Submarine groundwater discharge in tidal flats revealed by space-borne synthetic aperture radar

Most space-borne sensors cannot detect subsurface features. Groundwater is a typical subsurface feature, and its discharge to coastal ocean waters plays an important role in transporting terrestrial chemical constituents and providing habitats for various species of fauna and flora. This is the first paper to report observational evidence for submarine groundwater discharge (SGD) in tidal flats using space-borne synthetic aperture radar (SAR). Tidal flats are composed of high-moisture-saturated sediments and water puddles. These shallow water puddles were imaged effectively by using SAR systems. The presence of water puddles is usually indicated by low radar backscatter in SAR images due to specular reflections on the water surface. This effect was proved by comparing radar backscattering coefficients obtained from two space-borne SAR systems, TerraSAR-X and RADARSAT-2, with those obtained from two theoretical scattering models, IEM and Oh model. We observed relatively large, widely distributed water puddles in belt shape along the upper parts of the tidal flat, which were confirmed to be related to the discharge of groundwater. The results of this research suggest that SAR can be a powerful tool for observing and determining the areal distributions of possible groundwater discharge in large tidal flats, which is normally difficult to detect with traditional measurement tools or survey techniques for groundwater discharge. We firmly believe that this technique can reduce significantly the efforts of field work to confirm SGD in tidal flats.     

Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (> 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m × 100 m NOAH and AIEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters.     

合成孔径雷达图像的分类方法研究仿真

合成孔径雷达有多个通道,采集得到的远程雷达图象特征较多,图像合成后由于特征的不一致,造成图像维度很高.传统的图像分类方法在设置训练器时,一旦雷达图像特征维数很高,会使得传统的分类器难以训练至给定要求,模型的分类中心发生偏移,造成图像分类不准.提出了一种遗传因子优化的超高维度合成孔径雷达图像分类算法.利用遗传因子优化方法,对采集的雷达图像分类参数进行优化训练,从而获取最优参数.利用搜索空间划分方法,对采集的雷达图像进行分类处理.实验结果表明,改进的算法进行高精度合成孔径雷达图像分类,能够有效提高分类的准确性,从而为航天技术的发展提供依据.     

利用卫星SAR监测海上航行船舶

星载合成孔径雷达能够得到高分辨率的遥感图像。船舶目标以及船舶航迹在一些卫星ＳＡＲ海洋图像中清晰可见。文中就利用卫星ＳＡＲ对船舶目标和船舶航迹的监测问题进行了论述。首先简要介绍了船舶目标及其航迹的ＳＡＲ成像原理,然后对不同成像条件得到的航迹图像进行了分类,最后分析了船舶目标的检测、船舶航迹特征的检测以及船舶的相应参数的估计问题。通过对上述问题的讨论,认为利用卫星ＳＡＲ监测海上航行的船舶是可行的,而且是一项很有意义的工作。     

Estimated surface-wave contributions to radar Doppler velocity measurements of the ocean surface

For simulated ocean conditions, we estimate the magnitude of the Doppler velocity contributions produced by unresolved surface waves that typical spaceborne synthetic aperture radars (SAR) would measure. The mechanism for generating Doppler velocities is the correlation between wave phase and radar cross section. The contributions analyzed include those of linear gravity waves, second-order wave-wave interactions, Bragg-wave scatterers and breaking waves. For gravity waves, we consider both wave tilt and hydrodynamic modulation transfer functions (MTFs). We find that for nominal sea conditions, the Doppler velocity is significant, on the order of 1 m/s, and exhibits large variation as a function of incidence angle and look with respect to the sea direction. The most important contributors are gravity waves and the Bragg scatterers, followed by sea spikes. Effects produced by second-order wave solutions are argued to be inconsequential.     

