基于欧洲中尺度气象预报中心（ ECMWF）提供的ERA?Interim地表温度，利用经验正交函数（ EOF）等方法，分析了青藏高原四季地表温度的时空变化特征。结果发现：青藏高原春、夏、冬季地表温度变化以整体型为主，并且大部地区地表温度呈现升高的趋势；秋季地表温度略有下降趋势，并且以东部和西部地表温度的反向型异常变化最为显著。此外还发现，青藏高原不同季节地表温度的异常变化具有一定的联系，其中整体型变化可以持续3个季节。

The ERA?Interim surface temperature data from European Centre for Medium?Range Weather Forecasts ( ECMWF) are used to analyze the spatial and temporal variation of the seasonal surface temperature over the Tibet?an Plateau using Empirical Orthogonal Function(EOF).The results show that in spring,summer and winter,the sur?face temperature tends to increase in the whole Plateau without obvious spatial variation;while in autumn,surface temperature tends to decrease slightly in most area,and a significant opposite variation trend exists between the east?ern and western area of the Plateau. In addition,there are some definite relations between the surface temperature anomaly variations in different seasons,and the integral type can last for 3 seasons.

地表温度是研究区域地表能量平衡和资源环境变化的重要参数之一，为获取研究区的时空分布状况有重要的作用。对云南会泽县MODIS的昼夜两景影像数据进行辐射校正、定标等反演出地表温度图，通过31和32波段之间的波段运算显示出明显的地表温度空间差异和不同的地物类型；在已有的温度反演算法基础上，将地表温度数据引入到温度反演算法中，得出32波段反演的地表温度要高于31波段反演的地表温度，而且昼间两波段间的温度差要比夜间的两波段之间的温度差大，这是因为白天的可见光和近红外也存在辐射，而且白天的地表吸收温度比夜间高，辐射增大，导致白天的温差大于夜间的温差。利用地表实测温度数据与热红外遥感数据相结合，提高了研究区地表温度反演的精度，降低了反演难度。

The surface temperature is one of the important parameters of studying the regional surface energy balance as well as resources and environment changes , and it has an important role in obtaining the space -time distribution status of the study area . The surface temperature figures of Huize county in Yunnan province were retrieved through the radiation correction and calibration of data of the day and night two -scene MODIS images .The obvious spatial differences in the surface temperature and various types of surface features were shown by computing the band between band 31 and band 32.On the basis of the existing temperature retrieval algorithms, the surface temperature data were introduced into temperature retrieval algorithm , and it was obtained that:the surface temperature retrieved by band 32 was higher than that did by band 31, and the temperature difference between the two bands in the daytime was larger than that in the night , which were caused by higher surface a

地表温度是描述陆表过程和反映地表特征的重要参数。及时掌握区域和全球尺度上的地表温度时空分布是许多地表过程研究和热红外遥感应用的必需。热红外遥感是地表温度快捷获取的最佳手段,但仅局限于天空晴朗无云情形。云覆盖致使热红外遥感不能直接获取云覆盖像元地表温度,其反演结果为云顶温度或附加了云辐射强迫效应后的地表温度。如何精准获取热红外遥感图像中云覆盖像元地表温度信息,成为热红外遥感地表温度反演和应用亟待完善的难题。文章系统详细回顾了国内外热红外遥感图像中云覆盖像元地表温度估算方法,评述了各方法的特性,并指出今后应加强加深热红外遥感与被动微波遥感融合技术、数据同化技术、尺度效应和算法参数化中普适性假定等四方面研究。

Land surface temperature (LST) ,which reflects surface properties ,is one of the key parameters in the physics of land surface processes from local through global scales .LST is very required in time and space for a wide variety of scientific studies and thermal infrared (TIR) remote sensing applications .Satellite TIR channels are very available for LST retrieval but only in clear skies .However ,when the surface is obscured by clouds ,the actual retrieved LST for the corresponding pixel is ,or is con-taminated by ,the cloud top temperature .Lacking understanding of the complex relationships between clouds and LST ,the esti-mation of LST for cloud-covered pixels poses a big problem and challenge for thermal remote sensing scientists .In the present paper ,a review of algorithms and approaches related to LST retrieval for cloud-covered pixels from TIR data is presented ,and the characteristics of each method are also discussed .Directions for future research to improve the accuracy of

以3S技术为支持,采用TM数据、应用单窗算法对1989-2010年间北京市五环内地表温度( land surface temperature,LST)进行反演,并采用同步的MODIS 温度产品对反演结果进行对比验证。在此基础上,利用距平值分析和剖面线分析等方法揭示了20多a来北京市地表温度的时空分异总体特征；基于归一化水汽指数( normalized difference moisture index,NDMI)和归一化建筑指数( normalized difference building index,NDBI)分析了研究区内地表温度的时空变化特征,定量揭示并阐述了地表温度和NDMI及NDBI的相关关系。研究结果表明：反演温度可较为真实地反映研究区地表热量的空间差异；北京地表温度时空变化特征明显,同一时段、不同土地覆盖类型的地表温度差异较为显著,热岛效应明显；地表温度和NDBI呈显著的正相关,与NDMI则呈显著的负相关,NDMI和NDBI在表征地表热特征方面均是较好的指标。该结果为监测北京地表温度变化及其时空分布特征提供了依据。

Land surface temperature ( LST) is an important parameter in such fields as meteorology, hydrology and ecology, and the inversion of land surface temperature by use of remote sensing data is a simple and effective method. In this paper, with the support of the 3S technology, the authors took Beijing City as the study area to study the land surface temperature inversion and analyze spatial and temporal characteristics. Using TM data obtained from 1989 to 2010 within the 5th Ring Rroad of Beijing, the authors adapted the single-window algorithm in 20a to the inversion of land surface temperature, with a comparative study of the verification of synchronous MODIS temperature products. On such a basis, the anomaly analysis and hatches analysis were conducted to reveal the overall characteristics of temporal and spatial variation of Beijing surface temperature in more than 20 a. The characteristics of temporal and spatial variation of NDMI and NDBI index were analyzed based on 20 y

如何综合可见光波段信息提高地表温度的空间分辨率一直是热红外遥感应用研究的重要方向。以北京市Landsat TM图像为数据源,对比分析了SUTM和E-DisTrad模型地表温度分解的空间特征差异性和适用范围。结果表明：在植被覆盖较低、地表温度较高的中心城区,SUTM模型的地表温度分解效果更佳,最小均方根误差和平均绝对误差分别为1.522 K和1.191 K；在植被覆盖较高、地表温度较低的郊区,E-DisTrad模型的地表温度分解效果更好,最小均方根误差和平均绝对误差分别为1.768 K和1.173 K。2种模型都能有效地提高地表温度的空间分辨率,但是在植被覆盖不同的地区分解结果呈现一定的差异性。

Land surface temperature ( LST) is a vital parameter controlling the energy and water balance between atmosphere and land surface. LST image with high spatial resolution compatible with visible bands of Landsat TM is very important for the application of the LST image to many studies such as environmental monitoring. This paper examines the accuracy and applicability of two widely-used models for decomposition of LST images:SUTM and E-Distrad. Landsat TM data acquired in Beijing were used for the study. LST retrieved by the mono -window algorithm ( MWA) was used to compare the LST decomposition images by the two models. The results achieved by the authors indicate that SUTM is more applicable than E-Distrad in the regions with low vegetation cover and high LST such as downtown, while the latter is better than the former in the high vegetation cover and relatively cold areas such as water bodies. The RMSE and MAE are 1. 522 K and 1. 191 K respectively for SUTM and 1. 768 K and