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Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

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Advances in Land Remote Sensing

Thermal Infra Red (TIR) Remote sensing allows spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill-posed problem, with several parameters to be constrained from few information.

Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales.

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Authors and Affiliations

  1. Institute of Research for the Development, UMR LISAH SupAgro/INRA/IRD, Montpellier, France

    Frédéric Jacob

  2. College of Agriculture New Mexico State University, Las Cruces, NM, USA

    Thomas Schmugge

  3. National Institute for Agronomical Research, UMR CSE INRA/UAPV, 84914, Avignon, France

    Albert Olioso & Dominique Courault

  4. United States Department of Agriculture, US Arid Land Agricultural Research Center, 85238, Maricopa, AZ, USA

    Andrew French

  5. Department of Geo-system Engineering, University of Tokyo and Hitachi Ltd., Tokyo, Japan

    Kenta Ogawa

  6. ACRI-ST, Sophia Antipolis, France

    Francois Petitcolin

  7. Institute of Research for the Development, Center for Spatial Studies of the Biosphere, Toulouse, France

    Ghani Chehbouni

  8. Biospheric Sciences Branch, NASA's GSFC, 20771, Greenbelt, MD, USA

    Ana Pinheiro

  9. NOAA's National Climatic Data Center, 28801-5001, Asheville, NC, USA

    Jeffrey Privette

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  1. Frédéric Jacob
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  1. University of Maryland, College Park, MD, USA

    Shunlin Liang

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Jacob, F. et al. (2008). Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces. In: Liang, S. (eds) Advances in Land Remote Sensing. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6450-0_10

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