Brightness Temperatures of Snow Melting/Refreezing Cycles: Observations and Modeling Using a Multilayer Dense Medium Theory-Based Model

The ability of electromagnetic models to accurately predict microwave emission of a snowpack is complicated by the need to account for, among other things, nonindependent scattering by closely packed snow grains, stratigraphic variations, and the occurrence of wet snow. A multilayer dense medium model can account for the first two effects. While microwave remote sensing is well known to be capable of binary wet/dry discrimination, the ability to model brightness as a function of wetness opens up the possibility of ultimately retrieving a percentage wetness value during such hydrologically significant melting conditions. In this paper, the first application of a multilayer dense medium radiative transfer theory (DMRT) model is proposed to simulate emission from both wet and dry snow during melting and refreezing cycles. Wet snow is modeled as a mixture of ice particles surrounded by a thin film of water embedded in an air background. Melting/refreezing cycles are studied by means of brightness temperatures at 6.7, 19, and 37 GHz recorded by the University of Michigan Truck-Mounted Radiometer System at the Local Scale Observation Site during the Cold Land Processes Experiment-1 in March 2003. Input parameters to the DMRT model are obtained from snow pit measurements carried out in conjunction with the microwave observations. The comparisons between simulated and measured brightness temperatures show that the electromagnetic model is able to reproduce the brightness temperatures with an average percentage error of 3% (~8 K) and a maximum relative percentage error of around 8% (~20 K)     

Frequency dependence of scattering and extinction of dense media based on three-dimensional simulations of Maxwell's equations with applications to snow

The frequency dependence of scattering by geophysical media at microwave frequencies is an important topic because multifrequency measurements are used in remote sensing applications. In this paper, we study rigorously the frequency dependence of scattering by dense media using Monte Carlo simulations of the three-dimensional solutions of Maxwell's equations. The particle positions are generated by deposition and bonding techniques. The extinction, scattering, and absorption properties of dense media are calculated for dense media of sticky and nonsticky particles. Numerical solutions of Maxwell's equations indicate that the frequency dependence of densely packed sticky small particles are much weaker than that of independent scattering. Numerical results are illustrated using parameters of snow in microwave remote sensing. Comparisons are made with extinction measurements as a function of frequency.     

Inversion of snow parameters from passive microwave remote sensingmeasurements by a neural network trained with a multiple scatteringmodel

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense-media multiple-scattering model. The input-output pairs generated by the scattering model are used to train the neural network. Simultaneous inversion of three parameters, mean-grain size of ice particles in snow, snow density, and snow temperature from five brightness temperatures, is reported. It is shown that the neural network gives good results for simulated data. The absolute percentage errors for mean-grain size of ice particles and snow density are less than 10%, and the absolute error for snow temperature is less than 3 K. The neural network with the trained weighting coefficients of the three-parameter model is also used to invert SSMI data taken over the Antarctic region     

Passive microwave remote sensing of snow constrained byhydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations     

Retrieval of snow parameters by iterative inversion of a neuralnetwork

The inversion of snow parameters from passive microwave remote sensing measurements is performed, using an iterative inversion of a neural network (NN) trained with a dense-media multiple-scattering model. Inversion of four parameters is performed based on five brightness temperatures. The four parameters are mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. Iterative inversion of a data-driven forward NN model is justified on a theoretical and methodological basis. An error analysis is performed, comparing iterative inversion of a forward model with the use of an explicit inverse for the retrieval of independent snow parameters from their corresponding measurements. The NN iterative inversion algorithm is further illustrated by reconstructing a synthetic terrain of snow parameters from their corresponding measurements, inverting all four parameters simultaneously. The reconstructed parameter contours are in good agreement with the original synthetic parameter contours     

Towards the Definition of Optimum Sensor Specifications for Microwave Remote Sensing of Snow

The interaction of nicrowaves with snow strongly depends on parameters such as snow wetness and the size and structure of snow grains. Therefore microwave radiometry and scatterometry are excellent tools for remote sensing of the snowcover. Multifrequency radiometry can be used to classify snow as was shown with ground-based measurements of the period April-June 1977 at a high altitude Alpine test site. The continuation of the measurement program yielded data of 3 additional snow seasons with widely varying snow conditions, therefore the present information has become representative for alpine regions. Relationships between the brightness temperature and the water equivalents show a similar variation with snow type as in other snow regions, so that the range of validity of our data set is not restricted to the Alps. The problem of discriminating regions of wet snow from snow-free land is found to be solvable with microwave scatterometry. Two cluster analyses in factorial spaces of both the ground truth and the microwave data sets demonstrate the potential of microwave sensors to classify snow which is a prerequisite for snow algorithms retrieving hydrologic parameters. The results are used to define sensor specifications with optimum sensitivity for microwave remote sensing of snow.     

