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.     

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.     

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.     

基于X射线脉冲星导航试验卫星观测数据的到达时间估计

中国首颗X射线脉冲星导航试验卫星（X-ray Pulsar Navigation-1, XPNAV-1）搭载了两种体制的X射线探测器,其主要任务是在轨开展X射线脉冲星的探测以及进行脉冲星导航体制的验证。为了实现到达时间（Time of Arrival,TOA）的高精度估计,提出了利用阵列信号处理领域的多重信号子空间分类（Multiple Signal Classification,MUSIC）方法进行脉冲星导航的TOA估计,进行了试验仿真验证,并且对XPNAV-1观测到的蟹状星云（Crab）脉冲星的光子数据进行处理。在观测时间为协调世界时（Coordinate Universal Time,UTC）57727.0约简儒略日（Modified Julian Day,MJD）到UTC57812.0MJD内,选取了0.5~10keV频段内的121段光子观测数据,对这些数据进行了脉冲轮廓折叠,得到了Crab脉冲星的折叠轮廓,然后分别利用互相关法和MUSIC方法进行折叠轮廓的TOA估计,最后对比了两种方法的估计结果。     

Investigation of GPS-TEC measurements using ANN method indicating seismo-ionospheric anomalies around the time of the Chile (Mw = 8.2) earthquake of 01 April 2014

In 94 km NW of Iquique in Chile (19.610°S, 70.776°W) a powerful earthquake of M_w = 8.2 took place at 23:46:47 UTC (20:46:47 LT) on April 01, 2014. Using GPS-TEC (Total Electron Content) measurements, potential unusual variations around the time and location of the Chile earthquake have been detected based on the median and Artificial Neural Network (ANN) methods. The indices D_st, K_p, A_p and F10.7 were used to distinguish pre-earthquake anomalies from the other anomalies related to the solar-geomagnetic activities. Using the median method, striking anomalies in time series of TEC data are observed 4 days before the earthquake at 14:00 and 16:00 UTC. The ANN method detected a number of anomalies, 4 (02:00 and 16:00 UTC) and 13 (24:00 UTC) days preceding the earthquake. The results indicate that the ANN method due to its capability of non linear learning is quite promising and deserves serious attention as a robust predictor tool for seismo-ionospheric anomalies detection.     

Comparison of satellite orbit ephemerides for use in GPS meteorology

This paper discusses GPS (Global Position System) meteorology. The research presented is based on a comparison of values of precipitable water vapour PWV, based on GPS measurements using final and predicted ephemerides of satellite orbits. We analysed recent year’s improvement in predicting ephemerides. We compared the data outputs from a radiosonde using GPS receiver measurements directly from the meteorological station from which the radiosondes were launched. The results indicate a high quality of the predicted ephemerides. This finding makes predicted ephemerides highly usable for near real-time estimations of PWV. To use PWV in meteorological forecast applications, this high speed of PWV values supply is necessary.     

Validation on MERSI/FY-3A precipitable water vapor product

The precipitable water vapor is one of the most active gases in the atmosphere which strongly affects the climate. China's second-generation polar orbit meteorological satellite FY-3A equipped with a Medium Resolution Spectral Imager (MERSI) is able to detect atmospheric water vapor. In this paper, water vapor data from AERONET, radiosonde and MODIS were used to validate the accuracy of the MERSI water vapor product in the different seasons and climatic regions of East Asia. The results show that the values of MERSI water vapor product are relatively lower than that of the other instruments and its accuracy is generally lower. The mean bias (MB) was ?0.8 to ?12.7?mm, the root mean square error (RMSE) was 2.2–17.0?mm, and the mean absolute percentage error (MAPE) varied from 31.8% to 44.1%. On the spatial variation, the accuracy of MERSI water vapor product in a descending order was from North China, West China, Japan -Korea, East China, to South China, while the seasonal variation of accuracy was the best for winter, followed by spring, then in autumn and the lowest in summer. It was found that the errors of MERSI water vapor product was mainly due to the low accuracy of radiation calibration of the MERSI absorption channel, along with the inaccurate look-up table of apparent reflectance and water vapor within the water vapor retrieved algorithm. In addition, the surface reflectance, the mixed pixels of image cloud, the humidity and temperature of atmospheric vertical profile and the haze were also found to have affected the accuracy of MERSI water vapor product.     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.     

基于北斗导航检测平台的时间量值传递系统研究

研究了基于北斗卫星的北京市导航检测平台的时间量值传递系统,它以卫星共视法作为数据比对的条件,采用修正值预估技术和相位补偿技术来提高铷钟的准确度与稳定度。本系统可实时地将地方原子时标与UTC（NIM）进行比对溯源,实现北斗卫星导航检测平台的时间量值可靠传递,保证地方原子时标与国家时间基准UTC（NIM）同步差在±10ns以内,以满足北斗导航产品研制、开发和应用的需要。     

基于北斗导航检测平台的时间量值传递系统研究

研究了基于北斗卫星的北京市导航检测平台的时间量值传递系统,它以卫星共视法作为数据比对的条件,采用修正值预估技术和相位补偿技术来提高铷钟的准确度与稳定度。本系统可实时地将地方原子时标与UTC（NIM）进行比对溯源,实现北斗卫星导航检测平台的时间量值可靠传递,保证地方原子时标与国家时间基准UTC（NIM）同步差在±10ns以内,以满足北斗导航产品研制、开发和应用的需要。     

利用青海湖对FY—1C、FY—2B气象卫星热红外通道进行在轨辐射定标

介绍利用青海湖辐射校正场对FY－1C、FY2B气象卫星热红外通道进行在轨辐射定标，先用CE312野外热红外辐射计在水面测量水表辐亮度，再经大气订正传递到卫星入瞳处，大气订正包括大气吸收削弱和大气产生热发射影响，这两部分对卫星信号的贡献通过辐射传输模式MODTRAN37计算出来，同时进行CE312与卫星通道光谱响应匹配，最终得到卫星入瞳处的表观辐亮度，这个辐亮度与卫星通道的计数值得到该通道绝对定标系数。我们对两颗卫星进行了多次定标，结果表明利用青海湖进行的在轨定标与星上定标系数相差5％左右，相当于3K的亮温差。     

低轨航天器弹道系数估算及热层大气模型误差分析

利用低轨（LEO）航天器在轨期间两行轨道根数（TLEs）数据,结合经验大气密度模型NRLMSISE00,反演计算得到其在轨期间的弹道系数B’,以31年B’的平均值代替弹道系数真值,分别通过标准球形目标卫星对比以及物理参数基本相同的非球形目标卫星对比,对弹道系数真值进行了检验;利用不同外形目标卫星弹道系数在不同太阳活动周内的变化规律,结合太阳和地磁活动变化,估计经验大气密度模型的误差分布. 结果表明,利用反演弹道系数31年的平均值来代替真值,其在理论值的正常误差范围内;大气密度模型误差在210～526km高度范围内存在相同的变化趋势,且模型误差随高度增加而增大;在短周期内B’变化与太阳活动指数F_10.7存在反相关性;密度模型不能有效模拟2008年出现的大气密度异常低. 以上结果表明,经验大气密度模型结果需要修正,尤其是在太阳活动峰年和谷年,此外,磁暴期间模型误差的修正对卫星定轨和轨道预报等也具有重要意义.      

ISCCP cloud analysis algorithm intercomparison

The International Satellite Cloud Climatology Project (ISCCP) will provide a uniform global climatology of satellite-measured radiances and derive a climatology of cloud radiative properties from these radiances. For this purpose, a pilot study of cloud analysis algorithms was initiated to define a state-of-the-art algorithm for ISCCP. This study compared the results of applying the nine different algorithms to the same satellite radiance data. The comparison allowed for a sharper understanding of the process of detecting clouds and shows that all algorithms can be improved by better information about clear sky radiance values (essentially equivalent to surface property information) and by better understanding of cloud size distribution variations. The dependence of all methods on cloud size distribution led to selection of an advanced bispectral threshold technique for ISCCP because this method is currently better understood and more developed. Further research on cloud algorithms is clearly suggested by these results.     

