Special Issue "Applications of Artificial Intelligence and Remote Sensing in Soil Environment Monitoring"
Deadline for manuscript submissions: 15 March 2024 | Viewed by 48
Interests: proximal and remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: proximal sensing; UAS; machine learning
Interests: satellite remote sensing; digital soil mapping; spatial modeling of soil-water-environment relations
The soil environment is essential for supporting life on Earth, from agriculture and food production to ecosystem health and environmental sustainability. As a unique site for the interaction of Earth's spheres, soils play an important role in climate evolution, energy balance, and water, carbon and nitrogen cycles. Large-scale, high-precision, and high-quality soil monitoring is essential for environmental protection, sustainable land and water management, and climate change mitigation. Although the accurate monitoring of the soil environment remains a challenging task due to complex spatio-temporal variations and difficult data acquisition, the development of remote sensing and artificial intelligence technologies now make this possible.
With the continuous advancement of remote sensing technology, there have been significant breakthroughs in data acquisition capabilities, producing mass amount of highly dimensional and diverse data, enabling more in-depth applications using artificial intelligence methods, such as machine learning, deep learning or feature extraction. The combination of remote sensing and artificial intelligence is a powerful and innovative approach that brings significant benefits in terms of efficiency, accuracy and scalability across multiple domains. This integration allows us to harness the vast amounts of data collected with space and terrestrial sensors and enhances our ability to understand soil properties, dynamics, and functions and to address soil-related environmental challenges. Therefore, conducting scientific research using this synergistic approach is conducive to developing an intelligent method for more accurate environmental monitoring.
The main objective of this Special Issue is to provide a scientific platform to discuss recent advances in the application of remote sensing and AI techniques for monitoring the soil environment. Theoretical and applied papers are welcome, as well as contributions on new advanced artificial learning and data science techniques from the remote sensing research community.
Dr. Asa Gholizadeh
Dr. János Mészáros
Dr. Katalin Takács
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 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.
- soil environment monitoring
- remote sensing
- machine learning
- artificial intelligence
- big data
- data mining
- digital soil mapping
- ground truth
- spatio-temporal modelling