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Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

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REMOTE SENSING 2022年第18期14卷 4444-4444页

作者： Adeniran, Ibrahim Ademola Zhu, Rui Yang, Jinxin Zhu, Xiaolin Wong, Man Sing Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Guangzhou Univ Sch Geog & Remote Sensing Guangzhou 510006 Peoples R China

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWA(L8)) and split-window algorithm (SWA(L8)) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWA(S3) and SWA(H8)). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to -1.80 K when SWA(H8) was compared with MWA(L8), despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an arra

关键词： land surface temperature mono-window algorithm split-window algorithm Landsat-8 Himawari-8

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New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

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International Journal of Digital Earth 2020年第12期13卷 1430-1453页

作者： Huazhong Ren Jiaji Dong Rongyuan Liu Yitong Zheng Jinxin Guo Shanshan Chen Jing Nie Yan Zhao Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijingPeople’s Republic of China China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingPeople’s Republic of China China Translation Telecommunications and Information Center BeijingPeople’s Republic of China

Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes *** overcome this problem,a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input,by combining the split-window(SW)and temperature–emissivity separation(TES)*** SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands,and such radiance is applied to the TES algorithm to retrieve LST and *** hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER).Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020,***,the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm,involving 1%–2%uncertainty in atmospheric *** hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products,indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground ***,the hybrid algorithm is applied to at two places.

关键词： Land surface temperature and emissivity TES algorithm split-window algorithm hybrid algorithm

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Relationship between land surface temperature and urbanization in Vilnius district

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2021年第8期193卷 472-472页

作者： Mirsanjari, Mir Mehrdad Mohammadyari, Fatemeh Visockiene, Jurate Suziedelyte Zarandian, Ardavan Malayer Univ Dept Environm Sci Malayer Iran Malayer Univ Fac Nat Resources Malayer Iran Vilnius Gediminas Tech Univ Dept Geodesy & Cadaster Sauletekio Av 11 LT-10223 Vilnius Lithuania Res Ctr Environm & Sustainable Dev RCESD Dept Environm Tehran Iran

The present study aims to evaluate the effect of vegetation on land surface temperature (LST) in different land uses and covers in Vilnius district in 1999 and 2019. To that end, in addition to mono-window and split-window algorithms that help estimate the LST, the variables digital elevation model (DEM), slope, heat load index (HLI), distances from the road and the water, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) affecting the surface temperature were used. Furthermore, the random forest regression (RFR) method was applied to evaluate the effect of the mentioned variables on the LST. The performance model was also assessed by using the mean absolute (MAE), mean squared (MSE), and root mean square error (RMSE). Based on the results, NDVI and NDWI indexes had the greatest impact on the temperature of Vilnius city, respectively. The study area images were categorized as built-up area, cropland, semi-forest land, dense forest land, water bodies, pastures, and green urban areas. It was found that the pastures in 1999 and the built-up class in 2019 received the highest temperature from the land surface and that the classes characterized by natural land cover such as forest land and agricultural and water bodies had a relatively low surface temperature. NDVI response curves in both 1999 and 2019 indicated that the higher the density of vegetation on the land surface, the lower the surface temperature. A lower rate of urbanization, a higher density of vegetation and consequently, a lower the temperature of the land surface were recorded for 1999 in comparison with 2019. Therefore, urbanization was demonstrated to play a significant role in changes in LULC and the increase in LST.

关键词： Land surface temperature split-window algorithm Mono-window algorithm Random forest regression NDVI Land use

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Validation of Sentinel-3 SLSTR Land Surface Temperature Retrieved by the Operational Product and Comparison with Explicitly Emissivity-Dependent algorithms

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REMOTE SENSING 2021年第11期13卷 2228-2228页

作者： Perez-Planells, Lluis Niclos, Raquel Puchades, Jesus Coll, Cesar Gottsche, Frank-M. Valiente, Jose A. Valor, Enric Galve, Joan M. Univ Valencia Fac Phys Dept Earth Phys & Thermodynam 50 Dr Moliner E-46100 Burjassot Spain Karlsruhe Inst Technol KIT IMK ASF Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Fdn Ctr Estudios Ambientales Mediterraneo CEAM 14 Charles R Darwin E-46980 Paterna Spain Univ Castilla La Mancha Inst Reg Dev GIS & Remote Sensing Grp Campus Univ SN E-02071 Albacete Spain

Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of -0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product.

关键词： emissivity LST SLSTR split-window algorithm in-situ validation

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An Approach for the Retrieval of Land Surface Temperature from the Industrial Area Using Landsat-8 Thermal Infrared Sensors 10

An Approach for the Retrieval of Land Surface Temperature fr...

