The relationship between land surface temperature and land use/land cover in Guangzhou,China

The integration of remote sensing, geographic information system, landscape ecology and statistical analysis methods was applied to study the urban thermal environment in Guangzhou. Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Barren Index (NDBaI) and Modified Normalized Difference Water Index (MNDWI) were used to analyze the relationships between land surface temperature (LST) and land use/land cover (LULC) qualitatively. The result revealed that, most urban built-up lands were located in the middle part, and high LST areas mostly and were in the middle and southern parts. Therefore, the urbanization and thermal environment in the middle and southern parts need to be determined. Land surface temperature increased with the density of urban built-up and barren land, but decreased with vegetation cover. The relationship between MNDWI and LST was found to be negative, which implied that pure water would decrease the surface temperature and the polluted water would increase the surface temperature. A multiple regression between LST and each indices as well as the elevation was created to elevate the urban thermal environment, which showed that NDVI, NDBI, NDBaI, MNDWI were effective indicators for quantifying LULC impacts on LST.     

Analysing transition of land surface temperature and derived indices with respect to elevation values in Kohima Saddar

Human activities have affected the urban environment resulting in a drastic change in the surface temperature. The impact of urban heat islands is noticeable in urban areas than in rural areas. The thermal band of Landsat 8 data is used to retrieve the spatial distribution of land surface temperature (LST) over Kohima Sadar for the years 2009, 2015 and 2020 with the Mono-window algorithm. Urban Thermal Field Variance Index (UTFVI) is used to assess the ecological condition in the area impacted by LST. Cartosat-1 Digital Elevation Model (Carto DEM) is used to understand the variations of LST and indices values with reference to the elevation profile located at different random points. The variations in the land cover are categorized as per the values of normalized difference vegetation index (NDVI) and built-up density index (BUI). This work estimates the influence of elevation over LST, vegetation, and the built-up area. Results implies a negative correlation between LST and NDVI whereas a positive correlation between LST and BUI. Likewise, NDVI and BUI show a strong negative correlation. It is observed that LST is independent of elevation profile but the variation of LST depends on the impact of change in topography urbanization, deforestation, and afforestation. There is no significant relationship of elevation with the variations in NDVI and BUI values. It is observed that the impact of emissivity influences the estimation of LST values. For the locations having the highest and lowest LST, NDVI, and BUI values, 50 random points are generated for the entire region, and validation is executed with the google earth historical image.

     

基于植被指数和土地表面温度的干旱监测模型

干旱是一种周期性发生的自然现象,其发生过程中有关参数如地表覆盖度、温度和土壤表层含水量等可以通过遥感的途径进行定量反演,而这些参数客观地反映了地表的综合特征。综述了运用遥感反演产品---土地表面温度和归一化植被指数在干旱监测中的应用前景和进展,分析了距平植被指数、条件植被指数、条件温度指数和归一化温度指数等干旱监测方法的优缺点,在前人研究的基础上,提出了条件植被温度指数的干旱监测模型,探讨了其应用前景。     

Study of land surface temperature and spectral emissivity using multi-sensor satellite data

In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km², is a part of Singhbhum-Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50°) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show difference of LST up to 2°C. The results of the present study indicate that continuous monitoring of LST and emissivity can be undertaken with the aid of multi-sensor satellite data over a thermally homogeneous region.     

Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.     

基于HJ-1B卫星数据的疏勒河上游流域地表温度反演

地表温度是描述地表和大气之间物质交换和能量平衡的关键参数之一. 利用疏勒河上游流域2010年7月25日过境的HJ-1B卫星数据, 采用修正的Jimenez-Mufloz&Sobrino普适性单通道算法反演了疏勒河上游流域的地表温度. 与MODIS LST产品的对比验证表明, 该算法在研究区有较高的精度. 同时, 对不同土地利用类型的地表温度的分析表明, 土地利用类型不同, 其地表温度存在一定的差异, 其中, 稀疏草地的地表温度最高, 裸岩次之. 研究发现, 在河流和冰川区, 修正的单通道算法可能高估了表面温度. 同时, 对模型反演所需的关键地表参数的敏感性分析发现, 算法在一定范围(0~0.01)内对比辐射率不太敏感, 但超过该范围则变得非常敏感, 而对大气水汽含量比较敏感. 因此, 一定程度上, 地表比辐射率和大气水汽含量的精度对HJ-1B卫星数据反演地表温度的准确性起主要作用.     

