2019年8月16日诸城超级单体风暴双偏振参量结构特征分析

利用青岛S波段双偏振多普勒天气雷达探测资料和常规气象观测资料以及区域自动气象站观测资料,对2019年8月16日发生在山东省诸城市的一次长寿命超级单体风暴双偏振结构特征进行了分析。结果表明:超级单体风暴发生在东北冷涡和地面中尺度辐合线共同作用背景之下,对流有效位能偏低,但风垂直切变非常强,这种配置有利于超级单体风暴的形成与维持。诸城超级单体风暴持续3 h左右并伴有深厚持久的中气旋,旺盛阶段最强反射率因子、基于单体的垂直累积液态含水量、强中心高度和单体顶部高度平均分别为74.1 dBz、67.9 kg/m2、6.3 km和11.3 km。对偏振特征分析表明,风暴低层60 dBz以上回波区对应偏小的差分反射率（Z_dr）、小的相关系数（CC）和大的差分相移率（K_dp）,湿（或干）冰雹和液态雨滴共存。此外,低层入流缺口附近有明显Z_dr弧存在。风暴中层强上升气流区内有明显的有界弱回波区,其顶部达到7 km左右。有界弱回波区内相关系数较小,其周围有明显的Z_dr环和CC环,Z_dr环顶部达到?10℃层高度。0℃层高度之上存在深厚的Z_dr柱和K_dp柱,顶部都达到?20℃层高度,Z_dr柱位于有界弱回波区东侧,K_dp柱位于西侧。?10—?20℃层,Z_dr柱对应强的水平极化反射率因子（35—60 dBz）和小的差分相移率,表明含有少数偏大的液态或湿冰粒子,而K_dp柱对应更强的水平极化反射率因子（55—72 dBz）和小的差分反射率,表明含有一定数量的小的液态或（和）湿冰粒子及大的冰雹粒子。风暴低层强反射率核后侧径向上如果出现显著差分反射率负值区,可作为特大冰雹（直径≥50 mm）的识别依据;如果对应异常大的差分相移率,表明含有浓度较高的雨滴和包有水膜的冰雹粒子。      

On the reduction and presentation of data in astronomical two-channel photopolarimetry

Many different methods exist for reducing data obtained when an astronomical source is studied with a two-channel polarimeter, such as a Wollaston prism system. This paper presents a rigorous method of reducing the data from raw aperture photometry, and evaluates errors both by a statistical treatment, and by propagating the measured sky noise from each frame. The reduction process performs a hypothesis test for the presence of linear polarization. The probability of there being a non-zero polarization is obtained, and the best method of obtaining the normalized Stokes Parameters is discussed. Point and interval estimates are obtained for the degree of linear polarization, which is subject to positive bias; and the polarization axis is found.     

Scattering property based contextual PolSAR speckle filter

Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data.     

PolSAR相干斑抑制中的非局部均值滤波研究进展

为促进非局部均值滤波在PolSAR相干斑抑制技术领域的应用与发展,首先简要阐述了非局部均值滤波的基本理论;然后系统梳理了非局部均值在PolSAR相干斑抑制处理中应用的历史沿革,最后深入探讨了现有研究工作的不足之处及其解决之道,为该方向进一步开展研究工作提供有益参考。     

基于全极化Radarsat-2影像的建筑物震害评估

利用震后灾区全极化SAR影像可快速提取建筑物震害信息,为应急救援的快速有效实施提供重要的灾情信息支持。本文建立基于极化散射矩阵的Pauli-Wishart监督极化分类的建筑物震害信息提取和以街区为尺度的震害程度评估方法,包括Pauli分解、Wishart监督分类和遥感震害指数提取,并利用玉树县城区2010年4月14日青海玉树7.1级地震震后全极化Radarsat-2影像,提取了建筑物震害信息。经统计,确定结果总体分类精度达到0.81,Kappa值为0.61,表明本文提取建筑物震害的方法是可行的。     

The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.     

Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley,California

Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (>40%), while surface scattering (>40%) dominated for alfalfa and winter wheat, although double-bounce scattering (>30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (>40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (>40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.     

利用全极化微波辐射计资料反演台风境内海面风场

作为一种新兴的被动遥感技术,全极化微波辐射计不仅可以提供海面风速产品,还可以提供海面风向产品.以往利用全极化微波辐射计观测亮温进行海面风场反演仅在晴空条件下进行,本文通过对观测亮温结合台风区域海面风场的分布特征进行分析,验证了全极化微波辐射计具有在台风等恶劣天气条件下进行海面风场观测的能力.基于敏感性分析实验,确定使用6.8 GHz和10.7 GHz等低频通道组合可进行台风区域内海面风场反演.其中,海面风速反演使用基于统计的多元线性回归算法,同时对海面温度、大气水汽含量、云中液态水含量及降水强度等物理量进行反演计算,为海面风向反演做准备.海面风向反演使用物理统计法进行,借鉴散射计风向反演使用的最大似然估计法.通过在全极化辐射传输前向模型中加入降水对大气透过率的影响、设计第三和第四Stokes通道亮温环境影响修正函数,在实现台风区域内海面风向反演的同时减小了反演误差.通过对“云娜”台风境内海面风场进行数值计算,验证了本文反演算法的可行性,并对反演误差的空间分布特征进行了分析.将2004年各台风过程的海面风场反演结果与散射计风场产品进行对比,海面风速和海面风向反演的均方根误差分别为1.64 m·s-1和18.02°.     

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.     

Forest parameter estimation in the Pol-InSAR context employing the multiplicative-additive speckle noise model

This paper addresses the problem of speckle noise on single baseline polarimetric SAR interferometry (Pol-InSAR) on the basis of the multiplicative-additive speckle noise model. Considering this speckle noise model, a novel filtering technique is defined and studied in terms of simulated and experimental Pol-InSAR data. As demonstrated, the use of the multiplicative-additive speckle noise model does not lead to a corruption of the useful information but to an improvement of its estimation. The performance of the algorithm is analyzed in terms of the physical parameters retrieved from the filtered data, that in this work correspond to the forest height and the ground phase. In case of experimental data, the retrieved forest height is compared and validated against Lidar ground truth measurements.