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10th Institution-of-Geospatial-and-Remote-Sensing-Malaysia(IGRSM) International Conference and Exhibition on Geospatial and Remote Sensing (IGRSM)

作者： Dahiru, M. Z. Hashim, Mazlan Univ Teknol Malaysia Geosci & Digital Earth Ctr INSTEG Res Inst Sustainable Environm RISE Johor Baharu Malaysia Univ Teknol Malaysia Fac Geoinformat & Real Estate Johor Baharu Malaysia Adamawa State Polytech Yola Adamawa State Nigeria

The thermal imagine provides data with synoptic coverage for investigating thermal information from hot sources for detecting, mapping energy loss from the industrial area. This study attempts to retrieved heat loss from the industrial area using Landsat-8 TIRS experimented at an industrial area of Pasir Gudang, Peninsular Malaysia, the main objective is to investigate the sensitivity of Landsat-8 TIR for detecting industrial thermal energy within the various range of targets of different temperatures. An estimated heat map with absolute surface temperature values is the final output. Apart of the pre-processing of Landsat-8 TIRS data, data are processed for the retrieval of land surface temperature, then subjected to a downscaling process to final 30 x 30 m pixels, hence enable to merge with all Landsat-8 bands for visualization and validation of results. The split window algorithm (SWA) is used for the temperature retrieval from band 10 and 11, with other driven parameters. The Multiple Adaptive Regression Splines (MARS) model for spatial downscaling was adopted in this study. The generated thermal energy map was then validated over selected targets in the field and compared to corresponding downscaled MODIS LST product (MODIS11A2). TIR bands applied with SWA generated 13.7 degrees C temperature dynamic range from 22.35 similar to 51.36 degrees C in comparison with MODIS LST product values range from 27.17 similar to 37.65 degrees C). Results indicated good agreement between the generated thermal energy map with the in-situ validations (RMSE=0.43 degrees C). It is therefore concluded that derived Land surface temperature map derived is suitable for study industrial thermal environment at 1:50,000 degrees 100,000 scales, adequately to be used for environmental impact assessment.

关键词： Remote sensing split-window algorithm downscaling

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Bi-Spectral Infrared algorithm for Cloud Coverage over Oceans by the JEM-EUSO Mission Program

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SENSORS 2021年第19期21卷 6506-6506页

作者： Santalices, David Briz, Susana de Castro, Antonio J. Lopez, Fernando Univ Carlos III Madrid Lab Infrared Av Univ 30 Madrid 28911 Spain

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrat

关键词： remote sensing infrared camera cloud coverage split-window algorithm JEM-EUSO

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Cross-calibration of Chinese Gaofen-5 thermal infrared images and its improvement on land surface temperature retrieval

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 101卷 102357-102357页

作者： Ye, Xin Ren, Huazhong Liang, Yanzhen Zhu, Jinshun Guo, Jinxin Nie, Jing Zeng, Hui Zhao, Yanhua Qian, Yonggang Peking Univ Inst Remote Sensing & Geog Informat Syst Sch Earth & Space Sci Beijing 100871 Peoples R China Peking Univ Beijing Key Lab Spatial Informat Integrat & Its A Beijing 100871 Peoples R China China Natl Space Adm Earth Observat Syst & Data Ctr Beijing 100101 Peoples R China Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China Chinese Acad Sci Acad Optoelect Key Lab Quantitat Remote Sensing Informat Technol Beijing 100094 Peoples R China

Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new crosscalibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.

关键词： Land surface temperature Radiometric calibration split-window algorithm Residual optimization Gaofen-5 satellite

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The GLASS Land Surface Temperature Product

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第2期12卷 493-507页

作者： Zhou, Ji Liang, Shunlin Cheng, Jie Wang, Yujia Ma, Jin Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China Beijing Normal Univ State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Beijing Normal Univ Fac Geog Sci Beijing 100875 Peoples R China Univ Maryland Dept Geog Sci College Pk MD 20742 USA

Land surface temperature (LST) is one of the most important geophysical parameters at the earth's surface. Satellite LST products are beneficial for scientific communities with a variety of applications. The Global Land Surface Satellite (GLASS) LST product has been generated for 1983, 1993, 2003, and 2013 from historical NOAA-7 and NOAA-14 AVHRR data and recent Terra/Aqua MODIS data. The spatial resolution of the instantaneous GLASS LST product is 0.05 degrees for 1983 and 1993 and is 1 km for 2003 and 2013. The algorithm for generating the GLASS LST product is based on a multialgorithm ensemble approach, which combines nine split-window algorithms with good performance in training, testing, and sensitivity analysis. The validation results based on in situ measurements demonstrate that the GLASS-AVHRR LST product has an accuracy of 2.89 K at Barrow site;the GLASS-MODIS LST product has an accuracy of 1.82-2.15 K at six grassland/cropland SURFRAD sites at nighttime, which is a similar accuracy to the official MODIS LST product. Systematic underestimation of LST at Desert Rock, NV (arid shrubland) has been found and has been attributed to uncertainties associated with land surface emissivities and other input datasets. Further intercomparison indicates that the GLASS-MODIS LST product agrees well with the official MODIS LST, with mean bias deviations/root mean square deviations of -1.19 K/1.87 K and 1.89 K/2.27 K for the two granules under examination. Nevertheless, the validation of the GLASS LST product is currently in a preliminary stage, and more in-depth examinations are still needed .