Satellite monitoring of spatio-temporal dynamics of China’s coastal zone eco-environments: preliminary analysis on the relationship between the environment,climate change and human behavior

In this paper, temporal dynamics of eco-environmental changes in coastal areas of China during 1981–2000 are investigated based on four key surface parameters including normalized difference vegetation index (NDVI), thermal index, moisture index and surface broadband albedo derived from quantitative remote sensing techniques and meteorological data. Firstly, land surface temperature (LST) and land surface broadband albedo are retrieved by the split-window algorithms and high-order polynomial regression method, respectively, using NOAA/AVHRR series images. Then, moisture index and thermal index, indicators of climate and moisture conditions in the study area, are computed from meteorological data and LST using principal component analysis (PCA). Finally, long-term dynamics of these eco-environmental factors and the reasons responsible for these changes are analyzed further. The results show that during the years from 1981 to 2000, the study area experienced a gradual increase in annual NDVI and climate factors and a decrease in surface annual broadband albedo, which indicates that the coastal thermal and moisture conditions and the subsistence conditions of natural vegetation have changed to a considerable extent. According to the results, a warming and wetting tendency over the last two decades is obvious in the China’s coastal zone that are mainly due to land use changes as of growing urbanization, exhaust emissions from industries and transportations and, partly global climate change. Uncontrolled rapid development of the study area may be blamed for these negative changes as a major driving force. The positive feedback mechanisms between albedo, NDVI and climate factors also partly explain these changes. This study suggests that the method integrating biophysical parameters retrieved from remote sensed images and meteorologic data provides a novel and feasible way to monitor large scale eco-environmental changes.

Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island,Malaysia

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.     

Iterative selective spatial variance reduction of MYD11A2 LST data

The aim of this research effort is to develop a method that will allow to map and evaluate thermal anomalies in SW USA from the MYD11A2 night land surface temperature (LST) imagery being available for the year 2014, that present higher spatial (1 km) and temporal (46 images per year) resolution than the MYD11C3 LST data (12 images per year at 5.6 km spatial resolution). The fact that is MYD11A2 LST imagery is projected to a rectangular grid did not affect the X, Y and elevation (H) spatial decorrelation stretch. Principal component analysis and linear regression models isolated and removed the X, Y, H (spatial) dependent variance included in the data while metrics devised verified the selective spatial variance reduction. The reconstructed 46 LST images represent the amount the LST deviates from the X, Y and H predicted for the year 2014. The thematic information content of the reconstructed LST images is verified by cluster analysis and mapped the spatial extend and the temporal variability of thermal anomalies within the study area. The positive thermal anomaly clusters are spatially arranged mainly west of Sierra Nevada in Great Basin Section where extensional tectonics create a series of titled elongated mountain blocks along the N to S direction in between basins bounded by normal faults, while the negative thermal anomaly clusters are spatially arranged along the coastal region, further north and in the western region far from the tilted mountain tectonic blocks of the Great Basin Section. The spatial maps that define regions with (positive or negative) thermal anomalies and distinct mean land response could assist landcover studies and support urban and rural planning in the context of emerging climatic change.     

遥感技术在陆面过程研究中的应用进展

探讨了当前陆面过程 (LSP)研究的特点 ,指出遥感在陆面过程研究中的应用以及陆面过程国际合作实验是突出的特点 ,进而对遥感技术的陆面参数获取、地表能量通量的计算以及与 LSP模式的结合研究及进展进行了综述。根据不同特征的地表参数选择光学遥感或微波遥感已成共识 ,而综合利用不同遥感数据获取同一种地表参数也已成为研究热点 ,当前及今后发射的携载多种遥感仪器的众多遥感卫星为此项研究提供了条件 ;遥感与 LSP模式的结合研究是遥感在陆面过程研究中深入应用的一个方面 ,国际陆面过程合作实验是这项研究的重要保证。     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

