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Evaluation of Land Surface Temperature Retrieval from Landsat 8/TIRS Images before and after Stray Light Correction Using the SURFRAD Dataset

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REMOTE SENSING 2020年第6期12卷 1023-1023页

作者： Guo, Jinxin Ren, Huazhong Zheng, Yitong Lu, Shangzong Dong, Jiaji Peking Univ Sch Earth & Space Sci Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Peking Univ Beijing Key Lab Spatial Informat Integrat & Its A Beijing 100871 Peoples R China

Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval.

关键词： land surface temperature Landsat 8 stray light correction split-window algorithm single-channel algorithm

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HIGH TEMPORAL RESOLUTION LAND SURFACE TEMPERATURE RETRIEVAL FROM GLOBAL GEOSTATIONARY SATELLITE DATA 39

HIGH TEMPORAL RESOLUTION LAND SURFACE TEMPERATURE RETRIEVAL ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Ruibo Li, Hua Bian, Zunjian Cao, Biao Du, Yongming Sun, Lin Liu, Qinhuo Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Shandong Univ Sci & Technol Geomat Coll Qingdao 266590 Peoples R China

ISBN: (纸本)9781538691540

In this paper, in order to produce long term fully global land surface temperature (LST) product, the generalized split-window (GSW) algorithm and dual-window (DW) algorithm was used to retrieve LST from different geostationary (GEO) satellite data, including the FY-2E/4A, MTSAT-2/Himawari-8, MSG2, and GOES13/15. First, the coefficients of the GSW and DW algorithm were obtained from a simulation database constructed using the MODTRAN 5.2 and the SeeBor V5.0 atmospheric profile database. Second, the emissivity was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset (GED). Finally, the LST results of FY-4A AGRI and Himawari-8 AHI were retrieved and cross-validated. The results show that the LST algorithms developed in this work are capable of generating accurate high temporal resolution LST retrieval from global GEO satellite data.

关键词： geostationary satellite land surface temperature split-window algorithm land surface emissivity cross-validation

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Systematic modeling of impacts of land-use and land-cover changes on land surface temperature in Adama Zuria District, Ethiopia

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2019年第3期5卷 805-817页

作者： Tafesse, Belete Suryabhagavan, K. V. Addis Ababa Univ Sch Earth Sci POB 1176 Addis Ababa Ethiopia

Land surface temperature (LST) has been increasing year after year globally. The present investigation was aimed to study the impact of land-use/land-cover changes on LST in the Adama Zuria District in Ethiopia. Land-use/land-cover, LST and Normalized Difference Vegetation Index were extracted from Landsat TM (1989), Landsat ETM + (1999) and Landsat 8 OLI/TIRS (2016) using GIS and remote sensing tools. Land surface temperature was assessed using split window algorithm. Land-use/land-cover changes that occurred during 1989-2016 in the study area were evaluated and analyzed using geospatial tools and verified by field data. Results indicated that farmland covered more than 60% during the study period (1989-2016) followed by shrub land (>12%). Most areas with lower LST in 1989 were changed to higher LST in 1999 and 2016 in response to different changes in land-use/land-cover pattern. By linking land-use/land-cover pattern changes and LST using zonal statistics, LST is found to have negative relationship with the extent of vegetation cover. Land surface temperature results showed that the northwestern, south, lake Koka area and along Awash river relatively low LST that ranged between 9 and 21 degrees C in response to the high NDVI values. The eastern, Adama town and western part of the study area showed high LST of up to 42 degrees C. Visual comparison of 1989, 1999 and 2016 images showed that the land-use/land-cover type and NDVI status play a major role for the variability of LST values. Correlation between LST and land-use changes has indicated that changes to settlement/urban land-use/land-cover have influenced LST proportionately. Relevant measures are to be taken by the bodies concerned to minimize land-use/land-cover changes to gain effective control over increasing LST in the study area.

