Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing

Dear Colleagues,

In recent years, artificial intelligence has become the core driving force behind a new wave of industrial transformation; this will further unleash the enormous energy of technological innovation. The combination of artificial intelligence and specific industries will lead to the emergence of novel technologies and products, profoundly changing human thinking and production patterns, and achieving an overall leap in social and industrial productivity. Considering the potential and significance of deep learning in the fields of geology and agriculture, in order to promote the application of artificial intelligence in the fields of geology and agriculture, it is necessary to accelerate the deep integration of artificial intelligence and remote sensing technology, provide key technical support for meteorological forecasting, agricultural monitoring, and agricultural disaster prediction, and thus facilitate global disaster monitoring and food security. Cross-disciplinary research is only in its preliminary stages, and the majority of deep learning applications in geosciences are still “black boxes”, with most applications lacking physical significance, interpretability, and universality.

This Special Issue aims to study the application of artificial intelligence methods in the retrieval of remote sensing key parameters in geology and agriculture. Topics may address anything from the retrieval of surface temperature or soil moisture, to atmospheric water vapor content and rainfall in the atmosphere.

Hence, submissions describing remote sensing parameters retrieved from multi-source data (such as multispectral, hyperspectral, thermal infrared, and microwave) at multiple scales are welcome. Articles may address, but are not limited, to the following topics:

• Surface Temperature
• Near-Surface Air Temperature
• Surface Emissivity
• Soil Moisture
• Vegetation Moisture Content
• Water Vapor Content
• Precipitation
• LAI
• Drought and Flood

Prof. Dr. Kebiao Mao
Dr. Sayed M. Bateni
Dr. Jongmin Park
Dr. Lixin Dong
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 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.