基于DTW的HJ-1B卫星地表温度产品质量检验

HJ-1B卫星热红外数据应用广泛,但其地表温度反演产品的质量检验工作尚不完善。以黑河流域为研究区,利用普适性单通道算法得到HJ 1B地表温度,基于7个地面站点（下垫面为荒漠、沙漠、植被、农作物、雪地和湿地）同步观测资料和MODIS地表温度产品（MOD11A1）,引入动态时间规整方法（DTW）对站点处HJ 1B地表温度进行验证。试验结果表明：HJ 1B地表温度反演产品与地面观测值的偏差值在沙漠和荒漠站点为1K以内,均方根误差在05K左右;对于植被和农作物站点的偏差在2K以内,均方根误差为1~2K;基于DTW的验证对时序不匹配的数据评价结果与现有指标表现一致。HJ-1B地表温度反演产品与地面站点的相对偏差均低于其与MODIS地表温度反演产品的相对偏差。     

基于高分四号数据的多时相多通道云检测算法

针对高分四号（GF-4）卫星影像波段较少导致传统云检测算法难以区分云与冰雪像元的问题,提出一种多时相多通道云检测算法。该算法首先对GF-4卫星影像进行辐射定标和配准,然后利用云与典型地表的光谱差异得到潜在云像元,之后利用序列GF-4卫星影像之间的差异识别出移动的云像元,最后利用中红外波段反演地表亮度温度来去除冰雪像元。该算法在海南、辽宁和安徽3个研究区域进行验证,并将检测结果与传统单时相云检测算法、支持向量机（SVM）云检测算法和实时差分（RTD）云检测算法的检测结果进行对比。结果表明,该算法优于其他3种云检测算法,准确识别率均达到90%以上,误检率均低于5%,有利于GF-4卫星影像的进一步利用。     

基于VMD-MA的GNSS-MR雪深反演方法

针对利用全球卫星导航系统多径反射(GNSS-MR)技术反演雪深过程中信噪比(SNR)序列趋势项分离不佳和反演结果波动较大的问题，提出一种基于变分模态分解(VMD)和移动平均(MA)的雪深反演方法。VMD算法通过自适应高通滤波有效分离SNR序列的趋势项，MA算法对初始反演结果进行平滑处理从而减少随机波动。选用瑞典KIRU站2021年前5个月GLONASS不同频段的SNR观测值开展实验，研究所提方法的可行性。结果表明：基于VMD算法的反演结果与气象站原位雪深相关系数超过0.95，均方根误差(RMSE)最低约5 cm，较传统算法减少近40%；经MA算法平滑处理后，反演精度进一步提高。考虑到GNSS站和气象站之间的差异性，选取GPS SNR反演结果作为参考数据源，不同参考数据源取得了一致的实验结论,验证了所提方法的可行性和有效性。     

基于散射光强度的碳黑团聚体分形结构和粒径分布同时反演

利用散射光信号实现碳黑团聚体分形结构和粒径分布参数的同时反演，在火焰辐射换热模拟和污染物测定方面有着重要应用价值。反演的正问题基于瑞利-德拜-甘斯多分散分形团聚体（RDG-PFA）散射理论，研究了2种信号方案，包括多角度散射及多角度散射-准直透射率。反演前，对比2种信号方案的残余适应度值分布发现，散射与透射信号同时使用有效减弱了反问题的病态性。反演过程基于协方差矩阵自适应的演化策略（CMA-ES）算法，该算法具有很强的局部搜索能力，为快速且稳定地反演各个目标参数提供了保障。反演结果表明了CMA-ES算法较大搜索空间内的可行性和普适性，同时也证明了采用多角度散射-准直透射率的组合信号有效提高了目标参数的反演精度。      

