Online Global Land Surface Temperature Estimation from Landsat
1
Remote Sensing Lab, Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas (FORTH), N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
2
Jet Propulsion Laboratory, California Institute of Technology, MS 183-501, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1208; https://doi.org/10.3390/rs9121208
Received: 29 September 2017 / Revised: 15 November 2017 / Accepted: 15 November 2017 / Published: 23 November 2017
(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)
This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.
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Keywords:
land surface temperature; Landsat; global study; web application; MODIS emissivity; ASTER emissivity; NDVI
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Parastatidis, D.; Mitraka, Z.; Chrysoulakis, N.; Abrams, M. Online Global Land Surface Temperature Estimation from Landsat. Remote Sens. 2017, 9, 1208. https://doi.org/10.3390/rs9121208
AMA Style
Parastatidis D, Mitraka Z, Chrysoulakis N, Abrams M. Online Global Land Surface Temperature Estimation from Landsat. Remote Sensing. 2017; 9(12):1208. https://doi.org/10.3390/rs9121208
Chicago/Turabian StyleParastatidis, David; Mitraka, Zina; Chrysoulakis, Nektrarios; Abrams, Michael. 2017. "Online Global Land Surface Temperature Estimation from Landsat" Remote Sens. 9, no. 12: 1208. https://doi.org/10.3390/rs9121208
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