Land Surface Temperature Retrieval and Its Spatial Heterogeneity in Summer in Chongqing

Land Surface Temperature(LST)is an important parameter in land surface energy budget.In order to improve the accuracy of LST retrieval by remote sensing methods in summer in urban districts of Chongqing with hot and humid atmosphere condition.an improved methodology was presented with the improved atmospheric transmittance estimated on MODTRAN software using the atmospheric profile data of MERRA in urban districts of Chongqing.LST was retrieved from Landsat 8 TIRS band 10 data using single-window algorithm which apply the improved and unimproved atmospheric transmittance respectively.Then the retrieved LST was compared with the 0 cm soil temperatures observedby 4 meteorological stations.Finally,the spatial heterogeneity of LST was analyzed.The result indicated that：(1)The scheme proposed in this paper can improve LST retrieval in summer in urban districts of Chongqing.The Mean Absolute Error(MAE)decrease from 4.89 K to 1.73 K.(2)The retrieved LST has spatial heterogeneity with different terrain factors.Its lapse rate is about 1.17 K/100 m.It decreases with the increase of slope.Moreover,it has obvious differences with aspect.The flat slope>sunny slope>semi-sunny slope>semi-shady slope>shady slope.There also existed highly significant correlation between the LST and hill shade.The LST increases with the decrease of hill shade.(3)Influenced by land cover,the spatial distribution of LST showed significant differences.The average LST inthe built\|up area is highest,while the wet land is lowest.The difference of average LST in other land cover types is little.     

Spatial Downscaling Land Surface Temperature based on Copula

A Copula is used to construct a bivariate distribution describing the relation between coarse\|scale and fine\|scale rainfall or soil moisture.This distribution is then used to downscale rainfall or soil moisture.In order to explore the feasibility of spatial downscaling Land Surface Temperature (LST)based on Copula,we implemented LST downscaling based on Copula and ASTER LST and MODIS LST products at Yingke oasis\|desert area in the middle streams of the Heihe River Basin.The downscaled LST was calibrated by the ground observations from HiWATER\|MUSOEXE experiment.The results show that the downscaling method based on Copula is able to achieve the LST downscaling in general,but the method can’t obtain the fine\|scale LST correctly at the interface between oasis and desert.The accuracy of LST obtained from thermal infrared satellite image was improved significantly by the method.The MAE and RMSE in LST are reduced from 2.99 K,and 3.89 K to 1.5 1K,and 2.36 K,respectively.     

Downscaling of Remotely Sensed Land Surface Temperature with the BP Neural Network

Downscaling algorithms based on statistical models have been widely utilized to address the issue of coarse-resolution Land Surface Temperature (LST).However,most methods (e.g.,TsHARP algorithm) could be affected by land environment,including land cover,seasons.In this study,a Back Propagation (BP) neural network was introduced for LST downscaling in a specific area with complex land covers.The method comprises two steps.First,five reprehensive spectral indices were selected to training according to three typical land cover,including vegetation,building,and water.And the structure of network was trained using coarse-resolution spectral indices and LST.Second,high-resolution spectral indices were input to the network to get a high-resolution LST.A stratified linear regression downscaling with land-cover classification was conducted for comparative evaluation.The comparative results showed that in urban,vegetation,and water areas,the Root Mean Square Error (RMSE),determination coefficient (R²),and relative accuracy for the proposed approach (BP neural network) were better than those for stratified linear regression.Finally,the verification results show that RMSE and bias of the algorithm are 0.98 ℃ and 0.51 ℃,which is obviously better than the result of stratified linear regression (RMSE is 2.9 ℃ and Bias is 1.7 ℃).It shows that this method has a higher downscaling accuracy.And the approach is potential for producing high-resolution LST for the study on urban thermal environment.     

