Uncertainty Quantification and Propagation in Vegetation Earth Observation

Dear Colleagues,

Accurate monitoring of vegetation structure, functioning, and productivity using Earth observation (EO) requires not only reliable estimates of biophysical and physiological variables, but also a robust characterization of their associated uncertainties. As vegetation products increasingly support climate science, ecosystem assessment, agriculture, and operational decision-making, understanding and communicating uncertainty has become essential for ensuring the credibility, interpretability, and fitness-for-purpose of EO-derived information.

This Special Issue focuses on the characterization, propagation, and interpretation of uncertainty throughout the complete vegetation EO chain. Contributions are encouraged that investigate uncertainty sources associated with measurements, atmospheric correction, radiative transfer modeling, machine learning, inversion procedures, spatial and temporal aggregation, and downstream ecological inference. Particular emphasis is placed on distinguishing between aleatoric uncertainty, arising from measurement noise and intrinsic variability, and epistemic uncertainty, arising from incomplete knowledge, model assumptions, limited training data, and out-of-distribution conditions.

The Special Issue also highlights the often overlooked role of uncertainty in reference and in situ observations used for calibration and validation. Errors and representativeness issues in field measurements, flux tower observations, laboratory analyses, and sampling strategies can propagate through retrieval and learning frameworks and ultimately affect the reliability of EO products. Contributions addressing uncertainty quantification and propagation in ground observations, reference datasets, and cross-scale validation activities are therefore particularly welcome.

Submissions may address uncertainty in the estimation and reconstruction of vegetation variables and ecosystem indicators, including leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), chlorophyll and biochemical traits, solar-induced chlorophyll fluorescence (SIF), evapotranspiration, gross primary productivity (GPP), and ecosystem carbon fluxes. Methodological studies involving radiative transfer models, hybrid retrieval approaches, machine learning, Bayesian inference, ensemble methods, Gaussian processes, Monte Carlo techniques, and uncertainty propagation frameworks are especially encouraged.

The Special Issue seeks contributions on topics including, but not limited to, the following:

• Characterization of measurement, retrieval, structural, and representativeness uncertainties;
• Distinction and quantification of aleatoric and epistemic uncertainty;
• Uncertainty of in situ observations and reference datasets used for calibration and validation;
• Propagation of uncertainty across the EO processing chain;
• Bayesian, ensemble-based, and probabilistic retrieval approaches;
• Uncertainty-aware machine learning and hybrid physical–statistical methods;
• Out-of-distribution detection and model trustworthiness;
• Spatial and temporal scaling effects and uncertainty propagation;
• Uncertainty in Level-3 and Level-4 vegetation products and data fusion frameworks;
• Validation methodologies and uncertainty-aware benchmarking;
• Calibration, reliability diagnostics, and interpretability of uncertainty estimates;
• Uncertainty communication and quality indicators for operational EO products;
• Integration of uncertainty information into ecosystem monitoring and decision-support systems.

By bringing together advances from remote sensing, machine learning, radiative transfer modeling, and ecosystem science, this Special Issue aims to promote a more rigorous and transparent treatment of uncertainty in vegetation EO and to support the development of trustworthy and operationally relevant environmental information products.

Dr. Jochem Verrelst
Dr. Luke A. Brown
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.

• uncertainty quantification
• vegetation earth observation
• epistemic and aleatoric uncertainty
• machine learning and Bayesian inference
• radiative transfer modeling