Recent Progress in GIS and Remote Sensing for Agricultural Applications

Dear Colleagues,

Monitoring agricultural activities can provide valuable information to farmers, investors, and decision makers. In recent years, space-based crop identification and monitoring have played a significant role in agricultural studies due to the development of Remote Sensing and GIS technology. Moreover, recent progress in digital technologies, such as cloud computing, data fusion, deep learning, artificial intelligence, social media, and cyber-infrastructure, has enhanced the spatial and temporal scale of crop mapping with new tools and methods for facilitating and promoting new innovative approaches in agriculture. This Special Issue will enable papers exploring the progress in innovative crop mapping and monitoring research and applications to be published.

Papers that are suitable for the Special Issue must address relevant topics in agricultural mapping and monitoring and include sound implementation and validation procedures. We welcome submissions that describe state-of-the-art approaches in the progress in GIS and remote sensing for agricultural-related applications, including, but not limited to, the following:

• Research on remote sensing crop mapping theory, methodology, and practices;
• Geospatial information for stratification and sampling;
• Data fusion, calibration, validation, and ground truths for agricultural monitoring;
• Crop mapping, condition monitoring, acreage estimation, and yield modeling;
• Cropland evapotranspiration, soil moisture, and drought monitoring and assessment;
• Remote sensing-based agricultural disaster monitoring, assessment, mitigation, and emergency response;
• Global climate and environmental change and its impacts on agriculture sustainability and food security;
• Remote sensing monitoring and modeling on agricultural greenhouse gases;
• Agricultural environment and public health;
• Crop/plant disease detection, monitoring, and assessment;
• Cloud computing and big data in agriculture-related applications;
• Agricultural land use and land cover change;
• Agricultural deep learning and artificial intelligence technology;
• Spatial data uncertainty analysis.

Dr. Li Lin
Dr. Chen Zhang
Prof. Dr. Liping Di
Dr. Zhengwei Yang
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-anonymized 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.