Land Surface Temperature Retrieval Research Articles

A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms

Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Contribution of urban functional zones to the spatial distribution of urban thermal environment

With the accelerated rate of urbanization, reasonable identification of urban functional zones has been increasingly important to urban development. In this study, we used OpenStreetMap and point of interest data combined with the classification standard of urban construction land, to identify the urban functional zones. In addition, Landsat 8 remote sensing images were utilized to retrieve land surface temperature, and the random forest algorithm was used to evaluate the contribution of different types of urban functional zones to the urban thermal environment. The results showed that the land surface temperature along Hunhe River in the central area of Shenyang gradually decreased. The temperature was high in the center of the experimental area and low in the north and south. Furthermore, in the main city zone of Shenyang, single functional zones accounted for 65.71% of the total study area, whereas mixed functional zones accounted for 34.29%. Among the various types of urban land, public service facility land contributed the most to the urban thermal environment with an impact of 21.65%, followed by residential, industrial, and commercial service facility lands, with impacts of 19.89%, 18.44%, and 17.58%, respectively. Additionally, the impact of road traffic land was 14.92%, whereas that of green square land was the lowest at 7.51%, 14.14% lower than that of public service facility land.

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.

Identifying urban ventilation corridors through quantitative analysis of ventilation potential and wind characteristics

Urban heat islands (UHIs) are mainly caused by mismanaged urban development. Urban ventilation corridors (UVCs) can effectively reduce UHIs. Hangzhou, a large, dense city in eastern China, is one of the nation's hottest cities and must urgently improve its ventilation. In this study, a newly developed method combining land surface temperature retrieval, GIS spatial analysis, and weather data was employed to design city-level UVCs. The functional space and compensation space of UVCs are identified based on the LandsatETM8 image data inversion of surface temperature. The contribution of affect factors, such as building density, building height, green land area, waterbody, roads, and urban terrain, to the urban ventilation potential is calculated using AHP and GIS. Areas with tremendous ventilation potential were identified as possible ventilation corridors by using an evaluation model and considering wind characteristics. Fifty-four high-temperature zones and 48 low-temperature zones were identified as functional and compensative spaces in Hangzhou, respectively. On the urban scale, green space (area >500ha), water bodies (area >300ha or width >100 m), and roads (width >50 m) were considered to be the factors that have the greatest impact on urban ventilation potential. The LCP method was used to verify the accuracy and rationality of the method proposed in this study to determine the city-scale UVC. UVC planning schemes and construction measures were proposed to provide rich information and a reference basis for the new general land and space planning of Hangzhou. However, to improve the accuracy of the potential ventilation results, the urban underlying surface criteria should be re-evaluated more comprehensively.

Optimizing TRISHNA TIR channels configuration for improved land surface temperature and emissivity measurements

In preparation of the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) mission, we conducted a thorough analysis of sensitivity for the Temperature-Emissivity Separation (TES) method to the position of the four TRISHNA spectral channels, notably to find an optimal spectral configuration. To that purpose, we designed a fast-computing end-to-end simulator including several components, which we implemented to simulate both pixel-size TRISHNA measurements and land surface temperature (LST) retrievals. Firstly, simulations were conducted over a wide range of realistic scenarii, notably by including vegetation canopy-scale cavity effect. Secondly, the experimental design included the features of second generation Mercury-Cadmium-Telluride (MCT) cooled detectors with lower instrumental noises and finer channels. Thirdly, as opposed to previous studies that used predefined spectral configurations to determine the most suited one, we conducted an optimization of the spectral configuration by crossing, on a pair basis, several positions of the four TIR channels over a range of wavelengths. Fourthly, we quantified the TES sensitivity to atmospheric perturbations, by comparing LST retrievals with and without atmospheric noise.We observed an overall moderate sensitivity of TES LST retrievals to the spectral channel positions, with a maximum RMSE variation of 0.31 K within the atmospheric spectral windows. Furthermore, the TES method was sensitive to three main parameters, namely the instrumental noise, the atmospheric downwelling irradiance, and the transmittance due to ozone and water vapor, with RMSEs larger than 1 K for specific channel locations. Moreover, by considering possible superimposition of two channels, we noted that the TES method could achieve similar performance by considering three or four channels. Eventually, our study enabled us to recommend a new spectral configuration for the TRISHNA TIR instrument, that is more robust to atmospheric perturbations and to uncertainties on channel positions and bandwidths.

