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Recent Advances in Quantitative Thermal Imaging Using Remote Sensing

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Dear Colleagues,

Thermal remote sensing has become an essential tool for the quantitative analysis of land, water, atmosphere, and infrastructure dynamics. Advances in sensor technology, calibration techniques, and data processing have greatly enhanced the accuracy and utility of thermal imagery across a wide range of applications. From monitoring Land Surface Temperature and evapotranspiration to detecting heat anomalies in urban areas, wildfires, or industrial systems, quantitative thermal imaging is now central to environmental science, energy diagnostics, and geospatial analysis. The emergence of high-resolution thermal sensors, integration with AI, and multisource data fusion further extend the frontiers of what thermal remote sensing can achieve.

This Special Issue aims to gather innovative research and review articles that advance the theory, methods, and applications of quantitative thermal imaging using remote sensing platforms (e.g., satellite, airborne, UAV-based). It aligns with the journal’s scope by promoting interdisciplinary approaches and technological developments that enable robust environmental monitoring, hazard detection, and sustainable planning. We seek contributions that not only showcase cutting-edge science but also provide practical insights into processing chains, calibration protocols, and validation strategies.

We welcome submissions on topics including, but not limited to the following:

Land Surface Temperature retrieval and modeling;
Calibration and correction techniques for thermal sensors;
Urban Heat Island detection and mitigation;
Wildfire detection and thermal anomaly tracking;
Integration of thermal data with machine learning and data fusion;
UAV and satellite-based thermal mapping in agriculture or hydrology;
Infrastructure and industrial monitoring using thermal imaging.

Both original research and review articles are encouraged.

Prof. Dr. Susana Lagüela López
Dr. Iván Garrido González
Guest Editors

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21 pages, 3462 KB

Open AccessArticle

Evaluating Airborne Thermal Infrared Hyperspectral Data for Leaf Area Index Retrieval in Temperate Forests

by Elnaz Neinavaz, Roshanak Darvishzadeh, Andrew K. Skidmore, Marco Heurich and Xi Zhu

Remote Sens. 2025, 17(23), 3820; https://doi.org/10.3390/rs17233820 - 26 Nov 2025

Viewed by 265

Abstract

The Leaf Area Index (LAI) is a key vegetation biophysical variable extensively studied using various remote sensing platforms and applications. Most studies focused on retrieving LAI using remote sensing data have primarily applied visible to shortwave infrared (0.3–2.5 µm) data. While we have previously retrieved LAI using thermal infrared (TIR 2.5–14 µm) hyperspectral data under controlled laboratory conditions, this study aims to evaluate the reliability of our earlier findings using in situ and airborne TIR hyperspectral data. In this study, 36 plots, each 30 × 30 m in size, were randomly selected in the Bavarian Forest National Park in southeastern Germany. The EUFAR-TIR flight campaign, conducted on 6 July 2017, aligned with field data collection using an AISA Owl TIR hyperspectral sensor at 3 m spatial resolution. Statistical univariate and multivariate approaches have been applied to predict LAI using emissivity data. The LAI was derived using six narrowband indices, computed from all possible combinations of wavebands between 8 µm and 12.3 µm, via partial least squares regression (PLSR) and artificial neural network (ANN) models, applying the Levenberg–Marquardt and Scaled Conjugate Gradient algorithms. The results indicated that compared to LAI estimation under controlled conditions, TIR narrowband indices demonstrated poor performance in estimating in situ LAI (R² = 0.28 and RMSE_CV = 0.02). Instead, it was observed that the PLSR model unexpectedly achieved higher prediction accuracy (R² = 0.86 and RMSE_CV = 0.36) in retrieving LAI compared to the ANN approach using the Levenberg–Marquardt algorithm (R² = 0.56, RMSE_CV = 0.71); however, it was outperformed by the Scaled Conjugate Gradient algorithm (R² = 0.83, RMSE_CV = 0.18). The results revealed that wavebands located at 8.1 µm, 9.1 µm, 9.85–9.95 µm, and 9.99–10.27 µm are equally effective in predicting LAI, regardless of sensor or measurement/environmental conditions. Our findings have important implications for upscaling LAI predictions, as the identified wavebands are effective across varying conditions and align with the capabilities of upcoming thermal satellite missions such as Landsat Next and Copernicus LSTM. Full article

(This article belongs to the Special Issue Recent Advances in Quantitative Thermal Imaging Using Remote Sensing)

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