Calibrating the GOCE accelerations with star sensor data and a global gravity field model

A reliable and accurate gradiometer calibration is essential for the scientific return of the gravity field and steady-state ocean circulation explorer (GOCE) mission. This paper describes a new method for external calibration of the GOCE gradiometer accelerations. A global gravity field model in combination with star sensor quaternions is used to compute reference differential accelerations, which may be used to estimate various combinations of gradiometer scale factors, internal gradiometer misalignments and misalignments between star sensor and gradiometer. In many aspects, the new method is complementary to the GOCE in-flight calibration. In contrast to the in-flight calibration, which requires a satellite-shaking phase, the new method uses data from the nominal measurement phases. The results of a simulation study show that gradiometer scale factors can be estimated on a weekly basis with accuracies better than 2 × 10⁻³ for the ultrasensitive and 10⁻² for the less sensitive axes, which is compatible with the requirements of the gravity gradient error. Based on a 58-day data set, scale factors are found that can reduce the errors of the in-flight-calibrated measurements. The elements of the complete inverse calibration matrix, representing both the internal gradiometer misalignments and scale factors, can be estimated with accuracies in general better than 10⁻³.     

The ITG-Goce02 gravity field model from GOCE orbit and gradiometer data based on the short arc approach

In this contribution, we describe the global GOCE-only gravity field model ITG-Goce02 derived from 7.5 months of gradiometer and orbit data. This model represents an alternative to the official ESA products as it is computed completely independently, using a different processing strategy and a separate software package. Our model is derived using the short arc approach, which allows a very effective decorrelation of the highly correlated GOCE gradiometer and orbit data noise by introducing a full empirical covariance matrix for each arc, and gives the possibility to downweight ‘bad’ arcs. For the processing of the orbit data we rely on the integral equation approach instead of the energy integral method, which has been applied in several other GOCE models. An evaluation against high-resolution global gravity field models shows very similar differences of our model compared to the official GOCE results published by ESA (release 2), especially to the model derived by the time-wise approach. This conclusion is confirmed by comparison of the GOCE models to GPS/levelling and altimetry data.     

A new isostatic model of the lithosphere and gravity field

Based on the analysis of various factors controlling isostatic gravity anomalies and geoid undulations, it is concluded that it is essential to model the lithospheric density structure as accurately as possible. Otherwise, if computed in the classical way (i.e. based on the surface topography and the simple Airy compensation scheme), isostatic anomalies mostly reflect differences of the real lithosphere structure from the simplified compensation model, and not necessarily the deviations from isostatic equilibrium. Starting with global gravity, topography and crustal density models, isostatic gravity anomalies and geoid undulations have been determined. The initial crust and upper-mantle density structure has been corrected in a least squares adjustment using gravity. To model the long-wavelength (>2000 km) features in the gravity field, the isostatic condition (i.e. equal mass for all columns above the compensation level) is applied in the adjustment to uncover the signals from the deep-Earth interior, including dynamic deformations of the Earths surface. The isostatic gravity anomalies and geoid undulations, rather than the observed fields, then represent the signals from mantle convection and deep density inhomogeneities including remnants of subducted slabs. The long-wavelength non-isostatic (i.e. the dynamic) topography was estimated to range from –0.4 to 0.5 km. For shorter wavelengths (<2000 km), the isostatic condition is not applied in the adjustment in order to obtain the non-isostatic topography due to regional deviations from classical Airy isostasy. The maximum deviations from Airy isostasy (–1.5 to 1 km) occur at currently active plate boundaries. As another result, a new global model of the lithosphere density distribution is generated. The most pronounced negative density anomalies in the upper mantle are found near large plume provinces, such as Iceland and East Africa, and in the vicinity of the mid-ocean ridge axes. Positive density anomalies in the upper mantle under the continents are not correlated with the cold and thick lithosphere of cratons, indicating a compensation mechanism due to thermal and compositional density.     

