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You are here: Home / Library / RBINS Staff Publications 2020 / Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS

Quinten Vanhellemont (2020)

Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS

ISPRS Journal of Photogrammetry and Remote Sensing, 166:390-402.

Remote sensing of Land Surface Temperature (LST) generally requires atmospheric parameters and the emissivity (∊) of the target to be estimated. The atmospheric up- and downwelling radiances and transmittance can be accurately modelled using radiative transfer models and profiles of relative humidity and temperature, either measured by radiosonde probes or retrieved from assimilating weather models. The estimation of ∊ is a large source of uncertainty in the resulting LST product, and there are various approaches using multi-angle observations, multispectral optical or multispectral thermal infrared imagery. In this paper, the estimation of LST from the Thermal InfraRed Sensor (TIRS) on board Landsat 8 is evaluated using more than 6 years of in situ temperature measurements from a network of 14 Autonomous Weather Stations (AWS) in Belgium. ∊ is estimated from concomitant atmospherically corrected imagery from the Operational Land Imager (OLI) using two new neural network approaches trained on ECOSTRESS spectra, and an established NDVI based method. Results are compared to using ∊=1 and the ASTER Global Emissivity Dataset. LST retrievals from L8/TIRS perform well for all emissivity data sources for 500 matchups with AWS subsoil temperature measurements: Mean Differences 0.8–3.7 K and unbiased Root Mean Squared Differences of 2.9–3.5 K for both B10 and B11. The use of unity emissivity gives the best results in terms of MD (0.8 K) and unb-RMSD (3 K). Similar ranges of unb-RMSD are found for 500 matchups with broadband radiometer temperatures (2.6–3.1 K), that have lower absolute MD values (−2.2–0.6 K). For the radiometer temperatures, both the neural net approaches gave lowest MD, in the best case ±0.1 K. The present investigation can hence recommend the neural nets to derive ∊ for the retrieval of LST over the AWS in Belgium. Using published matchup results from other authors however, no single source of ∊ data performed better than ∊=1, but this could be due to their low number of matchups. Further efforts for estimating representative pixel average emissivities are needed, and establishing a denser in situ measurement network over varied land use, with rather homogeneous land cover within a TIRS pixel, may aid further validation of a per pixel and per scene ∊ estimates from multispectral imagery. AWS data seems valuable for evaluation of satellite LST, with the advantage of a much lower cost and higher potential matchup density compared to conventional radiometers.

RBINS Publication(s), RBINS Collection(s), Open Access, Impact Factor, Peer Review
Atmospheric correction, Emissivity, Landsat 8, Neural network, OLI, Surface temperature, Thermal infrared, TIRS