非线性半参数模型在GPS系统误差处理中的应用

本文总结了当前测量界对系统误差的处理方法,提出了用非线性半参数核估计来处理GPS双差定位中系统误差的方法,即用非线性半参数模型中的非参数分量表达双差模型中残余的系统误差。最后通过算例检验了此方法对于提取GPS双差定位中残余的系统误差的有效性。     

Efficient propagation of error covariance matrices of gravitational models: application to GRACE and GOCE

We have applied efficient methods for computing variances and covariances of functions of a global gravity field model expanded in spherical harmonics, using the full variance–covariance matrix of the coefficients. Examples are given with recent models derived from GRACE (up to degree and order 150), and with simulated GOCE derived solutions (up to degree and order 200).     

利用高分辨率水文模型提高GRACE卫星的空间分辨率

为克服GRACE卫星数据空间分辨率粗糙的局限性,本文以高空间分辨率的PCR-GLOBWB数据为基础,构建了加权、乘权及误差分配降尺度方法,将GRACE卫星在河北省的水储量变化数据的空间分辨率由0.50°提高至0.05°。结果表明：加权降尺度方法不仅保留了GRACE数据的原始空间分布特征,还刻画了局部细节特征;误差分配降尺度方法结果较为理想,但在格网交界处的信号存在跳跃现象;乘权降尺度方法表现最差,与GRACE存在明显差异。经实测数据验证可知,加权降尺度方法与实测值拟合程度最好,相关系数最高可达0.81。本文为获取河北省高空间分辨率地下水储量数据提供了有效保障。     

A comparison of coincident GRACE and ICESat data over Antarctica

In this study, we present a comparison of coincident GRACE and ICESat data over Antarctica. The analysis focused on the secular changes over a 4-year period spanning from 2003 to 2007, using the recently reprocessed and publicly available data sets for both missions. The results show that the two independent data sets possess strong spatial correlations, but that there are several factors that can significantly impact the total derived ice mass variability from both missions. For GRACE, the primary source of uncertainty comes from the modelling of glacial isostatic adjustment, along with the estimates of C _2,0 and the degree one terms. For ICESat, it is shown that assumptions about firn density, rate biases, and the sampling interval of the various laser campaigns can have large effects on the results. Despite these uncertainties, the similarities that do exist indicate a strong potential for the future refinement of both GIA and mass balance estimates of Antarctica.     

Estimation and testing of higher-order spatial autoregressive panel data error component models

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system

Advances in earth observation (EO) and spatially distributed hydrological modelling provide an opportunity to improve modelling of controlled water systems. In a controlled water system human interference is high, which may lead to incorrect parameterisation in the model calibration phase. This paper analyses whether assimilation of EO actual evapotranspiration (ETa) data can improve discharge simulation with a spatially distributed hydrological model of a controlled water system. The EO ETa estimates are in the form of eight-day ETa composite maps derived from Terra/MODIS images using the ITA-MyWater algorithm. This algorithm is based on the surface energy balance method and is calibrated for this research for a low-lying reclamation area with a heavily controlled water system: the Rijnland area in the Netherlands. Data assimilation (DA) with the particle filter method is applied to assimilate the ETa maps into a spatially distributed hydrological model. The hydrological model and DA framework are applied using the open source software SIMGRO and PCRaster-Python respectively. The analysis is done for a period between July and October 2013 in which a high discharge peak followed a long dry-spell. The assimilation of EO ETa resulted in local differences in modelled ETa compared to simulation without data assimilation, while the area average ETa remained almost the same. The modelled cumulative discharge graphs, with and without DA, showed distinctive differences with the simulation, with DA better matching the measured cumulative discharge. The bias of simulated cumulative discharge to the observed data reduced from 14% to 4% when using DA of EO ETa. These results showed that assimilating EO ETa may not only be effective in the more common applications of soil moisture and crop-growth modelling, but also for improving discharge modelling of controlled water systems.     

