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。     

TIMED/SABER与AURA/MLS临近空间探测温度数据比较

利用AURA/MLS数据（V4.2）和TIMED/SABER数据（V2.0）对20~92km高度的大气温度进行比较分析,计算AURA/MLS数据与TIMED/SABER数据的温度绝对偏差,并分析平均温度偏差在不同季节中随经度、纬度和高度的变化特征.结果表明:20~80km高度的平均温度偏差在±6K以内,相对偏差在3%以内;80~90km高度平均温度偏差减小至-10K以下,相对偏差在9%以内.中低纬度地区平均温度偏差廓线的变化趋势一致,从20km高度的-3K左右的负偏差逐渐增加,在45~50km高度的平流层顶处有较明显的3K左右的正偏差峰值.平均温度偏差随纬度变化明显,随经度变化很小.研究结果可为卫星数据的应用提供参考依据.      

澳大利亚东南部森林山火HJ卫星遥感监测

以2009年2月发生在澳大利亚东南部的森林山火为研究对象,利用HJ-1B遥感影像识别森林山火,分析HJ-1B在林火灾害事故中的监测能力,通过对HJ-1B IRS B07设计参数及数据特点进行分析,提出适用于HJ-1B卫星林火监测的归一化火点指数(Ku)算法.研究表明:Ku值大于0.40为潜在可能的火点像元,云耀斑和地表虚假高温点是影响林火监测的主要噪声.由于HJ-1B没有获取到研究区域未着火前的影像数据,利用MODIS(Moderate-resolution Imaging Spectroradiometer)空间分辨率为250 m的通道1和通道2计算植被指数,其结果能较好的应用于HJ-1B林火监测算法中.通过对比分析HJ-1B林火监测结果和MODIS林火产品MOD14认为,HJ-1B能更好的监测出澳大利亚东南部森林火灾,反映出火灾的局部空间分布和细节特征.     

CYGNSS/SMAP数据融合半经验模型的土壤湿度反演

星载GNSS反射信号(GNSS-R)的土壤湿度反演易受陆地多变环境因素影响，目前，对于星载GNSS-R土壤湿度反演中误差分析及反演模型外推性能分析较少。综合多种误差修正模型，包括GNSS卫星发射功率误差、植被和地表粗糙度对反射信号强度的衰减，通过修正提高陆地点反射率的准确性，建立了反射率-土壤湿度的CYGNSS/SMAP数据融合的反演半经验模型。实现了一年高精度外推反演，反演偏差为-0.003 7 cm³/cm³，均方根误差(RMSE)为0.026 4 cm³/cm³，相关系数为0.963 6。提出了分季节的外推模型，提高了低含水量季节的外推精度。实验区域的经度为90°E～130°E，纬度为20°N～38°N，利用2019年10月至2020年9月的CYGNSS/SMAP数据进行训练，外推2020年10月至2021年9月的土壤湿度。经误差模型修正反射率后，模型的反演偏差提升6.80%，均方根误差提升3.30%。针对实验区域内冬、春季土壤含水量较低时反演精度差的问题，提出了同季节外推的分季节训练模型，相...     

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.      

自主导航星座旋转误差的差分校正技术

基于星间观测的自主导航星座存在整体旋转不可测问题,一旦星座发生整体旋转,将导致地面用户定位结果存在偏差.针对这一问题建立了地面用户定位误差模型,运用定位原理和球坐标系变换,从数学上推导了星座整体旋转偏差和地面用户定位偏差之间的关系,在此基础上提出了基于差分原理的旋转误差校正技术,并给出了系统组成和校正算法.使用Walker 12/3/1星座的仿真表明,导航星座旋转误差将导致地面用户的大地经度出现相同角度的偏差,地面用户使用差分校正技术后可有效校正这一误差,在星座整体旋转误差小于1'（相当于赤道地区31 m的水平误差）的条件下,地面用户经差分校 正后的水平误差小于 1.5 m,高程误差小于0.003 m.     

