Radiative Transfer Models for Remote Sensing Land Surface Parameter Estimation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 7
Special Issue Editors
Interests: cloud and shortwave radiation parameter retrieval; cloud-radiation interaction
Interests: remote sensing; topographic effect; vegetation
Interests: vegetation biophysical parameters; time series analysis; deep learning model; vegetation dynamics
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; imaging science; photographic technology; geology; environmental sciences; ecology engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Radiative transfer models (RTMs) are pivotal in remote sensing for accurately estimating surface parameters such as surface reflectance, the leaf area index (LAI), and solar radiation, which are critical for understanding Earth’s complex land surface processes. These models simulate the interaction of electromagnetic radiation with the atmosphere, vegetation, soil, and topography, enabling the precise retrieval of surface characteristics from satellite, airborne, or drone-based sensors. In challenging environments, such as mountainous areas, where topography significantly influences solar illumination, atmospheric scattering, and surface reflectance, RTMs are essential for correcting distortions and improving estimation accuracy. Recent advancements in high-resolution sensors (e.g., Sentinel-2, Landsat 8, and hyperspectral platforms) and computational techniques, including machine learning and deep learning, have enhanced the capability of RTMs to model complex radiative interactions at finer spatial and temporal scales. This Special Issue addresses the growing importance of RTMs in advancing remote sensing applications, particularly for environmental monitoring and ecosystem studies, where accurate surface parameter estimation is crucial for informed, sustainable resource management.
This Special Issue aims to showcase cutting-edge research on the development, application, and validation of radiative transfer models for surface parameter estimation using remote sensing data. It seeks to explore innovative approaches that integrate RTMs with emerging technologies, such as machine learning, high-resolution imagery, and multi-sensor data fusion, to enhance the accuracy and applicability of surface parameter data retrieval. This subject aligns closely with the scope of Remote Sensing, which emphasizes advancements in sensor technology, data processing, and environmental applications.
We welcome the submission of original research articles, review papers, and technical notes that address, but are not limited to, the following themes:
The development of advanced RTMs;
Topographic corrections;
Machine learning integration;
Surface reflectance and energy budget components;
Ecosystem and hydrological applications;
Validation and uncertainty analysis.
Submissions are encouraged to include case studies, methodological advancements, or interdisciplinary approaches that bridge remote sensing and environmental sciences. We particularly welcome contributions that address challenges in rugged or heterogeneous terrain, where RTMs can significantly improve data quality and application outcomes.
Dr. Xinyan Liu
Dr. Yichuan Ma
Dr. Guodong Zhang
Dr. Anxin Ding
Guest Editors
Manuscript Submission Information
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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
- radiative transfer model
- surface parameter estimation
- physical model
- satellite product
- machine learning
- moderate and high resolution
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