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Retrieval of precise land surface temperature from ASTER night-time thermal infrared data by split window algorithm for improved coal fire detection in Jharia Coalfield, India

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GEOCARTO INTERNATIONAL 2022年第3期37卷 926-943页

作者： Singh, Narendra Chatterjee, R. S. Kumar, Dheeraj Panigrahi, D. C. Mujawdiya, Ritesh Indian Sch Mines Dept Min Engn Indian Inst Technol Dhanbad Bihar India Indian Inst Remote Sensing Geosci Dept Dehra Dun Uttarakhand India

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST.

关键词： Land surface temperature land surface emissivity split window algorithm modified split-window covariance and variance ratio ASTER TIR data Jharia Coalfield

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EVALUATION OF CLOUD TYPE CLASSIFICATION BASED ON split window algorithm USING HIMAWARI-8 SATELLITE DATA 39

EVALUATION OF CLOUD TYPE CLASSIFICATION BASED ON SPLIT WINDO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Purbantoro, Babag Aminuddin, Jamrud Manago, Naohiro Toyoshima, Koichi Lagrosas, Nofel Sumantyo, Josaphat Tetuko Sri Kuze, Hiroaki Chiba Univ Ctr Environm Remote Sensing Chiba Japan Indonesian Inst Aeronaut & Space LAPAN Remote Sensing Technol & Data Ctr Jakarta Indonesia Univ Jenderal Soedirman Fac Math & Nat Sci Dept Phys Purwokerto Indonesia

ISBN: (纸本)9781538691540

Precise evaluation of cloud types is indispensable for the detailed analysis of the Earth's radiation budget. The split window algorithm (SWA) is an algorithm that has been widely employed for cloud type classification from meteorological satellite imagery. In this study, we apply the SWA to analyze the clouds that appear in the Japan area using the imagery of Himawari-8 meteorological satellite. The brightness temperature (BT) information from band 13 (BT13, 10 mu m) and band 15 (BT15, 12 mu m) are employed with the BT difference (BTD) between these two bands (BTD13-15). For daytime analysis, the albedo of band 1 (0.47 mu m) is also used to discriminate the cloudy and cloud-free areas. The validation of the resulting cloud type (SWA13-15), which includes ten classes including cloud-free condition, is carried out using the space-borne lidar data concurrent with the satellite observations. In addition, two different classifiers, namely, the sequential minimal optimization (SMO) and Naive Bayes (NB) classifiers are tested with the results of SWA. When about 10% of 2 million data points are used for training the classifiers, the test results reveal that the correctly classified points are 97.0% and 89.5% for the first dataset (observed in July 2015) and 97.4%, and 92.1% for the second dataset (July 2016) for SMO and NB, respectively.

关键词： Himawari-8 split window algorithm brightness temperature cloud type classification machine learning

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Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using split window algorithm

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REMOTE SENSING 2019年第24期11卷 2944-2944页

作者： Purbantoro, Babag Aminuddin, Jamrud Manago, Naohiro Toyoshima, Koichi Lagrosas, Nofel Sumantyo, Josaphat Tetuko Sri Kuze, Hiroaki Chiba Univ Ctr Environm Remote Sensing Chiba 2638522 Japan Indonesian Inst Aeronaut & Space LAPAN Remote Sensing Technol & Data Ctr Jakarta 13710 Indonesia Univ Jenderal Soedirman Fac Math & Nat Sci Dept Phys Cent Java 53123 Indonesia

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 mu m) of MODIS and band 1 (0.47 mu m) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 mu m are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 mu m) and 32 (12.02 mu m) of MODIS (SWA31-32) and band 13 (10.4 mu m) and 15 (12.4 mu m) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA

关键词： Himawari-8 AHI Aqua MODIS Terra MODIS cloud mask cloud type classification split window algorithm brightness temperature

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Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model

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DATA IN BRIEF 2019年 27卷 104773-104773页

作者： Athick, A. S. Mohammed Abdul Shankar, K. Naqvi, Hasan Raja Acad Sinica Earth Syst Sci Program TIGP Taipei 11529 Taiwan Natl Cent Univ Grad Inst Hydrol & Ocean Sci Taoyuan Taiwan Acad Sinica Res Ctr Environm Changes Taipei Taiwan Adama Sci & Technol Univ Sch Appl Nat Sci Dept Appl Geol Adama Ethiopia Jamia Millia Islamia Fac Nat Sci Dept Geog New Delhi 110025 India

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as split-window, Mono-window (SW), Single-Channel (SH), among others. LST was estimated using Mono-window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation. (C) 2019 Published by Elsevier Inc.

关键词： Land surface temperature (LST) Normalized difference vegetation index (NDVI) Mono window split window algorithm Spectral radiance model

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Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2016年第2期2卷 1-11页

作者： Sahana, Mehebub Ahmed, Raihan Sajjad, Haroon Jamia Millia Islamia Fac Nat Sci Dept Geog New Delhi India

The paper utilized Landsat 5 TM and Landsat 8 OLI for analyzing land use/land cover change and its impact on land surface temperature in Sundarban Biosphere Reserve, India. split window algorithm and spectral radiance model were used for determining land surface temperature from Landsat 8 OLI and Landsat 5 TM, respectively. The land use land cover change analysis revealed phenomenal increase in the waterlogged areas followed by settlement and paddy and a decrease in open forest followed by deposition and water body. The distribution of average change in land surface temperature shows that water recorded highest increase in temperature followed by deposition, open forest and settlement. Overlay of the transect profiles drawn on land use/land cover change map over land surface temperature map revealed that the land surface temperature has increased in those areas which were transformed from open forest to paddy, open forest to settlement, paddy to settlement and deposition to settlement. The study demonstrated that increase in non-evaporating surfaces and decrease in vegetation have increased the surface temperature and modified the temperature of the study area.

