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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhang, Wanchang Zhu, Yefei Xu, Shijin Chinese Acad Sci E Asia Atmospher Phys Reg Climate Environm Res Temperate Beijing 100029 Peoples R China Nanjing Univ Dept Geosci Nanjing 210093 Peoples R China

ISBN: (纸本)9781424412112

An algorithm for retrieval of surface net radiation with Landsat TM/ETM+ data was developed in two parts in this study. First of all, net short wave radiation was computed with an improved algorithm for atmospheric correction based on dark object scheme associated with topographic information of the target site to account for the topographic affect on radiant regimes, and then a thermal radiant transfer model for accurate estimation of net long wave radiation over mountainous region by using surface temperature derived from the extended mono-window algorithm to integrate with surface energy balance equations was established. The thermal radiation from surrounding surface thus can be estimated, and atmospheric downward long wave radiation was calculated by means of an empirical formula related to surface temperature. With these two terms, Pixel based surface net radiation can be obtained. An evident advantage of this model is that only two atmospheric parameters, i.e. atmospheric mean effective temperature and surface temperature, are needed. The proposed approach was experimentally applied to a mountainous region located in the Hanjiang Basin, south western China. 6S code was utilized to the Landsat ETM+ image acquired over the region to simulate the surface and atmospheric radiative parameters under different geographic zones at selected climatic conditions to examine the performance of the proposed model. It was suggested that the maximum error in albedo extraction by the,proposed algorithm is about 17% with mean error less than 10%, the maximum error in retrieved short wave net radiation and long wave net radiation is about 15% and 12% with mean error less than about 11% and 9% respectively, which suggested the proposed approach an valid and operational mean in quantitative reversion of surface long wave radiation with satisfied accuracy.

关键词： mono-window algorithm mountainous thermal radiant transfer model net long-wave radiation net short wave radiation

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Improvement of mono-window algorithm for Retrieving Land Surface Temperature from HJ-1B Satellite Data

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Chinese Geographical Science 2010年第2期20卷 123-131页

作者： ZHOU Ji ZHAN Wenfeng HU Deyong ZHAO Xiang State Key Laboratory of Earth Surface Processes and Resource Ecology College of Resources Science ＆ Technology Beijing Normal University Beijing 100875 China College of Resource Environment and Tourism Capital Normal University Beijing 100048 China State Key Laboratory for Remote Sensing Science Beijing Normal University and Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.

关键词： land surface temperature mono-window algorithm HJ-1 B satellite remote sensing

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An Improved mono-window algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

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REMOTE SENSING 2015年第4期7卷 4268-4289页

作者： Wang, Fei Qin, Zhihao Song, Caiying Tu, Lili Karnieli, Arnon Zhao, Shuhe Chinese Acad Agr Sci Inst Agroresources & Reg Planning Beijing 100081 Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210093 Jiangsu Peoples R China Ben Gurion Univ Negev J Blaustein Inst Desert Res Dept Environm Phys Remote Sensing Lab IL-84990 Midreshet Ben Gurion Ben Gurion Israel Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China

The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of similar to 1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were -0.05 K and 0.84 K, respectively, which were less than the -2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of

关键词： Landsat 8 TIRS data land surface temperature thermal infrared mono-window algorithm split-window algorithm

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Modeling Land Surface Temperature with a mono-window algorithm to Estimate Urban Heat Island Intensity in an Expanding Urban Area

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ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL 2022年第1期9卷 1-21页

作者： Abdullah, Shahriar Barua, Dhrubo Noakhali Sci & Technol Univ Dept Environm Sci & Disaster Management Noakhali 3814 Bangladesh

We have estimated the amplitude and impact of urbanization on Chittagong metropolitan area (CMA), and investigated the correlation between land cover features and land surface temperature (LST). We have also located the urban heat island (UHI) and calculated the UHI intensity. Support vector machine (SVM) for land cover classification and mono-window algorithm (MWA) for LST estimation were applied to Landsat images of years 1990, 2000, 2010 and 2020. Validation of classification and temperature was also done by various statistical parameters giving credible results. The findings reveal that in Chittagong, between 1990 and 2020, the urban area has raised by almost 12%, whereas vegetative areas have decreased by about 14%. With the rise of the urban area, the temperature has also shifted from 19.8 degrees C to 23.7 degrees C. Moreover, the UHI intensity has also raised from 1 degrees C to 2.3 degrees C, which is the indication of an unplanned and unsustainable urban development. Evidently, the whole city can turn into a UHI if proper steps are not taken immediately.

