双/多基地天气雷达探测小椭球粒子群的雷达气象方程

首先导出小椭球散射的方向函数及侧向散射截面的表达式。然后,考虑发射水平偏振波与垂直偏振波这两种情况下,当小椭球粒子群旋转轴作一致铅直取向和在空间作均匀随机取向时,分别建立适用于双/多基地雷达接收子站的雷达气象方程。主要结果为:(1)当入射波以不同的仰角及方位角射到旋转轴任意取向的椭球上时,会在椭球的3个轴上产生不同的极化电偶极矩分量,使小椭球散射方向函数不仅与散射方向有关,还与天线和粒子两个直角坐标之间的配置情况有关。(2)无论入射波偏振方式是水平还是垂直,小椭球粒子的总侧向散射截面恒是散射波在该方向上造成的侧向散射截面之和。(3)双基地雷达气象方程与单基地雷达气象方程比较,其差别在于侧向散射截面与后向散射截面的不同,另外,有效照射体积不同,子站与单基地站天线方向性函数不同,使双基地雷达气象方程中多出与双基地角有关的一个因子。(4)主站天线辐射在半功率点内不论均匀与否,双基地雷达气象方程中的侧向散射截面均代表总的侧向散射截面且依赖入射波偏振方式。(5)给出了小椭球群旋转轴一致铅直取向情况下的双基地雷达气象方程,这些方程与入射波偏振方式、主站发射天线辐射在半功率点内均匀与否有关。入射波为垂直偏振时一致铅直取向的椭球群的侧向散射截面可能由包含铅直分量在内的3个正交分量组成。这具体依赖于主站天线仰角。但入射波为水平偏振时到达子站天线处的回波功率与主站天线仰角无关。(6)给出了小椭球群旋转轴在空间无规则取向时的双基地雷达气象方程,这些方程与主站天线仰角、入射波偏振方式等无关,但与主站发射天线辐射在半功率点内均匀与否有关。     

Orthorectification of VHR optical satellite data exploiting the geometric accuracy of TerraSAR-X data

Orthorectification of satellite data is one of the most important pre-processing steps for application oriented evaluations and for image data input into Geographic Information Systems. Although high- and very high-resolution optical data can be rectified without ground control points (GCPs) using an underlying digital elevation model (DEM) to positional root mean square errors (RMSEs) between 3 m and several hundred meters (depending on the satellite), there is still need for ground control with higher precision to reach lower RMSE values for the orthoimages. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This is due to the fact that the SAR antenna measures distances which are mainly dependent on the terrain height and the position of the satellite. The latter can be measured with high precision, whereas the satellite attitude need not be known exactly. If the used DEM is of high accuracy, the resulting geocoded SAR data are very precise in their geolocation. This precision can be exploited to improve the orientation knowledge and thereby the geometric accuracy of the rectified optical satellite data. The challenge is to match two kinds of image data, which exhibit very different geometric and radiometric properties. Simple correlation techniques do not work and the goal is to develop a robust method which works even for urban areas, including radar shadows, layover and foreshortening effects. First the optical data have to be rectified with the available interior and exterior orientation data or using rational polynomial coefficients (RPCs). From this approximation, the technique used is the measurement of small identical areas in the optical and radar images by automatic image matching, using a newly developed adapted mutual information procedure followed by an estimation of correction terms for the exterior orientation or the RPC coefficients. The matching areas are selected randomly from a regular grid covering the whole imagery. By adjustment calculations, parameters from falsely matched areas can be eliminated and optimal improvement parameters are found. The original optical data are orthorectified again using the delivered metadata together with these corrections and the available DEM. As proof of method the orthorectified data from IKONOS and ALOS-PRISM sensors are compared with conventional ground control information from high-precision orthoimage maps of the German Cartographic Survey. The results show that this method is robust, even for urban areas. Although the resulting RMSE values are in the order of 2-6 m, the advantage is that this result can be reached even for optical sensors which do not exhibit low RMSE values without using manual GCP measurements.     

基于Delphi和MapX组件的新一代天气雷达拼图系统

以辽宁省电子地图为基础数据,利用Borland Delphi为主要软件开发平台,基于COM技术的可编程OCX控件MapX开发了辽宁省新一代天气雷达拼图系统。该系统发挥了地图控件显示处理地理信息的优势和面向对象语言编程的灵活性,实现了新一代天气雷达拼图在电子地图上的"显示和缩放"、"漫游"和"图层控制"等功能。论述了雷达产品的坐标系转换原理、地图投影的选择、雷达重叠区域回波处理和栅格图像的配准以及MapX提供的数据绑定和临时图层生成方法,实现了雷达数据的地图加载。     

一种半导体激光天气雷达的波形设计

本文利用霍尔序列设计一种半导体激光天气雷达的发射波形,解决了用m序列时无法解决的信号压缩比要求和系统运算速度限制之间的矛盾。文中给出了波形的设计和产生方法及其性能模拟结果。     

SAR影像几何校正

SAR影像的几何校正是阻碍其应用的瓶颈问题，目前所用的方法都有其局限性。介绍描述SAR构像几何的坐标系统及距离－多普勒方程，同时，介绍卫星轨道及描述卫星轨道的参数。为提高几何校正的精度，提出一种由几个离散点的值内插整个弧段上卫星状态向量的轨道模拟算法。在此基础上，给出利用DEM进行SAR影像几何校正的方法。该方法以时间参数为自变量，迭代解算距离－多普勒方程，计算DEM中点每一点对应的影像坐标，最后进行了实验与讨论。     

