TY  - JOUR
AU  - Rasul, Azad
PY  - 2026
DA  - 2026/09/20
TI  - Machine Learning and Deep Learning Approaches in Thermal Remote Sensing: A Systematic Review (2018–2026)
JO  - Journal of Geoscience and Earth Observation
T2  - Journal of Geoscience and Earth Observation
JF  - Journal of Geoscience and Earth Observation
VL  - 1
IS  - 2
SP  - 137
EP  - 157
DO  - 10.62762/JGEO.2026.270724
UR  - https://www.icck.org/article/abs/JGEO.2026.270724
KW  - thermal remote sensing
KW  - machine learning
KW  - deep learning
KW  - land surface temperature
KW  - PRISMA 2020
KW  - convolutional neural network
AB  - This systematic review, conducted in accordance with the PRISMA 2020 guidelines, maps the applications of machine learning (ML) and deep learning (DL) within thermal remote sensing (RS) from January 2018 to March 2026. Despite significant growth in this field driven by high-resolution satellite missions and open-source frameworks, no comprehensive PRISMA-compliant synthesis has previously focused exclusively on this domain during the post-2018 period. Through a structured search of Scopus and Google Scholar, we identified 193 peer-reviewed studies meeting inclusion criteria, of which 93 provided full-text access for in-depth methodological appraisal and 100 were analyzed at the metadata level. Our analysis reveals a concentrated architectural landscape, with convolutional neural networks (CNNs), long short-term memory networks (LSTM/BiLSTM), and support vector machines/regression (SVR/SVM) predominating. Application-wise, the literature skews heavily toward sea surface temperature (SST) forecasting and land surface temperature (LST) retrieval and downscaling, while critical areas such as wildfire detection, evapotranspiration estimation, and permafrost monitoring remain notably understudied. A key concern is the severe deficit in open science practices: fewer than 5% of the full-text accessible studies reported code availability, raising substantial reproducibility challenges. While the field demonstrates technical maturity, it remains architecturally conservative, presenting clear opportunities for exploring emerging approaches like physics-informed neural networks and transformer-based models. The findings underscore an urgent need for the community to address reproducibility gaps and to expand ML/DL applications into underserved domains, thereby broadening the scientific impact and operational utility of thermal RS in the coming years.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Rasul2026Machine,
  author = {Azad Rasul},
  title = {Machine Learning and Deep Learning Approaches in Thermal Remote Sensing: A Systematic Review (2018–2026)},
  journal = {Journal of Geoscience and Earth Observation},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {137-157},
  doi = {10.62762/JGEO.2026.270724},
  url = {https://www.icck.org/article/abs/JGEO.2026.270724},
  abstract = {This systematic review, conducted in accordance with the PRISMA 2020 guidelines, maps the applications of machine learning (ML) and deep learning (DL) within thermal remote sensing (RS) from January 2018 to March 2026. Despite significant growth in this field driven by high-resolution satellite missions and open-source frameworks, no comprehensive PRISMA-compliant synthesis has previously focused exclusively on this domain during the post-2018 period. Through a structured search of Scopus and Google Scholar, we identified 193 peer-reviewed studies meeting inclusion criteria, of which 93 provided full-text access for in-depth methodological appraisal and 100 were analyzed at the metadata level. Our analysis reveals a concentrated architectural landscape, with convolutional neural networks (CNNs), long short-term memory networks (LSTM/BiLSTM), and support vector machines/regression (SVR/SVM) predominating. Application-wise, the literature skews heavily toward sea surface temperature (SST) forecasting and land surface temperature (LST) retrieval and downscaling, while critical areas such as wildfire detection, evapotranspiration estimation, and permafrost monitoring remain notably understudied. A key concern is the severe deficit in open science practices: fewer than 5\% of the full-text accessible studies reported code availability, raising substantial reproducibility challenges. While the field demonstrates technical maturity, it remains architecturally conservative, presenting clear opportunities for exploring emerging approaches like physics-informed neural networks and transformer-based models. The findings underscore an urgent need for the community to address reproducibility gaps and to expand ML/DL applications into underserved domains, thereby broadening the scientific impact and operational utility of thermal RS in the coming years.},
  keywords = {thermal remote sensing, machine learning, deep learning, land surface temperature, PRISMA 2020, convolutional neural network},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}