关键词： Urban heat island Land surface temperature mono-window algorithm Support vector machine Urbanization Chittagong

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Improvement of mono-window algorithm for Land Surface Temperature Retrieval Integrated with Sub-pixel Mapping for Landsat Imagery 4

Improvement of Mono-window Algorithm for Land Surface Temper...

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4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

作者： Dai, Xiaoyan Guo, Zhongyang Chen, Chen Fudan Univ Dept Environm Sci & Engn Shanghai Peoples R China East China Normal Univ Minist Educ Key Lab Geog Informat Sci Shanghai Peoples R China

ISBN: (纸本)9781509014798

Since a large proportion of pixels are often composed of mixed land cover types within remote sensing images, how to eliminate the impact of the error resulting from pixel mixing effect in the estimation of land surface emissivity on the accuracy of land surface temperature (LST) retrieval from remote sensing data is a key problem to resolve firstly in process of LST retrieval. Based on the local relationship between the thermal radiance of one pixel and that of its components satisfying the Planck's radiance function, in this paper, the mono-window algorithm was improved by integrating with mixed-pixel classification and sub-pixel mapping for Landsat imagery. Validation indicates that the improved mono-window algorithm is able to provide more accurate LST than the original algorithm for Landsat TM/ETM+ imagery. By applying the improved algorithm to the Landsat image of Shanghai, the result revealed the spatial heterogeneity characteristics of UHI effect in Shanghai city.

关键词： land surface temperature mono-window algorithm sub-pixel mapping Landsat imagery remote sensing

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Enhancing landsat 8 land surface temperature retrieval in coastal regions using MODIS atmospheric water vapor data

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2025年第8期46卷 3137-3161页

作者： Wang, Tianyi Gao, Zhiqiang Ning, Jicai Tian, Xinpeng Wang, De Wang, Yueqi Jiang, Xiaopeng Luan, Xianyi Chinese Acad Sci Yantai Inst Coastal Zone Res CAS Key Lab Coastal Environm Proc & Ecol Remediat Yantai 264003 Shandong Peoples R China Chinese Acad Sci Yantai Inst Coastal Zone Res Shandong Key Lab Coastal Environm Proc Yantai Shandong Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

The Landsat series has long served as a critical data source for remote sensing-based land surface temperature (LST) retrieval. Accurate LST estimation requires parameters like emissivity and atmospheric transmittance, whose uncertainties can introduce significant errors. This study presents the MODIS three-channel weighted algorithm, which offers robust atmospheric water vapour retrieval capabilities, particularly in regions with high spatial variability such as coastal areas. By analysing variations in atmospheric water vapour across different land cover types, we incorporate these values into Qin's mono-window algorithm for LST calculation and validate the results through two approaches: UAV-based validation and cross-validation. The UAV-based measurements were used to directly compare observed and modelled LST, achieving a high accuracy with an RMSE of 1.01. Cross-validation against existing satellite-derived LST products further confirmed the model's reliability, demonstrating robust consistency with an RMSE of 1.259 K. This integration provides a reliable solution for addressing atmospheric variability in heterogeneous landscapes, with potential applications in climate monitoring and land management.

关键词： Land surface temperature MODIS mono-window algorithm atmospheric water vapour retrieval coastal zone

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Modeling the spatiotemporal heterogeneity of land surface temperature and its relationship with land use land cover using geo-statistical techniques and machine learning algorithms

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第49期30卷 106917-106935页

作者： Bindajam, Ahmed Ali Mallick, Javed Talukdar, Swapan Shahfahad Shohan, Ahmed Ali A. Rahman, Atiqur King Khalid Univ Coll Engn Dept Architecture & Planning Abha Saudi Arabia King Khalid Univ Coll Engn Dept Civil Engn POB 394 Abha 61411 Saudi Arabia Jamia Millia Islamia Dept Geog Fac Nat Sci New Delhi India

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

关键词： Support vector machine mono-window algorithm LISA model Parallel coordinate plot Abha-Khamis Mushyet

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Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa

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JOURNAL OF APPLIED REMOTE SENSING 2023年第3期17卷