关键词： Global Land Surface Satellite (GLASS) land surface temperature (LST) National Oceanic and Atmospheric Administration (NOAA) AHVRR split-window algorithm Terra/Aqua MODIS

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Analysis of land surface temperature trend and climate resilience challenges in Tehran

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2019年第12期16卷 8585-8594页

作者： Tayebi, S. Mohammadi, H. Shamsipoor, A. Tayebi, S. Alavi, S. A. Hoseinioun, S. Univ Tehran Fac Geog Dept Phys Geog Tehran Iran Islamic Azad Univ Sci & Res Branch Fac Nat Resources & Environm Dept Environm Law Tehran Iran Shahid Beheshti Univ RS & GIS Ctr Remote Sensing & GIS Tehran Iran Univ Melbourne Melbourne Vic Australia

Urban heat island is one of the most important consequences of climate change in cities. Various factors accelerate temperature increase in urban surfaces. Widespread use of impermeable materials with high heat capacity;albedo and trapping of incoming solar radiation in urban structure which prevents reflection;reduction in cooling at night due to increased building heights;and changes in the complex urban geometry are among these factors. The aim of this study is to investigate the surface temperature of Tehran in a 30-year period (1984-2014). The required images and land surface temperature maps were prepared using a split-window algorithm on thermal bands of landsat images for four periods. All images had been taken at 11:00-14:00 in summer. Temperature factors were calculated for different neighborhoods, and two patterns were obtained. One pattern showed the formation of urban heat island in barren lands, large workshop areas, and areas with military land use, and the other one showed the formation of urban heat island in dense building population areas with limited green space. The results indicate an increase in the area of the regions with a higher temperature in the city. In 1984, the smallest area belonged to the above 48 degrees C temperature category (1.8 ha), while in 2014 the below 25 degrees C category had the smallest area (0 ha). It was observed that the area of the former category reached over 1740 ha in 2014. Neighborhood temperature factors show that 17 Shahrivar and Khajeh Nasir-Hoghoughi neighborhoods are the hottest neighborhoods in Tehran. The findings in this study may persuade urban managers scientifically to make preparations for changing the life style focusing on urban macro-scale planning which is a widely emerging issue resulted from recent effects on Tehran's climate resilience. Considering the importance of urban planning priorities in urban ecosystem services, it is essential to make use of all urban elements and fill the empty gap b

关键词： Surface temperature Urban heat island split-window algorithm Tehran neighborhoods Urban climate resilience

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Validation of Collection 6 MODIS land surface temperature product using in situ measurements

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REMOTE SENSING OF ENVIRONMENT 2019年 225卷 16-29页

作者： Duan, Si-Bo Li, Zhao-Liang Li, Hua Goettsche, Frank-M Wu, Hua Zhao, Wei Leng, Pei Zhang, Xia Coll, Cesar Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr Beijing 100081 Peoples R China Hebei GEO Univ Sch Land Resources & Urban Rural Planning Shijiazhuang 050031 Hebei Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China KIT Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu 610041 Sichuan Peoples R China Univ Valencia Fac Phys Dept Earth Phys & Thermodynam Burjassot 46100 Spain

Land surface temperature (LST) is an important physical quantity at the land-atmosphere interface. Since 2016 the Collection 6 (C6) MODIS LST product is publicly available, which includes three refinements over bare soil surfaces compared to the Collection 5 (C5) MODIS LST product. To encourage the use of the C6 MODIS LST product in a wide range of applications, it is necessary to evaluate the accuracy of the C6 MODIS LST product. In this study, we validated the C6 MODIS LST product using temperature-based method over various land cover types, including grasslands, croplands, cropland/natural vegetation mosaic, open shrublands, woody savannas, and barren/sparsely vegetated. In situ measurements were collected from various sites under different atmospheric and surface conditions, including seven SURFRAD sites (BND, TBL, DRA, FPK, GCM, PSU, and SXF) in the United States, three KIT sites (EVO, KAL, and GBB) in Portugal and Namibia, and three HiWATER sites (GBZ, HZZ, and HMZ) in China. The spatial representativeness of the in situ measurements at each site was separately evaluated during daytime and nighttime using all available clear-sky ASTER LST products at 90 m spatial resolution. Only six sites during daytime are selected as sufficiently homogeneous sites despite the usually high spatial thermal heterogeneity, whereas during nighttime most sites can be considered to be thermally homogeneous and have similar LST and air temperature. The C6 MODIS LST product was validated using in situ measurements from the selected homogeneous sites during daytime and nighttime: except for the GBB site, large RMSE values ( > 2 K) were obtained during daytime. However, if only satellite LST with a high spatial thermal homogeneity on the MODIS pixel scale are used for LST validation, the best daytime accuracy (RMSE < 1.3 K) for the C6 MODIS LST product is achieved over the BND and DRA sites. Except for the DRA site, the RMSE values during nighttime are < 2 K at the selected homogeneou

关键词： Land surface temperature MODIS Temperature-based validation method split-window algorithm In situ measurements

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