关键词： Land-use land-cover Land surface temperature NDVI split-window algorithm

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Land Surface Temperature Retrieval of the Geothermal Area in Eastern Liaoning, China, Based on Thermal Infrared Remotely Sensed Data of MODIS

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2018年第7期46卷 1023-1034页

作者： He, Jinxin Zheng, Bofu Li, Wenqing Yang, Linmao Sun, Huanzhao Jilin Univ Coll Earth Sci Changchun 130061 Jilin Peoples R China

With the depletion of fossil fuel energy, geothermal resource exploration has become an important research hotspot around the world. Thermal infrared remote sensing technology is a new trend in the field of geothermal resources investigation and exploration, with the advantages of quick, economical study of large areas that have variations in geothermal resources and heat distribution. Based on the MODIS thermal infrared remotely sensed data, the split-window algorithm was used to retrieve the surface temperature of the geothermal region in Eastern Liaoning, China. The temperature inversion images at four seasons during the year of 2016 were selected, and the high temperature zones in the inversion results were compared with the geothermal features and fault structures in the study area. It was found that the spatial distribution relationships were highly similar. In conclusion, the distribution of geothermal resources can be inferred by analyzing the inversion results of regional temperatures and geological structures.

关键词： Geothermal characteristics Land surface temperature Thermal infrared remote sensing MODIS split-window algorithm

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Estimation of Land Surface Temperature Using FengYun-2E (FY-2E) Data: A Case Study of the Source Area of the Yellow River

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第8期10卷 3744-3751页

作者： Song, Xiaoning Wang, Yawei Tang, Bohui Leng, Pei Chuan, Sun Peng, Jian Loew, Alexander Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Ludwig Maximilian Univ Munich LMU Dept Geog D-80337 Munich Germany Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100864 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agriinformat Minist Agr Beijing 100081 Peoples R China

Land surface temperature (LST) is a key variable used for studies of water cycles and energy budgets of land-atmosphere interfaces. This study addresses the theory of LST retrieval from data acquired by the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) in two thermal infrared channels (IR1: 10.29-11.45 mu m and IR2: 11.59-12.79 mu m) using a generalized split-window algorithm. Specifically, land surface emissivity (LSE) in the two thermal infrared channels is estimated from the LSE in channels 31 and 32 of the moderate-resolution imaging spectroradiometer (MODIS) product. In addition, an eight-day composition MODIS LSE product (MOD11A2) and the daily MODIS LSE product (MOD11A1) are used in the algorithm to estimate FY-2E emissivities. The results indicate that the LST derived from MOD11A1 is more accurate and, therefore, more appropriate for daily cloud-free LST estimation. Finally, the estimated LST was validated using the MODIS LST product for the heterogeneous source area of the Yellow River. The results show a significant correlation between the two datasets, with a correlation coefficient (R) varying from 0.60 to 0.94 and a root mean square error ranging from 1.89 to 3.71 K. Moreover, the estimated LST agrees well with ground-measured soil temperatures, with an R of 0.98.

关键词： FengYun-2E (FY-2E) land surface temperature (LST) moderate-resolution imaging spectroradiometer (MODIS) split-window algorithm

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Angular effect of MODIS emissivity products and its application to the split-window algorithm

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2011年第4期66卷 498-507页

作者： Ren, Huazhong Yan, Guangjian Chen, Ling Li, Zhaoliang Beijing Normal Univ State Key Lab Remote Sensing Sci Sch Geog Beijing 100875 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed. By performing a statistical analysis of 5-year MODIS emissivity products over most of East Asia, we generated the empirical relationships between the directional emissivity, land cover, and seasonal variations. Two look-up tables (LUTs) of directional emissivity were created for typical land cover types and applied to the generalized split-window algorithm to modify the MODIS LST. The results showed that the angular effect of emissivity could introduce a significant bias of -1-3 K to the 1 km resolution LST. Finally, the spatial scale effects of emissivity were analyzed, and it was found that the temperature differences caused by scale effects fell within +/-0.5 K for most pixels if 5 km emissivity was used in 1 km LST retrieval. Therefore, wide use of the LUTs can be expected. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： MODIS emissivity Angular effects split-window algorithm

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VALIDATION OF LANDSAT-8 TIRS LAND SURFACE TEMPERATURE RETRIEVED FROM MULTIPLE algorithmS IN AN EXTREMELY ARID REGION 36

VALIDATION OF LANDSAT-8 TIRS LAND SURFACE TEMPERATURE RETRIE...

