Integrating Satellite Data, Radiative Transfer Models and Artificial Intelligence for Land Surface Observation
A Special Issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 September 2026 | Viewed by 262
Editors
Interests: deep learning and advanced analytical methods to interpret earth system dynamics
Special Issue Information
Dear Colleagues,
As satellite constellations (e.g., MODIS, VIIRS, Sentinel, Landsat, and geostationary platforms) generate observations at unprecedented spatial–temporal–spectral resolutions, the challenge has shifted from data scarcity to the transform of heterogeneous, sensor-specific measurements into reliable geophysical variables that can support climate, ecosystem, agriculture, and energy applications. Traditional remote sensing pipelines built on radiative transfer theory offer interpretability and physical consistency, but they are often computationally intensive, sensitive to input uncertainties, and difficult to scale globally. In parallel, AI/ML methods excel at learning from large archives but can overfit to sensor artifacts or atmospheric conditions when trained without physical constraints.
This Special Issue aims to advance hybrid, physics-guided AI pipelines for producing accurate, scalable, and traceable land surface information. The topic sits squarely within Remote Sensing’s scope—spanning sensor calibration and inter-calibration, atmospheric correction, retrieval algorithms, data fusion, and AI-enabled Earth observation. By uniting RTM-based physical rigor with data-driven learning, this Special Issue advances core remote sensing science while delivering application-ready products for climate, ecosystem, agriculture, and energy communities.
A core emphasis is on tightly coupling satellite observations with radiative transfer models (RTMs)—using RTMs to generate synthetic training data, constrain inversions, correct for geometry and aerosols, or harmonize measurements across sensors—while leveraging AI/ML architectures (transformers, diffusion models, and physics-informed networks) to improve accuracy, generalization, speed, and uncertainty quantification. Studies on sensor inter-calibration, spatiotemporal gap filling, and cross-domain transfer that demonstrate how physically guided AI can accelerate global product generation while preserving the interpretability and traceability required by climate services and long-term monitoring programs at national scales are also welcomed. Application-driven papers are encouraged, including those on vegetation and crop monitoring, carbon and energy flux estimation, and wildfire and drought assessments. Submissions that release open datasets, benchmark protocols, or reproducible pipelines to enable closer collaboration between the remote sensing, radiative transfer, and AI communities are also valued.
Dr. Ruohan Li
Dr. Aolin Jia
Guest Editors
Manuscript Submission Information
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Keywords
- radiative transfer models
- artificial intelligence
- remote sensing
- physics-guided machine learning
- surface energy budget
- land surface interaction
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