Recent Advances in Thermal Infrared Remote Sensing

Dear Colleagues,

With the development of remote sensing technology, a series of Earth observation satellites has been launched in recent years. Also, thermal infrared remote sensing measurements have greatly improved in terms of spectral, spatial, and temporal resolution. These improvements will soon produce a clearer picture of the land surface than ever before. At this time, we need to synthesize the current status of the field and illustrate future trends and prospects, so as to exploit new applications of thermal infrared remote sensing. This is a good opportunity to discuss the modeling and application of thermal infrared remote sensing observations.

Thermal infrared remote sensing data have been used to derive surface parameters for a long time. The technology can be traced back to the early 1970s. The main parameters of interest in thermal infrared observation include soil moisture, land surface emissivity, land surface temperature, and evapotranspiration. Because these parameters can reflect the results of all surface-atmosphere interactions and energy fluxes between the surface and atmosphere on both regional and global scales, knowledge of such parameters is critical for the accurate modeling of energy fluxes between the surface and the atmosphere, and for other land process applications (e.g., hydrology, climatology, agronomy, and ecology, among others).

On the basis of different assumptions and approximations, various methods have been proposed to derive those parameters (e.g., the NDVI-based emissivity method for land surface emissivity, the split-window algorithm for land surface temperature, and the VI-Ts triangle/trapezoidal feature space for evapotranspiration). However, there is still no “best method” for retrieving those parameters from space. All of the methods either rely on statistical relationships or assumptions and constraints to solve the inherent, underdetermined retrieval problem. These solutions are not always workable across all circumstances. It is therefore necessary to select the optimum one for a particular case by accounting for sensor characteristics, the required accuracy, computation time, and the availability of auxiliary information. The birth of hyper-spectral, fine-spatial, and multi-temporal thermal infrared data would introduce more advantages and convenience in terms of retrieval and application. Nevertheless, selected topics are being planned to demonstrate the state of the art reflecting the retrieval of land surface parameters from thermal infrared remote sensing measurements and the growing interest in the analyses and applications of those parameters.

Prof. Dr. Zhao-Liang Li
Prof. Dr. Jose A. Sobrino
Dr. Xiaoning Song
Guest Editors

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