星载微波ERS-1和多通道SSM/I观测海洋的相关性和随机粗糙面复合模型的数值模拟

欧洲遥感卫星（ERS）和美国防卫气象卫星计划（DMSP）空对地微波遥感是当今研究全球大气地表微波散射辐射和反演地球物理与水文参数的主要数据来源．本文研究了ERS－1散射计和SSM／I多通道辐射计在中国海域观测到的后向散射和热辐射数据,论证了同一地区同一时间段内ERS主动散射计和SSM／I被动辐射计观测数据的相关性．用海域主、被动遥感数据的比较,阐述了主、被动联合多通道分析方法有利于监视和分析复杂地表和海面在时间和空间尺度上的变化．用带泡沫散射层的双尺度随机粗糙面的复合模型计算后向散射和热发射,用以数值模拟ERS和SSM／I数据．并讨论了后向散射与热辐射数值模拟结果的相关性,以及与星载微波遥感器实际观测结果的比较．     

Investigating the uncertainty of satellite altimetry products for hydrodynamic modelling

Satellite altimetry products are increasingly used in many hydraulic applications, and recent studies demonstrate their suitability for the calibration of hydraulic models. The study investigates the effect of satellite‐data uncertainty on the calibration of a quasi‐two‐dimensional (quasi‐2D) model of the middle‐lower portion of the Po river (~140 km). We refer to extended (~16 years of observations) ERS and ENVISAT altimetry products (i.e. River and Lake Hydrology data, RLH) to investigate the effect of (i) record length (i.e. number of satellite measurements at a given satellite track) and (ii) data uncertainty (i.e. altimetry measurements errors) on the calibration of the quasi‐2D model. We first present an assessment of ERS and ENVISAT altimetry errors and then perform the investigations in a Monte Carlo framework by generating datasets of synthetic altimetry products. The results of our analysis further emphasize the suitability of satellite data for the calibration of hydraulic models, providing also a quantitative assessment of the effect of the uncertainty of altimetry products. The analysis highlights the higher accuracy of ENVISAT data, which ensures a stable calibration with ~1.5 years of data (Mean Absolute Error, MAE, lower than 0.4 m, ~0.2 m of which results directly from the uncertainty of ENVISAT data). ERS‐based calibrations become stable with longer series (~3.5–5 years of data), and the negative effect of uncertainty in ERS data is higher (i.e. MAE of 0.6–0.9 m, of which 0.4–0.6 m results from the uncertainty of ERS measurements). Copyright © 2015 John Wiley & Sons, Ltd.     

Large-Scale Errors in ERS Altimeter Data

A method is described for mapping time-uncorrelated large-scale errors in satellite altimeter sea surface heights. Standard deviations of differences between pairs of successive measurements at track crossovers are computed, and the functional dependence of these deviations on absolute time difference is used to estimate the errors of individual measurements. This is first applied to all of ERS-1,2 altimeter data in the Pacific Ocean, yielding average errors of 3.2 cm in the deep ocean (>1 km) and 4.7 cm in the shallow seas (<1 km). The procedure is repeated for variable latitude bands, each with a full range of possible time differences, yielding a meridional profile of computed errors, ranging from 2.6 cm near the Antarctic continent (67–60S) and South Subtropical regions (25–5S) to 3.5 cm in the Antarctic Circumpolar Current (60–45S) and the Northern Hemisphere Subtropical and Subpolar Gyres. Finally, coarse-resolution maps of these errors are produced by subdividing the Pacific Ocean into latitude-longitude bins, each large enough to contain a sufficient number of samples for the functional fits. The larger errors are in Northwest and Subtropical Pacific, especially in South China Sea (4.3 to 4.5 cm) and off northern Australia (5.4 cm), while the smaller errors (2.5 to 3 cm) are in Northeast Pacific, central Tropical Pacific and near Antarctica in Southeast Pacific Ocean. These are lower bounds on altimeter errors, as they do not include contributions from time-correlated errors. We find that the computed error fields are not correlated with sea level standard deviations, thus disproving the notion that altimeter error variance can be scaled with the variance of sea surface height data.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.     