基于DBN模型的遥感图像分类

遥感图像分类是地理信息系统(geographic information system, GIS)的关键技术,对城市规划与管理起到十分重要的作用.近年来,深度学习成为机器学习领域的一个新兴研究方向.深度学习采用模拟人脑多层结构的方式,对数据从低层到高层渐进地进行特征提取,从而发掘数据在时间与空间上的规律,进而提高分类的准确性.深度信念网络(deep belief network, DBN)是一种得到广泛研究与应用的深度学习模型,它结合了无监督学习和有监督学习的优点,对高维数据具有较好的分类能力.提出一种基于DBN模型的遥感图像分类方法,并利用RADARSAT-2卫星6d的极化合成孔径雷达(synthetic aperture radar, SAR)图像进行了验证.实验表明,与支持向量机(SVM)及传统的神经网络(NN)方法相比,基于DBN模型的方法可以取得更好的分类效果.     

基于云理论的遥感图像土地利用分割分类方法研究

传统的遥感图像土地利用分类技术自动化和智能化的程度较低,遥感图像土地利用分类问题是一个包含随机性和模糊性的不确定性问题,而云模型把模糊性和随机性集成到一起,构成定性和定量相互间的映射。据此,本文探索将云理论引入到遥感图像土地利用分割分类技术中,建立了基于灰度的云映射空间,实现对遥感图像的分类。同时以武汉市南湖地区进行了实证分析,进一步阐述了模型构建过程,通过分类结果的评价和对比,探索该方法在本领域中的适用性。     

训练样本数目选择对面向对象影像分类方法精度的影响

面向对象遥感影像分类中的样本选择与基于像素的方法有很大不同,基于统计学理论,研究了面向对象方法的样本数量选择问题。首先,针对面向对象方法的特点,对影像特征空间进行分析,结果表明面向对象方法中要求训练样本的数量可以显著地减少。然后,在遥感影像分类实验中,借助样本数量与波段数目的关系,验证了理论分析的结果。     

基于GF-1和Sentinel-1A的漓江流域典型地物信息提取

漓江流域是桂林山水的核心,保护漓江流域生态环境已成为国家战略。以漓江流域为研究区域,以GF-1多光谱影像和SAR影像为数据源,采用小波融合算法将GF-1多光谱影像和SAR VV极化的后向散射影像进行影像融合,再利用随机森林算法分别对GF-1多光谱影像、GF-1和Sentinel融合影像构建典型地物高精度识别模型,提取与漓江流域生态环境紧密相关的河流、针叶林、阔叶林、水田、旱地以及居民地等地物类型。研究结果表明:①在95%置信区间内,基于GF-1影像分类的总体分类精度达到96.15%,基于GF-1和Sentinel-1A后向散射系数的影像总体分类精度达到了94.40%;②河流、阔叶林和旱地在基于GF-1多光谱影像的分类精度中分别达到了97.74%、93.20%、90.90%,比基于融合GF-1多光谱和SAR的数据分别高出7.57%、8.96%和1.22%,其余地物类型两者分类精度相近;③GF-1多光谱和SAR数据的融合中,利用了小波变换进行图像融合,发现融合图像的喀斯特地貌突出,增加了地物特征的差异性。     

Identification of Typical Land Features in the Lijiang River Basin with Fusion Optics and Radar

Lijiang River is the core of Guilin's landscape. Protecting the ecological environment of Lijiang River Basin has become a national strategy. In this paper, Lijiang River Basin was used as the research area. The GF-1 multispectral image and SAR image were used as the data source. The wavelet fusion algorithm was used to fuse the GF-1 multispectral image and the SAR VV polarized backscatter image. Using random forest algorithm to construct a high-precision recognition model for GF-1 multispectral imagery, GF-1 and sentinel fusion images. The model can extract rivers, coniferous forests, broad-leaved forests, paddy fields, drylands, residential land and other land types that are closely related to the ecological environment of the Lijiang River. The results show that ①the overall accuracy based on GF-1 image classification reaches 96.15% in the 95% confidence interval, and the overall accuracy based on GF-1 and sentinel-1A backscatter coefficient reaches 94.40%. ②The classification accuracy of rivers, broad-leaved forests and drylands based on GF-1 multispectral images reached 97.74%, 93.20%, and 90.90%. They are 7.57%, 8.96%, and 1.22% higher than those based on the fused GF-1 multispectral and SAR data, respectively. The classification accuracy of the other features is similar. ③In the fusion of GF-1 multispectral and SAR data, wavelet transform was used for image fusion. It was found that the karst topography of the fusion image was prominent, which increased the difference of the features of the ground features.     

独龙江流域TM图像的分区分类方法探讨

高山峡谷地区山高坡陡、地形破碎,大量的阴影及混合像元的存在,给遥感分类带来了困难。为了减少和消除这些因素的影响,采用分区分类方法,从NDVI中选择恰当的阈值,利用该值从原始图像上划分出植被区和非植被区并分别进行分类,试验结果表明,其分类精度(74.63%)较传统的直接分类精度(61.59%)有较大提高,是山区遥感图像分类行之有效的方法之一。     

基于高分辨率遥感影像的面向对象城市土地覆被分类比较研究

针对高分辨率遥感影像的城市土地覆被信息提取,根据分类目的与精度要求的不同,分别引入了优化与广义两种面向对象分类方案,并对分类的结果进行分析比较。结果表明:①优化方案的分类结果总体上要比广义方案好,前者的总体精度为86.50%,相比后者的80.50%提高了6.0%,而总体Kappa系数提高了0.0851,但是该方案效率低,可移植性差;②广义方案的分类结果虽然精度略低,但是该方案具有很强的适用性与可移植性,能够在精度可控范围内,很大程度提高分类效率,实现系统而有效的自动分类;③广义方案得到的分类结果具有一致的精度,在利用其建立城市生态模型中能够保证数据之间的系统性与鲁棒性。因此,利用优化方案能够提高分类结果的绝对精度,而广义方案对于实时精确获取城市土地覆被信息、小尺度上定量监测与评价城市化的生态后果以及有效开展城市土地规划与管理具有更重要的意义。     

基于正样本和未标记样本的遥感图像分类方法

传统分类器的构建需要正样本和负样本两类数据。在遥感影像分类中,常出现这样一类情形：感兴趣的地物只有一种。由于标记样本耗时耗力,未标记样本往往容易获取并且包含有用信息,鉴于此,提出了一种基于正样本和未标记样本的遥感图像分类方法（PUL）。首先,根据正样本固有特征并结合支持向量数据描述（SVDD）从未标记集筛选出可信正负样本,再将其从未标记集中剔除;接着将其带入SVM训练,根据未标记集在分类器中的表现设立阈值,再从未标记集中筛选出相对可靠的正负样本;最后是加权SVM（Weighted SVM）过程,初始正样本及提取出的可靠正负样本权重为1,SVM训练筛选出的样本权重范围0~1。为验证PUL的有效性,在遥感影像进行分类实验,并与单类支持向量机（OC-SVM）、高斯数据描述（GDD）、支持向量数据描述（SVDD）、有偏SVM（Biased SVM）以及多类SVM分类对比,实验结果表明PUL提高了分类效果,优于上述单类分类方法及多类SVM方法。     

基于卷积神经网络的青海湖区域遥感影像分类

科学准确的获取青海湖区域土地覆盖分类对于研究该区域生态环境变化有着重要的意义.本文使用30米分辨率的LandSat 8 OLI青海湖区域遥感影像数据展开相关研究,30米分辨率属于中等分辨率,当前中分遥感影像的分类方法尚存在特征提取困难、分类精度不高等问题.本文借鉴GoogLeNet Inception结构,设计并提出了一种卷积神经网络模型进行特征提取及分类,分析了用于样本生成的邻域窗口尺寸对分类结果的影响,并与最大似然分类和SVM分类方法进行比较.结果表明,在窗口尺寸为9×9时,CNN模型的总体分类效果最好,且CNN的分类结果明显优于最大似然分类方法和SVM.     