Microwave remote sensing of a two-layer random medium

A two-variable expansion technique is used to solve for the mean Green's function from the Dyson equation under the nonlinear approximation. The Bethe-Salpeter equation then gives rise to a set of modified radiative transfer (MRT) equations which accommodate coherent effects essential to bounded media. It is found that the nonlinear approximation, instead of the more popular bilocal approximation, should be used for the case of bounded media. The two approximations yield identical results for unbounded media. The MRT equations are then solved for a two-layer random medium. The MRT equations give rise to simple and useful solutions which are applicable to both active and passive microwave remote sensing.     

Backscattering enhancement and clustering effects of randomlydistributed dielectric cylinders overlying a dielectric half space basedon Monte-Carlo simulations

Backscattering enhancement can exist in volume-surface interactions where a double bounce can arise from one volume scattering and one surface scattering. An important quantity to be determined in backscattering enhancement is the angular width. The authors study backscattering enhancement of the volume-surface interaction by performing Monte-Carlo simulations of scattering by vertical dielectric cylinders overlying a dielectric half space using the Foldy-Lax multiple-scattering equations. The results indicate that the angular width of backscattering enhancement for a scattering layer with a small optical thickness is of the order of the wavelength divided by the layer thickness giving an appreciable angular width of the order of 10-30 degrees that can be important for remote sensing applications. The effects of clustering of scatterers leading to collective scattering and absorption effects are also studied     

Retrieval of dry-snow parameters from microwave radiometric data using a dense-medium model and genetic algorithms

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy.     

相位相关法及其在偏振图像配准中的应用研究

分析了遥感偏振探测的原理和与遥感强度探测相比所具有的优越性,指出了偏振图像的特点。采用偏振测量方法,可以获得地物的偏振信息,有助于增强对地物的探测能力,但是对图像像元不对准有很高的敏感性,为了得到可靠的偏振信息,配准的精度应该至少到达0．1个像素。介绍了一种基于傅立叶变换的配准方法：相位相关法,并把该方法应用于偏振图像配准中。相位相关法根据傅立叶变换的平移性质,能够解决具有平移关系的图像配准问题,实验证明该方法具有对噪声和图像灰度变化不敏感的优点。     

偏振遥感的回顾与展望

主要回顾了偏振遥感的产生与发展历程及其在植被、岩矿、土壤、水体等方面的研究成果,并展望了偏振遥感发展的未来。众所周知,传统遥感是将地球视为朗伯体,故采取垂直方式获取地表的二维信息,其目的是为了更有利于对遥感信息进行计算机处理。事实证明,把地球当做朗伯体颇欠合理,来自地球表面的信息具有明显得三维空间分布特征。李小文院士对BRDF进行了深入研究,并取得了令人瞩目的成果。偏振反射与二向性反射是一对孪生姊妹.BRDF主要反映的是光谱的角度信息,而偏振反射不但反映光谱的角度信息,同时也反映光谱的偏振信息。当地表某个部分为朗伯面时,其光谱的角度信息与偏振信息均为零。分析比较偏振遥感与多角度、高光谱遥感以及微波遥感的内在联系与差异,不难发现上述三种遥感是偏振遥感的特例.     

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.     

Automated delineation of dry and melt snow zones in Antarctica using active and passive microwave observations from space

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated.     

