Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions - Archive ouverte HAL
Article Dans Une Revue Remote Sensing of Environment Année : 2023

Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions

Résumé

Satellite observations in the Thermal Infra-Red (TIR) domain provide valuable information on Land Surface Temperatures, Evapo-Transpiration and water use efficiency and are useful for monitoring vegetation health, agricultural practices and urban planning. By 2030, there will be 3 new high-resolution global coverage satellite TIR missions in space, all of them with fields of view larger than ± 30°. Directional anisotropy in TIR can affect the estimation of key application variables, such as temperature, and are typically studied by means of field campaigns or physical modelling. In this work, we have evaluated directional effects using simultaneous measurements from Landsat-8 and the ± 45° field of view MASTER airborne TIR sensor from NASA. Differences as high as 6K are observed in the surface temperatures derived from these simultaneous observations. Those differences are attributed to directional effects, with the greatest differences associated with hotspot conditions, where the solar and satellite viewing directions align. Five well studied parametric directional models have then been fitted to the temperature differences, allowing the amplitude of the measured directional effects to be reduced below 1K, with small variations between models. These results suggest that a simple correction for directional effects could be implemented as part of the ground segment processing for the upcoming missions.
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Dates et versions

hal-04073733 , version 1 (19-04-2023)
hal-04073733 , version 2 (22-06-2023)
hal-04073733 , version 3 (02-08-2023)

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Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. Remote Sensing of Environment, 2023, 297, pp.113765. ⟨10.1016/j.rse.2023.113765⟩. ⟨hal-04073733v3⟩
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