Improving land surface temperature prediction using spatiotemporal factors through a genetic-based selection procedure (Case Study: Tehran, Iran)

In recent decades, land surface temperature (LST) has been considered as an important satellite product for thermal monitoring of the environment. Despite the great development of LST retrieval technology from satellite data, especially from Landsat images, up-to-date thermal information cannot always be obtained. Predicting LST values for future times is a pressing issues to understand and mitigate the effects of climate change, especially in urban environments. This highlights the importance of LST forecasting methods. Accordingly, in this study a combination of Genetic Algorithm and Geographically and Temporally Weighted Regression (GTWR) is presented to predict LST based on nine selected spatiotemporal factors affecting LST in the urban areas. The genetic algorithm is used to determine weights of different factors in the LST prediction with respect to the GTWR as the regression model. Then, these weights are used to obtain predicted LST values for experimental points. Landsat-derived LST bands is used to train the genetic algorithm and extract the factors’ weights. Tehran city in the summer is selected as the case study in our work. Landsat LST data from 2013 to 2016 are used for training. To assess the performance of our scheme, we apply it in 2017. We find a correlation coefficient of 0.99 and a root-mean-square error of 0.5 K with respect to the Landsat data. The results of this study also indicate that LST prediction using weighted factors is superior to using all factors with equal contributions.

Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager

ABSTRACT This paper extends a new temperature and emissivity separation (TES) algorithm for retrieving land surface temperature and emissivity (LST and LSE) to the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4A, China’s newest geostationary meteorological satellite. The extended TES algorithm was named the AGRI TES algorithm. The AGRI TES algorithm employs a modified water vapor scaling (WVS) method and a recalibrated empirical function over vegetated surfaces. In situ validation and cross-validation are utilized to investigate the accuracy of the retrieved LST and LSE. LST validation using the collected field measurements showed that the mean bias and RMSE of AGRI TES LST are 0.58 and 2.93 K in the daytime and −0.30 K and 2.18 K at nighttime, respectively; the AGRI official LST is systematically underestimated. Compared with the MODIS LST and LSE products (MYD21), the average bias and RMSE of AGRI TES LST are −0.26 K and 1.65 K, respectively. The AGRI TES LSE outperforms the AGRI official LSE in terms of accuracy and spatial integrity. This study demonstrates the good performance of the AGRI TES algorithm for the retrieval of high-quality LST and LSE, and the potential of the AGRI TES algorithm in producing operational LST and LSE products.

Urban green space mediates spatiotemporal variation in land surface temperature: a case study of an urbanized city, Bangladesh.

Rapid urbanization with an increasing rate of urban built-up area is decreasing urban green space resulting in changing urban microclimate conditions showing increasing land surface temperature. A better understanding of these effects is important to formulate effective strategies in addressing the impact of increasing built-up area. Land surface temperature patterns in an urbanized city in Bangladesh (Mymensingh district) were investigated using Landsat satellite sensor data from 1988 to 2016. A total of nineteen Landsat satellite images were used to retrieve land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The radiative transfer equation (RTE) model was applied to derive LST for the years 1988, 1992, 1999, 2004, 2008, 2012, and 2016. Further, the Landsat-derived LST results were compared with MODIS Terra satellite outputs (MOD11A1) for the validation of our study results. Our results showed NDVI higher in 2008 and lower in 2004, LST maximum in 1988 and minimum in 2008, and NDBI higher in 2004 and lower in 2012. Seasonally, summer was characterized by higher LST and winter by lower LST, while NDVI was higher in autumn and lower in winter, however, NDBI was higher in winter and lower in autumn. Spatially, a relatively higher LST and NDBI was observed in the southwest, followed by central, and northern regions, whereas the trend was opposite for NDVI. Using Pearson's correlation, results showed a strong significant negative correlation between LST and NDVI and a positive significant correlation between LST and NDBI. Further, simple linear regression analysis revealed that LST decreased with increasing NDVI most quickly in 2012, followed by the years 2016, 2008, 1992, 1988, 1999, and 2004. On the other hand, LST increased with increasing NDBI most quickly in 1999, followed by the years2016, 1988, 1992, 2012, 2004, and 2008. Thus, long-term observation suggested that urbanization had driven a decrease in green space while simultaneously increasing the land surface temperature within an urbanized area. This study has concluded that the protection of urban green spaces is needed as an effective step toward addressing adverse effects of regional climate change and desertification.