基于TechDemoSat-1卫星的GPS反射信号海面高度反演

针对星载GPS反射信号（GPS-R）海面测高的误差问题，基于星载GPS-R实测数据进行星载海面测高模型和误差修正模型的研究，并验证其有效性。利用TechDemoSat-1（TDS-1）数据，使用时延多普勒图（DDM）海面高度反演技术，着重分析了星载GPS-R海面高度反演中的各类误差，并建立了相应的误差模型。对星载GPS-R海面高度反演模型进行优化，采用DTU15全球平均海面模型、DTU全球海潮模型验证反演精度。结果证明:优化后反演模型得到的全球海面高度反演结果的平均绝对误差（MAD）为6.05 m，精度提高了约29%，有效提高了海面高度反演的精度。研究成果对于推广星载GNSS反射信号（GNSS-R）的海面测高应用具有一定的意义。      

基于GPS多星三频数据融合的GNSS-IR土壤湿度反演方法

土壤湿度的监测是全球卫星导航系统干涉测量法（GNSS-IR）的关键应用之一。传统的GNSS-IR土壤湿度反演方法一般只针对单颗卫星的单一频段，未充分利用不同轨道、不同频率卫星信号的差异性与互补性。针对此问题，提出了一种将GPS多星的L1、L2和L5频段数据加权融合进行联合反演的方法，该方法利用基于最小方差的自适应融合算法得到加权因子，并通过现场实验进行了方法验证。结果表明:在测试集上所提出的反演方法相比于传统的Larson方法，相关系数提高了24.69%，均方根误差下降了22.28%，与均值融合法相比，相关系数提高了26.77%，均方根误差下降了23.26%，证明了所提方法能有效提高反演精度。      

欧洲发射星上自主项目-2卫星

“星上自主项目”（PROBA）卫星系列属于欧洲航天局（ESA）的技术验证计划，资金来自通用支持技术计划（GSTP）。该系列卫星是小型、低成本卫星，用于验证新的航天器技术、研究技术、研发方法，同时还可携带科学有效载荷。     

改进粒子群优化的卫星导航选星算法

为提高选星算法的性能，提出一种基于人工鱼群算法的粒子群优化（PSO）选星算法。该算法利用人工鱼群算法良好的全局收敛特性，克服了粒子群优化算法易陷入局部最优的缺点。将每种卫星组合看作空间中的一个粒子，选取几何精度因子（GDOP）作为适应度函数。利用所提算法更新粒子自身位置，优化卫星组合与几何精度因子。利用实际数据对所提算法进行验证和对比，结果表明:改进的选星算法在保障选星效率的同时，选星结果的准确性优于标准的粒子群优化选星算法。      

基于树模型机器学习方法的GNSS-R海面风速反演

GNSS-R是基于GNSS卫星反射信号的一种新技术.GNSS-R技术可以运用到海面风场反演中，传统的GNSS-R技术反演海面风场主要有波形匹配和经验函数两种方法，风速反演精度约为2m·s-1.波形匹配方法耗时多，计算量大；经验函数方法通常只使用少量物理观测量，会造成信息浪费，损失一定的反演精度.为了提高海面风速的反演精度，引入机器学习领域常用的树模型算法决策树、随机森林、GBDT等对海面风速进行预测.利用GNSS-R与ECMWF数据构成训练集和验证集，训练集用于模型学习，验证集用于检验模型的反演效果.实验结果显示，决策树和随机森林预测误差约为0.6m·s-1，GBDT等算法的预测误差约为2m·s-1，满足风速反演要求.与GNSS-R传统反演方法相比，机器学习树模型算法效果更好，在验证集上表现稳定且误差较小.因此，可以将机器学习树模型算法运用到海面风速反演中.      

ROPP反演软件算法及其精度分析

介绍了ROPP反演软件中无线电掩星反演的算法与精度分析. 采用COSMIC卫星2008年1 月1日全天的附加相位数据, 反演得到折射率、温度、压强与湿度等参数, 并与CDAAC 相应结果进行对比. 实验结果表明, 在30km高度以下, 折射率、压强和湿度的相对 误差在2%以内, 温度误差不超过2K.      