基于星载激光雷达和雷达高度计数据的南极冰盖表面高程制图

南极数字高程模型DEMs(Digital Elevation Models)是研究极区大气环流模式,南极冰盖动态变化和南极科学考察非常重要的基础数据。目前,科学家已经发布了五种不同的南极数字表面高程模型。这些数据都是由卫星雷达高度计,激光雷达和部分地面实测数据等制作而成。尽管如此,由于海洋与冰盖交接的南极冰盖边缘区随时间的快速变化,有必要根据新的卫星数据及时更新南极冰盖表面高程数据。因此,我们利用雷达高度计数据(Envisat RA-2)和激光雷达数据(ICESat/GLAS)制作了最新的南极冰盖高程数据。为提高ICESat/GLAS数据的精度,本文采用了五种不同的质量控制指标对GLAS数据进行处理,滤除了8.36%的不合格数据。这五种质量控制指标分别针对卫星定位误差、大气前向散射、饱和度及云的影响。同时,对Envisat RA-2数据进行干湿对流层纠正、电离层纠正、固体潮汐纠正和极潮纠正。针对两种不同的测高数据,提出了一种基于Envisat RA-2和GLAS数据光斑脚印几何相交的高程相对纠正方法,即通过分析GLAS脚印点与Envisat RA-2数据中心点重叠的点对,建立这些相交点对的高度差(GLAS-RA-2)与表征地形起伏的粗糙度之间的相关关系,对具有稳定相关关系的点对进行Envisat RA-2数据的相对纠正。通过分析南极冰盖不同区域的测高点密度,确定最终DEM的分辨率为1000 m。考虑到南极普里兹湾和内陆地区的差异性,将南极冰盖分为16个区,利用半方差分析确定最佳插值模型和参数,采用克吕金插值方法生成了1000 m分辨率的南极冰盖高程数据。利用两种机载激光雷达数据和我国多次南极科考实测的GPS数据对新的南极DEM进行了验证。结果显示,新的DEM与实测数据的差值范围为3.21—27.84 m,其误差分布与坡度密切关系。与国际上发布的南极DEM数据相比,新的DEM在坡度较大地区和快速变化的冰盖边缘地区精度有较大改进。     

Combination of temporal gravity variations resulting from superconducting gravimeter (SG) recordings, GRACE satellite observations and global hydrology models

Gravity recovery and climate experiment (GRACE)-derived temporal gravity variations can be resolved within the μgal (10^?8 m/s ²) range, if we restrict the spatial resolution to a half-wavelength of about 1,500 km and the temporal resolution to 1 month. For independent validations, a comparison with ground gravity measurements is of fundamental interest. For this purpose, data from selected superconducting gravimeter (SG) stations forming the Global Geodynamics Project (GGP) network are used. For comparison, GRACE and SG data sets are reduced for the same known gravity effects due to Earth and ocean tides, pole tide and atmosphere. In contrast to GRACE, the SG also measures gravity changes due to load-induced height variations, whereas the satellite-derived models do not contain this effect. For a solid spherical harmonic decomposition of the gravity field, this load effect can be modelled using degree-dependent load Love numbers, and this effect is added to the satellite-derived models. After reduction of the known gravity effects from both data sets, the remaining part can mainly be assumed to represent mass changes in terrestrial water storage. Therefore, gravity variations derived from global hydrological models are applied to verify the SG and GRACE results. Conversely, the hydrology models can be checked by gravity variations determined from GRACE and SG observations. Such a comparison shows quite a good agreement between gravity variation derived from SG, GRACE and hydrology models, which lie within their estimated error limits for most of the studied SG locations. It is shown that the SG gravity variations (point measurements) are representative for a large area within the accuracy, if local gravity effects are removed. The individual discrepancies between SG, GRACE and hydrology models may give hints for further investigations of each data series.     

