GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition)

Dear Colleagues,

Due to the great support for and interest in the previous Special Issue, we can now announce a second edition of “GIS and Remote Sensing in Soil Mapping and Modeling"; we would like to thank all the authors and co-authors who contributed to the successful first edition of this Special Issue and look forward to receiving more experts' innovative contributions.

Soil is one of the most important natural resources on our planet and is essential for sustainable agriculture and food production. However, the mapping and modeling soil properties across large areas can be a challenging task due to the complex nature of soil variability. Therefore, the use of advanced GIS and remote sensing technologies can greatly aid soil mapping and modeling.

We encourage submissions that highlight the use of cutting-edge GIS and remote sensing technologies, as well as those that address the practical applications of soil mapping and modeling in real-world scenarios. Overall, this Special Issue will provide a platform for researchers and practitioners to share their knowledge and experiences in the field of soil mapping and modeling, contributing to the advancement of this important area of research. This Special Issue invites original research articles, reviews, and case studies on the following topics:

• Remote sensing data for soil mapping and modeling;
• GIS-based soil mapping and modeling;
• Machine learning and artificial intelligence for soil mapping and modeling;
• Spatial and temporal analysis of soil properties;
• Integration of soil data with other environmental data;
• Uncertainty and error analysis in soil mapping and modeling;
• Applications of soil mapping and modeling in agriculture, forestry, and land-use planning.

Dr. Xiaoling Wu
Dr. Chong Luo
Dr. Liujun Zhu
Dr. Xiaoji Shen
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 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.

• GIS
• remote sensing
• soil science
• soil moisture
• spatial modeling
• climate change
• precision agriculture

• GIS and Remote Sensing in Soil Mapping and Modeling in Remote Sensing (11 articles)

Title: Advances in Hyperspectral Remote Sensing for Soil Organic Carbon Estimation: A Systematic Review and Future Directions
Authors: Correspondence: [email protected]
Affiliation: Hongdou Liu, Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Brisbane, Australia Lizhen Cui, College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China Anquan Xia, College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China Rajiv Pandey, Indian Council of Forestry Research & Education, Dehradun, India Ram C. Dalal, School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, Australia Yunru Lai, Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, Australia Songchao Chen, Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China Matthias Kuhnert, Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK Tong Li*, School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, Australia Yushu Xia, Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Campaign, 1102 S. Goodwin Ave, Urbana, IL 61801, USA Yash P. Dang, School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, Australia
Abstract: Soil organic carbon (SOC) is a cornerstone of soil health and climate resilience. Hyperspectral remote sensing, with its unmatched spectral resolution, is revolutionizing SOC estimation. This review provides a detailed analysis of research advancements in five key areas: sensor platforms, data processing, remote sensing mapping, quantitative inversion models, and validation and calibration techniques. Specifically, we examine the hyperspectral platforms in use today, including satellite, airborne, and UAV systems; discuss data preprocessing techniques; explore common mapping technologies and SOC inversion models; and assess the methods used to validate and calibrate these models for accurate SOC estimation. Despite significant progress, challenges such as high data acquisition costs and limited model scalability persist. We propose that future research should focus on enhancing multi-source data fusion, developing novel algorithms, and establishing robust long-term SOC monitoring systems. This review provides essential insights and sets a strategic path for advancing SOC estimation, paving the way for improved environmental management and sustainability.