An Operational Land Surface Temperature Retrieval Methodology for Chinese Second-Generation Huanjing Disaster Monitoring Satellite Data

The Chinese second-generationHuanjing disaster monitoring satellite (HJ-2A) was launched on September 27, 2020, and underutilized due to the lack of accurate operational methodologies for land surface temperature (LST) retrieval.In this article, an operational LST retrieval method is proposed to retrieve LSTs from HJ-2A thermal infrared observations. The LST retrieval methodology involves two main steps. The land surface emissivities (LSEs) over all land cover types are obtained with the improved normalized difference vegetation index-based threshold method, and then the LST is retrieved operationally from the adjacent infrared bands.The algorithm coefficients for LST retrieval are from regression analysis of radiative transfer simulations, and LSTs could be retrieved based on thermal images without any additional auxiliary data. The simulation results demonstrated that the root-mean-square errors (RMSEs) of LST retrieval were less than 2.4 K in all subranges, and the minimum RMSE for the two emissivity groups (high- (low-) emissivity group) was 0.16 K (0.20 K) and appeared in the tractable subrange with water vapor content (WVC) varying from 0 to 1.5 g/cm² and view zenith angle (VZA) being 0°. Furthermore, an error analysis was performed, the results showed that the LSE, NEΔT, and atmospheric water vapor uncertainty of 1%, 0.2 K, and 20% caused the LST retrieval errors with 0.88–1.21 K (0.84–1.19 K), 0.1 K (0.09 K), and 0.006 K (0.008 K) for the high- (low-) emissivity group, respectively, with WVC∊[0–1.5] g/cm² and VZA = 0°. Finally, the retrieved LSTs were applied to seven images of the Wuhai, Geermu, Dunhuang, and Baotou sites from January to March and cross validated by the moderate resolution imaging spectroradiometer (MODIS) LST products. From the cross-validated results, it can be found that the RMSEs of the retrieved LSTs and the MODIS LST products were between 2.3 and 3.7 K, and the mean RMSE value was 2.89 K.

Land Surface Temperature Retrieval From Landsat 8 Thermal Infrared Data Over Urban Areas Considering Geometry Effect: Method and Application

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

A Land Surface Temperature Retrieval Method for UAV Broadband Thermal Imager Data

Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB’s core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and −0.02 K, respectively, is highly consistent with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> LST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.

Retrieval of Land Surface Emissivities Over Partially Vegetated Surfaces From Satellite Data Using Radiative Transfer Models

Land surface emissivity (LSE) is a key variable for land surface temperature (LST) retrieval from satellite data. In this study, five emissivity radiative transfer models (RTMs) for vegetation canopies are investigated together with a classification-based methodology to produce canopy LSE maps over the Iberian Peninsula with EOS Aqua – MODIS data. The five canopy RTMs are: FR97, Mod3, REN15, 4SAIL and CE-P. The analysis of the RTMs performance with satellite data gain interest over partially vegetated surfaces, for which these models can obtain accurate emissivities. The sensitivity analyses showed that FR97, REN15, 4SAIL and CE-P models have higher uncertainty for low LAIs, while Mod3 model increase the uncertainty with LAI. The produced LSEs were first intercompared with emissivities from the MODIS MYD21A1 product, which is obtained with the Temperature and Emissivity Separation (TES) method. The RTMs agreed with the TES emissivities within the given uncertainty. Additionally, the RTM emissivities were compared with MYD11A1 and MYD11B1 MODIS products and the IREMIS and CAMEL databases, and they were used to estimate the LST at three specific homogeneous sites: shrubland, vineyard and olive orchard. The LSTs estimated with each modelled emissivity and emissivity products were validated against reference data at these sites. All RTMs provided accurate LST data, equal or even better than the MODIS products, with median values of differences between -0.7 and 0.4 K depending on the site. Therefore, the canopy emissivity RTMs used in this study, together with the classification-based methodology, showed to be suitable for satellite LST retrieval.