青藏高原地表温度时空变化分析

使用MODIS地表温度(LST)产品对青藏高原地表温度的空间分布和年际变化进行分析。通过一种融合时空信息的方法对LST缺失像元进行重建恢复,重建后有效像元比例达到97%以上。用正弦和线性分段函数法将4个瞬时时刻的LST观测值拟合为日平均LST,经地面0cm土壤温度观测数据验证,拟合后的均方根误差(RMSE)在1K以内。建立以年为周期的余弦函数模型,刻画了LST在一年内的季节波动,并得到LST的年平均值、振幅和峰值日期3个参数。分析了各参数在空间上的分布和多年的变化趋势。结果显示:LST年平均值与海拔高度、纬度和下垫面类型相关性较大;年内振幅从青藏高原东南部到西北部呈升高趋势;水体的峰值日期相比其他地物类型有明显的延迟。多年变化斜率分析显示,整个青藏高原的年平均LST以每年0.015K的速度升高,振幅以每年0.076K的速度增长,反映出受气候变化的影响,极端气候出现的概率明显增大,而峰值日期有所提前。     

Inversion of Land Surface Temperature and Its Application in Urban Agglomeration Thermal Environment Monitoring based on Tiangong-2 Data

According to the characteristics of thermal infrared spectrum of wide band imager of Tiangong-2 data,a spilt-window algorithm applied to Tiangong-2 data was proposed.Taking the South of Jiangsu urban agglomeration as the research area,the inversion of the land surface temperature was carried out.On this basis,the spatial distribution characteristics of thermal environment of South of Jiangsu urban agglomeration were analyzed through SUHI index.The results show that the split-window algorithm can be effectively applied to the thermal infrared of Tiangong-2 data.The root mean square error of land surface temperature inversion is within 1 K.The result of land surface temperature are in good agreement with the types of land use in the research area,and the temperature of the building land is the highest and the water is the lowest.A global heat island is formed in Suzhou-Wuxi-Chanzhou,and the intensity and spatial distribution of the heat island in the urban agglomeration are monitored by SUHI index.     

MODIS数据地表温度反演劈窗算法比较

针对MODIS数据,分析比较了QIN和Wan-Dozier两种劈窗算法地表温度(LST)反演精度和误差分布。首先利用辐射传输模型MODTRAN4.0,结合TIGR大气廓线数据,评价两种算法绝对精度,然后基于误差传递理论分析评价二者的总精度,最后对两种算法的LST反演结果进行比较。研究表明针对所有廓线数据,两种算法绝对精度相差不大,但Wan-Dozier算法绝对精度受地表温度和水汽含量变化的影响程度要大于QIN算法;两种算法总精度相差不大,且主要误差源均为算法绝对精度和地表比辐射率精度,QIN算法反演结果对地表比辐射率的敏感性要略高于Wan-Dozier算法;两种算法得到研究区LST分布情况基本一致,均可表现空间LST分布差异,其中水体和裸土的LST反演结果差异较大,城镇和植被平均温度差异在0.5 K以内。     

微波被动遥感陆面降水统计反演算式的比较

用TRMM 卫星上微波降水雷达PR、微波辐射计TM I 资料和河南省站点小时雨量资料, 对几种陆面降水的统计反演算式进行比较验证。通过资料匹配分析显示, 仅用地面站点小时雨量资料和微波亮温的关系难以建立较好的陆面反演降水算式。结合时空匹配较好的卫星资料, 建立了新的算式, 并且和其它算式比较验证。比较分析结果表明, 无论是估测能力还是误差方面, 新算式都比已有算式有改善; 对于较弱(< 5 mm/h ) 或较强(> 10 mm/h ) 的降雨, 新算式比已有的算式有明显的改进(10%～20% ) , 误差减少至少25%; 对中等雨量(5～10 mm/h ) 的估算效果也有一定改善, 但和原有算式一样估算能力较低。算式的建立和验证过程还表明: 将微波低频和高频通道组合起来, 可增加反演陆面降水的信息, 能明显提高对陆面降水的估测能力。     

