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Towards an Operational, split window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9

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REMOTE SENSING 2020年第2期12卷 224-224页

作者： Gerace, Aaron Kleynhans, Tania Eon, Rehman Montanaro, Matthew Rochester Inst Technol 54 Lomb Mem Dr Rochester NY 14624 USA

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

关键词： Landsat land surface temperature split window algorithm TIRS thermal

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Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data

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REMOTE SENSING 2020年第4期12卷 627-627页

作者： Liu, Mengdi Yin, Xiaobin Xu, Qing Chen, Yuxiang Wang, Bowen Hohai Univ Coll Oceanog Nanjing 210098 Peoples R China Beijing PIESAT Informat Technol Co Ltd Beijing 100195 Peoples R China Hohai Univ Key Lab Coastal Disaster & Def Minist Educ Nanjing 210098 Peoples R China Chinese Acad Fishery Sci South China Sea Fisheries Res Inst Guangzhou 510300 Peoples R China Beijing Natl Day Sch Beijing 10039 Peoples R China

Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono window algorithm (MW) and the split window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1-4 degrees C higher than the surrounding water and exceeds 6 degrees C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that durin

关键词： Landsat 8 destriping warm drain-off split window algorithm tide

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Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

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ARABIAN JOURNAL OF GEOSCIENCES 2018年第14期11卷 391-391页

作者： Zareie, Sajad Rangzan, Kazem Khosravi, Hassan Sherbakov, Vladimir Modestovich St Petersburg State Univ Inst Earth Sci St Petersburg Russia ShahidChamran Univ Fac Earth Sci Ahvaz Iran Univ Tehran Dept Arid & Mountainous Reg Reclamat Fac Nat Resources Coll Agr & Nat Resources Karaj Iran

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 degrees C, and Vidal's method presented the lowest accuracy to derive LST with RMSE 4.11 degrees C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.

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

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EVALUATION OF THE MUSYQ LAND SURFACE TEMPERATURE PRODUCT IN AN ARID AREA OF NORTHWEST CHINA 39

EVALUATION OF THE MUSYQ LAND SURFACE TEMPERATURE PRODUCT IN ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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

ISBN: (纸本)9781538691540

In this study, we present an operational algorithm to retrieve the land surface temperature (LST) from MODIS thermal infrared data using physically retrieved emissivity product. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. First, the emissivity in the MODIS two split window channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity dataset. Then, the LST was retrieved using a modified generalized split -window algorithm. The MuSyQ MODIS LST product and the C6 MxD11 LST product were evaluated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces.

关键词： MODIS land surface temperature split window algorithm land surface emissivity evaluation

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Comparison of Three algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

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SENSORS 2019年第22期19卷 5049-5049页

作者： Wang, Lei Lu, Yao Yao, Yunlong Northeast Forestry Univ Coll Wildlife Resources Harbin 150040 Heilongjiang Peoples R China Key Lab Garden Plant Germplasm Dev & Landscape Ec Harbin 150040 Heilongjiang Peoples R China Heilongjiang Univ Sci & Technol Coll Architectural Engn Harbin 150022 Heilongjiang Peoples R China

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

关键词： Landsat 8 TIR data land surface temperature mono-window algorithm split window algorithm single-channel method sensitivity analysis

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Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data

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SENSORS 2018年第2期18卷 376-376页

作者： Hu, Yuanyuan Zhong, Lei Ma, Yaoming Zou, Mijun Xu, Kepiao Huang, Ziyu Feng, Lu Univ Sci & Technol China Sch Earth & Space Sci Lab Atmospher Observat & Climate Environm Res Hefei 230026 Anhui Peoples R China Chinese Acad Sci Inst Tibetan Plateau Res Key Lab Tibetan Environm Changes & Land Surface P Beijing 100101 Peoples R China CAS Ctr Excellence Tibetan Plateau Earth Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Meteorol Adm Inst Trop & Marine Meteorol Guangzhou 510080 Guangdong Peoples R China

During the process of land-atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li's SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is t

关键词： FengYun 2C split window algorithm land surface temperature the Tibetan Plateau

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LAND SURFACE TEMPERATURE RETRIEVED FROM COMBINED MID-INFRARED AND THERMAL INFRARED DATA 37

LAND SURFACE TEMPERATURE RETRIEVED FROM COMBINED MID-INFRARE...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Qian, Yong-Gang Li, Kun Wang, Ning Ma, Ling-Ling Liu, Yao-Kai Li, Wei Ren, Lu Qiu, Shi Li, Chuan-Rong Tang, Ling-Li Chinese Acad Sci Acad Optoelect Key Lab Quantitat Remote Sensing Informat Technol Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781509049516

