Special Issue "Remote Sensing of Plant Functional Traits"
Deadline for manuscript submissions: 1 August 2020.
Plant functional traits reflect key state variables of plant physiological performance. Recently, new generation vegetation models have used leaf traits to represent functional diversity across and within plant types and forms. Based on the radiation absorption and scattering properties of leaves, most functional relevant traits such as pigment contents (e.g., chlorophylls, carotenoids) and leaf nitrogen content can be retrieved from the spectral signal. With recent advancements in remote sensing technologies and data model approaches, a number of physiological attributes of vegetation can be successfully monitored at the appropriate temporal and spatial scales. For instance, the maximum carboxylation capacity (key trait related to photosynthesis) can be inferred from the reflectance via the inversion of the radiative transfer model. As a purely empirical approach, a range of statistical models (e.g., random forest regression, partial least square regression, among the most promising methods) provide an alternative means to squeezing the most relevant information out from the whole reflectance spectra, which offers a clear advantage against traditional multispectral approaches. With the constellation of remote sensing products, this emerges as a unique opportunity with extended applications in a range of research areas, including plant functional ecology.
In this Special Issue, we welcome studies linking spectral reflectance to plant functional traits. From a remote sensing application perspective, we particularly encourage studies detailing the functional convergency/divergency of specific plant traits across plant forms and environmental conditions. This includes scale studies from proximal sensing up to all the ways to satellite observations. In addition, we encourage studies covering the following topics:
- Novel retrieval approaches of plant functional traits;
- Uncertainties in measuring or modeling plant traits. Evaluating confounding factors such as the effects of leaf structure, plant architecture, as well as other properties at the community levels on the retrieval of plant functional traits;
- Direct comparisons of empirical and statistical or model inversion approaches. Hyperspectral against multispectral approaches for the retrieval of plant traits;
- Linkages between sun-induced chlorophyll fluorescence and plant functional traits.
Dr. Oscar Perez-Priego
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.
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 2200 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.