- 标题
- 摘要
- 关键词
- 实验方案
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[Advances in Intelligent Systems and Computing] Advanced Computing and Systems for Security Volume 883 (Volume Eight) || Remote Sensing Technology for Evaluation of Variations in Land Surface Temperature, and Case Study Analysis from Southwest Nigeria
摘要： Recent studies have shown that evaluation of the changes in the Land Surface Temperature (LST) of any area can be a re?ector of changes in urbanisation trend, industrial activities, population change and natural factors. Subsequently, many researches have evolved over time, especially with development in remote sensing, digital image processing and geographical information systems. This chapter is aimed at providing information on the relevance and challenges of remote sensing as a geospatial technology that is capable of being used for monitoring LST at different spatial and timescales. The case study analysis indicated that the results from the remote sensing processing of the imageries re?ect signi?cant in?uence of the spatial resolutions of selected imageries. The challenges of huge image data gaps, cloud cover, coarse spatial and temporal resolution, limited night-time data for evaluation of night-time urban heat island—for both technical and security reasons, in?uenced the reliability of the study results. The study recommended policies for improvement in the applications and utilisation of the geospatial technology in many developing countries, including Nigeria based on its strengths.
关键词： Nigeria,Image sensors,Land surface temperature,Image analysis
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of 1-Km All-Weather Land Surface Temperature Over the Tibetan Plateau
摘要： Land surface temperature (LST) immensely affects the energy balance and water cycle on the earth’s surface. Merging thermal infrared (TIR) and passive microwave (MW) remote sensing provides the possibility to obtain all-weather LST with moderate resolutions. However, due to difficulties in downscaling MW LST, current methods merging TIR LST and MW LST into such an all-weather LST are limited over large areas with very complicated land surfaces (e.g. the Tibetan Plateau). By fully considering the influence of the topography on estimation of merged LSTs, this study revises the recently-developed physical method for generating the 1-km all-weather LST and applies it over the Tibetan Plateau to merge MODIS (1 km) and AMSR2 (10 km) observations. Results show that the merged LST has accuracy of 0.99 K-3.22 K when validated against in-situ LSTs from five ground stations with various land cover types. This study would be beneficial for continuously monitoring LST and improving spatio-temporal resolutions for associated land surface process studies requiring high-quality all-weather LST over large scales.
关键词： MODIS,Spatial correlations,AMSR2,Land surface temperature (LST),Passive microwave remote sensing
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE SENSORS - Montreal, QC, Canada (2019.10.27-2019.10.30)] 2019 IEEE SENSORS - Evanescent field waveguide particle detector : Simulations concerning size and shape dependence
摘要： The midinfrared (MIR) spectral region (3–5 μm), which penetrates most haze layers in the atmosphere and is less sensitive to variations in atmospheric water vapor, seems to be appropriate for retrieving land surface temperature (LST). However, there are currently few studies of LST retrieval with MIR data because it is difficult to eliminate solar irradiance from the total energy measured in the MIR during the daytime. This paper proposes a physics-based method to retrieve LST from MODIS daytime MIR data. The bidirectional reflectivity describing the reflected solar direct irradiance is determined using the method by Tang and Li. The directional emissivity, representing the surface emitted radiance, is determined by a kernel-driven bidirectional reflectance distribution function model, i.e., RossThick-LiSparse-R. Intercomparisons using the MODIS-derived LST product MYD11_L2, for the Baotou experimental site in Urad Qianqi, Inner Mongolia, China, have a maximum root-mean-square error (RMSE) of 1.69 K and a minimum RMSE of 1.31 K, for four scenes of MODIS images. Furthermore, in situ LSTs measured at the Hailar field site in northeastern Inner Mongolia, China, were also used to validate the proposed method. Comparisons of the LSTs retrieved from MODIS daytime MIR data and those calculated using in situ measurements have a bias and RMSE of ?0.17 K and 1.42 K, respectively, which indicates that the proposed method can accurately retrieve LST from MODIS daytime MIR data.