A causal analysis of error in viewsheds from USGS digital elevation models

Users of GIS are faced with the ongoing and difficult problem of estimating the validity of a GIS output given the uncertainty of the quality of digital mapped data. This research project examines the manner in which error from a digital elevation model (DEM) results in error in an estimate of a viewshed. The data set includes two DEMs for the same study area. A 10-metre DEM serves as the control model to test a United States Geological Survey (USGS) DEM. The amount and spatial pattern of the error in the test DEM is determined by comparing it to the control DEM. A viewshed analysis is then performed at a set of 61 sites. After determining the relative accuracy of the viewsheds thus estimated, a causal model of viewshed error is developed, which links the sources of the DEM error with the error in the viewshed. The most important factor in DEM error is terrain roughness. The error in the viewsheds is a result of the error being propagated from the DEM, with landscape characteristics of the viewpoinr playing a role due to their effect on the error in the DEM. The conceptual path model developed sets the stage for a quancitative approach that will attempt to predict viewshed error from the landscape characteristics without direct knowledge of the error in the DEM.     

利用GRACE数据进行京津冀地区地面沉降与地下水耦合分析

地下水的过量开采已导致京津冀地区出现严重的地面沉降,为了分析地下水与地面沉降之间的耦合关系,首先利用GRACE-FO数据与GLDAS数据反演出京津冀地区2016—2019年地下水变化时序;然后利用MCTSB-InSAR技术反演出该地区同时段的沉降变化时序。通过试验分别获取地下水与地面沉降的差分变化序列和变化趋势线,并引入非弹性存储系数分析地下水对地面沉降影响力的变化规律。结果表明:①当地下水迅速升高或降低时,地面沉降速率减小或增大;当地下水持续升高时,地面沉降接近停止。②沉降越严重的区域,地下水与地面沉降变化趋势的相关性越强。③由地下水变化引起沉降的能力,随沉降等级升高而变大,且该能力在不同沉降等级中随时间变化的趋势也不同。     

Derivation of tree skeletons and error assessment using LiDAR point cloud data of varying quality

The architecture of trees is of particular interest for 3D model creation in forestry and ecolocical applications. Terrestrial (TLS) and mobile laser scanning (MLS) systems are used to acquire detailed geometrical data of trees. Since 3D point clouds from laser scanning consist of large data amounts representing uninterpreted topographical information including noise and data gaps, an extraction of salient tree structures is important for further applications. We present a fully automated modular workflow for topological reliable reconstruction of tree architecture. Object-based point cloud processing such as branch extraction is combined with tree skeletonization. Branch extraction is performed using a segmentation procedure followed by segment-based analysis of form indices derived from eigenvector metrics. Extracted branch primitives are simplified and connected to line features during skeletonization. The modular workflow allows comprehensive parameter tests and error assessments that are used for a calibration of the module parameters with respect to various characteristics of the input data (e.g noise, scanning resolution, and the number of scan positions). The estimated parameter settings are validated using an exemplary MLS data set. The quality of input point cloud data, strongly influencing the quality of the skeleton results, can be improved by the presented branch extraction procedure. The potential for data improvement increases with increasing point densities. For our object-based appoach, we can show that the presence of erroneous structures and filtering artifacts have the strongest influence onto the quality of the derived skeletons. In contrast to traditional skeletonization approaches, the existance of data gaps has less influence onto the results.     

Impact of GPS radio occultation data assimilation on regional weather predictions

The impact of GPS radio occultation (RO) data assimilation on severe weather predictions in East Asia is introduced and reviewed. Both the local observation operator that assimilates the retrieved refractivity as local point measurement, and the nonlocal observation operator that assimilates the integrated retrieved refractivity along a straight raypath have been utilized in WRF 3DVAR to improve the initial analysis of the model. A general evaluation of the impact of these approaches on Asian regional analysis and daily prediction is provided in this paper. In general, the GPS RO data assimilation may improve prediction of severe weather such as typhoons and Mei-yu systems when COSMIC data were available, ranging from several points in 2006 to a maximum of about 60 in 2007 and 2008 in this region. Based on a number of experiments, regional model predictions at 5 km resolution were not significantly influenced by different observation operators, although the nonlocal observation operator sometimes results in slightly better track forecast. These positive impacts are seen not only in typhoon track prediction but also in prediction of local heavy rainfall associated with severe weather over Taiwan. The impact of 56 GPS RO soundings on track prediction of Cyclone Gonu (2007) over the Indian Ocean is also appealing when compared to other tracks assimilated with different observations. From a successive evaluation of skill scores for real-time forecasts on Mei-yu frontal systems operationally conducted over a longer period and predictions of six typhoons in 2008, assimilation of GPS RO data appears to have some positive impact on regional weather predictions, on top of existent assimilation with all other observations.     