Altimeter Signal-to-Noise for Deep Ocean Processes in Operational Systems

The ocean signal for this study is the sea surface height due to the slowly varying (greater than 5-day) ocean processes, which are predominantly the deep ocean mesoscale. These processes are the focus of present assimilation systems for monitoring and predicting ocean circulation due to ocean fronts and eddies and the associated environmental changes that impact real time activities in areas with depths greater than about 200 m. By this definition, signal-to-noise may be estimated directly from altimeter data sets through a crossover point analysis. The RMS variability in crossover differences is due to instrument noise, errors in environmental corrections to the satellite observation, and short time period oceanic variations. The signal-to-noise ratio indicates that shallow areas are typically not well observed due to the high frequency fluctuations. Many deep ocean areas also contain significant high frequency variability such as the subpolar latitudes, which have large atmospheric pressure systems moving through, and these in turn generate large errors in the inverse barometer correction. Understanding the spatial variations of signal to noise is a necessary prerequisite for correct assimilation of the data into operational systems.     

Combined Sea Surface Determination Based on Various Geometric and Dynamic Techniques

Abstract

The combined use of Global Positioning System (GPS) differential positioning as well as ERS‐1 altimeter data is considered in implementing geodetic vertical datums and their unification. The article describes concepts, techniques, practical realization, and associated questions and problems. Particular aspects in view of small sea surface perturbations in offshore areas in determining sea surface components (variable and steady state) are discussed. The combinations of tide gauge data with altimetry and (mainly) GPS positioning for geodetic purposes are discussed in detail. Special attention is devoted to the associated reference systems as well as to the combination of dynamic (level and nonlevel surfaces) with geometric quantities. The discussion is based on a specific ERS‐1 project supported by the National Science Foundation. Implications and practical impact of the project are outlined.     

Exploring The Generation Of Digital Elevation Models From Same-Side Ers Sar Images: Topographic And Temporal Effects

This paper presents an attempt to explore the generation of digital elevation models (DEMs) by radargrammetry from same-side ERS-1/2 synthetic aperture radar (SAR) images. Two ERS-1 and two ERS-2 C-band (wavelength 5·6 cm) SAR images along parallel descending orbits, acquired in 1996 and 1997, are used to form four stereopairs. The test area is western Hong Kong which is dominated by mountains/hills that are largely covered by evergreen-broadleaf trees. The stereopairs covering the same area but with different time intervals (varying from 16 days to 1·5 years) have an intersection angle of 4·5° and a relatively small overlap of 30% (30 km). In addition to the effect of topography, the effect of temporal correlation on the accuracy of the resulting DEMs is also analysed for the first time. From the experimental results obtained in this study, the following conclusions may be drawn: (a) with same-side ERS-1/2 SAR images, an accuracy of 30 to 35 m is achievable; (b) the accuracy of a resulting DEM degrades almost linearly with an increase in slope when the terrain slope is below 30°, and in the areas with slope over 30°, the accuracy of the DEM is significantly degraded and even becomes unacceptable; (c) the worst accuracy is obtained in the foreslope directions and the best accuracy is obtained in the backslope directions; (d) the time interval between two SAR images has no significant effect on the accuracy of the DEM in general; (e) the time interval between two SAR images has no significant effect on the reliability of image matching.     

面向震害检测的SAR相干分析处理平台的设计与实现

根据震区相干分析的需求,在Windows系统平台和VC++6.0编译环境中,实现了用户交互式操作的基础平台。该平台主要包括数据读取、影像配准和相干性分析与评价等功能模块,并以ERS数据为试验,验证了平台的高可靠性和高效性。     

船台测量场中公共点的粗差判别方法

针对船台公共点含粗差时对测量场整体精度的影响,提出了一种稳健的公共点粗差判别方法。该方法先通过莱以特准则初步筛选得到可疑点,再以罗曼诺夫斯基准则对可疑点进行判别,确定异常点并剔除,最后采用SVD算法计算转站参数。通过仿真试验进行验证,结果表明,该方法能准确地剔除异常点,得到可靠的转站参数估值,从而提高测量场整体精度。     

Usage of ERS SAR data over the Singhbhum Shear Zone,India for structural mapping and tectonic studies

The extraction of lineaments and anomalous patterns in the Singhbhum Shear Zone, Jharkhand, India, has multifaceted applications for mineral exploration as well as for geological interpretation of neotectonic movements. ERS-1 SAR data are very useful for such applications because of their structural information content. A comparative study has been attempted with ERS, Landsat and IRS images for the interpretation of various geological structures over the Singhbhum Shear Zone. The Rose diagram generated from this study has shown major trends that matched well with the geological map of the area and the associated tectonic boundary as well as with the results obtained from ground based studies.