关键词： Land use changes Land surface temperature split window algorithm Spectral radiance model Sundarban Biosphere Reserve

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Land Surface Temperature retrieval from Landsat 8 TIRS-comparison between split window algorithm and SEBAL method 3

Land Surface Temperature retrieval from Landsat 8 TIRS-compa...

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3rd International Conference on Remote Sensing and Geoinformation of the Environment (RSCy)

作者： Kamran, Khalil Valizadeh Pirnazar, Mojtaba Bansouleh, Vida Farhadi Univ Tabriz GIS & RS Dept Tabriz East Azarbayjan Iran

ISBN: (纸本)9781628417005

Land surface temperature (LST) is a key parameter in the physics of the earth surface through the process of energy and water exchange with the atmosphere, which plays an important role in a wide variety of scientific studies, such as ecology, hydrology, and global change studies. Thermal infrared (TIR) remote sensing provides a unique method for obtaining LST information at the regional and global scales since most of the energy detected by the sensor in this spectral region is directly emitted by the land surface. Landsat 8 Thermal Infrared Sensor (TIRS), because of two adjacent thermal bands, band 10 between 10.30-11.30 mu m and band 11 between 11.30-12.30 mu m, and availability of its images is a good source for retrieving LST. In this study, we compared two different methods for LST inversions from TIRS including the split window method and the SEBAL method for Tehran, Iran.. Results show that the LST inverted from the split window method has the accuracy with RMSE lower than 1.17 degrees C, while the SEBAL method has the accuracy with 3.27 degrees C, so the split window algorithm is appropriate method for this study area.

关键词： Land Surface Temperature TIRS split window algorithm SEBAL Tehran

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Comparing different space-borne sensors and methods for the retrieval of land surface temperature

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EARTH SCIENCE INFORMATICS 2021年第2期14卷 985-995页

作者： Rehman, Arif U. R. Ullah, Sami Liu, Qijing Khan, Muhammad Sadiq Beijing Forestry Univ Coll Forestry Beijing 100083 Peoples R China Shaheed Benazir Bhutto Univ Sheringal Dept Forestry Dir 18050 Pakistan Univ Peshawar Natl Ctr GIS & Space Applicat GIS & Space Applicat Geosci G SAG Lab NCE Geol Peshawar Pakistan Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and split window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 degrees C, followed by MODIS with RMSE = 0.36 degrees C, and SCB10 with RMSE = 0.38 degrees C. Secondly, SCmean (Mean of B-10 and B-11), and RTEmean (Mean of B-10 and B-11) generate nearly the same accuracy with RMSE = 0.53 degrees C, and RMSE = 0.54 degrees C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 degrees C, RMSE = 0.88 degrees C, and RMSE = 0.91 degrees C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

关键词： Landsat-8 TIRS MODIS Radiative transfer equation Single Channel method split window algorithm

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Urban Heat Island Monitoring and Impacts on Citizen's General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach

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REMOTE SENSING 2020年第8期12卷 1350-1350页

作者： Mirzaei, Mohsen Verrelst, Jochem Arbabi, Mohsen Shaklabadi, Zohreh Lotfizadeh, Masoud Malayer Univ Res Inst Grapes & Raisin RIGR Environm Pollut Grape Environm Sci Dept Malayer *** Iran Univ Valencia Image Proc Lab IPL Parc Cient Valencia 46980 Spain Shahrekord Univ Med Sci Sch Hlth Dept Environm Hlth Engn Shahrekord *** Iran Shahrekord Univ Med Sci Social Determinants Hlth Res Ctr Shahrekord *** Iran

Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens' health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens' health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 +/- 2.3 degrees C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens' health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education.

关键词： urban heat island land surface temperature split window algorithm general health questionnaire-28 Isfahan metropolis

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Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval algorithms and Emissivity Models and Toolbox Implementation

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REMOTE SENSING 2020年第2期12卷 294-294页

作者： Sekertekin, Aliihsan Bonafoni, Stefania Cukurova Univ Dept Geomat Engn TR-01950 Ceyhan Adana Turkey Univ Perugia Dept Engn Via G Duranti 93 I-06125 Perugia Italy

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono window algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel algorithm (SCA) and split window algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images;fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMS

关键词： land surface temperature (LST) land surface emissivity (LSE) performance evaluation Mono window algorithm radiative transfer equation Single channel algorithm split window algorithm Landsat SURFRAD data

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Comparison of the accuracy of daytime land surface temperature retrieval methods using Landsat 8 images in arid regions

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INFRARED PHYSICS & TECHNOLOGY 2021年 115卷 103692-103692页

作者： Aliabad, Fahime Arabi Zare, Mohammad Malamiri, Hamidreza Ghafarian Yazd Univ Fac Nat Resources & Desert Stud Dept Arid Lands Management Yazd Iran Yazd Univ Dept Geog Yazd Iran

Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5. different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 degrees C. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2-3, 3-4 and more than 5 degrees C Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2. in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 degrees C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 degrees C In general, the algorithms that use atmospheric transmitt

关键词： Mono window algorithm Single channel algorithm split window algorithm Validation

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