This paper addressed the retrieval of land surface temperature (LST) from combined mid-infrared and thermal infrared data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP). To efficiently remove the effect of the direct solar radiance, a relationship between direct solar radiance and water vapor content, view zenith angle and solar zenith angle is proposed to improve the retrieve accuracy. Then, a split-window algorithm from combined mid-infrared and thermal infrared data is used to correct for the atmospheric effects and retrieve the LST with the aid of emissivity provide by VIIRS product. Finally, comparison of the standard VIIRS LST product and the retrieved LST from the proposed algorithm, a good agreement was shown. Analysis indicated the root mean square error (RMSE) of the LST over these land cover types is 2.04K for desert and 1.84K for vegetation, respectively.

关键词： Temperature mid-infrared and thermal infrared split window algorithm

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Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

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REMOTE SENSING 2015年第9期7卷 12215-12241页

作者： Liu, Yuling Yu, Yunyue Yu, Peng Goettsche, Frank M. Trigo, Isabel F. Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20740 USA NOAA NESDIS Ctr Satellite Applicat & Res College Pk MD 20740 USA Karlsruhe Inst Technol D-76344 Eggenstein Leopoldshafen Germany IPMA P-1749077 Lisbon Portugal

The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of -0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation;(2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type;(3) The VIIRS LST quality can be degraded when significant brightness temperature difference between the two split window channels is observed;(4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type.

关键词： VIIRS LST EDR split window algorithm surface type dependency LST uncertainty

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Analysis of principal elements of land surface temperature retrieval from AVHRR over Tibetan Plateau 17

Analysis of principal elements of land surface temperature r...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII part of the International Symposium on Remote Sensing

作者： Huang, Qingni Cao, Guangzhen Dong, Lixin China Meteorol Adm Natl Satellite Meteorol Ctr Key Lab Radiometr Calibrat & Validat Environm Sat Beijing 100081 Peoples R China

ISBN: (纸本)9781628418477

Recently, Tibetan plateau (TP) has become a hot area of climate change research. And Land Surface Temperature (LST) is one of key factors in the research. In order to get a long time-series, high spatial resolution and high accuracy LST dataset, we carried out analysis of influence essential factor of LST retrieval from AVHRR oriented Tibetan plateau area. First, choose MODTRAN5.2 to simulate the impact of land surface, atmospheric, geometric factors on bright temperatures of channel 4 and channel 5 for special features of TP using stand atmospheric models. Result showed that emissivity, boundary temperature, water vapor amount and view zenith angle were the principal elements of bright temperature. Second an improved algorithm from Wanz-Dozier split window model was established considering these factors. At last, differences between LST retrieval result considering different factors were given.

关键词： Land Surface Temperature retrieval Tibetan plateau split window algorithm

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Research on the Retrieval of Land Surface Temperature based on MODIS Data

Research on the Retrieval of Land Surface Temperature based ...

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5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Wang, Kang Li, Liangchao Guo, Yanni Jin, Lanhai Xidian Univ Sch Sci Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781467360791;9781467360777

Land Surface Temperature(LST) is a key parameter of the research on regional and global ground surface physical process, which synthetically reflects the result of energy and material exchange ([1]).In the interaction and energy exchange between land surface and atmosphere, LST is also the crucial factor of the Land Surface Energy Balance, which is affected by the Surface Radiation Balance and has important application value and plays an important significance on the ecological environment, agricultural meteorology, crop yield estimation, drought monitoring, disaster monitoring, urban environment and hydrological characteristics etc. This paper utilizes the split-window algorithm and related theories to retrieve the land surface temperature of southeast coast of China based on the MODIS data. MODIS 1B data of Terra satellite at 13:19 28/09/2011 is selected. The study area is between 21.0 degrees N similar to 23.0 degrees N and 117.0 degrees E similar to 123.0 degrees E ([2]).To begin with, the author has retrieved the atmospheric transmittance and modified its impact factors from the MODIS data. Then the author calculated the vegetation coverage through the calculating vegetation index and respectively estimated the Surface Emissivity according to the type of vegetation coverage. Finally, the LST was retrieved by adopting the split-window algorithm and then the author analyzed the influence of clouds on the retrieval of LST.

关键词： MODIS data split window algorithm the retrieval of LST

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