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     

湖泊叶绿素高光谱空谱联合遥感反演

高光谱遥感提供的精细光谱信息给水色遥感参数反演提供了广阔的前景,然而光谱分辨率高但空间分辨率较低的特点,使得目前的高光谱水色遥感反演模型和算法普遍缺乏对空间信息的有效利用,模型的精度和稳定性往往难以保证。以巢湖为研究区,利用HJ-1A卫星HSI高光谱遥感数据,结合地面实测样点数据,在深入分析叶绿素光谱特性基础上构建基于空间八邻域与遗传算法的水体叶绿素a高光谱遥感反演模型,并以matlab7.0为平台,联合光谱指数与遗传算法求解叶绿素a浓度反演模型参数,经空间邻域分析与遗传迭代,求出叶绿素浓度最优解。结果表明,遗传算法摒弃了传统的搜索方式,以光谱信息为基础,在邻近空间域上采用模拟进化方式对水色空间进行随机优化搜索,跳出了局部极值点,能够有效提高模型反演的精度。     

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...     

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     

基于遗传BP神经网络算法的主被动遥感协同反演土壤水分

提出了一种基于遗传神经网络算法的主被动遥感协同反演地表土壤水分的方法.首先,建立一个BP神经网络,并采用遗传算法对BP网络的节点权值进行了优化.然后分别将TM数据(TM3,TM4,TM6)、不同极化和极化比的(VV,VH,VH/VV)ASAR数据作为神经网络的输入,土壤水分含量作为网络的输出,用部分实测数据对网络进行训练并反演得到研究区土壤水分布图.最后,利用地面实测数据分别对遗传神经网络优化算法的有效性和主被动遥感协同反演的效果进行了验证,结果表明,新优化算法是有效可行的,且TM和ASAR协同反演的结果比两者单独反演的结果明显要好,体现了主被动遥感协同反演土壤水分的优势与潜力.     

Sensitivity of passive microwave snow depth retrievals to weather effects and snow evolution

Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.     

Estimation of surface snow properties using combined millimeter-wave backscatter and near-infrared reflectance measurements

Knowledge of surficial snow properties such as grain size, surface roughness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using combined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared and millimeter-wave measurements for accurate estimation of the relevant snow properties. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 nm and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested using simulated data, and is seen to perform well under noise-free conditions. Under noise-added conditions, a signal-to-noise ratio of 32 dB or greater ensures acceptable errors in snow parameter estimation.     

A prototype AMSR-E global snow area and snow depth algorithm

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.     

基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.     

Inferring snow wetness using C-band data from SIR-C's polarimetricsynthetic aperture radar

In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25°-70° and for surface roughness with rms height ⩽7 mm and correlation length ⩽25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs     

Bayesian algorithm for microwave-based precipitation retrieval: description and application to TMI measurements over ocean

A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.     

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.     

Integrating in situ and multiscale passive microwave data for estimation of subgrid scale snow water equivalent distribution and variability

New multiscale research datasets were acquired in central Saskatchewan, Canada during February 2003 to quantify the effect of spatially heterogeneous land cover and snowpack properties on passive microwave snow water equivalent (SWE) retrievals. Microwave brightness temperature data at various spatial resolutions were acquired from tower and airborne microwave radiometers, complemented by spaceborne Special Sensor Microwave/Imager (SSM/I) data for a 25/spl times/25 km study area centered on the Old Jack Pine tower in the Boreal Ecosystem Research and Monitoring Sites (BERMS). To best address scaling issues, the airborne data were acquired over an intensively spaced grid of north-south and east-west oriented flight lines. A coincident ground sampling program characterized in situ snow cover for all representative land cover types found in the study area. A suite of micrometeorological data from seven sites within the study area was acquired to aid interpretation of the passive microwave brightness temperatures. The in situ data were used to determine variability in SWE, snow depth, and density within and between forest stands and land cover types within the 25/spl times/25 km SSM/I grid cell. Statistically significant subgrid scale SWE variability in this mixed forest environment was controlled by variations in snow depth, not density. Spaceborne passive microwave SWE retrievals derived using the Meteorological Service of Canada land cover sensitive algorithm suite were near the center of the normally distributed in situ measurements, providing a reasonable estimate of the mean grid cell SWE. A realistic level of SWE variability was captured by the high-resolution airborne data, showing that passive microwave retrievals are capable of capturing stand-to-stand SWE variability if the imaging footprint is sufficiently small.     