基于梯度提升决策树模型的Sentinel-1图像浅海水深反演

利用合成孔径雷达(Synthetic Aperture Radar, SAR)反演浅海水深在海洋遥感中极具挑战性。本文采用梯度提升决策树(Gradient Boosting Decision Tree, GBDT)为核心的机器学习算法, 使用Sentinel-1、全球水深数据、风场和流场数据来反演杭州湾和长江口南缘相连的浅海区域的水深。首先分析反演的最佳风速和迭代次数, 再对0~10 m、10~20 m、20~30 m、30~40 m、40~50 m的分段水深和0~10 m、0~20 m、0~30 m、0~40 m、0~50 m的总体水深用相关系数、均方根误差和平均绝对误差进行精度评价, 最后分析反演水深的空间分布特征。结果表明: 反演的最佳风速约为3.78 m/s, 并且GBDT模型达到最佳精度时的迭代次数远小于其他模型, 最佳迭代次数为4。分段水深中, 40 m以内的相关系数都高于0.8, 其中以10~20 m的相关系数最高, 为0.9; 40~50 m则最低, 为0.73。40~50 m的平均绝对误差和均方根误差均为最大, 分别为1.89 m和2.24 m, 20~30 m的平均绝对误差和均方根误差均为最小, 分别为0.75 m和0.96 m。在总体水深中, 虽然随水深区间的扩大, 相关系数会逐渐增加, 但是平均绝对误差和均方根误差的精度都随水深区间的扩大而下降, 且在0~50 m区间内的平均绝对误差和均方根误差最大, 分别为1.06 m和1.59 m, 因此反演的最佳区间为0~40 m。该区域的水深从杭州湾海岸线开始由浅及深阶梯增加, 反演结果能够较好的表现研究区内的实际水深分布情况, 比较符合当前区域的水下地形特征。     

Quantitative assessment of remotely sensed global surface models using various land classes produced from Landsat data in Istanbul

Digital elevation model(DEM) is the most popular product for three-dimensional(3D) digital representation of bare Earth surface and can be produced by many techniques with different characteristics and ground sampling distances(GSD). Space-borne optical and synthetic aperture radar(SAR) imaging are two of the most preferred and modern techniques for DEM generation. Using them, global DEMs that cover almost entire Earth are produced with low cost and time saving processing. In this study, we aimed to assess the Satellite pour l'observation de la Terre-5(SPOT-5), High Resolution Stereoscopic(HRS), the Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER), and the Shuttle Radar Topography Mission(SRTM) C-band global DEMs, produced with space-borne optical and SAR imaging. For the assessment, a reference DEM derived from 1∶1000 scaled digital photogrammetric maps was used. The study is performed in 100 km2 study area in Istanbul including various land classes such as open land, forest, built-up land, scrub and rough terrain obtained from Landsat data. The analyses were realized considering three vertical accuracy types as fundamental, supplemental, and consolidated, defined by national digital elevation program(NDEP) of USA. The results showed that, vertical accuracy of SRTM C-band DEM is better than optical models in all three accuracy types despite having the largest grid spacing. The result of SPOT-5 HRS DEM is very close by SRTM and superior in comparison with ASTER models.     

Solar radar astronomy with the low-frequency array

Initial studies of the Sun's corona using a solar radar were done in the 1960s and provided measurements of the Sun's radar cross-section at about 38 MHz. These initial measurements were done at a time when the large-scale phenomenon known as a coronal mass ejection was unknown; however, these data suggest that coronal mass ejections (CMEs) may have been detected but were unrecognized. That solar radar facility, which was located at El Campo, TX, no longer exists. New solar radar investigations are motivated by our modern understanding of CMEs and their effects on the Earth. A radar echo from an Earthward-directed coronal mass ejection may be expected to have a frequency shift proportional to velocity; thus providing a good estimate of arrival time at Earth and the possible occurrence of geomagnetic storms. Solar radar measurements may also provide new information on electron densities in the corona. The frequencies of interest for solar radars fall in the range of about 10–100 MHz, corresponding to the lower range planned for the low-frequency array. In combination with existing or new high-power transmitters, it is possible to use the low-frequency array to re-initiate radar studies of the Sun's corona. In this report, we review the basic requirements of solar radars, as developed in past studies and as proposed for future investigations.     

探地雷达探测地下目标时的波速估计

在利用探地雷达(GPR)进行工程检测过程中，电磁波在地下介质中的传播速度是影响埋深计算精度的关键参数之一。本文通过吸取几种波速求取方法的优点，给出一种精度高、操作性强的波速求取法——刚性界面反射系数法；并利用此法，由LTD探地雷达实测数据求得整条测线的速度曲线。经检验，借助于少量钻芯数据，利用此法可使公路面层厚度检测的相对误差小于5％。     

Prediction of topsoil properties at field-scale by using C-band SAR data

Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σ_VV^o and σ_VH^o, while those with low prediction accuracy showed high values of σ_VV^o and low values of σ_VH^o. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.