scholarly journals Retrieval of Land Surface Temperature From Landsat 8 Data of the Dandong-liaoyang Geothermal Area

2020 ◽  
Author(s):  
Yiming Luan
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Fossil Fuels ◽  
Hot Springs ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Geothermal Resources ◽  
Thermal Infrared Remote Sensing

Abstract In recent years, with the intensification of problems like the depletion of traditional fossil fuels and environmental degradation, the development of new energy sources has become a key long-term strategy in China. Geothermal energy has attracted much attention due to its advantages of abundance and low environmental impact. Based on infrared data sensed remotely by the Landsat 8 satellite, this paper reports a verification of the atmospheric-correction method for extracting the surface temperature of the Dandong-Liaoyang geothermal region in all months of 2014. The method combines the abnormal points in the inversion results with the local sites of hot springs, structures, local historical air temperatures, and land surface temperature/emissivity data (MOD11_L2). Results showed that these data sources were spatially distributed in similar ways, which indicates that these results can be used to identify promising geothermal resources from publically available thermal-infrared remote-sensing data.

scholarly journals A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5659
Author(s):  
Yue Jiang ◽  
WenPeng Lin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Thermal Infrared Remote Sensing ◽  
Retrieval Algorithms ◽  
Split Window Algorithm

In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

LAND SURFACE TEMPERATURE INVESTIGATION AROUND IKOGOSI WARM SPRING NIGERIA USING LANDSAT8 DATA

2021 ◽  
Vol 5 (2) ◽  
pp. 264-272
Author(s):  
Y. A. Bello ◽  
K. M. Lawal ◽  
B. B. M. Dewu ◽  
A. E. Ikpokonte
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hot Spot ◽  
Geophysical Methods ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Geothermal Resource ◽  
Geothermal Resources ◽  
Warm Spring

Ikogosi warm spring (IWS) is among the most visited geothermal resource by tourists in Nigeria. On that basis, it has attracted so much attention from researchers using various geophysical methods, except the retrieval of the land surface temperature (LST) from remote sensing data. This work aimed at computing LST to delineate hot zone around Ikogosi geothermal resources. The split-window approach was used to compute the LST from Landsat 8 data. The interpretation of Landsat8 data revealed that the central region of the study area is of high LST, and the temperature then drops towards the southwest direction. The result also shows that the warm spring is situated around a region with high land surface temperature (about 29 °C) which is an indication of a geothermal reservoir. The supervised classification of the LST yields two zones of the high density of pixels with high temperature, hot spot zones. The hot spot zone west of IWS is believed to be the heat source of IWS as it has high LST, and it is closer to IWS while the hot spot zone NW of IWS shows an indication of a viable geothermal resource, high LST

scholarly journals Inversion and Analysis of Land Surface Temperature based on Landsat - a case study of BeiBei District in Chongqing

2020 ◽  
Vol 165 ◽  
pp. 03006
Author(s):  
Zhou Yang ◽  
Liu Na-na
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Exchange Process ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Temperature Inversion ◽  
Correction Method ◽  
Inversion Method ◽  
Temperature Area

Land surface temperature is the surface of the earth’s energy change and the exchange process, which is an important index for a lot of scientific research. In this paper, the surface temperature changes of BeiBei district in Chongqing in the past 20 years were inverted in 6 time phases. The surface temperature inversion method of Landsat remote sensing data was studied, and the atmospheric correction method was adopted to conduct the inversion by using Landsat5TM and landsat8OLI-TIRS image data. The results showed that from 2004 to 2014, the area of high temperature area increased year by year, and the area of low temperature area also increased year by year.

Land Surface Temperature Measurement Based on Grid Service & Remote Sensing Data

2010 ◽  
Vol 108-111 ◽  
pp. 943-947
Author(s):  
Shao Bin Zhan ◽  
Yong Sheng Liang ◽  
Hong Ying Huo ◽  
Yue Fang Gao
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Grid Service ◽  
Atmospheric Effects ◽  
Correction Model ◽  
Important Significance

Land surface temperature (LST) is a key parameter of surface physical process and plays an important significance to the energy balance in the world. In this paper, based on the knowledge of atmospheric radiative transfer and the comparability of two satellites, 7 undetermined variables was solved. Then, eliminate the atmospheric effects on the remote sensing images by atmospheric correction model. The retrieval of atmospheric elements and the surface parameters, will also be presented. It’s a effective way to measure the LST. Following the disposal information and plentiful data increasing, the powerful computing resource was required. The computing capability in single computer fall short of demand. So the grid computing conception is introduced. We build a LST measurement system by grid service, it can solve the problem commendably.

scholarly journals Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1436
Author(s):  
Lucas Ribeiro Diaz ◽  
Daniel Caetano Santos ◽  
Pâmela Suélen Käfer ◽  
Nájila Souza da Rocha ◽  
Savannah Tâmara Lemos da Costa ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Wrf Model ◽  
Landsat 8 ◽  
Accuracy Evaluation ◽  
Vertical Profiles ◽  
Radiosonde Station

This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval.

scholarly journals Spatio-Temporal Change of Land Surface Temperature at Tiris Geothermal Potential Area, East Java, Indonesia.

2019 ◽  
Vol 3 (1) ◽  
pp. 13-21
Author(s):  
Andre Prayogo ◽  
Sukir Maryanto ◽  
Ahmad Nadhir
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Landsat 8 ◽  
Geothermal Potential ◽  
Geological Information

AbstractOne of the areas that have geothermal potential in Indonesia is Tiris because there are found some manifestation in the form of hot springs. Several studies are needed to determine its geothermal potential before exploitation is carried out. Some previous studies have been carried out in the area, one of which uses Landsat 7 remote sensing data. There are other studies that state that knowledge of geology is needed to implement remote sensing in determining geothermal areas. This study uses 3-years data from Landsat 8 and geological information from the regional geological map of the study area. The result show changes in the value of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) from year to year, where each year the NDVI value decrease which is interpreted as reduced vegetation in the study area. From the distribution of LST values in the study area, it was found that there were hot spots that had higher temperatures than the surrounding area. When geological information and LST distribution map overlaid with regional geological maps, it is known that the hot spots inside the research area are possible to be a geothermal reservoir.

scholarly journals Spaceborne Thermal Remote Sensing for Characterization of the Land Surface Temperature of Manmade and Natural Features

Proceedings ◽  
2020 ◽  
Vol 67 (1) ◽  
pp. 2
Author(s):  
Sakshi Jain ◽  
Shashi Kumar
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Forest Cover ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Doon Valley ◽  
Window Method ◽  
Urban Settlements

The changes in land surface temperature (LST) concerning time and space are mapped with the help of satellite remote sensing techniques. These measurements are used for determining several geophysical parameters including soil moisture, evapotranspiration, thermal inertia, and vegetation water stress. This study aims at calculating and analyzing the LST of manmade and natural features of Doon Valley, Uttarakhand, India. The study area includes the forest range of Doon Valley, agricultural areas, and urban settlements. Spaceborne multitemporal thermal bands of Landsat 8 were used to calculate the LST of various features of the study area. Split-window algorithm and emissivity-based algorithms were tested on the Landsat-8 data for LST calculation. The study also explored the effect of atmospheric correction on the temperature calculation. The land surface temperature determined using an emissivity based method that did not provide atmospheric correction was found to be less accurate as compared to the results by the split-window method. The LST for urban settlements is higher than the forest cover. A temporal analysis of the data shows an increase in the temperature for October 2018. The study shows the potential of the spaceborne thermal sensors for the multitemporal analysis of the LST measurement of manmade and natural features.

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