作者： Djikpo, Vignon Adelphe Rosos Teka, Oscar Azihou, Fortune Toko, Ismaila Oumorou, Madjidou Sinsin, Brice Univ Abomey Calavi Fac Agron Sci Lab Appl Ecol Cotonou Benin Univ Abomey Calavi Inst Geog Land Management & Environm Lab Cartog Cotonou Houeyiho Benin Univ Abomey Calavi Ecole Polytech Abomey Calavi Dept Environm Management Cotonou Benin

Land surface temperature (LST) is an important climate variable used to assess the effects of climate change. This research project aims to compare the results of mono-window (MW) and split-window (SW) algorithms against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Collection 2 Level-2 surface temperature (L8-C2L2) products and identify the most suitable techniques. In-situ measurements of the earth's surface temperature and relative humidity have been recorded in 2020. LSTs have been validated using root mean square error (RMSE) based on the in-situ meteorological data. Validation analysis has indicated that the SW algorithm combined with in-situ micro-scale atmospheric water vapor content values was more accurate for LST. In the overall study area, the RMSE values of 1.09 degrees C, 3.97 degrees C, 4.36 degrees C, and 6.80 degrees C have been calculated for SW, MODIS, L8-C2L2, and MW LSTs, respectively. These results have demonstrated that the SW algorithm outperformed the other LST products. The maximum difference between the in-situ earth surface temperature and the SW algorithm was 0.79 degrees C. These findings are essential for comparing different data-driven approaches and identifying the most efficient techniques. The study's significance lies in identifying the most appropriate method for LST retrieval, which can aid in climate change studies and inform decision-making processes.(c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： land surface temperature Landsat imagery moderate resolution imaging spectroradiometer mono-window algorithm split-window algorithm

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Dynamic trends in land surface temperature and land use/land cover transitions in semi-arid metropolitan city, Jaipur

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第1期197卷 1-38页

作者： Khajuria, Neha Kaushik, S. P. Kurukshetra Univ Dept Geog Kurukshetra 136119 India

The increasing surface heat in metropolitan areas is one of the biggest issues, especially as natural surfaces are being replaced by impermeable concrete surfaces. This study uses Landsat data (1991-2022) to examine the spatio-temporal dynamics of LST and LULC in Jaipur, highlighting the impact of urban expansion and the city's semi-arid nature on the thermal landscape. We have used the maximum likelihood classifier for supervised LULC classification and the mono-window algorithm for retrieving LST. The evaluation is done using buffer analysis. Furthermore, to assess the interrelationship between LST and LULC indices (NDVI & NDBI), regression analysis is used. The CA-ANN model is employed to project LSTs of 2032 and 2042. The findings indicate that the built-up land in the study area grew by 52.80% from 1991-2022. Most of this expansion has come at the expense of agriculture/open land, and vegetation cover. The mean LST in the city has risen by 5.9 degrees C, with the inner zone (B1) increasing from 35.44 degrees C to 41.93 degrees C, indicating urbanisation-induced heat stress. In the outer zones (B5-B6), dry sandy and rocky soils contribute to elevated temperatures. Water bodies show the lowest LST, while open and barren lands have the highest. LST exhibit a positive correlation with NDBI and a weak negative correlation with NDVI. Predictions indicate that by 2042, about 99% of the urban landscape will encounter surface temperatures above 40 degrees C, with 28.79% exceeding 45 degrees C. Raised temperatures could exacerbate the UHI effect, leading to serious health and environmental concerns.

关键词： mono-window algorithm LST (Land Surface Temperature) LULC (Land use/Land cover) UHI (Urban Heat Island) CA-ANN (Cellular Automata-Artificial Neural Network)

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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

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SUSTAINABLE CITIES AND SOCIETY 2019年 45卷 686-692页

作者： Shirani-Bidabadi, Niloufar Nasrabadi, Touraj Faryadi, Shahrzad Larijani, Adnan Roodposhti, Majid Shadman Univ Alabama Civil & Environm Engn 01 Sparkman Dr Huntsville AL 35899 USA Univ Tehran Coll Engn Sch Environm Tehran Iran Isfahan Univ Technol Dept Min Esfahan Iran Univ Tasmania Sch Technol Environm & Design Discipline Geog & Spatial Sci Churchill Ave Hobart Tas 7005 Australia

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 land surface temperature (LST) of Isfahan in 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： Urban heat island Remote sensing mono-window algorithm Land surface temperature Isfahan

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