关键词： MODIS,midinfrared (MIR),Daytime,land surface temperature (LST)
更新于2025-09-23 15:19:57
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Temperature and Emissivity Separation Algortihm for Chinese Gaofen-5 Satelltie Data
摘要： In this paper, we proposed a temperature and emissivity separation (TES) algorithm for the simultaneous retrieval of land surface temperature and emissivity (LST&E) from the thermal infrared data of Chinese GaoFen-5 (GF-5) satellite’s Multiple Spectral-Imager (MSI) payload. In order to improve the accuracy of the TES algorithm, a water vapor scaling (WVS) method for atmospheric correction was adopted. The Seebor V5.0 global atmospheric profile database and MODTRAN 5 were used to simulate the WVS coefficients. A total of 11 ASTER scenes were used to simulate the MSI images and concurrent ground measurements acquired in the HiWATER experiment were used to validate the algorithm. The results showed that the bias and root mean square error (RMSE) in the retrieved LST were 0.47 K and 1.70 K, respectively, and the absolute emissivity differences between MSI and the ground measurements were smaller than 0.01 for the four MSI TIR bands, which demonstrated that the proposed algorithm can be used to retrieve high accurate and high spatial resolution LST&E from GF-5 MSI data.
关键词： atmospheric correction,TES,GF-5,Land Surface Emissivity,Land Surface Temperature
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Downscaling Land Surface Temperature by Using Random Forest Regression Algorithm
摘要： This study proposes a land surface temperature (LST) downscaling method to downscale the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) from 990m to 90m by using random forest (RF) regression algorithm. The LST product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with 90m resolution, serves as the validation reference at the finer scale. The proposed method is based on the relationship between LST and a variety of surface parameters including band reflectance, spectral indices, land cover types and terrain factors. The proposed downscaling method is evaluated in Segovia, Spain, the Pe?arora mountain region. Comparison between downscaled LST and referenced LST proves that the proposed method shows a great accuracy in downscaling LST. Furthermore, another downscaling method, an algorithm for sharpening thermal imagery (TsHARP), is also implied to get finer resolution LST to make a more complicated comparison with the proposed method. The results are evaluated by root mean squared error (RMSE) and bias, which demonstrate that the accuracy and robustness of RF downscaling method compared with TsHARP.
关键词： Land surface temperature,random forest,downscaling
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Refined Generalized Split-Window Algorithm for Retrieving Long-Term Global Land Surface Temperature from Series NOAA-AVHRR Data
摘要： Long-term global land surface temperature (LST) is a very important data source for climate change study. By adding a quadratic term of two adjacent channels’ brightness temperature difference, this paper proposed to use a refined generalized split-window (GSW) algorithm to retrieve LST from series NOAA-AVHRR data. Results of the simulation analysis and the sensitive analysis indicated that the refined GSW method had a high retrieval accuracy and a robust performance. The overall root mean square errors (RMSEs) varied from 0.55 K to 0.59 K for NOAA 7-AVHRR to NOAA 19-AVHRR data. In terms of the wet atmosphere, the refined algorithm had a better ability than the GSW algorithm, and the proportion of sub-ranges with RMSE below 0.5 K was 61.7%. Most RMSE errors were within 0.2 K and 0.7 K for sensor noise (NEΔT) = 0.1 K and NEΔT = 0.2 K, respectively, compared with the cases of no NEΔT. Given the uncertainties of emissivity around 1%, the errors were mainly within [0.9K, 1.2K] for dry atmosphere and [0.3K, 0.7K] for wet atmosphere.
关键词： land surface temperature (LST),long-term,AVHRR-AVHRR,generalized split-window (GSW)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Land Surface Temperature Retrieval from the Infrared Measurements of Advanced Himawari Imager on Himawari-8
摘要： This work addresses Land Surface Temperature (LST) retrieval from the infrared measurements of Advanced Himawari Imager (AHI) on Himawari-8 satellite using the Generalized Split-Window (GSW) algorithm. First, a radiative transfer modeling experiment is conducted using the moderate spectral resolution atmospheric transmittance algorithm and computer model (MODTRAN) 4.0 fed with the SeeBor V5.0 atmospheric profile database to simulate the brightness temperatures in the AHI channels 14 (centered at about 11.2 μm) and 15 (centered at about 12.3 μm) related to Land Surface Emissivities (LSEs) and Total Precipitable Water (TPW). Then, the unknown coefficients of the GSW algorithm are obtained through multi-variable linear regression, in which the simulated data are grouped into several sub-ranges to improve algorithm accuracy. Next, LSTs are derived from the clear-sky AHI measurements in September 2016 over a study area with longitude from 100°E to 145°E and latitude from 15°N to 45°N, where LSEs are deduced from the MOD11C1 V6 product using the baseline fit method, and TPWs are extracted from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data. Finally, the derived LSTs are cross-validated with the MOD11C1 V6 product. The results show that the GSW algorithm developed in this work can accurately retrieve LST from the AHI measurements, and the error is 0.39±1.62 K against the MOD11C1 V6 product.