城市不透水面与地表温度定量关系的遥感分析

作为城市下垫面的最主要组成部分,城市不透水面是引发城市热岛效应的主要因素,但目前不透水面与地表温度之间的定量关系仍不明确,不同研究的结论也不尽相同。选取了上海、广州、北京、长沙、兰州、福州等6个代表我国不同区域的城市作为研究区来进一步研究城市不透水面与城市热环境的定量关系。采用Landsat ETM+影像和线性光谱混合分析法提取出各个研究区的不透水面,利用热红外波段反演出各研究区的地表温度,采用多种回归模型和大样本量对二者之间的相关关系进行定量分析;在此基础上讨论了不透水面对城市热环境的影响机制。结果表明：不透水面与地表温度呈明显的正相关关系,并且以指数函数为最佳拟合模型,回归方程的相关系数均为0.750以上,最高可达0.954 1;高不透水面盖度地区的升温幅度更大,比低不透水面盖度地区高出0.600~1.700 ℃,其原因主要是高不透水面盖度地区的植被覆盖率低,蒸腾蒸发作用小。     

Recent trends of temperature and precipitation proxies in Saudi Arabia: implications for climate change

The objective of the present study was to reconstruct a short-term (12–14 years) trend of surface temperature and precipitation patterns using their surrogates as provided by satellite images for selected locations along the Red Sea mountains in Saudi Arabia. Time series land surface temperature (LST) and normalized difference vegetation index (NDVI) data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were temporally plotted to delineate the trend and the decadal rates of change of both parameters. Results showed that real climate change is reported in the study area during the study period. There is a net increasing in the surface temperatures by 0.45 to 1.2 °C/decade and a net decrease in annual rainfall between 2001 and 2014. Findings of the present study show that the region is under a warming of the climate and a declining of wetness, which coincide with the air temperature and rainfall trends obtained from meteorological stations.     

西藏地表温度时空演变特征及影响因子

地表温度（land surface temperature, LST）是反映生态环境状况的重要指标。西藏作为气候变化的敏感地区,掌握其LST的时空变化有利于深入了解西藏热环境演化过程,为长期监测高原基础生态变化提供帮助。研究基于谷歌地球引擎获取西藏2000—2020年的MODIS LST数据,采用归一化分级方法对LST进行5个等级的划分,利用趋势分析、热力空间分析以及重心迁移等方法分析了研究区近20年来的LST时空演变特征。同时,选取归一化植被指数（normalized difference vegetation index, NDVI）、裸土指数（bare soil index, BI）、垂直不透水面指数（perpendicular impervious surface index, PISI）、湿度（WET）以及高程（digital elevation model, DEM）等5个影响LST的地表参数,结合多尺度地理加权回归,探讨了LST影响因子的作用尺度与作用效力。结果表明：2000—2020年,西藏LST均值由18.72 ℃上升至20.28 ℃,年均增长0.09 ℃,LST呈现微弱上升态势。20年来,LST在所有年份皆具有西北高、东南低的空间分布格局,LST增温趋势亦表现为西北高、东南低的分布特征。低温区和高温区空间分布聚集,形状简单、规则;次低温区、中温区以及次高温区空间分布破碎,形状复杂。2000—2020年各温区重心分布具有明显的方向性,且各温区重心迁移轨迹具有显著差异。特别是,低温区重心与高温区重心迁移轨迹呈现出由相向而行到背向而行的转变,反映出研究区东西部区域LST差距经历了由缩小到扩大的过程。DEM和WET对LST具有负向影响,BI、PISI和NDVI具有正向影响,常数项在不同生态区具有不同的影响性质。DEM具有较小的作用尺度以及最强的作用效力,常数项具有最小的作用尺度以及仅次于DEM的作用效力。     

New ASTER derived thermal indices to delineate mineralogy of different granitoids of an Archaean Craton and analysis of their potentials with reference to Ninomiya's indices for delineating quartz and mafic minerals of granitoids—An analysis in Dharwar Craton,India

