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Estimation of Land Surface Temperature

Contact measurements of emissivity values of the surfaces using thermal radiometers and spectral assessment of samples supplementedLST estimation procedures in this study.Since the temperature values obtained as atsatellite temperature Tʙ,referenced to black bodies,the land cover classes were assigned emissivity values derived through field measurements.Thus the emissivity corrected land surface temperatures can be calculated as [8]:

St = Tʙ ∕1+ [λ* Tʙ∕ρ]Ιnɛ
λ = Wavelength of emitted radiance,ρ =h * c / σ (1.438 * 10^-2 m K)
h = Planck’s constant (6.626 * 10^-34 Js)
σ = Boltzmann constant (1.38 * 10^-23 J/K, c = velocity of light (2.998 * 10^8 m/s)

Source: Estimation of Spatial Variability of Land Surface Temperature using Landsat 8 Imagery, P Rajendran, K Mani, 2015 [PDF]

Can anyone tell which algorithm is used for Land Surface Temperature retrieval?

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Conversion to At-Satellite Brightness Temperature

http://landsat.usgs.gov/Landsat8_Using_Product.php

TIRS band data can be converted from spectral radiance to brightness temperature using the thermal constants provided in the metadata file:

T = K2 ln( K1 +1) Lλ where:

T = At-satellite brightness temperature (K)

Lλ = TOA spectral radiance (Watts/( m2 * srad * μm))

K1 = Band-specific thermal conversion constant from the metadata

(K1_CONSTANT_BAND_x, where x is the thermal band number)

K2 + Band-specific thermal conversion constant from the metadata (K2_CONSTANT_BAND_x, where x is the thermal band number)

Standard Landsat 8 data products provided by the USGS EROS Center consist of quantized and calibrated scaled Digital Numbers (DN) representing multispectral image data acquired by both the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS).

This is the tutorial. This website provided a full step-by-step tutorial.

https://blogs.esri.com/esri/arcgis/2014/01/06/deriving-temperature-from-landsat-8-thermal-bands-tirs/

You would then need to convert to land temperature using https://www.youtube.com/watch?v=uDQo2a5e7dM

  • Thank you for your reply. Already seen the YouTube link you provided. But some portion is not clear to me. I want to know which type of algorithm is that Single Channel, Split Window or radiative transfer ?? – user48079 Jan 17 '16 at 15:12
  • Give us a time in the video so we can check for you. – If you do not know- just GIS Jan 17 '16 at 19:06
  • No answer still now! – user48079 Jan 19 '16 at 13:53
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The paper utilized Landsat 5 TM and Landsat 8 OLI for analyzing land use/land cover change and its impact on land surface temperature in Sundarban Biosphere Reserve, India. Split window algorithm and spectral radiance model were used for determining land surface temperature from Landsat 8 OLI and Landsat 5 TM, respectively. The land use land cover change analysis revealed phenomenal increase in the waterlogged areas followed by settlement and paddy and a decrease in open forest followed by deposition and water body. The distribution of average change in land surface temperature shows that water recorded highest increase in temperature followed by deposition, open forest and settlement. Overlay of the transect profiles drawn on land use/land cover change map over land surface temperature map revealed that the land surface temperature has increased in those areas which were transformed from open forest to paddy, open forest to settlement, paddy to settlement and deposition to settlement. The study demonstrated that increase in non-evaporating surfaces and decrease in vegetation have increased the surface temperature and modified the temperature of the study area. Split window algorithm: https://www.researchgate.net/publication/301797360_Analyzing_land_surface_temperature_distribution_in_response_to_land_useland_cover_change_using_split_window_algorithm_and_spectral_radiance_model_in_Sundarban_Biosphere_Reserve_India

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