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IEEE International Geoscience and Remote Sensing Symposium

作者： Yuanyuan Chen Si-Bo Duan Zhao Wei Zhao-Liang Li Key Laboratory of Agri-informatics Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Institute of Mountain Hazards and Environment Chinese Academy of Sciences

ISBN: (纸本)9781479979301

Land surface temperature (LST) is a crucial parameter in analyzing and evaluating climate change at various scales, the surface energy balance, soil moisture, evapotranspiration and urban heat islands. Currently, methods for its estimation from space have continuously been developed, while most studies focus on the split-window (SW) algorithms. According to the published works, some approximations and assumptions were used to develop SW algorithms. This paper investigated and revised the error caused by these approximations and assumptions with the help of TIGR 2000 database and MODTRAN 4.0 software. Then a new SW method to estimate LST from FY-3A/VIRR was proposed in this paper. The primarily accuracy evaluation of the proposed method shows that the root mean square error (RMSE) of LST estimation using TIGR atmospheric profiles is 0.768 K, with the bias of -0.122 K.

关键词： Land surface temperature (LST) split-window algorithm Revision FY-3A/VIRR revision procedure derivation revision Hubble Space Telescope surface temperature windows windows Heat islands Land algorithms approximation

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Land Surface Temperature Retrieval from Landsat 8 TIRS-Comparison between Radiative Transfer Equation-Based Method, split window algorithm and Single Channel Method

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REMOTE SENSING 2014年第10期6卷 9829-9852页

作者： Yu, Xiaolei Guo, Xulin Wu, Zhaocong Univ Saskatchewan Dept Geog & Planning Saskatoon SK S7N 5C8 Canada Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.

关键词： Landsat 8 TIRS split-window algorithm single channel method SURFRAD NCEP MODIS

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CRYOGENIC LAND SURFACE PROCESS DETECTION IN SIBERIAN HIGH LATITUDE MOUNTAIN PERMAFROST LANDSCAPE BY TIME SERIES LANDSAT THERMAL IMAGERY

CRYOGENIC LAND SURFACE PROCESS DETECTION IN SIBERIAN HIGH LA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gadal, Sebastien Zakharov, Moisei Avignon Univ Aix Marseille Univ Univ Cote Azur CNRSESPACEUMR 7300 F-84000 Avignon France

ISBN: (纸本)9798350320107

In recent years, the accumulation of cloud-free remote sensing thermal infrared data for high-latitude permafrost regions makes it possible to use them to identify active cryogenic land surface processes (ACP) spurred by climate change and human activities. ACP is an extremely important indicator of excitations in the energy cycle of permafrost landscapes and changes in the carbon budget. In this paper, we test the time series of land surface temperature (LST) extracted from Landsat 8 OLI/TIRS datasets using a split window algorithm (SWA) to identify ACP for the study area in the mountains of Northeastern Siberia. We identified active cryogenic processes from thermal anomalies detected using standard deviation thresholds on 5 datasets for selected dates. Our results show that multi-year verified high-resolution LST data for the second half of the summer period can be applicable for identifying an area with active cryogenic processes in valley and disturbed permafrost landscapes, provided they can indicate local effects of processes associated with drainage degradation and vegetation change.

关键词： land surface temperature split-window algorithm thermal anomaly land surface cryogenic process Landsat

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Open-Source Software Implementation and Validation of the split-window Method for Automated Land Surface Temperature Retrieval from Landsat 8 Data

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2021年第9期57卷 1171-1178页

作者： Kazakov, E. E. Borisova, Yu, I State Hydrol Inst St Petersburg 199053 Russia St Petersburg State Univ St Petersburg 199034 Russia

An open software implementation of the algorithm for retrieving the land surface temperature (LST) from Landsat 8 remote sensing satellite data is presented using the split-window algorithm, supplemented by a covariance-variational technique to modeling the water-vapor content in the atmosphere. The implementation also uses the Simplified and Robust Surface Reflectance Estimation Method (SREM), a physical-based atmospheric correction method, and the FMASK cloud and cloud-shadow detection algorithm. All components of the complex algorithm are fully automated and do not require additional information, including external data on the state of the atmosphere. The implementation of the algorithm has been validated at various settings on the basis of ten ground stations in the United States that publish data on observations of the LST with a high time resolution;the average absolute error according to its results was 1.1 degrees C. The software component is developed in Python and is available in the public repository;the source code is distributed under an open license.

