山东半岛北斗基准站观测数据质量分析与评价

本文使用数据缺失天数统计软件与TEQC数据预处理软件评估了山东半岛31座北斗基准站2021年全年的观测数据质量,结果表明：大部分北斗基准站的观测数据质量良好,符合后续研究要求;23座北斗基准站全年观测数据质量良好,全年数据均可用于后续研究;HJBH、LJZK、LOKO、SDPL 4座北斗基准站在GPS周2142周和2185周出现仪器故障,后续研究应舍弃这2周数据;RZRS、ZNRS 2座北斗基准站全年数据质量较差,不应用于后续研究;WEIH、SDLA 2座北斗基准站在年积日158d和160d后数据质量明显提升,符合后续研究要求。北斗基准站应加强数据质量动态监测及巡检力度,确保站点数据质量稳定可靠。     

微波与光学遥感协同反演旱区地表土壤水分研究

土壤水分是水文循环中的关键因素,尤其对旱区的生态环境具有十分重要的意义。微波遥感是反演土壤水分的有效手段,而植被是影响土壤水分反演精度的重要因素。因此,对土壤水分的反演需要考虑植被的影响。本文以内蒙古乌审旗为研究区,利用Radarsat-2雷达数据与TM光学数据,对旱区稀疏植被覆盖地表土壤水分反演进行研究。利用TM数据,分别选取NDVI和NDWI指数对植被含水量进行反演,通过水云模型消除植被层对土壤后向散射系数的影响;在此基础上,根据研究区地表植被特性,提出一种基于AIEM 模型的反演土壤水分的改进算法,反演了不同粗糙度参数、不同极化（VV极化和HH极化）条件下的研究区土壤水分。反演结果与野外实测数据的对比结果表明,本文提出的基于地表植被特性的土壤水分改进算法,具有更好的适应性;土壤水分反演模式\begin{array}{c}M{v}_{\mathit{\sigma vv}\left(1\right)}^{\mathit{lh}}\end{array}（VV极化方式下采用NDVI去除植被影响的反演模式）更适合于旱区考虑稀疏植被覆盖影响的地表土壤水分的反演。     

基于CORS的高程参考框架的动态监测与维护

通过InSAR技术对山东省地表形变监测发现,黄三角、鲁西南等地区地表沉降明显,这些地球形变的发生,使测绘基准成果的可靠性变差,严重影响了测绘基准的应用服务。高程参考框架本身也是动态的,开展高程参考框架的动态监测与维护是必要的。通过对CORS站网2015—2019年的连续观测成果进行精细处理,2019年开展对CORS站、B级和C级点、水准点进行二等水准观测,通过2015年、2019年两期水准成果检核CORS站点正常高值,探索出一种基于CORS实现对大地高程参考框架的动态监测与维护的新方法,通过研究还发现,大多数水准点发生了沉降,少数水准点发生了抬升,CORS和水准的数据成果也验证了InSAR对地表形变的监测结果。     

CORS在线定位用户服务系统

首先对基于CORS的在线定位用户服务系统功能进行了较细致的分析,将其概括为7项基本功能和4项扩展功能。在此基础上提出了系统架构方案,将系统划分为3个部件和20个子部件,并对各组成部分的功能作了较详细的阐述。最后,针对用户数据上传与存储、基线解算和基线向量网平差等核心功能,提出了较具体的设计方案,并对软件实现与系统集成方法进行了阐述。     

遥感影像超分辨率重建的字典学习类算法

近年来基于字典学习的超分辨率重建技术已成为图像处理领域的研究热点,相比基于重建的超分辨率方法,基于学习的方法充分利用了先验知识,在放大倍数较高时,仍可取得较好的效果,因此被公认为一种非常有前途的方法。本文对国内外已有的基于字典学习的超分辨率重建方法进行了系统研究,梳理了3种基于字典学习超分重建算法的基本原理及优缺点。此外,本文根据遥感影像的特点,使用同一数据源进行字典学习,利用不同字典学习算法分别生成高、低联合字典对,采用不同尺寸大小及缩放倍数的测试图像,进行超分辨率重建,对各种算法的重建性能、鲁棒性和复杂度进行综合分析,进一步研究了各种算法对遥感影像不同应用需求的适用性。     

