scholarly journals The international surface temperature initiative global land surface databank: monthly temperature data release description and methods

2014 ◽  
Vol 1 (2) ◽  
pp. 75-102 ◽  
Author(s):  
J. J. Rennie ◽  
J. H. Lawrimore ◽  
B. E. Gleason ◽  
P. W. Thorne ◽  
C. P. Morice ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Monthly Temperature ◽  
Data Release ◽  
Global Land

scholarly journals Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

2021 ◽  
Vol 12 (5) ◽  
pp. 607-617
Author(s):  
A. Rajani, Dr. S.Varadarajan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Andhra Pradesh ◽  
Temperature Data ◽  
Sensor Data ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band  contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

A new approach for modeling near surface temperature lapse rate based on normalized land surface temperature data

2020 ◽  
Vol 242 ◽  
pp. 111746 ◽  
Author(s):  
Mohammad Karimi Firozjaei ◽  
Solmaz Fathololoumi ◽  
Seyed Kazem Alavipanah ◽  
Majid Kiavarz ◽  
Ali Reza Vaezi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Lapse Rate ◽  
Near Surface ◽  
New Approach ◽  
Temperature Lapse Rate ◽  
Surface Temperature Data

A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States

2012 ◽  
Vol 119 ◽  
pp. 315-324 ◽  
Author(s):  
William L. Crosson ◽  
Mohammad Z. Al-Hamdan ◽  
Sarah N.J. Hemmings ◽  
Gina M. Wade
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Data ◽  
Data Set ◽  
Surface Temperature Data

scholarly journals Online Global Land Surface Temperature Estimation from Landsat

2017 ◽  
Vol 9 (12) ◽  
pp. 1208 ◽  
Author(s):  
David Parastatidis ◽  
Zina Mitraka ◽  
Nektrarios Chrysoulakis ◽  
Michael Abrams
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Temperature Estimation ◽  
Global Land
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