Retrieving photometric properties of desert surfaces in China using the Hapke model and MISR data

The retrieval of photometric properties of desert surfaces is an important first step in the parameterization of land surface components of regional dust emission and global radiation models and in Earth system modeling. In this study, the values of Hapke's photometric parameters (ω, h, b, c, B0, and θ?) were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) instrument at locations in China's deserts. Four pixels represented the typical surface characteristics of the Taklimakan Desert, sand dunes of Kumtag Desert, relatively smooth areas of the Kumtag Desert and the aeolian sandy soil of Loulan. In contrast to earlier studies, we found that the retrieved parameter values were largely affected by the initial value. To combat this problem we used a Monte Carlo method with physical constraints and a conformity indicator to ensure physically meaningful inversion.The results showed that the angular domain of MISR observations was sufficiently large to determine confidently the values of Hapke's photometric parameters with the exception of the opposition effect width (h). Retrieved values for the single scattering albedo (ω) and macroscopic roughness (θ?) were consistent with qualitative observations about the structure and composition of the surface material and the nature of the dune forms, respectively. At Loulan, where the surface was smoother than other sites, retrieved values exhibited the strongest backward scattering. These results indicated that at the sensor scale, a rough surface (e.g., dunes) does not necessarily mean more backward scattering than a smooth surface. This finding has significant implications for empirical methods (e.g., using the normalized index of backward-scattered radiance minus forward-scattered radiance as an indicator to indicate surface roughness) which should be used carefully for analyzing surface roughness from remote sensing data. Future research is needed to 1) understand how surface roughness at the sub-pixel scale modifies the angular characteristics of reflectance and to 2) find practical methods for rapid whole image processing for mapping the photometric parameters.     

Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR)

Interferometric synthetic aperture radar (InSAR) techniques can successfully detect phase variations related to the water level changes in wetlands and produce spatially detailed high-resolution maps of water level changes. Despite the vast details, the usefulness of the wetland InSAR observations is rather limited, because hydrologists and water resources managers need information on absolute water level values and not on relative water level changes. We present an InSAR technique called Small Temporal Baseline Subset (STBAS) for monitoring absolute water level time series using radar interferograms acquired successively over wetlands. The method uses stage (water level) observation for calibrating the relative InSAR observations and tying them to the stage's vertical datum. We tested the STBAS technique with two-year long Radarsat-1 data acquired during 2006-2008 over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). The InSAR-derived water level data were calibrated using 13 stage stations located in the study area to generate 28 successive high spatial resolution maps (50 m pixel resolution) of absolute water levels. We evaluate the quality of the STBAS technique using a root mean square error (RMSE) criterion of the difference between InSAR observations and stage measurements. The average RMSE is 6.6 cm, which provides an uncertainty estimation of the STBAS technique to monitor absolute water levels. About half of the uncertainties are attributed to the accuracy of the InSAR technique to detect relative water levels. The other half reflects uncertainties derived from tying the relative levels to the stage stations' datum.     

View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data

Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05-06 February 2005) with opposite viewing geometries (− 42° and + 44°, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) (r = + 0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward (r of + 0.21) to the back scattering view (r of + 0.60). The same trend was observed for SR that increased from + 0.22 to + 0.58.     

