Calibration in Hyperspectral Remote Sensing: Bridging Theory and Application

Dear Colleagues,

Hyperspectral remote sensing, capable of acquiring contiguous spectral information for each pixel, has revolutionized our ability to quantify the biochemical and biophysical properties of the Earth's surface. The accuracy of these quantitative retrievals, however, is fundamentally contingent upon the precision of radiometric and spectral calibration. Traditional approaches often treat the processes of sensor calibration, atmospheric correction, and surface parameter inversion as sequential, independent steps. This disconnection can lead to the propagation and accumulation of errors, ultimately limiting the reliability of the final geophysical products. There is a growing recognition within the community that a more integrated approach is necessary. This involves jointly optimizing calibration parameters, atmospheric state variables, and surface characteristics to achieve a unified, physically consistent solution. Advancing this integrated inversion paradigm is critical for unlocking the full potential of hyperspectral data for precision agriculture, environmental monitoring, mineral exploration, and climate change studies.

This Special Issue aims to collate cutting-edge research that bridges the gap between sensor radiometric calibration and the quantitative retrieval of surface parameters from hyperspectral data. We welcome contributions that move beyond conventional sequential processing chains towards integrated, holistic inversion frameworks. The goal is to showcase novel methodologies, models, and applications that simultaneously or iteratively solve for calibration parameters and surface properties, thereby enhancing the overall accuracy and operational utility of hyperspectral remote sensing.

We invite the submission of high-quality original research articles and comprehensive review papers related to hyperspectral areas. Topics of interest include, but are not limited to, the following:

♦ Radiometric Calibration Advances: On-orbit, vicarious, and cross-calibration techniques integrated with surface parameter retrieval.
♦ Atmospheric Correction Integration: Methods that tightly couple atmospheric compensation with the inversion of surface reflectance and albedo.
♦ Novel Inversion Methodologies: Physical, statistical, and hybrid models for joint inversion of atmospheric and surface parameters.
♦ Validation and Uncertainty Quantification: Robust frameworks for validating integrated inversion results and quantifying end-to-end uncertainties.
♦ AI/ML-Enhanced Inversion: Application of deep learning, machine learning, and data assimilation techniques for unified inversion problems.
♦ Multi-Source Data Fusion: Synergistic use of hyperspectral data with LiDAR, multispectral, or SAR data to constrain and improve inversion accuracy.
♦ Applications of Hyperspectral: Case studies demonstrating the superiority of integrated approaches in key areas such as the following:

— Geology and Mineral Exploration: Mineral mapping, lithological discrimination, and identification of alteration zones associated with ore deposits.
— Vegetation biochemistry (e.g., chlorophyll and leaf area index).
— Soil properties and composition.
— Water quality and coastal monitoring.
— Urban material classification

Dr. Yaokai Liu
Prof. Qijin Han
Prof. Dr. Shuang Wang
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-anonymized 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.

• hyperspectral remote sensing
• radiometric calibration
• spectral calibration
• parameter inversion
• atmospheric correction
• mineral exploration
• vegetation biophysical parameters
• data fusion
• uncertainty quantification