Application of Remote Sensing and GIS in Agricultural Engineering

Dear Colleagues,

Currently, global food security is increasingly threatened by many uncertainties, such as drought, flood, heat wave, and air pollution. It is well recognized that the traditional means of agricultural production cannot meet the growing demand for high-quality food around the world. Fortunately, precision agriculture management and agricultural engineering applications with remote sensing and GIS provide a hopeful way of capturing crop growth. Recently, many new technologies (e.g., deep learning) and multisource satellite remote sensing data (e.g., Landsat, Sentinel-1/2, and Planet) are drawing more and more attention for practical application in agricultural engineering. In particular, crop growth is largely influenced by the surrounding water and heat conditions, while the carbon in the atmosphere is absorbed through the stage of crop growth. This means that agriculture production is an important bridge connecting carbon and water dynamics across the agroecosystem. Therefore, to advance the understanding of the role of remote sensing and GIS in agricultural engineering, it is necessary to (1) monitor and manage agriculture production using multisource satellite remote sensing images with advanced deep learning algorithms; (2) capture and quantify the carbon and water parameters during agriculture production; and (3) evaluate the impact of different water and heat conditions on agriculture production.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Precision agriculture;
• Digital agriculture;
• Global food security monitoring;
• Fusing multisource satellite remote sensing images;
• Deep learning model application;
• Agricultural engineering.

I/We look forward to receiving your contributions.

Dr. Jiang Chen
Dr. Lorena Nunes Lacerda
Dr. Lirong Xiang
Guest Editors

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