We processed five thermal infrared (TIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) “at-sensor radiance” (Level 1B) data to derive few new indices to delineate variation in quartz, feldspar and mafic minerals in three different variants of granitoid; commonly occurring quartz bearing intrusive rock group. In this regard, three indices named as quartz-bearing rock index (QRI), feldspar-bearing rock index (FRI) and mafic-bearing rock index(MRI) were proposed.QRI index was derived using band 10, band 12 and band 13((band 10/band 12) 1 (band 13/band 12)) of ASTER radiance image. MRI index was derived using band 12, band 13 and band 14 ((band 12/band 13) 1 (band 13/band 14)) of ASTER radiance bands while band 10, band 11 were combined to derive FRI ((band 10/band 11) index. Three indices were combined in false colour composite image (FCC) and three-dimensional scatter plot to delineate granite, alkali granite and mafic rich granodioritic gneiss from each other as these granitoids had variable abundances of quartz, feldspar and mafic minerals. QRI and MRI were compared with the corresponding quartz and mafic indices proposed by Ninomiya (2005). It was observed from the respective ratio images and their regression plots that MRI and Ninomiya's mafic index (NMI) were complementary to each other. On the other hand, QRI image was better in enhancing quartz enrichment in alkali granites than Ninomiya's quartz index (NQI).However, QRI index was comparable with the quartz index proposed by Rockwall and Hofstra (2008) in terms of delineating quartz enrichment in alkali granite. Mutual exclusive nature of mafic minerals and quartz in granitoids was also evident from the negative correlation between MRI and QRI indices of the granitoids. On the other hand, FRI and QRI were negatively correlated with low regression value. This was resulted due to the combined effect of inverse relation of abundance of two dominant feldspars with quartz in different granitoids. In granitoids, abundance of plagioclase is known to increase with decreasing quartz content in granodiorite and tonalite although alkali feldspar bearing granites are characterised with high silica content. Results of discrimination of granitoids using proposed indices were validated based on deriving emissivity spectra of rocks and comparing them with ASTER TIR band resampled laboratory spectra of respective granitoids in addition to use geological map of the study area. Emissivity spectra of granitoids were derived from emissivity image (derived using emissivity normalisation method) after geospatially tagging it with QRI–FRI–MRI image composite; which was used to delineate exposures of granitoids. Further, we also found that the QRI, MRI and FRI indices had poor temperature dependence; when these indices were compared with relative surface temperature image derived from radiance bands using emissivity normalisation algorithm. Therefore, proposed indices can be implemented for delineating mineralogical variations of granitoids irrespective of surface temperature condition. Hence, proposed indices may be used successfully to delineate different granitic intrusions and relating their mineralogical variations with metallogeny.     

Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis,Xinjiang, China

Land use and land cover (LULC) changes caused by human activities have strong influences on regional environment. Land surface temperate plays an important role in studying the impact of LULC changes on regional environment. In this paper, remotely sensed thermal infrared data were used to assess land surface temperature (LST) in the Weigan and Kuqa river oasis, Xingjiang, one of the important agricultural areas in the northwestern China. The present study deals with the extraction of LST and the relationship between LULC changes using Landsat 5 TM acquired on September 25, 1989, and September 6, 2011. The results indicate that the surface temperature of water body, bare land, and desert changed significantly between 1989 and 2011. In general, the LST was lower in 1989 than in 2011. There were no lower, higher, and highest temperature zones in 1989. However, the minimum temperature was 10.7 °C in 1989 and 15.8 °C in 2011. The maximum temperature was 29.3 °C in 1989 and 41.8 °C in 2011. Regarding the LULC types, the desert features in the Gobi Desert warmed more quickly than the oasis. So, the temperature of the oasis was lower than the surrounded areas, resulting in a so-called “cold island” phenomenon. Oasis cold island effect index (OCIEI) shows that stability of oasis had rising trend from 1989 to 2011. In addition, the impact of LULC changes on LST was analyzed and the driving forces were also analyzed from 1977 to 2011. This study is significant for further understanding of the energy exchange status of soil-plant-atmospheric system and the regional heat distribution in arid and semi-arid areas of the northwest China.     