The contribution of multitemporal SAR data in assessing hydrological parameters

The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations.     

参数敏感性分析在遥感及生态水文模型中的研究进展

参数敏感性分析SA（Sensitivity Analysis）是遥感、生态和水文模型不确定性分析UA（Uncertainty Analysis）的重要方法之一。本文梳理了遥感散射/辐射模型,以及遥感驱动的生态、水文模型研究中常用的敏感性分析方法,并总结了各类方法的优缺点和适用条件。从识别关键参数、不确定性分析和参数优化3个方面,分析了这些领域中参数敏感性分析研究的进展和存在问题,并介绍了最常用的敏感性分析平台。参数敏感性分析作为模型参数优化的先验知识之一,促进了模型和参数的优化。在不确定性和敏感性矩阵USM（Uncertainty and Sensitivity Matrix）的框架下,结合全局敏感性分析方法开展多阶段遥感反演、参数敏感性的尺度效应、参数敏感性的时空异质性研究更加需要关注。此外,还需要提高敏感性分析的计算效率和模式,来适应未来更加复杂的模型和迅速增长的数据量。     

资源三号卫星姿态四元数系统误差检验方法

根据资源三号卫星严格成像模型,建立姿态四元数系统误差检验模型,通过对登封地区2景影像进行试验,利用不同控制点数量求解姿态四元数系统误差,将姿态进行系统误差改正后,可以使卫星直接定位精度大幅提高,验证了姿态四元数系统误差检验方法的正确性和有效性。     

Analysis of remanent systematic error effects on circuit closures in geodetic spirit levelling

This paper is a summary of an unpublished paper and it attempts to give a complete solution of the problem posed by the presence of systematic errors in precise spirit levelling, using mathematical statistics. Distribution-free statistics, which do not require the precise knowledge of the distribution of the underlying populations, are used and to this end, a value of the expected systematic error in any given section is isolated from the total discrepancy of the independent mutual levelling operations thus facilitating a plausible least square adjustment of the geodetic network. The problem of weighting is considered in relation to the rank test-statistics. Complete mathematical derivations are not attempted in this summary.     

利用GRACE卫星Level-1B数据反演陆地水储量变化的方法研究

卫星重力探测技术为监测陆地水储量变化提供了新的技术手段。对利用GRACE卫星Level-1B数据反演地球陆地水储量变化的重力位差法和Mascon方法的数学模型作了详细推导分析,总结两种方法的特点和解算处理步骤。推导过程表明:重力位差法和Mascon方法在反演时只采用卫星飞临研究区域上空时的观测数据,能够提高反演结果的空间分辨率,比传统的球谐系数法更具优势;Mascon方法在解算时还引入了时空约束方程,进一步提高了反演结果的时空分辨率。     

Analysis of systematic error influences on accuracy of airborne laser scanning altimetry

The error sources related to the laser rangefinder, GPS and INS are analyzed in details. Several coordinates systems used in airborne laser scanning are set up, and then the basic formula of system is given.This paper emphasizes on discussing the kinematic offset correction between GPS antenna phase center and laser fired point. And kinematic time delay influence on laser footprint position, the ranging errors, positioning errors, attitude errors and integration errors of the system are also explored. Finally, the result shows that thekinematic time delay can be neglected as compared with other error sources. The accuracy of the coordinates is not only influenced by the amplitude of the error, but also controlled by the operation parameters such as flight height, scanning angle amplitude and attitude magnitude of the platform.