关键词： land surface temperature remote sensing Landsat 8 TIRS SREM FMASK split-window algorithm

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Estimation of land surface temperature in response to land use/land cover transformation in Kolkata city and its suburban area, India

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INTERNATIONAL JOURNAL OF URBAN SCIENCES 2022年第4期26卷 604-631页

作者： Biswas, Souvik Ghosh, Soumen Forest Survey India Minist Environm Forest & Climate Change Dehra Dun Uttarakhand India Univ Burdwan Dept Geog Bardhaman 713104 W Bengal India

The land transformation in Kolkata city and its suburban area is mainly due to intensive population pressure and rapid urban sprawling. Consequently, the land surface temperature (LST) is continuously increasing and gradually intensifying the effects of the urban heat island. The aim of this study is to assess the spatiotemporal variation of LST in response to land use land cover change (LULC) during 1995-2020. The maximum likelihood classifier was used for the supervised classification of LULC and the accuracy assessment was done using the confusion matrix. Quin's Mono-window algorithms for Landsat TM data of 1995 and 2010 and split-window algorithms for Landsat 8 OLI data of 2020 were applied to retrieve LST. Several spectral indices such as Normalized difference built-up index (NDBI), Normalized difference vegetation index (NDVI), and Modified normalized difference water index (MNDWI) were calibrated and pixel-specific overlay analysis was done for correlation study between spectral indices and LST. This work revealed that the rapid urban sprawling causes massive land transformation in the study area. The land conversions from trees outside forests (TOF) and agricultural land to built-up were significantly contributing to an overall increase in the mean LST during 1995-2020. The mean LST was comparatively high over Kolkata city than its suburban area. During 1995-2020, the mean LST was increased by nearly 8.43 degrees C in the summer season and 4.32 degrees C in the winter season. The increasing rate of LST was found relatively high over the built-up (7.06 degrees C), agricultural land without crop (5.55 degrees C), and open land (5.54 degrees C). However, it was comparatively low over TOF (4.66 degrees C) and water bodies (3.68 degrees C). The LST was positively correlated to NDBI and negatively correlated to NDVI and MNDWI. In order to combat urban warming, this study will promote green city initiatives through sustainable land use planning.

关键词： Land-use land-cover land surface temperature Mono-window algorithm split-window algorithm spectral indices Kolkata city

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An extended SW-TES algorithm for land surface temperature and emissivity retrieval from ECOSTRESS thermal infrared data over urban areas

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REMOTE SENSING OF ENVIRONMENT 2023年第1期290卷

作者： Ru, Chen Duan, Si-Bo Jiang, Xiao-Guang Li, Zhao-Liang Huang, Cheng Liu, Meng Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr Beijing 100081 Peoples R China CNRS ICube UMR 7357 UdS F-67412 Illkirch Graffenstaden France

Land surface temperature (LST) over urban areas is an essential parameter in the monitoring of urban thermal environment. Urbanization has resulted in a more complex geometric structure of urban surface than natural surface. However, in the conventional LST retrieval algorithms, the impact of urban geometric structure is usually neglected. In this study, an extended algorithm that combines the split-window algorithm (SW) and the temperature and emissivity separation algorithm (TES) was proposed to retrieve LST and land surface emissivity (LSE) over urban areas simultaneously from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared (TIR) data. In the extended SW-TES algorithm with considering urban geometry effect, the brightness temperature at ground level was obtained using the SW algorithm, and the LST and LSE were retrieved simultaneously by using the TES algorithm. The results indicate that LST (LSE) retrieved by the extended SW-TES algorithm with urban geometry effect correction has generally lower (higher) values than that without urban geometry effect correction. The LST differences mostly vary from 0.3 K to 1.2 K, whereas the LSE differences are mainly concentrated between-0.01 and -0.06. Furthermore, the root mean squared error (RMSE) between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction and LST/LSE obtained from the ECOSTRESS level-2 LST/LSE product decrease with the increase of sky view factor (SVF). The largest RMSE is approximately 1.6 K for LST and 0.03 for LSE in the SVF of 0.1, which represents an urban pixel surrounded by high buildings within its adjacent pixels. There are no evident differ-ences between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction using five and three ECOSTRESS TIR bands. The LST differences are mainly concentrated between-0.2 K and 0.2 K, whereas the LSE differences mostly range from 0.001 to 0.0