Accuracy Evaluation of the Landsat 9 Land Surface Temperature Product

Having a good knowledge of the uncertainty in the land surface temperature (LST) product will help to encourage its use in a wide number of applications, including urban heat islands, geo-thermal detection, and surface energy balance. Landsat 9 was launched on 27 September 2021 and provides an LST product, which is generated by the radiative transfer equation (RTE) algorithm and has a spatial resolution of 30 m. In this study, we evaluated the performance of the Landsat 9 LST product by using a temperature-based (T-based) method and cross-validation. The T-based validation results showed that the average bias at the SURFRAD and BSRN sites was 0.24 K and that the corresponding root mean square error (RMSE) was 3.42 K. The Landsat 9 LST product was in good agreement with the Landsat 7/8 LSTs, with an average bias of 0.25/0.08 K, an RMSE of 0.51/1.04 K, and a mean absolute error (MAE) of 0.38/0.64 K. The comparable performance of the Landsat 7/8/9 LST products can be explained by the consistent LST retrieval algorithm. The absolute differences in the LST between Landsat 9 LST and MOD11 (MOD21) LST images were between 0.01 (0.65) and 2.50 K (1.76 K), whereas the RMSE values were between 1.40 (1.80) and 3.65 K (3.26 K). The specific heat capacity and thermal inertia of the different land surface covers can explain the significant biases. The above evaluation results are consistent with the initial performance testing of Thermal Infrared Sensor-2 (TIRS-2) by the National Aeronautics and Space Administration (NASA) and the US Geological Survey (USGS). Although the released Landsat 9 LST product showed good performance in the preliminary evaluation, the split-window algorithm may be a better option for Landsat 9 LST retrieval, as the TIRS-2 data addressed stray light incursion. Since there are no official validation results that have been published, this study provides a third-party performance evaluation of the Landsat 9 LST product and will benefit research fields that require Landsat series LST products.

PKULAST-An Extendable Model for Land Surface Temperature Retrieval From Thermal Infrared Remote Sensing Data

Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from thermal infrared (TIR) remote sensing data. In this study, we developed a python package, dubbed Peking University LAnd Surface Temperature (PKULAST) model to address the retrieval of LST from TIR remote sensing data with different LST retrieval algorithms, such as Single Channel algorithm, Split Window algorithm, and the Temperature and Emissivity Separation algorithm. Extendable data structures in the model covers common conceptual models used in LST algorithms implementation, such as spectral response functions, surface spectral libraries (ASTER, ECOSTRESS etc.), atmospheric profiles (NCEP, ERA5, MERRA-2, etc.), making it a convenience to construct the processing chain of developing new LST retrieval algorithms and generating LST products. The ASTER imageries are tested to assess the performance of the PKULAST to derive accurate LST. The LST result is then evaluated using long-term ground-based longwave radiation observations collected at seven sites from 2000 to 2021 under cloud-free scenes, and the result shows that the PKULAST model can obtain LST with an uncertainty of 2.7 K in bias, and 4.4 K in RMSE. With the help of predefined extendable data structures and workflows for different sensors, atmospheric profile libraries and surface spectral libraries, new algorithms for retrieving LST can be developed easily. The PKULAST promises to be a tool that seamlessly interoperates with developing LST retrieval algorithms and generating LST scientific products.

Split-Window Algorithm for Land Surface Temperature Retrieval From Landsat-9 Remote Sensing Images

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature at the top of the atmosphere in two adjacent TIR channels and thus is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides 2-channel TIR images with the same 100m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this paper, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 K to 2.87 K in various regions.

