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Hyperspectral Remote Sensing for Soil Property Retrieval: Advances and Applications

This special issue belongs to the section “Environmental Remote Sensing“.

Special Issue Information

Dear Colleagues,

Healthy soils are fundamental to food security, ecosystem services, and climate resilience. Traditional soil monitoring, based on laboratory analyses and in situ sampling, remains accurate but is costly, labor-intensive, and spatially limited. Hyperspectral remote sensing has emerged as a powerful alternative, offering rapid, non-destructive, and spatially explicit characterization of soil properties. The development of advanced airborne and spaceborne sensors (e.g., EnMAP, PRISMA, DESIS, and the forthcoming CHIME mission), along with UAV-based imaging systems, has created new opportunities for soil monitoring at multiple scales. Coupled with methodological advances in chemometrics, machine learning, and data fusion, these technologies are transforming how soil functions and degradation processes are studied and managed.

This Special Issue seeks to highlight recent advances and applications of hyperspectral remote sensing for soil property retrieval. It aims to provide a platform for interdisciplinary contributions that enhance our understanding of soil processes, promote digital soil mapping, and support sustainable land management under changing environmental conditions. By bringing together innovative methods, case studies, and reviews, the Special Issue aligns closely with the journal’s scope of advancing remote sensing applications in agriculture, environment, and ecosystem monitoring.

We invite contributions on a broad range of topics, including but not limited to, the following:

  • Retrieval of soil physical, chemical, and biological properties from hyperspectral data;
  • Development and use of soil spectral libraries;
  • UAV-, airborne-, and spaceborne-based soil spectroscopy;
  • Integration of hyperspectral data with multispectral, LiDAR, and in situ observations;
  • Advances in machine learning and statistical modeling for soil property prediction;
  • Monitoring soil degradation, salinization, and contamination;
  • Applications in precision agriculture, land restoration, and climate-smart management.

Both original research articles and comprehensive reviews are welcome. Case studies from diverse pedo-climatic zones and contributions from interdisciplinary teams are particularly encouraged.

Dr. Mohammadmehdi Saberioon
Guest Editor

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-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.

Keywords

  • hyperspectral remote sensing
  • soil spectroscopy
  • digital soil mapping
  • UAV- and satellite-based sensing
  • machine learning and data fusion
  • precision agriculture

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Remote Sens. - ISSN 2072-4292