关键词： Land surface temperature,Advanced Himawari Imager (AHI),the Generalized Split-Window (GSW) algorithm,cross-validation
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of Land Surface Temperature from Chinese Gaofen-5 Satellite Data
摘要： This work addressed the estimation of Land Surface Temperature (LST) from Chinese Gaofen-5 (GF-5) satellite Thermal Infrared (TIR) data, using a Generalized Split-Window (GSW) algorithm. The numerical values of the GSW coefficients were obtained using a statistical regression method from synthetic data simulated with an accurate atmospheric radiative transfer model MODTRAN 5 over a wide range of atmospheric and surface conditions. The LST, mean emissivity, and atmospheric Water Vapor Content (WVC) were divided into several tractable sub-ranges to improve the fitting accuracy. The experimental results showed that the combination of two adjacent channels CH8.20 (centered at 8.20 μm) and CH8.63 (centered at 8.63 μm) was comparable with the combination of two adjacent channels CH10.80 (centered at 10.80 μm) and CH11.95 (centered at 11.92 μm) for estimating LST using the GSW algorithm, with Root Mean Square Errors (RMSEs) below 0.8 K, provided that the Land Surface Emissivities (LSEs) are known. Particularly, for the high emissivity surfaces under wet and hot atmospheric conditions (WVC>3.0g/cm2), two not adjacent channels combination of CH8.63 and CH11.95 could also be used to estimate LST with RMSEs within 0.5 K.
关键词： Generalized Split-Window (GSW),channels combination,Land Surface Temperature (LST),Gaofen-5,Thermal Infrared (TIR)
更新于2025-09-10 09:29:36
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Evaluating the spatial distribution and the Intensity of urban heat island using remote sensing, Case study of Isfahan city in Iran
摘要： In recent years, urbanization has widely been developed and resulted in great loss of natural resources on earth and many other consequences such as temperature rise which exposes the cities to a phenomenon called heat island. Urban Heat Island (UHI) can be identified and analyzed by thermal remote sensing. In the present study, satellite images of Landsat 7 ETM+ (1999 and 2006) and Landsat 8 (2013 and 2016) were used to retrieve the earth surface temperature of Isfahan, Iran by means of the mono-window algorithm. Analysis of thermal levels showed an increase in the minimum temperature of 2016 compared to 1999. Furthermore, the results revealed that the heat island ratio (URI) in Isfahan in four different years followed a rising trend, moving from 0.16 in 1999 to 0.3 in 2016. The findings of this study indicated that the areas influenced by UHI are often in northern and southern parts of the city where vegetation cover is very sparse, the land is arid, and industrialization and regional settlements are booming.
关键词： Mono-window algorithm,Isfahan,Land surface temperature,Urban heat island,Remote sensing
更新于2025-09-10 09:29:36
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High-resolution land surface modeling utilizing remote sensing parameters and the Noah UCM: a case study in the Los Angeles Basin
摘要： In the current work we investigate the utility of remote-sensing-based surface parameters in the Noah UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat–MODIS data are utilized to generate high-resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter data sets are directly substituted for the land-use/lookup-table-based values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) ?elds is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal ?uctuations of the default and measured parameter sets lead to signi?cant errors in the model predictions of monthly ET ?elds (RMSE = 22.06 mm month?1). Results indicate that implemented satellite-derived parameter maps, particularly GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE= 11.77 mm month?1). GVF plays the most signi?cant role in reproducing the observed ET ?elds, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model’s capability to predict the LST differences between fully vegetated pixels and highly developed areas.
关键词： remote sensing,land surface modeling,Noah UCM,evapotranspiration,urban canopy model,Los Angeles Basin,land surface temperature
更新于2025-09-09 09:28:46