Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Applications of Remote Sensing and Machine Learning for Digital Soil...
share announcement

Applications of Remote Sensing and Machine Learning for Digital Soil Mapping

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 3156

Special Issue Editors

Dr. Jing Geng

E-Mail Website
Guest Editor
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
Interests: digital soil mapping; multi-source remote sensing; soil properties prediction; soil nutrients cycling
Dr. Yongsheng Hong

E-Mail Website
Guest Editor
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Interests: remote sensing; digital soil mapping; pedometrics; biogeochemical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Yiyun Chen

E-Mail Website
Guest Editor
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: soil-landscape relationships; machine learning and AI; legacy soil data utilization; precision agriculture; multi-scale landscape metrics; remote sensing-derived variables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

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. Agriculture is an international peer-reviewed open access monthly 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 2600 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.

Keywords

  • machine/deep learning
  • remote sensing
  • digital soil mapping
  • soil property prediction
  • big data analytics
  • soil resource management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 80170 KiB  
Article
Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)
by Liangwei Cheng, Mingzhi Yan, Wenhui Zhang, Weiyan Guan, Lang Zhong and Jianbo Xu
Agriculture 2024, 14(9), 1578; https://doi.org/10.3390/agriculture14091578 - 11 Sep 2024
Viewed by 695
Abstract
Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous [...] Read more.
Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted R2) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg−1, respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning for Digital Soil Mapping)
►▼ Show Figures

Figure 1

25 pages, 17857 KiB  
Article
Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils
by Ali Sakhaee, Thomas Scholten, Ruhollah Taghizadeh-Mehrjardi, Mareike Ließ and Axel Don
Agriculture 2024, 14(8), 1298; https://doi.org/10.3390/agriculture14081298 - 6 Aug 2024
Viewed by 918
Abstract
Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter [...] Read more.
Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning for Digital Soil Mapping)
►▼ Show Figures

Figure 1

20 pages, 1831 KiB  
Article
Deep Learning with a Multi-Task Convolutional Neural Network to Generate a National-Scale 3D Soil Data Product: The Particle Size Distribution of the German Agricultural Soil Landscape
by Mareike Ließ and Ali Sakhaee
Agriculture 2024, 14(8), 1230; https://doi.org/10.3390/agriculture14081230 - 26 Jul 2024
Viewed by 855
Abstract
Many soil functions and processes are controlled by the soil particle size distribution. Accordingly, nationwide geoinformation on this soil property is required to enable climate-smart and resilient land management. This study presents a new deep learning approach to simultaneously model the contents of [...] Read more.
Many soil functions and processes are controlled by the soil particle size distribution. Accordingly, nationwide geoinformation on this soil property is required to enable climate-smart and resilient land management. This study presents a new deep learning approach to simultaneously model the contents of the three particle sizes of sand, silt, and clay and their variations with depth throughout the landscape. The approach allows for the consideration of the natural soil horizon boundaries and the inclusion of the surrounding landscape context of each soil profile to investigate the soil–landscape relation. Applied to the agricultural soil landscape of Germany, the approach generated a three-dimensional continuous data product with a resolution of 100 m in geographic space and a depth resolution of 1 cm. The approach relies on a patch-wise multi-target convolutional neural network (CNN) model. Genetic algorithm optimization was applied for CNN parameter tuning. Overall, the effectiveness of the CNN algorithm in generating multidimensional, multivariate, national-scale soil data products was demonstrated. The predictive performance resulted in a median root mean square error of 17.8 mass-% for the sand, 14.4 mass-% for the silt, and 9.3 mass-% for the clay content in the top ten centimeters. This increased to 20.9, 16.5, and 11.8 mass-% at a 40 cm depth. The generated data product is the first of its kind. However, even though the potential of this deep learning approach to understand and model the complex soil–landscape relation is virtually limitless, its limitations are data driven concerning the approximation of the soil-forming factors and the available soil profile data. Full article
(This article belongs to the Special Issue Applications of Remote Sensing and Machine Learning for Digital Soil Mapping)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop