Special Issue "The Use of Proximal and Remote Sensing Techniques for the Detection and Mapping of Contaminants in Soils"
Deadline for manuscript submissions: 31 December 2022 | Viewed by 3723
Interests: soil dynamics; soil tillage; soil compaction; numerical modelling; proximal soil sensing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Estimation and Mapping of Soil Properties Based on Multi-Source Data Fusion
Special Issue in Sensors: Precision Agriculture and Sensor Systems
Special Issue in Agronomy: Soil Sustainability in the Anthropocene
Special Issue in Land: Measurement of Within-Field Spatial Variability for Evaluating Soil Degradation
2. Department of Earth & Environmental Sciences, Faculty of BioScience Engineering, Celestijnenlaan 200E, 3001 Heverlee, Belgium
Interests: water–soil–crop modelling; remote sensing; data mining
Interests: remote sensing; proximal sensing; precision agriculture; machine learning; data fusion
Soil contamination with potentially toxic elements (PTEs), petroleum hydrocarbons (PHCs), and microplastics poses a threat to the environment and human health. Proper methods and tools are essential to measure and map soil contaminants for effective management and pollution risk assessment. Proximal and remote sensing (RS) techniques are promising tools for detecting, monitoring, and mapping soil contamination at high spatial resolutions and temporal intervals. Massive amounts of RS (e.g., multispectral and hyperspectral) data become available with different spatial, spectral, and temporal resolutions, which play a potential vital role in the detection and monitoring of soil contaminants at different scales. Proximal soil sensing (PSS) offers high sampling resolution with real-time measurement. Generally, PSS includes spectroscopic methods, such as visible and near infrared (vis–NIRS), mid-infrared (MIRS), X-ray fluorescence (XRFS), gamma ray, and laser-induced breakdown spectroscopy (LIBS) in addition to geophysical methods such as electrical resistivity, electromagnetic induction, and ground penetrating radar. PSS techniques provide rapid and accurate laboratory and/or field measurements that can be optimized and combined with advanced data analytics such as machine learning methods. Besides the sensors, data fusion techniques have greatly advanced the monitoring of soil contaminates that require more extensive temporal and spatial information not available with a single sensor or data source. The integration of multi-sensor and data fusion (e.g., RS, PSS, or RS and PSS) with digital soil mapping techniques will provide a better tool for accurately monitoring and mapping soil contaminates at various spatial and temporal scales. The suggested methods may provide new insights into the pollution process and different options for land management practices on contaminated sites. This emerging field needs to be developed to overcome the environmental factors that impact the accurate quantification of PTEs, PHCs, and microplastics, using better in situ measurements and mapping.
This Special Issue focuses on the potential of RS and PSS technologies and advanced machine learning techniques for modeling and mapping soil contaminates, including PTEs, PHCs, and microplastics, for site-specific land reclamation. We invite papers on both fundamental and applied research related to the use of individual and combined sensing tools for soil contaminant analysis with the capabilities of detecting and monitoring soil contamination for better risk assessment and environmental management. We also invite papers dedicated to new proximal sensors that can be used in PTEs, PHCs, and microplastics analysis that are aimed at better detection and mapping at different scales. In particular, research articles that cover but not limited to the following topics are welcome:
- Remote sensing technologies for estimating and mapping soil contaminates at topsoil layers.
- Proximal soil sensing tools, including common (see above-mentioned list of technologies) and emerging techniques for the measuring and mapping of HMs, high salt concentrations, PHCs, and microplastics in soils.
- Sensors and data fusion techniques for modeling soil contaminates.
- Digital mapping of soil contaminants using remote sensing technology.
- The fusion of different combinations of remote and proximal sensing for monitoring and management of soil pollution, including risk assessment.
- Cloud computing and big data analytics for monitoring environmental pollution.
Prof. Dr. Abdul M. Mouazen
Prof. Dr. Anne Gobin
Dr. Said Nawar
Dr. Yiyun Chen
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. 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 2500 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.
- potentially toxic elements
- petroleum hydrocarbons
- remote sensing
- proximal sensing
- machine learning
- data Fusion
- soil analysis