局地气候区视角下的城市热环境研究

PDF HTML阅读 XML下载 导出引用 引用提醒 局地气候区视角下的城市热环境研究 DOI: 10.5846/stxb202105181297 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41771178，41671151）；辽宁省高等学校创新人才支持计划（LR2017017）；辽宁省教育厅项目（H201783628） Exploration of urban thermal environment based on local climate zone Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市化发展带来热岛效应，影响区域气候变化，地表温度可以反映地表增温程度，更能直接影响人类居住舒适度。运用Landsat8 TIRS热红外遥感数据和气象数据反演地表温度，以建筑数据、遥感影像为基础，通过GIS空间分析、决策树分类等方法划分局地气候区，从区域角度定量分析不同类型气候区地表温度分异规律。结果表明：（1）北京、天津、石家庄密集型建筑分别占比27.54%、21.95%、25.09%，且以中低层为主，城市公园包含了主要的绿地和水体。（2）在空间分布上，市中心地表温度高于郊区，热岛效应显著，森林、河流是主要低温区。（3）不同气候区的地表温度存在差异，建成区总体高于自然地表；其中建筑区域内表现为紧密型低层（LCZ3）平均地表温度最高，稀疏型高层（LCZ4）地表温度最低，北京、天津、石家庄分别相差1.53℃、2.30℃、2.22℃；植被和水体能够降低地表温度，裸土和铺设路面的地表温度始终较高。因此应充分考虑建筑布局，合理利用植被和水域分布，减少热量聚集，以改善城市生态环境。 Abstract:Urbanization brings about heat island effect, which affects regional climate change. Land surface temperature can reflect the degree of surface warming, and can directly affect human living comfort. This study retrieved land surface temperature (LST) based on Landsat 8 TIRS thermal infrared remote sensing data and meteorological data, and the local climate zones (LCZ) were divided by GIS spatial analysis and decision tree classification. We quantitatively studied LST differentiation rules of different types of local climate zones from the regional perspective. The results showed that (1) most of the buildings in study area were low- and middle-level, with the intensive buildings accounting for 27.54%, 21.95% and 25.09%, respectively. Urban park contained the main green and water space. (2) In terms of spatial distribution, LST in the city center was higher than that in the suburbs. Forest and river were the main low temperature areas, and the heat island effect was significant. (3) LST in built-up LCZs were generally higher than that of natural LCZs. Among built-up LCZs, LCZ3 (compact low rise) had the highest LST, while LCZ4 (sparse high-rise) had the lowest LST, with a difference of 1.53℃, 2.30℃ and 2.22℃ in Beijing, Tianjin and Shijiazhuang, respectively. Vegetation and water can lower LST, however, the LST of bare soil and paved road were always higher. Therefore, building layout should be fully considered and vegetation and water distribution should be rationally utilized to reduce heat accumulation and improve urban ecological environment. 参考文献 相似文献 引证文献

The Utility of Simpler Spatial Disaggregation Models for Retrieving Land Surface Temperature at High Spatiotemporal Resolutions

The thermal infrared (TIR) sensors aboard geostationary satellites provide multi-temporal land surface temperature (LST) observations for characterizing the diurnal temperature cycle (DTC) at a coarse spatial resolution. This study aims to test if simpler spatial disaggregation models developed to improve the spatial resolution of LST data can be applied across the temporal domain to obtain LST at relatively higher spatial resolution and to capture the DTC. LST from geostationary satellites Kalpana-1 and INSAT-3D were downscaled from 8- and 5-km spatial resolutions respectively to 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired during its daytime overpass. Two disaggregation models, DisTrad and NL-DisTrad and a data fusion model spatiotemporal integrated fusion model (STITFM) were selected for the purpose. The downscaled LSTs were compared against the MODIS LST observations from Terra nighttime and Aqua daytime overpasses in addition to in situ data at four sites in India. The results indicated that the DisTrad model (RMSE: 3.61 K, R²: 0.95) was able to capture the spatiotemporal patterns with an accuracy comparable to that of the STITFM model (RMSE: 3.22 K, R²: 0.96). However, the NL-DisTrad performed inconsistently, leading to higher errors.

Research on Retrieval of Surface Temperature Based on Landsat8 Data Atmospheric Correction Method

With the development of remote sensing technology, various land surface temperature retrieval algorithms have been proposed one after another. How to use remote sensing data to retrieve the changing trend of land surface temperature and then plan the development of future cities has become a research hotspot. This paper takes Jinan City, China, as an example, selects 2017 and 2018 Landsat8 OLI and TIRS remote sensing images, uses ENVI software, uses atmospheric correction method to perform temperature inversion, and combines the measured data of 10 measurement sites in Jinan City and the inversion results of this research Comparing the data. Using linear regression analysis, the correlation coefficients between the inverted temperature and the measured land temperature are 0.8815 and 0.8756, respectively, which means that the correlation is relatively high. The atmospheric correction method is used to invert the surface temperature of Ji Nan City. The rationality of this policy provides a reference for effectively responding to regional or global climate change.

Land Surface Temperature Retrieval Based on Thermal Infrared Channel Decomposed Split-Window Algorithm

Land Surface Temperature Retrieval Based on Thermal Infrared Channel Decomposed Split-Window Algorithm