Classification of new-ice in the Greenland Sea using Satellite SSM/I radiometer and SeaWinds scatterometer data and comparison with ice model

In the ice covered waters of the Greenland Sea the polarisation ratio of QuikSCAT SeaWinds Ku-band (13.4 GHz) scatterometer measurements and the polarisation ratio of DMSP-SSM/I 19 GHz radiometer measurements are used in combination to classify new-ice and mature ice. In particular, the formation of the new-(frazil/pancake)-ice ‘Odden’ (8° W, 75° N) March 11th-18th, 2001, is used in the study. The results of the ice cover classification in the Greenland Sea are compared to model parameters from a sea ice model. The classification of each ice pixel is performed using its backscatter and radiative properties as reflected in the polarisation ratio. Our results based on these comparisons show that the transformation into older mature (sheet) ice occurs within 5-10 days. During one day the new-ice cover increased by 33 000 km². The new-ice appears in March 2001 as a peninsula (maximum extent 56 000 km²) appended to the belt of older ice drifting along the East Coast of Greenland. These results are consistent with the ice model and with Radarsat images. Furthermore, using the ice model it is demonstrated that the new-ice/mature ice threshold in the classification corresponds to the physical transition of the ice cover from pancake ice to a consolidated young-ice sheet. The classification of each pixel into ice or water is done using two scatterometer parameters, namely the polarisation ratio and the daily standard deviation of the backscatter.     

基于面向对象分类方法的乌苏里江流域中俄跨境区域湿地景观动态研究

选取乌苏里江中下游干流50km缓冲区为研究区,以1989、2000和2013年的Landsat TM、OLI遥感影像为数据源,利用面向对象的分类方法获取湿地信息;在GIS技术支持下,选取斑块类别面积(CA)、面积所占比例(PLAND)、最大斑块面积指数(LPI)、平均斑块面积(AREA-MN)、斑块数(NP)、斑块密度(PD)等景观格局指数,分析了研究区1989~2013年湿地的景观格局动态特征。结果表明:面向对象的分类方法能够准确提取湿地信息,并且有效地避免"椒盐现象";1989~2013年,该区水田、水库/坑塘、运河/水渠、灌丛沼泽增加和河流、湖泊、草本沼泽、森林沼泽减少;大量草本沼泽转为水田为该区湿地变化主要特征;1989年湿地类型以草本沼泽为主,2013年水田的斑块面积略高于草本沼泽斑块面积;湿地景观变化区主要分布于中国一侧的挠力河流域和穆棱河流域;天然湿地景观破碎化程度逐年加深,人工湿地景观趋于聚集,人为干扰是导致湿地景观格局变化的主要动力;中俄两国境内湿地景观变化程度差异显著,中国境内大量天然湿地转为人工湿地;俄罗斯境内的湿地变化微小,天然湿地保存较好。农田垦殖是导致两国湿地景观破碎程度差异的主要原因。     

Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record

Variability and trends in lake ice dynamics (i.e. lake ice phenology) are related to climate conditions. Climate influences the timing of lake ice melt and freeze onset, ice duration, and lake thermal dynamics that feedback to the climate system initiating further change. Phenology records acquired in a consistent manner and over long time periods are required to better understand variability and change in climate conditions and how changes impact lake processes. In this study, we present a new technique for extracting lake ice phenology events from historical satellite records acquired by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. The technique was used to extend existing in-situ measurements for 36 Canadian lakes and to develop records for 6 lakes in Canada's far north. Comparison of phenology events obtained from the AVHRR record and in-situ measurements show strong agreement (20 lakes, 180 cases) suggesting, with high confidence especially in the case of break-up dates, the use of these data as a complement to ground observations. Trend analysis performed using the combined in-situ and AVHRR record ∼ 1950-2004 shows earlier break-up (average — 0.18 days/year) and later freeze-up (average 0.12 days/year) for the majority of lakes analyzed. Less confidence is given to freeze-up date results due to lower sun elevation during this period making extraction more difficult. Trends for the 20 year record in the far north showed earlier break-up (average 0.99 days/year) and later freeze-up (average 0.76 days/year). The established lake ice phenology database from the historical AVHRR image archive for the period from 1985 to 2004 will to a certain degree fill data gaps in the Canadian in-situ observation network. Furthermore, the presented extraction procedure is not sensor specific and will enable continual data update using all available satellite data provided from sensors such as NOAA/AVHRR, MetOp/AVHRR, MODIS, MERIS and SPOT/VGT.