Influence of land surface parameters on the spatio-seasonal land surface temperature regime in rural West Bengal,India

The magnitude and causes of changes in the land surface temperature of rural areas have not been extensively studied. The thermal band of Landsat imagery is taken to extract winter, summer, and monsoon season land surface temperature (LST) and relate it to surface parameters over a 30-year period. From the extracted parameters constructed a prospective surface temperature (PST) model using Multivariate Adaptive Regression Splines. The Chandrabhaga river basin in West Bengal of the lateritic Rarh Tract at the Chota Nagpur Plateau fringe was chosen as the study area because it is far from urban influences, to avoid the well-known heat island effect. Over the study period, summer and winter average LST increased linearly by 0.085?°C/y and 0.016?°C/y respectively. These results were validated with air temperature (RMSE?=?x and y, respectively). Over time more of the area is in the higher temperature zones, e.g., in April 2011, 4% area exceeded >32°, whereas in 2015 this proportion reached 52%. PST models of all the seasons were moderate to highly correlate (0.57–0.87) with actual LST, showing the value of this model. It also revealed the relative importance of the regional factors. Based on this information factor management is a scientific step to restrict or minimize the temperature rise effect.     

Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city,Delhi-India

The present study is an assessment and identification of urban heat island (UHI) in the environment of one of the fastest urbanizing city of India, Delhi Metropolis, employing satellite image of ASTER and Landsat 7 ETM+ in the thermal infrared region 3–14 μm. Temporal (2001 and 2005) ASTER datasets were used to analyze the spatial structure of the thermal urban environment subsequently urban heat island (UHI) in relation to the urban surface characteristics and land use/land cover (LULC). The study involves derivation of parameters governing the surface heat fluxes, constructing statistics of ASTER thermal infrared images along with validation through intensive in situ measurements. The average images reveal spatial and temporal variations of land surface temperature (LST) of night-time and distinct microclimatic patterns. Central Business District (CBD) of Delhi, (Connaught Place, a high density built up area), and commercial/industrial areas display heat islands condition with a temperature greater than 4 °C compared to the suburbs. The small increase in surface temperature at city level is mainly attributed to cumulative impact of human activities, changes in LULC pattern and vegetation density. In this study the methodology takes into account spatially-relative surface temperatures and impervious surface fraction value to measure surface UHI intensity between the urban land cover and rural surroundings. Both the spatial and temporal variation in surface temperature associated with impervious surface area (ISA) has been evaluated to assess the effect of urbanization on the local climate.     

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.     

Landsat 8地表温度产品验证与分析

Landsat系列卫星热波段具有60~120m的空间分辨率，对各种环境监测起到了重要的作用。随着Landsat系列卫星在全球范围内地表温度（land surface temperature，LST）产品的发布，其验证工作也随之展开，然而对于长时间序列的精度验证工作仍然缺乏。以黑河流域中游为研究区，利用研究区内湿地站（SD）、戈壁站（GB）和大满超级站（CJZ）三个气象站的地面测量数据对2013-2016年清晰无云的31景Landsat 8地表温度产品进行了验证与分析，并将Landsat 8地表温度产品与广泛使用的普适性单通道算法（JMS）反演结果进行了对比。结果表明，Landsat 8地表温度产品与普适性单通道算法反演结果精度均较高，在各个站点处R2均优于0.949。基于所有站点分析，Landsat 8地表温度产品精度稍高于普适性单通道算法反演结果。     

基于航天器解体事件的空间碎片寿命算法

研究了针对航天器解体事件所生成的空间碎片的寿命计算方法.给出了基于NASA标准航天器解体模型的航天器解体算法.该算法生成的一系列碎片参数,将作为寿命计算的初始条件.总结了现有求解碎片寿命的算法,并提出了一种半分析算法.该算法运用平均根数法的思路,计算了在J2摄动项的影响下,碎片的半长轴和偏心率的变化率;并采用微分积分法预报半长轴和偏心率随时间的变化.为了适应时变大气模型,该算法限制了计算步长.通过与数值法的比较分析了算法的计算速度和精度.选用了3种大气模型:SA76、GOST和MSIS-00,分析了不同大气模型在计算碎片寿命之间的差异.通过与P-78卫星解体事件的实测数据对比验证了整个算法的正确性.