在洪水灾情监测中,快速准确的获取淹没区域和洪灾面积,对防汛救灾和灾后重建工作具有重要价值.本文以2017年美国圣路易斯洪水为例,基于Sentinel-1 SAR数据,利用变化检测和阈值相结合的方法实现大范围洪水淹没提取,将VV/VH极化数据分别与从同期Sentinel-2光学影像中获取的洪水淹没范围进行比较,评定极化方式的洪水适用性优劣程度.不同的SAR极化数据对洪水监测的适用性不同,通过绘制各极化不同时期的后向散射横断面线来分析多极化中的散射响应特征.研究表明:Sentinel-1 VV/VH极化数据均能以超过82％的高精度识别出洪水,VV极化洪水提取时产生的误判更少;在同样的区域,相较于VH,Sentinel-1 VV极化信号的散射程度小了约28％,在洪水中的信息敏感,更适用于洪水灾害的淹没范围监测.

在洪水灾情评估中,洪水淹没范围是最重要的信息之一.合成孔径雷达(SAR)全天时、全天候的特点使其成为洪涝灾害评估的重要数据来源.为了监测整个灾害期间的洪水淹没范围并追踪洪水的演变,选取2015年末英国约克主城区附近遭受洪灾前后的Sentinel-1 SAR影像,运用变化检测和阈值结合的方法,快速有效地进行洪峰期范围估计和42天洪涝期内的淹没范围监测.通过将淹没范围结果与2种验证数据进行比较来评估该方法的适用性,并着重分析了Sentinel-12种极化在洪水应用中的差异性.结果表明Sentinel-1的VV极化在约克洪水应用中产生的错误分类较少,与光学验证数据相比,总精度可达98.92％,具有较好的应用前景.

基于站点的传统土壤水分测量方法耗时耗力,土壤水分主动被动监测任务(SMAP)利用微波遥感提供高效及时的土壤水分产品.为了对比研究微波遥感产品的优劣势,对比分析现有的4种SMAP土壤水分产品,并利用实测数据检验不同产品的土壤水分反演精度.实验结果表明:SMAP主动产品精度较低;被动、主被动产品与地面实测结果具有良好的一致性,精度较高;增强被动产品精度最高.此外,还对土壤水分产品的误差来源进行了讨论,从而促进其在陆地水、能量和碳循环中的应用.

合成孔径雷达作为及时高效动态监测生态环境的新型手段,在生产实践中已被广泛应用.针对高校教育中雷达专业课程少、学生参与度不高、数据处理实践能力不强的问题,以翻转课堂教育理念为基础,由教师建立相关的课程网站,利用信息时代新途径改善课程教育模式;将合成孔径雷达干涉测量(InSAR)技术与应用课程的理论知识教育与实际区域案例教学相结合,辅助以Matlab软件进行雷达数据处理,提高学生编程能力,培养创新理念;课程考核方式由传统单一的纸质考试向上机实践操作转变,穿插以小组课题完成度作为最后结业考查;课堂教学之余向学生征集课程内容评价,突出以学生为主体的多元化教学,实现普及雷达技术并培养学生地理信息能力的目的.

One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction improves discrimination capability. This paper presents an approach towards the detection of built-up land change based on a single-channel SAR stack. The proposed methodology includes the generation of a change indicator, the Markov modelling procedure and the delineation of changes over built-up areas. The generation of the change indicator aims to provide a feature with abundant contrast between changed and stable areas, a high signal-to-noise ratio and detail preservation. To this end, all temporal information is converted into a map of the coefficient of variation. After error removal, this change detector is combined with a Markov random field (MRF) criterion function. Rather than MRF modelling by iteration with very complex stochastic models, we propose using SAR temporal trajectory under a hypothesis test framework and interferometric coherence series to establish conditional density for each class. Then, the Graph-cuts theory is applied to delineate the boundary between changed and stable areas, followed by a binary classification procedure based on speckle divergence to exclude natural areas. The technique is tested on both synthetic data and two TerraSAR-X datasets covering representative areas with rich texture. We found that in a complex built environment that is challenging for classical change indicators and state-of-the-art techniques, the presented method can provide smaller overall error with better detail preservation.