scholarly journals MONITORING OF LAND SURFACE TEMPERATURE IN KRASNOYARSK AND ITS SUBURBAN AREA BASED ON LANDSAT-8 SATELLITE DATA

2018 ◽  
pp. 177-182
Author(s):  
A.K. Matuzko ◽  
O.E. Yakubaylik
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Suburban Area ◽  
Landsat 8

scholarly journals Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

2021 ◽  
Vol 10 (04) ◽  
pp. 131-149
Author(s):  
Yaw A. Twumasi ◽  
Edmund C. Merem ◽  
John B. Namwamba ◽  
Olipa S. Mwakimi ◽  
Tomas Ayala-Silva ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Two Cities

scholarly journals Estimation of Land Surface Temperature Using LANDSAT 8 Satellite Data of Panchkula District, Haryana

2020 ◽  
pp. 47-66
Author(s):  
Ravi Kumar ◽  
Anup Kumar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Global Scale ◽  
Sensor Data ◽  
Landsat 8 ◽  
Heat Island ◽  
The Earth ◽  
Satellite Sensor Data

Land surface temperature (LST) represents hotness of the surface of the Earth at a particular location. Land surface temperature is useful for meteorological, climatological changes, heat island, agriculture, hydrological processes at local, regional and global scale. Presently many satellite sensor data are available for calculation of land surface temperature like Landsat 8 and MODIS. In the present study land surface temperature in Panchkula district of Haryana have been calculated using Landsat 8 satellite data of 5th May 2019 and 28th October 2019. Already available equations were used for computation of LST in the study area. LST in the study area varies from 18°C to 56°C. High LST is observed in cultivation land, urban area while low LST is observed in hilly forest area in the study area. In the study validation of LST could not be done because of not available of temperature data of studied dates, however, the result gives idea of land surface temperature on a particular day and location.

scholarly journals Spatiotemporal analysis of the land surface temperature distribution over the territory of Novosibirsk city based on Landsat data

2020 ◽  
Vol 223 ◽  
pp. 03011
Author(s):  
Elena A. Mamash ◽  
Igor A. Pestunov ◽  
Dmitri L. Chubarov
Keyword(s):  
Temperature Distribution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Spatiotemporal Analysis ◽  
Landsat 8 ◽  
Surface Temperature Distribution ◽  
Development Analysis ◽  
The City

The paper discusses the applicability of Landsat 8 data for analyzing the land surface temperature distribution over the territory of Novosibirsk. The satellite data is compared with the data from ground meteorological stations. Using cloud-based systems and methods for processing time series of satellite data, a composite image of the temperature field for the territory of Novosibirsk is built; trends and anomalies in the temperature distribution in the urban area are studied. The results could be applied in urban development analysis and management of the city territory.

scholarly journals Assessment of thermal conditions in Krasnoyarsk urban area with use of different satellite data and geographic information system

2019 ◽  
Vol 23 (Suppl. 2) ◽  
pp. 615-621 ◽  
Author(s):  
Alexandra Matuzko ◽  
Oleg Yakubailik
Keyword(s):  
Surface Temperature ◽  
Urban Area ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Temperature Anomalies ◽  
The City

Satellite data in the thermal infrared range are a powerful source of information for the analysis and determination of city urban area temperature anomalies. The article presents a technique for monitoring the land surface temperature on the basis of combination of ?Landsat 8? satellite thermal infrared data with PlanetScope satellite constellation high resolution data. Such combination of satellite data from several spacecrafts increase the detalization of temperature maps to the level of individual city blocks. Determination of the nature and boundaries of temperature anomalies will help to understand the causes of the unfavorable environmental situation in Krasnoyarsk, where, in addition to high industrial emissions, their influence and atmospheric processes, leading to the fact that impurities are delayed and concentrated over the city. The results shows that the temperature in the places of thermal anomalies is 5?-8? higher than the average land surface temperature of the city. Based on the results of the analysis of summer thermal multi-temporal space images, several thermal zones of different nature were outlined on the territory under consideration. This information can be used in planning the development of the city, the design of new urban neighborhoods.

scholarly journals LEAST SQUARE APPROACH FOR ESTIMATING OF LAND SURFACE TEMPERATURE FROM LANDSAT-8 SATELLITE DATA USING RADIATIVE TRANSFER EQUATION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 97-100
Author(s):  
Y. Jouybari-Moghaddam ◽  
M. R. Saradjian ◽  
A. M. Forati
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Least Square ◽  
Landsat 8 ◽  
Least Square Approach

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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