Applications of Remote Sensing and Machine Learning for Digital Soil Mapping

Dear Colleagues,

The convergence of agriculture, environmental science, and cutting-edge technology is ushering in a new era in soil management and conservation. Soil mapping serves as a fundamental activity underpinning numerous environmental and agricultural endeavors. Traditional approaches, while foundational, are often characterized by their time-intensive nature, labor demands, and a potential lack of dynamism in capturing soil properties. The integration of machine learning (ML) with remote sensing technology offers a groundbreaking alternative, enhancing the precision, efficiency, and scope of soil analyses. The aim of this Special Issue is to demonstrate the enhanced capabilities that machine learning and remote sensing technologies bring to digital soil mapping. It seeks to bridge ML and traditional soil science, fostering a multidisciplinary exchange that elevates our ability to forecast, scrutinize, and manage soil resources with unprecedented accuracy.

We are soliciting original research articles and reviews covering, but not limited to, the following topics:

• Integration of machine learning algorithms and remote sensing for soil property prediction (as well as for soil classification);
• Machine learning approaches for soil classification and taxonomy;
• Soil spectral library, including visible–near-infrared and mid-infrared spectroscopy;
• Proximal, airborne, and satellite remote sensing;
• Advanced analytics in soil science utilizing big data and artificial intelligence;
• Case studies demonstrating the impact of these technologies in agricultural and environmental contexts.

Dr. Jing Geng
Dr. Yongsheng Hong
Dr. Yiyun Chen
Guest Editors

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