Remote Sensing for Vegetation Biophysical and Biochemical Parameters Retrieval

Dear Colleagues,

Around 60% of the Earth’s land surface consists of biologically active vegetation. The status of the vegetation cover is demonstrated in functional traits. The retrieval and mapping of the traits of leaf and canopy vegetation play a critical role in assessing the functioning and dynamics of the ecosystems. Plant biochemical and biophysical parameters, such as leaf area index, leaf mass per area, biomass, leaf/canopy chlorophyll and water content, plant nitrogen and carbon content, and crop yield, are important for understanding the forest and agricultural ecosystem functioning, monitoring, and productivity.

Several retrieval algorithms are available, including parametric regression, nonparametric regression, and physically based approaches. The most widely used approach is PROSPECT, which simulates leaf optical properties, and SAIL, which has evolved over the last few decades as the most widely used canopy radiative transfer model (RTM). A combination of RTMs and machine learning regression (MLR) techniques enables a fast and robust retrieval of vegetation traits from remote sensing data by considering physical principles and statistical analysis. 

Advances in sensors on board satellites, airborne vehicles, and UAVs, characterized by their high spectral and spatial resolutions, such as multispectral (e.g., Landsat, Sentinel-2) and hyperspectral (PRISMA, DESIS, EnMAP), as well as cloud computing platforms (e.g., Google Earth Engine) and MLRs, have opened new opportunities for the retrieval of such traits in field, regional, and global-scale studies.

In this regard, spaceborne and airborne imaging spectroscopy, which provide continuous narrow spectral information (e.g., 400–2500 nm), are critical to detect complex vegetation traits such as leaf anthocyanin content, which is often not possible to estimate with multispectral remote sensing. This Special Issue aims to leverage these new technologies and methodologies in the retrieval of vegetation parameters.

In this Special Issue, original research articles, reviews, and short communications are welcome. Research areas may include (but are not limited to) the following:    

- Spaceborne, airborne, and UAV field imaging spectroscopy and its role for high-accuracy retrieval of vegetation traits;

- Advances in leaf (e.g., PROSPECT-PRO) and canopy (e.g., 4SAIL) RTMs and inversion techniques;

- Comparison of different sensors, algorithms, and methods for vegetation trait retrieval from Terrestrial Earth Observation data;

- Active learning and Principal Component Analysis (PCA) for model optimization by reducing sample and spectral dimension;

- Upscaling of vegetation traits from leaf to the canopy level by combining ground truth data, MLRs, and RTM outputs;

- The pixel-based uncertainty quantification for models’ transferability assessment using independent datasets on different conditions.

Dr. Nizom Farmonov
Prof. Dr. Bendix Jörg
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.

• imaging spectroscopy
• agriculture
• forest ecosystems
• radiative transfer modeling
• machine learning
• hybrid methods
• vegetation biophysical and biochemical parameters