关键词： Land surface temperature Urban geometry effect split-window algorithm Temperature and emissivity separation algorithm ECOSTRESS

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Satellite Imagery Recording the Process and Pattern of Winter Temperature Field in Yangtze Estuary Interrupted by a Cold Wave

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ATMOSPHERE 2023年第3期14卷 479-479页

作者： Chen, Ruirui Jiang, Xuezhong Chen, Jing East China Normal Univ State Key Lab Estuarine & Coastal Res Shanghai 200241 Peoples R China East China Normal Univ Sch Urban & Reg Sci Shanghai 200241 Peoples R China East Normal Univ Inst Global Innovat & Dev Shanghai 200241 Peoples R China

Sea surface temperature (SST) is a key marine ecological metric. An optimized split-window algorithm was used to invert the Yangtze Estuary's temperature field during a cold wave process. Additionally, MODIS SST inversion results were used to explore the effects of typical cold waves on the Yangtze Estuary's temperature field through the application of a temperature profile analysis method and a multiscale, multidirectional edge detection algorithm. According to the findings, (1) the cold wave altered the temperature field characteristics and the temperature front intensity, morphology, and spatial distribution pattern within the Yangtze Estuary for a short period. The strong temperature front displayed irregular edges and scattered patterns due to the combined effects of cold water masses produced by the cold wave and the tides and the warm water currents outside the mouth. (2) The cold wave caused significant short-term deviations in the Yangtze Estuary's SST. The cold water tongue stretched across the entire Yangtze Estuary, where the temperature dropped sharply with time, with the maximum cooling occurring outside the mouth, at 12.2 degrees C, and the minimum cooling occurring inside the mouth, at only 5.5 degrees C. (3) The spatially gradual warming of the Yangtze Estuary's SST from inside to outside the mouth became a low-lower-high pattern during cold waves. (4) The cold wave exhibited a greater influence on the strength, form, and distribution of the temperature front. Studying the effects of cold waves on the Yangtze Estuary's temperature field has significant theoretical and practical implications for understanding the changes in the winter temperature field, environmental protection, disaster mitigation, and prevention.

关键词： cold wave MODIS sea surface temperature split-window algorithm Yangtze Estuary

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Improving the Accuracy of Landsat 8 Land Surface Temperature in Arid Regions by MODIS Water Vapor Imagery

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ATMOSPHERE 2023年第10期14卷 1589-1589页

作者： Arabi Aliabad, Fahime Zare, Mohammad Ghafarian Malamiri, Hamidreza Ghaderpour, Ebrahim Yazd Univ Fac Nat Resources & Desert Studies Dept Arid Lands Management Yazd *** Iran Yazd Univ Dept Geog Yazd *** Iran Sapienza Univ Rome Dept Earth Sci Ple Aldo Moro 5 I-00185 Rome Italy Sapienza Univ Rome CERI Res Ctr Ple Aldo Moro 5 I-00185 Rome Italy Earth & Space Inc Calgary AB T3A 5B1 Canada

Land surface temperature (LST) is a significant environmental factor in many studies. LST estimation methods require various parameters, such as emissivity, temperature, atmospheric transmittance and water vapor. Uncertainty in these parameters can cause error in LST estimation. The present study shows how the moderate resolution imaging spectroradiometer (MODIS) water vapor imagery can improve the accuracy of Landsat 8 LST in different land covers of arid regions of Yazd province in Iran. For this purpose, water vapor variation is analyzed for different land covers within different seasons. Validation is performed using T-based and cross-validation methods. The image of atmospheric water vapor is estimated using the MODIS sensor, and its changes are investigated in different land covers. The bare lands and sparse vegetation show the highest and lowest accuracy levels for T-based validation, respectively. The root mean square error (RMSE) is also calculated as 0.57 degrees C and 1.41 degrees C for the improved and general split-window (SW) algorithms, respectively. The cross-validation results show that the use of the MODIS water vapor imagery in the SW algorithm leads to a reduction of about 2.2% in the area where the RMSE group is above 5 degrees C.

关键词： cross-validation Landsat Land surface temperature MODIS Sentinel-2 split-window algorithm water vapor retrieval Yazd

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ESTIMATION AND EVALUATION OF THE LAND SURFACE TEMPERATURE FROM FENGYUN-3 SERIES SATELLITE DATA IN NORTHWEST CHINA

ESTIMATION AND EVALUATION OF THE LAND SURFACE TEMPERATURE FR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tu, Hao Li, Hua Liu, Qinhuo Li, Ruibo Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China

ISBN: (纸本)9781665403696

In this study, we have developed an operational split-window algorithm for retrieving the land surface temperature (LST) from Chinese FengYun-3 (FY-3) series satellite data, with the purpose of generating long-term FY-3 LST products from 2009 to 2020. The refined generalized split-window (GSW) algorithm was selected and the coefficients of the algorithm were simulated using radiative transfer model MODTRAN 5.2 and Seebore v5.0 atmospheric profile database. The land surface emissivities (LSE) in the two SW channels were calculated using the ASTER Global Emissivity Database (GED), vegetation cover product and snow cover product based on the vegetation cover method. The developed FY-3 GSW algorithm was implemented in a MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product production system. The FY-3A and FY-3B LST products in northwest China were produced for 2013 and 2014, respectively, and the results were evaluated using ground measurements collected in four barren surface sites in the Heihe river basin. Both level 1 and recalibrated VIRR data were used for retrieving the LSTs. The results showed that the historical recalibration coefficients of the VIRR data can improve the accuracy of the LST retrievals.

关键词： FY-3 VIRR land surface temperature split-window algorithm land surface emissivity

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Long-Term Changes and Factors That Influence Changes in Thermal Discharge from Nuclear Power Plants in Daya Bay, China

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REMOTE SENSING 2022年第3期14卷 763-763页

作者： Zhang, Zhihua Wang, Difeng Cheng, Yinhe Gong, Fang Jiangsu Ocean Univ Sch Marine Technol & Geomat Lianyungang 222005 Peoples R China Minist Nat Resources Peoples Republ China Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzho Guangzhou 511400 Peoples R China

Thermal discharge (i.e., warm water) from nuclear power plants (NPPs) in Daya Bay, China, was analyzed in this study. To determine temporal and spatial patterns as well as factors affecting thermal discharge, data were acquired by the Landsat series of remote-sensing satellites for the period 1993-2020. First, sea surface temperature (SST) data for waters off NPPs were retrieved from Landsat imagery using a radiative transfer equation in conjunction with a split-window algorithm. Then, retrieved SST data were used to analyze seasonal and interannual changes in areas affected by NPP thermal discharge, as well as the effects of NPP installed capacity, tides, and wind field on the diffusion of thermal discharge. Analysis of interannual changes revealed an increase in SST with an increase in NPP installed capacity, with the area affected by increased drainage outlet temperature increasing to different degrees. Sea surface temperature and NPP installed capacity were significantly linearly related. Both flood tides (peak spring and neap) and ebb tides (peak spring and neap) affected areas of warming zones, with ebb tides having greater effects. The total area of all warming zones in summer was approximately twice that in spring, regardless of whether winds were favorable (i.e., westerly) or adverse (i.e., easterly). The effects of tides on areas of warming zones exceeded those of winds.

关键词： Daya Bay Nuclear Power Plants thermal discharge long-term changes Landsat radiative transfer equation split-window algorithm power plant installed capacity flood tide ebb tide wind field

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