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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Yujia Zhou, Ji Li, Mingsong Zhang, Xiaodong Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

ISBN: (纸本)9781509033324

With the rapid development of new satellite thermal sensors and applications of land surface temperature (LST), research on finding effective algorithms to retrieve accurate LST from satellite thermal infrared (TIR) data is becoming more and more important. In this study, multiple algorithms for retrieving LST from Landsat-8 Thermal Infrared Sensor (TIRS) data are validated and intercompared in an extremely arid region, Northwest China. According to the validation and intercomparison, we find that the radiative transfer equation (RTE) based method with TIRS band 1 (10.6-11.19 mu m) has the highest accuracy, while the single-channel (SC) method using TIRS band 2 (11.50-12.51 mu m) yielded the lowest accuracy. The accuracies of split-window (SW) algorithms are slightly lower than the RTE based method. However, the SW algorithms have better applicability than the RTE based method. The most suitable SW algorithm for Landsat-8 TIRS data in the study area is recommended. This study will be beneficial for developing the LST product from Landsat-8 data for the study area.

关键词： Land surface temperature (LST) thermal infrared remote sensing split-window algorithm radiative transfer equation Landsat8-TIRS arid region

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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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Retrieval of land surface temperature from Landsat 8 TIRS for the command area of Mula irrigation project

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ENVIRONMENTAL EARTH SCIENCES 2016年第16期75卷 1169-1169页

作者： Nikam, Bhaskar R. Ibragimov, Furkat Chouksey, Arpit Garg, Vaibhav Aggarwal, S. P. Indian Space Res Org Govt India Indian Inst Remote Sensing Water Resources Dept Dehra Dun 248001 Uttarakhand India Ctr Space Sci & Technol Educ Asia & Pacific CSSTE Dehra Dun 248001 Uttarakhand India

Application of satellite remote sensing in generating various geophysical parameters of land surface has gained tremendous importance in many branches of science and applied research. Out of all geophysical parameters of land surface derived from satellite remote sensing, the land surface temperature (LST) is of prime importance. LST acts as a governing parameter in water and energy exchange between land and atmosphere. It is an essential input to all the numerical weather prediction models, most of the process-based hydrological models and even in irrigation water management activities. Many algorithms for space-based LST retrieval and operational products of LST are available nowadays. However, these products are generally coarser in spatial and/or temporal resolutions. In the sector of irrigation water management, higher spatial resolution of all the geophysical products is prerequisites. Hence, in the present study, LST has been retrieved using two popular algorithms viz. radiative transfer theory (RTT) equation-based method and split-window (SW) algorithm. LST of Mula irrigation project command area has been retrieved using twenty images of Landsat 8 Thermal Infrared Sensor (TIRS) and Operation Land Imager (OLI) for entire Rabi/Winter season. Atmospheric parameters needed for the LST retrieval have been derived using online atmospheric correction tool. The land surface emissivity has been estimated using NDVI threshold technique. The standard daily LST product of MODIS has been used to cross-validate the LST retrieved from both the algorithms. LST retrieved using SW algorithm shows higher correlation coefficient with MODIS LST in entire command area and in agricultural land as well (0.938 and 0.925, respectively) compared to that of LST output of RTT method (0.902 and 0.894, respectively). The relative comparison of the LST products derived from RTT and SW algorithms showed that there is an average difference of +/- 3 degrees K between these two products. Howev

关键词： Land surface temperature split-window algorithm Land surface emissivity Landsat 8 TIRS MODIS

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Improvements of a COMS Land Surface Temperature Retrieval algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

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REMOTE SENSING 2015年第2期7卷 1777-1797页

作者： Cho, A-Ra Choi, Youn-Young Suh, Myoung-Seok Natl Inst Environm Res Dept Climate & Air Qual Res Inchon 404708 South Korea Kongju Natl Univ Dept Atmospher Sci Gongju Si 314701 Chungcheongnam South Korea

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying "dry," "normal," and "wet" cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of -0.03 K from 0.00K;the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year's MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from -1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K).

关键词： COMS land surface temperature MODIS split-window algorithm

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