盖面积和积雪深度的提取,雪水当量换算以及积雪遥感在融雪径流模拟、雪灾监测与评价、积雪对气候变化的影响研究等方面的应用。并对积雪遥感研究的发展趋势做了简要的分析与展望。     

Evaluation of passive microwave snow water equivalent algorithms in the depth hoar-dominated snowpack of the Kuparuk River Watershed, Alaska, USA

This research investigates the utility of passive microwave remote sensing instruments to accurately determine snow water equivalent (SWE) over large spatial extents. Three existing Special Sensor Microwave Imager (SSM/I) snow water equivalent algorithms produced by Chang, Tait and Goodison were evaluated for their ability to determine snow water equivalent in a snowpack containing substantial depth hoar, large faceted snow crystals. The Kuparuk River Watershed (8140 km²) test site on the North Slope of Alaska was chosen for its snowpack containing a think depth hoar layer and long history of ground truth data. A new regional snow water equivalent algorithm was developed to determine if it could produce better results than the existing algorithms in an area known to contain significant depth hoar. The four algorithms were tested to see how well they could determine snow water equivalent: (1) on a per pixel basis, (2) across swath-averaged spatial bands of approximately 850 km², and (3) on a watershed scale. The algorithms were evaluated to see if they captured the annual spatial distribution in snow water equivalent over the watershed. Results show that the algorithms developed by Chang and from this research are generally within 3 cm of the spatially averaged snow water equivalents over the entire watershed. The algorithms produced by Chang, Tait, and in this research were able to predict the basin-wide ground measured snow water equivalent value within a percent error range from −32.4% to 24.4% in the years with a typical snowpack. None of the algorithms produce accurate results on a pixel-by-pixel scale, with errors ranging from −26% to 308%.     

AMSR-E data inversion for soil temperature estimation under snow cover

Climate warming is the focus of several studies where the soil temperature plays an essential role as a state variable for the surface energy balance of the Earth. Many methods have been developed to determine summer surface temperature, but the determination in presence of snow is an ill-conditioned problem for microwave techniques because snow changes the emissivity of the surface. This project aims to improve the estimation of soil temperature, within the top 5 cm of the ground, under the snowpack using passive microwave remote sensing. Results show the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from the Advanced Microwave Scanning Radiometer—Earth Observing System, AMSR-E) for the estimation of soil temperature using a physical multilayer snow-soil model (SNTHERM) coupled with a snow emission model (HUT). The snow model is driven with meteorological measurements from ground-based stations as well as data generated from reanalysis. The proposed iterative retrieval method minimizes the difference between the simulated and measured brightness temperature using the soil temperature as a free parameter given by SNTHERM. Results are validated against ground-based measurements at several sites across Canada through several winter seasons. The overall root mean square error and bias in the retrieved soil temperature is respectively 3.29 K and 0.56 K, lower than the error derived from the snow-soil model without the use of remote sensing. The accuracy in detection of frozen/unfrozen soil under the snowpack is 78%, which is improved up to 81% if the spring melting period is not considered. This original procedure constitutes a very promising tool to characterize the soil (frozen or not) under snow cover, as well as its evolution in northern remote locations where measurements are unavailable.