Validation of non-linear split window algorithm for land surface temperature estimation using Sentinel-3 satellite imagery: Case study; Tehran Province,Iran

In recent years, land surface temperature (LST) has become critical in environmental studies and earth science. Remote sensing technology enables spatiotemporal monitoring of this parameter on large scales. This parameter can be estimated by satellite images with at least one thermal band. Sentinel-3 SLSTR data provide LST products with a spatial resolution of 1 km. In this research, direct and indirect validation procedures were employed to evaluate the Sentinel-3 SLSTR LST products over the study area in different seasons from 2018 to 2019. The validation method was based on the absolute (direct) evaluation of this product with field data and comparison (indirect) evaluation with the MODIS LST product and the estimated LST using the non-linear split-window (NSW) algorithm. Also, two emissivity estimation methods, (1) NDVI thresholding method (NDVI-THM) and (2) classification-based emissivity method (CBEM), were used to estimate the LST using the NSW method according to the two thermal bands of Sentinel-3 images. Then, the accuracy of these methods in estimating LST was evaluated using field data and temporal changes of vegetation, which the NDVI-THM method generated better results. For indirect evaluation between the Sentinel-3 LST product, MODIS LST product, and LST estimated using NSW, four filters based on spatial and temporal separates between pairs of pixels and pixel quality were used to ensure the accuracy and consistency of the compared pairs of a pixel. In general, the accuracy results of the LST products of MODIS and Sentinel-3, and LST estimated using NSW showed a similar trend for LST changes during the seasons. With respect to the two absolute and comparative validations for the Sentinel-3 LST products, summer with the highest values of bias (?1.24 K), standard deviation (StDv = 2.66 K), and RMSE (2.43 K), and winter with the lowest ones (bias of 0.14 K, StDv of 1.13 K, and RMSE of 1.12 K) provided the worst and best results for the seasons in the period of 2018–2019, respectively. According to both absolute and comparative evaluation results, the Sentinel-3 SLSTR LST products provided reliable results for all seasons on a large temporal and spatial scale over our studied area.     

Simulation of the land surface temperature from moon-based Earth observations

The land surface temperature (LST) is a key parameter for the Earth’s energy balance. As a natural satellite of the Earth, the orbital of the moon differs from that of current Earth observation satellites. It is a new way to measure the land surface temperature from the moon and has many advantages compared with artificial satellites. In this paper, we present a new method for simulating the LST measured by moon-based Earth observations. Firstly, a modified land-surface diurnal temperature cycle (DTC) method is applied to obtain the global LST at the same coordinated universal time (UTC) using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The lunar elevation angles calculated using the ephemeris data (DE405) from the Jet Propulsion Laboratory (JPL) were then applied to simulate the Earth coverage observed from the moon. At the same time, the modified DTC model was validated using in situ data, MODIS LST products, and the FengYun-2F (FY-2F) LST, respectively. The results show that the fitting accuracy (root-mean-square error, RMSE) of the modified DTC model is not greater than 0.72?°C for eight in situ stations with different land cover types, and the maximum fitting RMSE of the modified model is smaller than that of current DTC models. By the comparison of the simulated LST with MODIS and FY-2F LST products, the errors of the results were feasible and accredited, and the simulated global LST has a reasonable spatiotemporal distribution and change trend. The simulated LST data can therefore be used as base datasets to simulate the thermal infrared imagery from moon-based Earth observations in future research.     

Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZentrum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 15.0 cm to 7.0 cm. For the SSR clock product, the positioning accuracy of N, E, and U directions is better than 12.0 cm with visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 12.0 cm to 9.0 cm.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

基于BP神经网络的介质表面充电电位反演方法

为了实现全面、实时的在轨卫星充放电风险分析,基于在相同环境下,不同材料表面充电的关联性,利用BP神经网络建立了一种由Kapton材料表面充电电位反演卫星其他常用介质材料表面电位的模型。以Kapton材料的表面电位以及材料厚度为输入,其他介质材料的表面电位作为模型输出,使用COMSOL建立的表面充电模型对神经网络进行训练,将反演误差降低到10%以下,并利用Kapton与Teflon材料表面充电地面试验数据验证反演模型的准确性,结果显示Teflon表面电位的反演值与实测值间的相对误差小于16%。     

Substitution of satellite-based land surface temperature defective data using GSP method

Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.     

