一种适用于电离层电子密度重构的AMART算法

针对传统乘法代数重构算法（MART）迭代精度不高的问题,提出一种自适应电离层层析新算法。一方面,该算法根据射线穿越像素点的截距和电子密度值的综合影响,合理地分配迭代差值;另一方面,提出一种与电子密度值相关的自适应松弛因子,有效克服传播噪声对电子密度反演的影响。试验采用全球电离层图（GIM）数据和GPS双频观测数据分别从单射线迭代和多射线迭代两个方面对新方法的可行性和优越性进行验证。试验结果表明,相对于传统的MART算法,本文方法反演电子密度剖面更接近电离层测高仪观测结果。     

附加平滑约束的电离层层析反演

根据乘法代数重构算法,利用相邻像素之间的连续性及平滑性,采用附加平滑约束的方法来克服没有任何观测信息的像素对经验模型给出的初值的依赖。利用中国地壳运动观测网络的高精度双频GPS观测值,反演了中国区域上空电离层电子密度分布,将反演的结果同检验站的电离层延迟进行对比分析,并且将反演的电子密度剖面与相应的测高仪数据进行比较,验证了此方法的有效性和可靠性。     

一种基于传统MART的电离层层析新算法

层析算法研究是电离层层析技术的关键。针对传统的乘法代数重构算法(MART)存在精度不高的问题,提出了一个改进的电离层层析算法(IMART)。该算法一方面将像素内的截距值与电子含量作为组合量,构造了新的组合量;另一方面改变了松弛因子的获取方式,削弱了噪声对反演结果的影响。利用GPS实测数据,对该算法的可靠性和优越性进行了仔细验证。根据统计分析和定量比较的结果,发现该算法表现出显著的重构精度且相较于其他经典层析算法有18%的提高。     

基于CORS数据反演江苏上空电离层电子密度

本文利用IRI2007模型和江苏CORS网数据,结合附加约束的同时迭代重构算法,反演了地磁平静日江苏上空电子密度的结构分布。结果表明,整个研究区域电子密度在不同纬度和不同高度上存在明显的日变特征,电子密度最大值出现在北京时13～15时,随着时间推移,峰值幅度衰减;白天出现明显的E层,夜晚消失;反演结果说明了电离层三维层析技术为监测电离层时空结构提供了一种强有力的实验支持。     

基于CORS数据反演江苏上空电离层电子密度

本文利用IRI2007模型和江苏CORS网数据,结合附加约束的同时迭代重构算法,反演了地磁平静日江苏上空电子密度的结构分布。结果表明,整个研究区域电子密度在不同纬度和不同高度上存在明显的日变特征,电子密度最大值出现在北京时13～15时,随着时间推移,峰值幅度衰减;白天出现明显的E层,夜晚消失;反演结果说明了电离层三维层析技术为监测电离层时空结构提供了一种强有力的实验支持。     

一种融合测高仪与地面GPS数据的单站电离层反演方法

电离层层析成像技术非常适用于检测电离层电子密度的大尺度空间分布及其扰动。利用地面单站的GPSTEC值和另一站的数字测高仪观测数据，结合国际参考电离层（IRI），利用MART算法反演得到测站上空电子密度的垂直分布。利用白天和夜间的实测数据进行了CIT反演，结果表明了该方法的可靠性。     

2003～2006年磁暴期间欧洲区域电离层三维层析及演变分析

基于IGS跟踪站的GNSS观测资料,利用电离层三维层析技术,对2003~2006年间3次不同类型强磁暴期间欧洲区域上空电离层电子密度的三维时空分布进行了反演,反演结果精细地反映了电离层电子密度的三维结构。通过对电离层扰动演变过程的分析,表明电离层扰动情况与磁暴发展过程一致,同时发现磁暴引起的电离层扰动具有显著的纬度效应。另外,随着磁暴的发展,电子密度极大值所在高度也会发生变化。研究分析表明,电离层电子密度三维重构图像可以直观清晰地反映各经度面、纬度面和高度面上电子密度的分布和演变情况,能有效地辅助分析电离层在磁暴发生期间的扰动情况。     