通过AVHRR数据研究中国陆面温度分异规律

近年来国内外利用遥感方法在陆面温度精确反演中开展了大量的研究工作,采用了一个在大区域上适用的由NOAA？AVHRR数据反演陆面温度的方法,反演中国晴空条件下各月和全年平均陆面温度,分析了中国陆面温度的分异规律,并与气温的分异规律作了对比,同时对中国土地利用／土地覆盖变化研究（LUCC）样带上的陆面温度变化进行了分析,这项工作从晴空条件重新认识了地面温度场的空间分异,对于研究中国陆地土壤蒸发,植物光合作用,土地覆盖的分布具有重要的指示意义。     

关于地表反照率遥感反演的几个问题

分析了地表反照率对陆面辐射能收支以及区域和全球气候的影响,强调了地表反照率是遥感反演陆面参数时的第一重要参数,地表反照率或多波段遥感中不同谱段的地表反射率的准确反演常常是准确估算其它陆面参数如植被和土地利用/土地覆盖等状况的先决条件。在对当前关于反照率的概念及容易混淆的术语进行阐述和说明的基础上,简述了遥感反演地表反照率的步骤和主要难点的解决方法,进而对常用陆面过程模式计算地表反照率的过程作了分析,并将其结果与MODIS有关产品进行了比较,强调了遥感与陆面过程模式和气候模式的结合。     

Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.     

气温与陆地表面温度和光谱植被指关系的研究

Prihodko 和Goward (1997) 在假设气温与浓密植被冠层温度近似的基础上, 利用空间分辨率为1 km NOAA 影像13×13 像素窗口的植被指数和LST 的线性外推求得NDVI= 0. 86 时LST, 作为中间像素气温的估计值, 这种方法假设了两个前提, 即13×13 像元窗口的植被指数和LST 呈负线性相关; 高植被覆盖条件下的LST 与气温相等。 根据Parton 和Logan (1981) 提出的气温时间尺度转换模型将297 个气象观测站获得的最高和最低气温资料计算MODIS Terra 卫星过境时刻的气温, 利用MODIS 每天陆地表面温度(LST ) 产品、16 d 合成植被指数产品, 探讨气温、植被指数和LST 之间的关系, 对Prihodko & Gow ard 法的两个前提进行调研。结果表明: ①在晚上,LST 与植被指数之间相关性很小; 方差分析的结果表明晚上LST 与晚上气温差异不显著, 因此晚上的气温基本可以由LST 代替; ②在白天, 在地形平坦的平原地区, 植被覆盖度范围较大的情况下,LST 与植被指数呈负相关关系, 但是在地形复杂的青藏高原地区和植被覆盖度范围较小(如在沙漠地区) 的情况下, 植被指数与LST 的关系很不明确; ③在白天,LST 与气温的关系随着植被生长状况差异而不同, 在稀疏植被覆盖条件下,LST 大于气温;当植被指数> 0. 7 时, 获得的LST 与气温差异不显著, 这与前人研究成果一致。根据结果②和③, 我们认为Prihodok & Goward 模型应用于区域尺度上计算白天气温存在一定局限性, 特别是应用于我国地形复杂的青藏高原地区和植被稀少的西北荒漠地区。     

地表温度热红外遥感反演的研究现状及其发展趋势

区域性或全球性的地表温度, 只有通过遥感手段才能获得, 在诸多应用中是一个非常重要的参数。地表温度反演是热红外遥感研究的热点和难点之一, 大气校正、温度与比辐射率的分离是必须考虑的两个重要方面。近年来有关的研究非常多, 主要反演方法可分为5 类: 单通道方法、分裂窗(双波段) 方法、多波段温度- 比辐射率分离方法、多角度温度反演方法和多角度与多通道相结合的方法。这些方法都各有利弊, 如何提高反演的精度和模型的适用性是地表温度热红外遥感的未来发展趋势, 理论和实验相结合的多种信息源的综合应用成为必然的要求。     