GNSS-IR双频数据融合的土壤湿度反演方法

目前全球导航卫星系统反射信号干涉测量（GNSS-IR）土壤湿度反演研究仅针对单一频点展开,提出用熵值法将2个频点数据进行融合以改进土壤湿度反演精度。首先,利用频谱分析法分别解析出各频点的信噪比（SNR）序列的振荡频率,计算出对应的等效天线高度,并利用最小二乘法求解各频点信噪比序列相位;然后,通过熵值法进行2个频点的相位观测量融合;最后,利用融合结果与实测土壤湿度建立经验模型,实现土壤湿度反演。利用地基观测实验获得的全球定位系统（GPS）L1和L2信噪比数据对该方法进行了验证,结果表明:L1和L2双频融合反演结果平均标准差为0.6%,比L1单频反演结果提高64.73%,比L2单频反演结果提高32.12%;均方根误差为0.37%,比L1频点降低72.8%,比L2频点降低73.4%。      

地基Fabry-Perot中高层大气风速反演及误差分析

基于子午工程地基法布里-帕罗干涉仪（Fabry-Perot Interferometer,FPI）的气辉观测数据,结合地基独特的观测模式（天顶角为0° 的天顶方向和天顶角为45°的东西北南四个方向）对地基中高层大气风速进行反演,包括数据预处理、干涉环圆心确定、干涉环半径计算和风速反演. 将2010年5月6-13日8天十个环（十个干涉环同时参与反演）的反演结果与地基FPI风速实测数据进行比较,得到557.7nm,630.0nm,892.0nm三种谱线气辉的反演平均偏差分别为2.7m·s-1,5.5m·s-1,7.7m·s-1. 此外,基于反演算法对上述反演精度影响因素进行了分析. 研究发现,气辉辐射强度对风速的反演精度影响较大,气辉辐射越强,外环的半径计算精度越高,可参与的反演环数越多,则最终的风速反演精度越高. 而圆心偏差± 2pixel（五个环）和± 1pixel（十个环）及焦距变化（±10mm）对风速反演精度的影响相对较小,但当超出这一偏差范围,风速反演偏差会迅速增大.      

基于图像模拟的HJ星CCD相机交叉定标

针对同一HJ-1星的两台CCD相机,提出基于图像模拟的交叉辐射定标方法:通过建立两台CCD相机图像DC值的数学关系,以获取地面目标图像的已定标的CCD为标准对未同时获取图像的待定标的CCD进行交叉定标,得到了2008年10月A2CCD和B2CCD的定标系数.利用贡格尔场野外实验数据和敦煌场的MODIS数据对定标系数进行真实性检验,并对该方法的主要误差来源进行了分析.结果表明,交叉定标具有较高的精度和可信度,可以在保证定标精度的同时,提高定标的频次.     

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.     

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

基于氧气A波段临近空间大气温度反演的贝叶斯与最小二乘法对比

基于氧气A波段的临边辐射模拟数据进行临近空间大气温度廓线的反演，分析比较了贝叶斯和最小二乘两种不同反演算法的特点.80km以下，信噪比为66～337时:基于贝叶斯理论反演的三条谱线761.59，762.2，764.05nm的反演误差平均值分别为5.52，3.94，4.73K；采用最小二乘法的反演误差平均值分别为10.57，7.04，8.80K.信噪比为6～34时:基于贝叶斯理论反演的三条谱线的反演误差平均值分别为18.27，12.18，18.27K；采用最小二乘法的反演误差平均值分别为103.18，68.79，85.98K.研究结果表明，基于贝叶斯理论的反演方法，利用先验信息对反演结果进行约束和修正，在有噪声的情况下获得了更合理的解，从而提高了反演精度和抗干扰能力.这为星载探测临近空间大气温度的算法研究和开发提供了参考，也为提高光谱仪器信噪比并进而提高温度反演精度提供了理论基础.