基于混合罚函数法的多尺度电离层层析确权方法

在利用GNSS进行像素基电离层层析时,多尺度层析方法利用权重因子将反演区域不同像素层析模型结合在一起,最终得到电离层电子密度反演结果,可以有效地解决电离层层析过程中不适应问题和最终的电离层电子密度失真现象。在多尺度电离层层析中,不同像素尺度层析模型之间权重是影响最终的电离层电子密度精度的重要因素。为了获得高精度电离层层析模型,考虑到权重因子存在着等式和不等式限制条件,采用解决最优化问题的罚函数法确定不同像素尺度电离层层析模型之间的权重。通过采用实测GNSS观测数据进行电离层多尺度电离层层析,对比了多尺度层析模型的各个子模型建模精度并进行分析,同时将罚函数法获得的模型精度与其他确权方法进行了对比,该方法可以有效地应用于多尺电离层度层析,且最终的层析模型精度优于其他确权方法,更优于单尺度电离层层析模型精度。     

利用区域地基GPS的电离层层析成像研究

电离层是近地空间的重要组成部分,如何对电离层的异常扰动进行合理监测与预报一直是空间物理领域的研究课题。本文将计算机层析成像技术引入到电离层扰动监测中,利用大量的区域地基GPS观测数据,借助代数层析迭代算法反演得到三维电离层电子密度;并将层析结果与国际电离层参考模型IRI2007进行对比分析,结果表明:地基GPS层析所得的电离层电子密度与IRI2007基本一致,但层析结果精度略高于IRI2007模型。     

代数重构算法在GNSS水汽层析解算中的应用

全球导航卫星系统(GNSS)水汽层析反演技术是目前获取对流层水汽三维分布的重要方法。考虑到代数重构算法在迭代反演中具有节省计算机内存且计算稳定度高的优点,对代数重构算法在GNSS水汽层析中的应用进行了研究。研究结果表明,受水汽在对流层中的分布情况的影响,传统的加法代数重构算法在实际的层析解算中,会出现较大的重构误差,而乘法代数重构算法和调整了松弛参数向量的加法代数重构算法则大大提高了层析解算的精度;代数重构算法较附加约束条件的层析解算方法更易受到观测值误差的影响,但采用乘法代数重构算法可以获得优于加法代数重构算法的结果。     

A new practical approach to GNSS high-dimensional ambiguity decorrelation

Based on both the lower and the upper triangular Cholesky decomposition algorithms, the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation are defined, and the (inverse) paired Cholesky integer transformation is proposed. Then, for the case of high-correlation ambiguity, a multi-time (inverse) paired Cholesky integer transformation is given. In addition, a simple and practical criterion is presented to solve the uniqueness problem of the integer transformation. It is verified by an example that (1) the (inverse) paired Cholesky integer transformation is very convenient and very efficient in practical computation; (2) the (inverse) paired Cholesky integer transformation is better than both the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation; and that (3) the inverse paired Cholesky integer transformation outperforms the paired Cholesky integer transformation slightly in the most cases.     

Use oe aerial photograph for study of soil salinity and landscape relationship in parts of Haryana

In order to know the development of soil salinity and alkalinity in relation to landform, a systematic approach has been adopted by using aerial photo-interpretation techniques followed by field checks. The major land form units identified in the area are (i) alluvial upland (ii) low land (iii) back-swamp areas (iv) remanent horstland (v) interfiuve (vi) abandoned channel (vii) plain undifferentiated and (viii) plain with aeolian activity. salt accumulation is pronounced in landform units (i), (ii), (iii) and (iv) due to configurating slopes and basement ridge control, whereas in landform units (v) and (vi), a patchy distribution of salinity has been found. Landform units (vii) and (viii) are free from this hazard The elements of landscape have controlled the distriburion of salts within each landscape.     

Pre-harvest wheat yield prediction using agromet-spectral-trend-yield models for hoshiarpur and rupnagar districts of punjab

Wheat yield prediction using different agrometeorological indices, spectral index (NDVI, Normalized Difference Vegetation Index) and trend predicted yield (TPY) were developed in Hoshiarpur and Rupnagar districts of Punjab. On the basis of examination of Correlation Coefficients (R), Standard Error of Estimate (SEOE) and Relative Deviation (RD) values resulted from different agromet models, the best agromet subset were selected as Minimum Temperature (T_min), Maximum Temperature (T_max) and accumulated Heliothermal Units (HTU) in case of Hoshiarpur district and Minimum Temperature (T--_min), accumulated Temperature Difference (TD) and accumulated Pan Evaporation (E) for Rupnagar district at reproductive stage (2^nd week of March) of wheat. It was found that Agromet-Spectral-Trend-Yield model could explain 96 % (SEOE = 87 kg/ha) and 91 % (SEOE = 146 kg/ha) of wheat yield variations for Hoshiarpur and Rupnagar districts, respectively.     