Comparative Analysis on Three Land Surface Temperature Inversion Algorithm based on Landsat 8 Data over Sanheba Basin

The Land Surface Temperature (LST) of TIRS10 / Landsat 8 remote sensing data is studied and analyzed by combining the data and related parameters of Sanheba basin,and the LST inversion algorithm are used the Radiative Transfer Equation Method (RTE),Mono\|Window algorithm (MW) and Single\|Channel Method (SC).The parameters of the MW algorithm are corrected.The LST gray scale and density segmentation graphs,the histogram of LST and the cross validation flank are used to compare the results of the LST inversion algorithm.The results show that the three kinds of algorithms are similar to the linear fitting degree of LST,and the spatial distribution is consistent.The RTE and SC algorithm are close to each other,the average error of algorithm is 0~0.05 K.the LST of MW algorithm is higher than that of the other two algorithms,the average error of algorithm is 0~1.27 K.The LST of different land cover types in this basin is compared,and the inversion results can effectively reflect the details of the surface thermal field structure according to the different land cover types.The LST values obtained by these three algorithms are compared with the MODIS LST product values.The results show that there is a significant correlation between the LST values and the MODIS LST products.In this paper,3 kinds of the LST inversion algorithms are analyzed detailed accurate on TIRS10/Landsat 8 remote sensing data,provide a reference for other thermal infrared satellite data inversion LST algorithm,but also for the subsequent LST improve the accuracy of inversion basis.     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

MODIS数据反演地表温度的参数敏感性分析

在利用MODIS卫星遥感数据进行地表温度反演过程中，有两个基本参数需要确定，即地表比辐射率和大气透过率，尽管采用了比较合理的参数估计方法，但仍会有一些不可避免的因素导致误差的产生。为了进一步研究可能的参数误差对地表温度反演精度的影响，我们对该算法的两个参数进行敏感性分析。结果表明，当31、32两个波段的参数估计都有中等误差时，可能的地表温度误差对大气透过率和地表比辐射率都不敏感，所引起的地表温度误差大约为0．6～0．8℃，算法能够得到较高精度的地表温度反演结果。     

保局投影算法在图像检索中的应用研究

对国际上近来提出的保局投影（LPP）算法在图像检索中的最新应用研究进行了详细介绍;分析指明了几种基于LPP的图像检索算法的特点;设计并完成了基于LPP的图像检索算法图像检索效果的比较实验;最后根据实验结果总结了各类算法的优缺点。     

MODIS地表温度产品的验证研究——以黑河流域为例

分析了影响MODIS地表温度产品精度的主要因素,并对这些因素综合作用下的MODIS地表温度产品的精度验证方法进行了回顾和比较。针对MODIS地表温度产品在干旱半干旱地区误差偏大的状况,以黑河流域为例,对MODIS地表温度产品进行了验证。用于验证的地面观测数据包括自动气象站红外辐射温度计数据和长波辐射数据。这里结合具体的...     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

基于TM6数据的成都市地表温度反演

本利用Landsat5的TM6热红外数据，利用实时的地表温度对不同温度演算算法进行了回归检验，根据统计的原理对各种方法做出评价，并优先了一种方法进行成都市地面温度场的分布分析。本也就同一大气条件下的地温和气温之间的关系作了分析，认为用卫星影像的DN值代替气温研究热岛效应是不合适的。     

基于热红外遥感的城市热场形成机制及特征分析--以成都市为例

利用LANDSAT5的TM6热红外数据和实时的地表温度，优选了一种方法进行成都市地面温度场反演，根据反演结果对成都市地表温度场分布特征进行了分析，同时定量分析了植被覆盖和水域廊道对地表温度场分布的影响，定量分析的结果对城市区域布局和规划有积极意义。