Artificial Intelligence – based methods for temperature retrieval and related applications from remote sensing data
Manuscript submission deadline: 31 December 2026
Topic Editor
Juan Carlos Jimenez-Muñoz
Global Change Unit, Image Processing Laboratory, University of Valencia, Spain
About this Research Topic
This Special Issue is devoted to the latest advances in Artificial Intelligence (AI) algorithms applied to Thermal Infrared (TIR) Remote Sensing for Sea, Land and Atmospheric Processes characterization.
Algorithm developments for Earth Observation (EO) data have experienced an impressive advance because of increased computing capabilities that allow application of AI methodologies such as machine/deep learning. The exceptional juncture between current and future TIR missions and algorithm development opens a new frontier in the topic of “Big Earth data”, which will allow an unprecedented rapid and accurate monitoring of atmospheric and surface processes using TIR data.
The main objective of TIR missions is to provide the user community with accurate Sea/Land Surface Temperature (LST) products at both low-spatial and high-spatial resolutions. Traditional physics-based algorithms for LST retrieval are based on the radiative transfer equation. Application of machine learning algorithms to the derivation of LST products is very much focused on deep learning convolutional neural networks, but this research topic is still to be explored.
This Research Topic is aimed (but not limited to) to the retrieval of land and atmospheric parameters using the last generation of AI algorithms, such as SST and LST, Evapotranspiration, Atmospheric constituents from infrared soundings, among others.