Applications of dense media radiative transfer theory for passivemicrowave remote sensing of foam covered ocean

The effect of the foam covered ocean surface on the passive microwave remote sensing measurements is studied based on the electromagnetic scattering theory. In formulating an electromagnetic scattering model, the authors treat the foam as densely packed sticky air bubbles coated with thin seawater coating. The layer of foam covers the ocean surface that has air bubbles. They then use dense media radiative transfer (DMRT) theory with quasi-crystalline approximation (QCA) for densely distributed sticky moderate size particles to calculate the brightness temperatures of the foam-covered ocean surface. Results are illustrated for 19 GHz and 37 GHz and for both vertical and horizontal polarizations as a function of foam microstructure properties and foam layer thickness. Comparisons are also made with experimental measurements     

相位相关法及其在偏振图像配准中的应用研究

分析了遥感偏振探测的原理和与遥感强度探测相比所具有的优越性，指出了偏振罔像的特点。采用偏振测量方法，可以获得地物的偏振信息，有助于增强对地物的探测能力，但是对罔像像元小对准有很高的敏感性，为了得到可靠的偏振信息，配准的精度应该至少到达0．1个像素。介绍了一种基于傅妒叶变换的配准方法：相位相关法，并把该方法应用于偏振图像配准中。相位相关法根据傅立叶变换的平移性质，能够解决具有平移关系的图像配准问题，实验证明该方法具有对噪声和图像灰度变化不敏感的优点。     

新疆北疆地区雪水当量遥感定量研究

为了探讨光学遥感数据反演雪水当量可行性,2009和2010年2月对新疆北疆地区雪密度等参数进行了野外观测.运用连续统去除法对不同类型积雪光谱吸收特征进行分析得出1028nm、1252nm、1494nm和1940nm附近雪深对光谱吸收深度影响显著,积雪深度越大,光谱吸收深度越小.以500m 中分辨率成像光谱仪MODIS影...     

Bistatic Scattering and Emissivities of Lossy Dielectric Surfaces With Exponential Correlation Functions

Bistatic scattering and emissivities of surfaces with exponential correlation functions are studied numerically for 2-D geometries in a numerical Maxwell model with 2-D simulations. Surfaces with exponential correlation functions are important for the active and passive microwave remote sensing of land surfaces. Because of the fine-scale features with large slopes of such surfaces, numerical accuracy, which is particularly important for the calculation of emissivity in passive remote sensing, is ensured by a variety of procedures in this paper. The rooftop function and Galerkin's method with numerical integration of near-field impedance matrix elements are used. Cubic spline interpolation is employed to connect knots on random rough surfaces. Numerical accuracy convergence tests are performed for numerical solutions of Maxwell equations by varying the number of points from 13 to 103 points per wavelength in the dielectric medium corresponding to 50-400 points per free wavelength. Surface lengths of up to 100 and 200 free wavelengths and root mean square heights of up to 0.4 and 0.8 free wavelengths, respectively, are used at 5 and 10 GHz to capture all the essential features. Because of the large number of surface unknowns (up to 80 000), the multilevel UV method is further used to accelerate the matrix equation solver. Numerical results are illustrated for both bistatic scattering and emissivities as functions of frequencies and incidence and scattering angles for cases of interests in microwave remote sensing. Comparisons are made with the second-order small perturbation method and Kirchhoff's approximation to reestablish the regimes of validity of these methods     

Theoretical and Experimental Studies of Microwave Emission Signatures of Snow

The brightness temperature of snow in Finland has been studied theoretically and experimentally at 5,12, and 37 GHz for satellite remote sensing applications. A snow model consisting of ice spheres covered by a water shell has been used in theoretical calculations taking into account scattering and absorption. The brightness temperature of a natural snow field on the bare ground and on the ground covered with aluminum sheets has been measured from a tower. The experimental brightness temperatures are compared with calculated ones and show a reasonably good agreement. Experimental results also show that relatively small changes in the snow conditions cause large changes in the brightness temperature. Possible methods for using satellite observations in the remote sensing of snow are suggested.     

无源微波遥感用于地震预报的实验研究

对无源微波遥感用于地震预报在理论研究的基础上对不同岩性不同结构的岩石试件进行了加载实验，测量了岩石试件的微波辐射随载荷的变化．实验得出：岩石试件的微波辐射能量随岩石应力状态变化而显著变化；而且，不同的波段变化的幅度不同；同一波段不同极化方式的波随应力变化的变化量也不同；微波辐射能量的变化能够被卫星微波遥感探测器探测到．实验还发现，岩石试件临破裂前出现明显的微波辐射异常．     

裸地散射特性分析

本文详细地研究了裸地（农用耕地）的散射特性。根据实验现象提出了新的散射系数与入射角关系模型，与实验数据获得了很好的吻合性。通过分析裸地散射系数的雷达参数（入射角、极化、频率）和地面参数（粗糙度、土壤湿度）的响应特性，得到微波遥感土壤湿度时的最佳工作参数。