Geoid and high resolution sea surface topography modelling in the mediterranean from gravimetry,altimetry and GOCE data: evaluation by simulation

The determination of local geoid models has traditionally been carried out on land and at sea using gravity anomaly and satellite altimetry data, while it will be aided by the data expected from satellite missions such as those from the Gravity field and steady-state ocean circulation explorer (GOCE). To assess the performance of heterogeneous data combination to local geoid determination, simulated data for the central Mediterranean Sea are analyzed. These data include marine and land gravity anomalies, altimetric sea surface heights, and GOCE observations processed with the space-wise approach. A spectral analysis of the aforementioned data shows their complementary character. GOCE data cover long wavelengths and account for the lack of such information from gravity anomalies. This is exploited for the estimation of local covariance function models, where it is seen that models computed with GOCE data and gravity anomaly empirical covariance functions perform better than models computed without GOCE data. The geoid is estimated by different data combinations and the results show that GOCE data improve the solutions for areas covered poorly with other data types, while also accounting for any long wavelength errors of the adopted reference model that exist even when the ground gravity data are dense. At sea, the altimetric data provide the dominant geoid information. However, the geoid accuracy is sensitive to orbit calibration errors and unmodeled sea surface topography (SST) effects. If such effects are present, the combination of GOCE and gravity anomaly data can improve the geoid accuracy. The present work also presents results from simulations for the recovery of the stationary SST, which show that the combination of geoid heights obtained from a spherical harmonic geopotential model derived from GOCE with satellite altimetry data can provide SST models with some centimeters of error. However, combining data from GOCE with gravity anomalies in a collocation approach can result in the estimation of a higher resolution geoid, more suitable for high resolution mean dynamic SST modeling. Such simulations can be performed toward the development and evaluation of SST recovery methods.     

A harmonic spline model for local estimation of planetary gravity fields from line-of-sight acceleration data

The spline interpolation technique is applied to estimate locally the radial component of a planetary gravity field from residual acceleration data along a special direction, the direction of observation from the Earth. After the presentation of the theoretical framework, the method is tested on synthetic and real data in the case of Venus. It is shown that this spline technique can be used succesfully to build local models of radial gravity fields at the planet surface. Received: 13 March 1997 / Accepted: 17 November 1998     

多源数据融合的高时空分辨率植被指数生成

高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集,目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响,提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model),VISTFM采用模糊C聚类算法,对存量时序VI数据按土地利用类型划分为若干子类,从高低分辨率影像中随土地覆被类的变化规律提取子类,结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法,测试表明,VISTFM能够较好的捕获VI的中间变化过程,与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比,利用VISTFM获得的植被指数数据集具有更高的时空分辨率。     

Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

Dynamic orbit determination and gravity field model improvement from GPS, DORIS and Laser measurements on TOPEX/POSEIDON satellite

Summary. In the framework of the GRIM series of gravity field models, the CNES/GRGS GINS precise orbit determination software has been adapted to dynamic GPS data processing. That is simultaneous processing of all available observables (i.e. GPS, DORIS, Laser) and all available satellite orbits (i.e. GPS, TOPEX/POSEIDON) can now be performed. The TOPEX/POSEIDON (T/P) mission satellite is equipped with a GPS receiver, a DORIS receiver and a laser reflector that represents an unprecedented opportunity to compare and combine these three tracking systems to estimate orbital parameters and gravity field coefficients. Different combinations including : GPS + DORIS, DORIS + LASER, GPS + DORIS + LASER, have been tested and have shown small but systematic improvement in T/P orbit accuracy when GPS and DORIS data were processed simultaneously. Five tuned gravity field models have been generated by accumulating different combinations of T/P normal equations associated to the GPS, DORIS and Laser data. GPS data have a greater dynamic impact on gravity field spherical harmonics coefficient determination than DORIS and Laser data. Furthermore, the results obtained with the solutions including and T/P normal equations suggest that indeed these different tracking systems are somehow complementary in a dynamic sense. Received 30 March 1995; Accepted 19 September 1996     

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.     

Integrating data from remote sensing,geology and gravity for geological investigation in the Tarhunah area,Northwest Libya

Abstract

The present work deals with the integration of remote-sensing, surface-geology and gravity-survey data to improve the structural knowledge of the Tarhunah area, northwest Libya. Geological information and remote-sensing data provided information about the surface structure. A gravity survey was conducted to decipher the subsurface structure. The results revealed that a basin having a width of 39 to 48 km trends NE. A two-dimensional (2-D) schematic model shows that the basin gradually deepens toward the southwest. Faults determined from a horizontal gradient, tilt derivative, and Euler deconvolution show a depth range of 2.5 to 7.5 km. The integration and interpretation of the results indicate that volcanic activity was related to the tectonic activity of an anticlinal structure called the Jabal Uplift.     