Evaluation of multisensor data for delineating salt affected soils in arid rajasthan

The Landsat (MSS and TM), SPOT (PLA and MLA) and IRS (LISS-I and LISS-II) images of crop free period (April, May), rainfed crop (October) and rabi irrigated crop (January, February) have been evaluated for their capabilities of mapping (1) primary salt affected soils: (slightly, moderately and severely) (2) saline water irrigated saline soils, (3) sodic water irrigated sodic soils and (4) salt affected soils due to tank seepage in the arid region of Rajasthan. The moderately and severe salt affected soils could be mapped with Landsat, (IRS LISS-I) and SPOT, images of any season. However, the summer season imagery provided maximum extent of salt affected soils. The LISS-II imagery also provided delineation of slightly salt affected soils in addition to the moderate and severely salt affected soils. The delineation of saline and sodic water irrigated areas was possible by using Landsat False Colour Composite for the January month by their characteristic reflectance, existing cropping pattern and the quality of irrigation water being used in the area. The IRS (LISS-II) and SPOT PLA images for the May month were also used for mapping of saline and sodic water irrigated soils.     

A new differential code bias (C1–P1) estimation method and its performance evaluation

The current satellite clock products are computed using the ionosphere-free phase (L1/L2) and code (P1/P2) observations. Thus, if users conduct undifferenced positioning using these clock products together with C1 and P2 observations, the differential code bias (DCB) (C1–P1) should be properly compensated. The influence of DCB (C1–P1) on the undifferenced ambiguity solutions is investigated. Based on the investigation, we propose a new DCB (C1–P1) estimation method. Using it, the satellite DCB (C1–P1) can be computed. A 30-day (DOY 205–234, 2012) dual-frequency GPS data set is processed to estimate the DCB (C1–P1). Comparing the estimated results with that of IGS DCB products, the accuracy is better than 0.13 m. The performances of DCB (C1–P1) in the code-based single-point positioning, precise point positioning (PPP) convergence and wide-lane uncalibrated phase delay (UPD) estimation are investigated using the estimated DCB (C1–P1). The results of the code-based single-point positioning show that the influence of DCB (C1–P1) on the up direction is more evident than on the horizontal directions. The accuracy is improved by 50 % and reaches to decimeter level with DCB (C1–P1) application. The performance of DCB (C1–P1) in PPP shows that it can accelerate PPP convergence through improving the accuracy of the code observation. The computed UPD values show that influence of DCB (C1–P1) on UPD of each satellite is different, and some values are larger than 0.3 cycles.     

基于等式的等间隔GM(1,1)模型比较研究

本文归纳了基于等式的2种等间隔GM(1,1)模型,比较了这2种模型与常规模型的精度,算例显示:当发展系数a较小时,3种模型的后验差比值C和小误差概率P的精度一致;当发展系数较大时,基于等式的模型比常规模型的后验差比值C和小误差概率P的精度高,且基于等式的这2种模型的后验差比值C和小误差概率P精度基本相当.     

Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.     

Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices

Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index have promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis + NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI₁) (CVA = 79.55%) and Vis + NIR index (NDVI, NG, IPVI and GNDVI) (CVA = 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.     

Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti,India

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.     

CBERS02B卫星CCD传感器数据反演陆地气溶胶

研究利用CBERS02B卫星的CCD传感器数据反演陆地气溶胶的方法。采用的方法是暗像元法。具体步骤为: 根据地面采集的植被光谱数据, 结合CCD传感器特点, 建立浓密植被(暗像元)红蓝波段(CCD传感器的第三和第一波段)反射率与地表反射率之间的关系, 确定了暗像元识别的阈值, 讨论气溶胶光学厚度对暗像元识别的影响以及消除这种影响的方法; 利用6S进行辐射传输运算, 构建查找表; 根据CBERS02B卫星的CCD传感器数据, 从查找表插值得到气溶胶光学厚度, 并进行了算法的误差分析。用广西南宁市及北京地区附近的两景数据进行了实际的反演试验, 使用MODIS的气溶胶产品与反演结果进行比对。结果显示, CBERS02B卫星的CCD传感器数据能够较好的反演陆地气溶胶。