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Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations...
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Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1011

Special Issue Editors

Dr. Bo-Hui Tang

E-Mail Website
Guest Editor
1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: retrieval and validation of land surface temperature/emissivity; retrieval and validation of net surface radiation; radiative transfer modeling; quantitative estimation of land surface variables from middle infrared data; hyperspectral thermal infrared data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangyang Liu

E-Mail Website
Guest Editor
State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: retrieval and validation of land surface temperature; radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Tian Hu

E-Mail Website
Guest Editor
Department of Environment Research and Innovation, Luxembourg Institute of Science and Technology, 4362 Belvaux, Luxembourg
Interests: retrieval and validation of land surface temperature; remote sensing

Special Issue Information

Dear Colleagues,

Land Surface Temperature (LST) is a key physical variable in understanding land–atmosphere interactions, surface energy balance, and climate dynamics. Accurate retrieval of spatio-temporal LST plays a vital role in monitoring global climate change, water cycle processes, ecosystem health, and human–environment interactions. Despite advances in satellite, reanalysis, and ground-based observations, challenges remain in producing long time-series, high-resolution, space-time continuous, and accurate LST data, particularly in heterogeneous or mountainous regions. Recent developments in artificial intelligence (AI) provide promising avenues for addressing these challenges through enhanced modeling, data fusion, and gap-filling techniques. The integration of ground-based measurement, satellite observation, and artificial intelligence to achieve accurate LST retrieval in time and space is an important development direction for the current surface temperature extension and application.

This Special Issue aims to bring together cutting-edge research focused on innovative methods for spatio-temporal LST retrieval by integrating ground-based measurements, satellite remote sensing, and AI technologies. It aligns closely with the journal’s scope by advancing remote sensing applications, algorithm development, and Earth system monitoring. Contributions that enhance the accuracy, resolution, and applicability of LST products in environmental and climate-related fields are especially welcome.

Articles may address, but are not limited to, the following topics:

  • LST retrieval algorithms based on multi-source data
  • The coupling of artificial intelligence and physical models
  • AI and machine learning approaches for LST reconstruction
  • LST retrieval in complex terrains or under extreme conditions
  • Validation and uncertainty analysis of LST products
  • Applications of LST in hydrology, ecology, and climate studies

Article types include original research, reviews, methodological papers, and case studies.

Dr. Bo-Hui Tang
Dr. Xiangyang Liu
Dr. Tian Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land surface temperature (LST)
  • retrieval algorithm
  • complex terrain
  • urban area
  • ground-based observation
  • artificial intelligence
  • validation
  • reconstruction of data under clouds
  • temporal scale expansion

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 8374 KB  
Article
Cross-Domain Land Surface Temperature Retrieval via Strategic Fine-Tuning-Based Transfer Learning: Application to GF5-02 VIMI Imagery
by Peyman Heidarian, Hua Li, Zelin Zhang, Yumin Tan, Feng Zhao, Biao Cao, Yongming Du and Qinhuo Liu
Remote Sens. 2025, 17(23), 3803; https://doi.org/10.3390/rs17233803 - 23 Nov 2025
Viewed by 369
Abstract
Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates [...] Read more.
Accurate prediction of land surface temperature (LST) is critical for remote sensing applications, yet remains hindered by in situ data scarcity, limited input variables, and regional variability. To address these limitations, we introduce a three-stage strategic fine-tuning-based transfer learning (SFTL) framework that integrates a large simulated dataset (430 K samples), in situ measurements from the Heihe and Huailai regions in China, and high-resolution imagery from the GF5-02 Visible and Infrared Multispectral Imager (VIMI). The key novelty of this study is the combination of large-scale simulation, an engineered humidity-sensitive feature, and multiple parameter-efficient tuning strategies—full, head, gradual, adapter, and low-rank adaptation (LoRA)—within a unified transfer-learning framework for cross-site LST estimation. In Stage 1, pre-training with 5-fold cross-validation on the simulated dataset produced strong baseline models, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), Deep Neural Network (DNN), Transformer (TrF), and Convolutional Neural Network (CNN). In Stage 2, strategic fine-tuning was conducted under two cross-regional scenarios—Heihe-to-Huailai and Huailai-to-Heihe—and model transfer for tree-based learners. Fine-tuning achieved competitive in-domain performance while materially improving cross-site transfer. When trained on Huailai and tested on Heihe, DNN-gradual attained RMSE 2.89 K (R2 ≈ 0.96); when trained on Heihe and tested on Huailai, TrF-head achieved RMSE 3.34 K (R2 ≈ 0.94). In Stage 3, sensitivity analyses confirmed stability across IQR multipliers of 1.0–1.5, with <1% RMSE variation across models and sites, indicating robustness against outliers. Additionally, application to real GF5-02 VIMI imagery demonstrated that the best SFTL configurations aligned with spatiotemporal in situ observations at both sites, capturing the expected spatial gradients. Overall, the proposed SFTL framework—anchored in cross-validation, strategic fine-tuning, and large-scale simulation—outperforms the widely used Split-Window (SW) algorithm (Huailai: RMSE = 3.64 K; Heihe: RMSE = 4.22 K) as well as direct-training Machine Learning (ML) models, underscoring their limitations in modeling complex regional variability. Full article
(This article belongs to the Special Issue Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence)
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30 pages, 7441 KB  
Article
High-Resolution Mapping and Spatiotemporal Dynamics of Cropland Soil Temperature in the Huang-Huai-Hai Plain, China (2003–2020)
by Guofei Shang, Yiran Tian, Xiangyang Liu, Xia Zhang, Zhe Li and Shixin An
Remote Sens. 2025, 17(22), 3765; https://doi.org/10.3390/rs17223765 - 19 Nov 2025
Viewed by 386
Abstract
Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing region of China, we developed a monthly ST prediction framework [...] Read more.
Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing region of China, we developed a monthly ST prediction framework for two depths (0–5 cm and 5–15 cm) using Random Forest and recursive feature elimination with cross-validation. Based on ~3000 in situ records (2003–2020) and 19 geo-environmental covariates, we generated 1 km monthly cropland ST maps and examined their spatiotemporal dynamics. The models achieved consistently high accuracy (R2 ≥ 0.80; RMSE ≤ 1.9 °C; MAE ≤ 1.1 °C; NSE ≥ 0.8, Bias ≤ ±0.3 °C). Feature selection revealed clear month-to-month shifts in predictor importance: environmental variables dominated overall but followed a U-shaped pattern (decreasing then increasing importance), soil properties became more influential in spring–summer, and topography gained importance in autumn–winter. Interannually, cropland ST declined during 2003–2012 (−0.60 °C/decade at 0–5 cm; −0.52 °C/decade at 5–15 cm) but increased more rapidly during 2012–2020 (1.04 and 0.84 °C/decade, respectively). Seasonally, the largest amplitudes occurred in spring–summer (±0.5 °C at 0–5 cm; ±0.4 °C at 5–15 cm), with there being moderate fluctuations in autumn (±0.1 °C) and negligible changes in winter. These temporal dynamics exhibited pronounced spatial heterogeneity shaped by latitude, elevation, and soil type. Collectively, this study produces high-resolution monthly maps and a transparent variable-selection framework for cropland ST, providing new insights into soil thermal regimes and supporting precision agriculture and sustainable land management in the HHH Plain. Full article
(This article belongs to the Special Issue Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence)
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