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Land
Special Issues
Digital Soil Mapping and Precision Agriculture
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Digital Soil Mapping and Precision Agriculture

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land, Soil and Water".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1292

Special Issue Editors

Dr. Xiaobo Wu

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Guest Editor
College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Interests: digital soil mapping and remote sensing
Prof. Dr. Songchao Chen

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Guest Editor
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Interests: proximal soil sensing; soil spectroscopy; digital soil mapping; carbon sequestration; soil biogeochemical modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil is a critical natural resource underpinning global food security, ecosystems, and climate regulation. However, traditional soil mapping and agricultural practices often lack the resolution and adaptability needed to address contemporary challenges such as land degradation, climate change, and population growth. Recent advances in geospatial technologies, machine learning (deep learning), and sensor networks have revolutionized our capacity to analyze soil systems and optimize agricultural practices at unprecedented scales. Digital soil mapping (DSM) and precision agriculture (PA) are emerging as transformative approaches that integrate high-resolution data, predictive modeling, and site-specific management to enhance soil health, crop productivity, and environmental sustainability.

This Special Issue aims to compile original research and review articles that advance the science and application of DSM and PA, emphasizing their role in sustainable land management. We aim to address gaps in spatial soil data, model accuracy, and technology adoption in diverse agroecosystems.

This Special Issue welcomes contributions on, but not limited to, the following topics:

  • High-resolution soil property mapping using remote sensing, ML, or IoT.
  • AI/ML-driven predictive models for soil health and yield optimization.
  • Data/model fusion frameworks integrating multi-source geospatial and field data.
  • Environmental impacts and circular agriculture tied to PA systems.
  • Innovations in DSM (e.g., AI/ML applications, hyperspectral imaging, and UAV-based sensing).
  • Integration of multi-source data (remote sensing, in situ measurements, and legacy data).
  • Climate-resilient agricultural practices leveraging DSM/PA.
  • Case studies on regional-to-global DSM frameworks.

Submissions may include original research, reviews, case studies, or methodological papers. In particular, research on the influence of human factors on soil property mapping is encouraged.

Dr. Xiaobo Wu
Prof. Dr. Songchao 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. Land 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

  • digital soil mapping
  • precision agriculture
  • geospatial technologies
  • machine learning
  • soil health
  • sustainable land management
  • remote sensing

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

Published Papers (2 papers)

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Research

16 pages, 6698 KB  
Article
Interpretable Machine Learning and Remote Sensing Data Reveal Soil Biogeochemistry Patterns in Agricultural Systems
by Ruslan Suleymanov, Marija Yurkevich, Olga Bakhmet, Tatiana Popova, Andrey Kungurtsev, Denis Zakirov, Anastasia Vittsenko, Gaurav Mishra and Azamat Suleymanov
Land 2025, 14(9), 1881; https://doi.org/10.3390/land14091881 - 15 Sep 2025
Viewed by 418
Abstract
Soil condition represents a critical factor for ensuring sustainable agricultural development and food security. In this study, we examined the content of key soil properties and their patterns using an interpretable machine learning framework in combination with remote sensing data (Sentinel-2A) across several [...] Read more.
Soil condition represents a critical factor for ensuring sustainable agricultural development and food security. In this study, we examined the content of key soil properties and their patterns using an interpretable machine learning framework in combination with remote sensing data (Sentinel-2A) across several land use types in northwestern Russia. The analyzed soil properties in 64 samples included soil organic carbon (Corg), total nitrogen (N), mobile phosphorus (Pmob), total phosphorus (Ptot), and mobile potassium (Kmob) sampled across three land use types: cropland, hayfield, and forest. For machine learning interpretability, model-agnostic methods were utilized, including permutation and SHapley Additive exPlanations (SHAP) with spatial visualization. Our results revealed the highest concentrations of Corg (6.1 ± 4.3%), Kmob (78.3 ± 42.1%), and N (31.2 ± 14.5 mg/100 g) in forested areas, while both types of phosphorus (Ptot and Pmob) peaked in croplands (0.075 ± 0.024 and 0.023 ± 0.015%, respectively). The lowest values of Corg were observed in hayfields, and the lowest values of Kmob and N in croplands. Model validation demonstrated that Corg and N were predicted most accurately (R2 = 0.53 and 0.55, respectively), where SWIR bands from Sentinel-2A satellite imagery were key predictors. The generated soil property maps and spatial SHAP values clearly showed distinct patterns correlated with land use types due to distinct biogeochemical processes across landscapes. Our findings demonstrate how land management practices fundamentally alter soil parameters, creating diagnostic spectral signatures that can be captured through interpretable machine learning and remote sensing. Full article
(This article belongs to the Special Issue Digital Soil Mapping and Precision Agriculture)
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17 pages, 4097 KB  
Article
How Spectrally Nearby Samples Influence the Inversion of Soil Heavy Metal Copper
by Yi Liu, Tiezhu Shi, Yiyun Chen, Wenyi Zhang, Chao Yang, Yuzhi Tang, Lichao Yuan, Chuang Wang and Wenling Cui
Land 2025, 14(9), 1830; https://doi.org/10.3390/land14091830 - 8 Sep 2025
Viewed by 406
Abstract
Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu [...] Read more.
Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction (Rp2) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg−1. The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity. Full article
(This article belongs to the Special Issue Digital Soil Mapping and Precision Agriculture)
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