Relationship in metastasis of coal fire and land use/cover using thermal imagery and support vector machine classifier

The objective of the present study was to delineate temporal and spatial changes in the coal fire and land use/cover within Bastacolla area of Jharia coal field. Studying this variation helped to decipher interconnection among the dynamics of the coal fire and concomitant changes in land use/cover. The detection of coal fires during a span of 14 years along with transitioning land use/cover was cost-effective and enabled planning for management of coal resources and environment. Landsat series of satellite data of 2002, 2009, 2013, and 2016 were processed for generating land surface temperature profiles vis-a-vis classified land use/cover of the study area. A single cut-off temperature was derived for mapping of coal fires using land surface temperature profile from 2002 to 2016. The satellite images were classified using support vector machines, and for depicting land use/cover change, post-classification change detection was done. Classification accuracy obtained was excellent with kappa coefficient ranging from 0.897 for classified image of 2002 to 0.799 for classified image of 2016. Results revealed that coal fires had shifted to the central west part of the area. Furthermore, pockets of coal fire from northern and eastern part of the study area have diminished. OB dumps and coal quarry/coal dump may be attributed towards the spatial change in coal fire while; OB dumps showed connotation with the highest temperature zones. Ground verifications for temperature profiles and coal fires were carried out using thermal camera which enunciated good agreement with results.     

基于时序MODIS NDVI的黑河流域土地覆盖分类研究

归一化植被指数(NDVI)是植被生长状态及植被覆盖度的最佳指示因子,其时序数据也已成为基于生物气候特征开展大区域植被和土地覆盖分类的基本手段。基于时序NDVI数据的土地覆盖分类,即通过提取NDVI时间信号所包含的植被生物学参数,构建起一个包含植被生物学信息的分类特征空间。利用2006年重建得到的MODIS NDVI 16天合成时间序列数据,并结合1 km分辨率的DEM数据、野外实地调查资料等辅助数据,综合分析了不同土地覆盖类型对应的时序NDVI谱线及其第一、二谐波的特征阈值,建立决策树对黑河流域的土地覆盖开展分类研究。结果表明,基于时序MODIS NDVI谱线特征的决策树分类精度为78%,Kappa系数为0.74。利用1 km时序MODIS NDVI时间序列获得较为准确的黑河流域土地覆盖类型是可行的。     

Geological mapping of the Francistown area in northeastern Botswana by surface temperature and spectral emissivity information derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared data

We identified geological units in the Francistown area in northeastern Botswana by using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data, which contains both surface temperature and spectral emissivity information. A scatter plot of ASTER L1B daytime TIR digital values and L2 daytime temperature indicates that in the ASTER L1B daytime TIR data, bands 10, 11, and 12 contain spectral emissivity and temperature information, whereas surface temperature dominates the spectral emissivity information in bands 13 and 14. Visual interpretation of the ASTER TIR false color composite (FCC) images generated by assigning red, green, and blue to band 14:band 12:band 10 using L1B daytime data allowed us to identify mafic-to-ultramafic units and quartz-rich felsic units. Mafic-to-ultramafic units such as gabbro, dolerite, and dunite appear white in ASTER L1B daytime TIR FCC images due to their high spectral emissivities in the 8–9 μm region (bands 10 and 12) and high surface temperatures. Mafic-to-ultramafic units have higher surface temperatures than other geological units because they absorb more solar radiation due to their lower albedos and they have a lower thermal inertia. Quartz-rich felsic units such as granite and dry river sand appear reddish in the ASTER L1B daytime TIR FCC image because the spectral emissivity of quartz is lower in the 8–9 μm region (bands 10 and 12) than in the 10–12 μm region (band 14). Mafic-to-ultramafic and granitic units are important targets for mineral exploration because they are potential geological units to host or accompany mineralization. The proposed ASTER L1B daytime TIR FCC images can be prepared very simply and they provide valuable information for geological mapping and mineral exploration.     

热红外遥感的温度与发射率分离研究进展

热红外传感器测得的辐射亮度值是温度和发射率的函数,因此,对于热红外遥感反演地表温度,温度和发射率的分离是一个关键问题。总结了地表温度反演所取得的进展,综述了温度与反射率分离的几种主要方法,对比分析了各种方法的优缺点。最后,对地表温度反演的发展趋势进行了展望。