Differentiation between melt and freeze stages of the melt cycle using SSM/I channel ratios

Microwave remote sensing detection of snow melt and ablation generally focuses on the detection of liquid moisture in the snow-pack. For ablation estimation, it is important to determine if wet snow is in the process of melting or freezing. The different stages of the melt cycle are observed in the diurnal variation of T/sub b/ measurements from the Special Sensor Microwave Imager (SSM/I) over Greenland. SSM/I channel ratios exhibit patterns indicating that they are sensitive to melt and freeze stages of the daily melt cycle. The horizontal to vertical polarization ratio is sensitive to surface wetness associated with melting. The 19-37-GHz frequency ratio is sensitive to a frozen surface layer over wet snow which is associated with the freeze stage of the melt cycle. These observations are supported by conceptual models presented here and in in situ measurements from other investigators.     

Nimbus 7 SMMR Investigation of Snowpack Properties in the Northern Great Plains for the Winter of 1978-1979

An investigation of the capabilities of remote sensing of snowpack properties was conducted with brightness temperatures from the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and climatological data for the northern Great Plains for the winter of 1978-1979. The radiometer data included horizontally and vertically polarized brightness temperatures at the 0.81-, 1.66-, and 2.80-, and 4.54-cm wavelengths for both day and night overpasses, with a repeat coverage on the average of every two to three days. The brightness temperatures in each channel and the daily surface climatological elements of maximum and minimum air temperature, precipitation, snowfall, and snow depth were objectively analyzed to a 20-km grid with 35 rows and 42 columns. The analysis concentrated on temporal analyses of selected grid cells. Characteristic signatures were observed for initial snow accumulation, snow depth to about 20 cm, beginning of snow melting in the surface layers, and snow melt. The process of snow ripening was evident in the thawing and refreezing cycles of the snow surface layers. Discrimination of dry soil, wet soil, snow amount to 15 cm, and liquid water at the soil surface before runoff occurred was present with the use of both polarizations at the 0.81- and 1.66-cm wavelengths, although the longer wavelengths contained additional information on the state of the surface underlying the snow pack.     

一种高效的海量遥感栅格数据库的空间可视化检索算法

该文针对利用GIS现有空间查询接口进行海量遥感栅格数据库空间可视化检索效率低下的问题。在深入研究海量遥感栅格数据库的空间可视化检索特点的基础上,提出了一种直接针对关系数据库(RDBMS)存储过程的高效的海量遥感栅格数据库复杂空间可视化检索算法,并对算法进行了多个级别的性能优化。该算法可直接应用于海量遥感栅格数据库基于多边形、椭圆和线穿越等复杂空间可视化查询的应用环境。实验结果说明该算法具有稳定性和普适性。     

星载单光子激光雷达浅水测深技术研究进展和展望

在浅水测深技术中,星载激光测量系统可以覆盖一些机载/舰载系统难以到达的偏远水域,具有比被动光学影像水深测量精度更高、可全天时工作等独特的优势。以稀疏而少量的主动星载激光测量值为水深标定点,融合被动星载遥感影像,主被动融合的浅水测深是当前的趋势。本文首先介绍了星载单光子激光雷达的工作范围、物理参数和数据产品,概述了测量原理,综述了现有的星载单光子激光雷达测深的理论传输模型,对比了不同的点云数据去噪处理算法的优劣,归纳了星载融合测深反演技术在不同环境中的应用,总结了当前存在的问题,并对该技术未来的前景和发展方向进行了展望。     

Average areal water equivalent of snow in a mountain basin using microwave and visible satellite data

Satellite microwave and millimeter-wave data have been used to evaluate the average areal water equivalent of snow cover in the mountainous Rio Grande basin in Colorado. Areal water equivalent data for the basin were obtained from contoured values of point measurements and from elevation-zone water volume values generated by a reliable snowmelt-runoff model using data on visible snow-cover extent. A significant relationship between the difference in brightness temperature at two different frequencies (37 and 18 GHz) and a basin-wide average snow-water equivalent value was obtained. This relationship and microwave observations were used to estimate the average areal water equivalent of the snow cover.<>     

基于氧气A 带的高光谱卫星气溶胶层高优化反演

针对气溶胶被动卫星遥感中由于气溶胶模型的不确定性导致的反演误差, 引入了一种基于贝叶斯理论的新型 气溶胶层高反演算法, 并应用于哨兵5 先导(Sentinel-5P) 卫星的TROPOMI (TROPOspheric Monitoring Instrument) 载 荷。该算法基于不同候选气溶胶模型的模型证据(气溶胶模型的条件概率密度) 确定符合当前观测数据条件的气溶胶 模型, 并通过两种模型选择方案分别得到估算最大值解和估算平均值解作为反演结果。以TROPOMI 观测到的一次真 实野火事件为例, 反演结果和官方产品具有很好的空间一致性, 且明显降低了低估现象, 证明在气溶胶先验知识缺乏 的背景下该算法能够高效选择合适的气溶胶模型, 为今后高光谱卫星气溶胶层高反演的业务化数据处理提供了一种 新的解决方案。