A comparison of the tesseroid,prism and point-mass approaches for mass reductions in gravity field modelling

The calculation of topographic (and iso- static) reductions is one of the most time-consuming operations in gravity field modelling. For this calculation, the topographic surface of the Earth is often divided with respect to geographical or map-grid lines, and the topographic heights are averaged over the respective grid elements. The bodies bounded by surfaces of constant (ellipsoidal) heights and geographical grid lines are denoted as tesseroids. Usually these ellipsoidal (or spherical) tesseroids are replaced by “equivalent” vertical rectangular prisms of the same mass. This approximation is motivated by the fact that the volume integrals for the calculation of the potential and its derivatives can be exactly solved for rectangular prisms, but not for the tesseroids. In this paper, an approximate solution of the spherical tesseroid integrals is provided based on series expansions including third-order terms. By choosing the geometrical centre of the tesseroid as the Taylor expansion point, the number of non-vanishing series terms can be greatly reduced. The zero-order term is equivalent to the point-mass formula. Test computations show the high numerical efficiency of the tesseroid method versus the prism approach, both regarding computation time and accuracy. Since the approximation errors due to the truncation of the Taylor series decrease very quickly with increasing distance of the tesseroid from the computation point, only the elements in the direct vicinity of the computation point have to be separately evaluated, e.g. by the prism formulas. The results are also compared with the point-mass formula. Further potential refinements of the tesseroid approach, such as considering ellipsoidal tesseroids, are indicated.     

GRACE时变重力场位系数相关误差的滤波消除技术

利用UTCSR发布的2003-01~2013-07GRACE RL05月平均重力场模型,分析比较了高斯滤波、各向异性滤波、扇形滤波和维纳滤波,并结合去相关滤波在反演南极地区冰盖质量变化方面的差异。通过计算得到以下结论：①基于121组重力场模型阶方差分布得到维纳滤波与半径为300 km的高斯滤波效果最为接近,说明300 km滤波半径完全可以满足质量变化信号的提取;②在一定范围内,提高滤波半径能提高反演结果信噪比,建议南极区域的滤波半径为500 km;采用相同滤波半径,不同空间滤波算法计算的质量变化率基本一致,在南极区域可以选取任一滤波方法;③与其他算法相比,去相关滤波算法能在一定程度消除球谐系数中存在的系统误差,改善反演结果。     

Quantifying FES2004 S<Subscript>2</Subscript> tidal model from multiple space-geodesy techniques,GPS and GRACE,over North West Australia

Unmodeled sub-daily ocean S₂ tide signals that alias into lower frequencies have been detected in the analysis of gravity recovery and climate experiment (GRACE) space gravity fields of GRGS. The most significant global S₂ aliased signal occurs off the northwest coast of Australia in a shallow continental shelf zone, a region with high tidal amplitudes at a period of 161 days. The GRACE S₂ aliased equivalent water height grids are convolved with Green’s functions to produce GRACE aliased tidal loading (GATL) vertical displacements. The analysis of hourly global positioning system (GPS) vertical coordinate estimates at permanent sites in the region confirms the presence of spectral power at the S₂ frequency when the same ocean tide model (FES2004) was used. Thus, deficiencies in the FES2004 ocean tide model are detected both directly and indirectly by the two independent space geodetic techniques. Through simulation, the admittance (ratio of amplitude of spurious long-wavelength output signal in the GRACE time-series to amplitude of unmodeled periodic signals) of the GRACE unmodeled S₂ tidal signals, aliased to a 161-day period, is found to have a global average close to 100%, although with substantial spatial variation. Comparing GATL with unmodeled S₂ tidal sub-daily signals in the vertical GPS time-series in the region of Broome in NW Australia suggests an admittance of 110–130%.     

A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.