空-谱信息与稀疏表示相结合的高光谱遥感影像分类

针对传统的高光谱遥感影像分类中多依赖光谱信息而忽视空间信息以及提取的特征维数高的问题,提出了一种空-谱信息与稀疏表示相结合的分类算法。首先,利用最小噪声分离对原始影像进行降维,在此基础上,对主成分图上局部影像块内的所有像素进行重组,并用排序的方法得到旋转不变的空-谱特征。然后,对空-谱特征进行监督学习得到字典,并将提取的测试样本的空-谱特征编码到字典中以得到测试样本的稀疏表示。最后,使用支持向量机分类器(SVM)对高光谱影像进行分类。3组高光谱数据试验表明,与传统的分类方法比较,本文方法能有效提高分类精度。     

Robust partitioned block preconditioners for large‐scale geotechnical applications with soil–structure interactions

Soil–structure interaction problems are commonly encountered in geotechnical practice and remarkably characterized with significant material stiffness contrast. When solving the soil–structure interaction problems, the employed Krylov subspace iterative method may converge slowly or even fail, indicating that the adopted preconditioning method may not suit for such problems. The inexact block diagonal preconditioners proposed recently have been shown effective for the soil–structure interaction problems; however, they haven't been exploited to full capabilities. By using the same partition strategy according to the structure elements and soil elements, the partitioned block symmetric successive over‐relaxation preconditioners or partitioned block constraint preconditioners are proposed. Based on two pile‐group foundation problems and a tunnel problem, the proposed preconditioners are evaluated and compared with the available preconditioners for the consolidation analysis and the drained analysis, respectively. In spite of one additional solve associated with the structure block and multiplications with off‐diagonal blocks in the preconditioning step, numerical results reveal that the proposed preconditioners obviously possess better performance than the recently developed inexact block preconditioners. Copyright © 2013 John Wiley & Sons, Ltd.     

双套自动气象站数据评估及其优势探讨

象站双套运行的结果和优缺点进行了深入分析和探讨。从缺测率上分析了双套站数据的完整性,认为双套站可以明显降低数据的缺测率,具有保证数据完整性的优点。从差值的统计数字特征以及一致率、超差率和粗差率等指标上分析了两套数据的差异性,表明双套站大部分要素 (如气温、气压、相对湿度等) 具有差值小、差值变化幅度小、数据之间有较高的一致率、较低的粗差率和超差率等特点,但个别要素 (草面温度和5 cm地温) 差异略大。运行中通过对两套数据的差值、差值的滑动平均值、差值日变化特征的监控可以及时发现数据异常、探测仪器的系统性误差等情况,从而说明双套站具有保证数据质量的潜在优势,且从一定程度解决了目前自动气象站单套运行的不足之处。     

Preserving Continuity of Long-Term Daily Maximum and Minimum Temperature Observations with Automation of Reference Climate Stations using Overlapping Data and Meteorological Conditions

The seasonal-by-wind bias method for aligning time series of daily maximum and minimum temperatures from past conventional staffed and new automated sites using closely collocated, overlapping observations is presented for twenty-two modernized Reference Climate Stations in Canada. The method consists of adjusting for incompatible observing times and deriving biases from the daily “manual-minus-automated” temperature differences classified into seasons and wind-speed conditions. Most of the biases vary with the season, and many show limited wind dependency. Four sets of adjusted time series are prepared based on two-year and five-year overlapping data and on seasonal bias with or without wind conditions; the adjusted data are compared with the original observations. Based on the mean of the absolute differences and examination of box plots, the results show that, for this particular set of stations, the two-year versus five-year and seasonal versus seasonal-by-wind bias adjusted time series are overall similar. The largest contribution to the improvements in the adjusted observations came from matching the times of observation. Additionally, daily temperatures are adjusted using statistical methods applied with neighbouring station data but no overlapping observations at collocated stations; it is concluded that these do not necessarily resolve the bias between staffed and automated sites.