Climate studies from satellite observations: Special problems in the verification of earth radiation balance,cloud climatology,and related climate experiments

A body of techniques that have been developed and planned for use during the Earth Radiation Budget Experiment (ERBE), the International Satellite Cloud Climatology Project (ISCCP), and related climate experiments of the 1980's are reviewed. Validation and verification methods must apply for $\text{systems}$ of satellites. They include: (1) use of a normalization or intercalibration satellite, (2) special intensive observation areas located over ground-truth sites, and (3) monitoring of sun and earth by several satellites and/or several instruments at the same time. Since each climate application area has a hierarchy of user communities, validation techniques vary from very detailed methods to those that simply assure high relative accuracy in detecting space and time variations for climate studies. It is shown that climate experiments generally require more emphasis on long-term stability and internal consistency of satellite data sets than high absolute accuracy.     

基于偏心率和倾角矢量的双星隔离策略

针对中国地球静止轨道双星共位的需要, 研究了双星共位的工程实现问题, 提出了一种使用偏心率矢量和倾角矢量联合隔离实现双星共位的方法. 给出了基于偏心率矢量和倾角矢量联合隔离的基本方法、约束方程和工程实现的控制策略, 并通过模拟计算和工程实际应用情况, 验证了该方法的正确性.      

Multi precursors analysis associated with the powerful Ecuador (MW = 7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data

After DEMETER satellite mission (2004–2010), the launch of the Swarm satellites (Alpha (A), Bravo (B) and Charlie (C)) has created a new opportunity in the study of earthquake ionospheric precursors. Nowadays, there is no doubt that multi precursors analysis is a necessary phase to better understand the LAIC (Lithosphere Atmosphere Ionosphere Coupling) mechanism before large earthquakes. In this study, using absolute scalar magnetometer, vector field magnetometer and electric field instrument on board Swarm satellites, GPS (Global Positioning System) measurements, MODIS-Aqua satellite and ECMWF (European Centre for Medium-Range Weather Forecasts) data, the variations of the electron density and temperature, magnetic field, TEC (Total Electron Content), LST (Land Surface Temperature), AOD (Aerosol Optical Depth) and SKT (SKin Temperature) have been surveyed to find the potential seismic anomalies around the strong Ecuador (M_w = 7.8) earthquake of 16 April 2016. The four solar and geomagnetic indices: F10.7, D_st, K_p and a_p were investigated to distinguish whether the preliminary detected anomalies might be associated with the solar-geomagnetic activities instead of the seismo-ionospheric anomalies. The Swarm satellites (A, B and C) data analysis indicate the anomalies in time series of electron density variations on 7, 11 and 12 days before the event; the unusual variations in time series of electron temperature on 8 days preceding the earthquake; the analysis of the magnetic field scalar and vectors data show the considerable anomalies 52, 48, 23, 16, 11, 9 and 7 days before the main shock. A striking anomaly is detected in TEC variations on 1 day before earthquake at 9:00 UTC. The analysis of MODIS-Aqua night-time images shows that LST increase unusually on 11 days prior to main shock. In addition, the AOD variations obtained from MODIS measurements reach the maximum value on 10 days before the earthquake. The SKT around epicentral region presents anomalous higher value about 40 days before the earthquake. It should be noted that the different lead times of the observed anomalies could be acknowledged based on a reasonable LAIC earthquake mechanism. Our results emphasize that the Swarm satellites measurements play an undeniable role in progress the studies of the ionospheric precursors.