Advanced Statistical Methods in Environmental and Climate Sciences
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".
Deadline for manuscript submissions: 20 February 2026
Special Issue Editors
Interests: Viticulture suitability modeling and forest habitat analysis under climate change; integrated statistical-geospatial techniques for agricultural and ecological applications.
Interests: Remote sensing; GIS; forest monitoring.
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
The fields of environmental and climate science are facing increasingly complex and changing phenomena, with many processes interacting to create patterns that are difficult to understand over time and space.
The application of advanced statistical methods is essential for identifying these structures from both descriptive and predictive perspectives. This approach provides objective elements for decision-making processes.
Massive databases containing a very large volume of information, and a large number of interconnected variables allow for the use of sophisticated data analysis techniques in the context of climate change.
We are particularly interested in submissions that go beyond traditional statistical techniques to tackle complex, high-dimensional datasets. Our focus is on innovative methods for modeling and understanding the relationships between variables that manifest differently in space or time.
For this Special Issue, we welcome the submission of original research articles on topics including, but not strictly limited to, the following:
- Machine learning, including techniques particularly adept at identifying patterns and handling high-dimensional datasets, such as random forest, support vector machines (classification and regression), data assimilation, and downscaling.
- Multivariate and data management techniques, with a particular focus on issues such as high data dimensionality, high correlation between variables, and non-linearity.
- Remote sensing, satellite data, and teleconnection patterns.
- Spatiotemporal and time series models, including aspects such as Extreme Value Analysis (EVA), imputation of missing data, comparison of climate databases, and trend detection.
This Special Issue on "Advanced Statistical Methods in Environmental and Climate Sciences" invites cutting-edge research that applies and develops advanced statistical methodologies to address critical challenges in environmental and climate sciences.
We encourage submissions that present methodological innovations as well as demonstrate their application to real-world problems.
Dr. Ramón Álvarez-Esteban
Prof. Dr. Flor Álvarez-Taboada
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 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. Applied Sciences 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 2400 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.
Keywords
- Environmental and Climate Sciences, Remote sensing, Data handling, Time series, Multidimensional statistical methods, Machine learning
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