Special Issue "Integrating Remote Sensing and Geospatial Big Data for Soil Moisture Estimation"

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Soil-Sediment-Water Systems".

Deadline for manuscript submissions: 25 May 2023 | Viewed by 221

Special Issue Editors

Prof. Dr. Salvatore Manfreda
E-Mail Website1 Website2
Guest Editor
Department of Civil, Architectural and Environmental Engineering, University of Naples "Federico II", Napoli, Italy
Interests: distributed hydrological modeling; flood risk assessment; stochastic processes in hydrology; river monitoring and UAS-based monitoring
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Dr. Yijian Zeng
E-Mail Website1 Website2
Guest Editor
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
Interests: soil-water-plant-energy interactions; land-atmosphere interactions; soil moisture; earth observation; climate data records; data assimilation
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Dr. Ruodan Zhuang
E-Mail Website
Guest Editor
Department of Civil, Architectural and Environmental Engineering, University of Naples "Federico II", Napoli, Italy
Interests: remote sensing; soil moisture; UAS; machine learning

Special Issue Information

Dear Colleagues,

Soil Moisture (SM) is a vital element in the hydrological cycle and land–atmosphere interactions. Quantification of SM and its spatiotemporal variability is valuable for understanding water availability in agriculture, ecosystem states, river basin hydrology, and water resources management, with different requirements of scales and spatial or temporal resolution. Thus, the precise quantification of SM and the spatial–temporal variability at different scales are always receiving considerable attention.

In recent years, with the explosion of geospatial data (from remote sensing, modelling, etc.), and the development of big data processing techniques (machine learning, etc.), the monitoring and estimation of soil moisture at multiple scales can be beneficial. Some efforts have been done to fill the scaling and resolution gaps of soil moisture, as it is always worth taking a deep look into the comprehensive usage of all available data and integrating the usage of them (Su et al., 2020).

For this Special Issue, we are interested in contributions that integrate remote sensing and geospatial big data for soil moisture estimation, through either empirical research or conceptual/theoretical works including, but not limited to:

  • Remote sensing of soil moisture (satellites or UAS);
  • Soil moisture data fusion and assimilation;
  • Machine learning algorithms assessment;
  • Construction of soil moisture database;
  • Gap filling of soil moisture data;
  • Novel tools for geospatial data processing (GEE et al.).

Contributions to remote sensing and geospatial big data of soil moisture are especially welcome, but contributions from other natural sciences at the forefront of soil moisture estimation are also highly welcome. Machine learning and imagery/data processing in contributions are also desired.

Proposed titles and abstracts (250 words) can be submitted by 15 November 2023 to the guest editors, at [email protected], for possible feedback, if prospective authors want some feedback before preparing their manuscripts.

Prof. Dr. Salvatore Manfreda
Dr. Yijian Zeng
Dr. Ruodan Zhuang
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. Land is an international peer-reviewed open access monthly 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.

Keywords

  • remote sensing
  • soil moisture
  • big geospatial data
  • machine learning
  • geospatial data processing

Published Papers

This special issue is now open for submission.
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