The Past, Present and Possible Future of Thermal Remote Sensing
Abstract
Thermal infrared remote sensing has reached a turning point. A status audit completed for this review identifies 55 operational satellite platform deployments on 18 August 2026. The resulting dataset contains 174 named thermal infrared instrument designs mapped to 306 historical, operational and planned satellite platform deployments. Across the orbital record, the finest reported nominal spatial sampling decreased from 55 km for the Medium Resolution Infrared Radiometer aboard TIROS-2 in 1960 to 3.5 m for the mid-wave infrared imager aboard HotSat-2 in 2026, an improvement of more than four orders of magnitude. Public continuity missions are now complemented by specialized instruments on the International Space Station, commercial small satellites, aircraft, stratospheric balloons, drones and terrestrial systems. This review links that platform history to the governing physics of emitted radiation, detector and cooling technologies, calibration, atmospheric effects, emissivity and spatial resolution. It also examines the transition from classical machine learning to convolutional, recurrent, transformer, diffusion, foundation and vision–language approaches. The selected examples indicate that adoption in thermal applications has been uneven rather than uniformly delayed relative to other areas of Earth observation. These methods support image interpretation and reconstruction as well as quantitative retrieval, for which radiometric calibration, physical consistency and independent validation remain necessary. As sensor availability expands, scientific comparability increasingly depends on harmonization, cross-sensor transfer, uncertainty characterization and validation in physical units. We recommend three priorities: (i) open, cross-calibrated thermal archives; (ii) models that preserve the distinct physical meanings of thermal variables; and (iii) validation across sensors, regions and seasons using physical units, independent observations and quantified uncertainty.
