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Advances in Remote Sensing for Soil Property Mapping
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Advances in Remote Sensing for Soil Property Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 January 2026 | Viewed by 1430

Special Issue Editors

Dr. Miao Lu

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: remote sensing applications in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Shangrong Wu

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: remote sensing mapping; crop parameter inversion; crop yield simulation
Dr. Feng Liu

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: soil science; digital soil mapping; geophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate and current maps of soil properties play a crucial role in agricultural land management, soil production potential, and land development planning. During the last several decades, significant progress has been made in estimating soil properties by using digital soil mapping. A series of models based on remote sensing have been developed to predict soil properties from field to global scales. This Special Issue focuses on advances in science and the state-of-art of soil property mapping such as soil organic matter, soil moisture, soil salinity, soil water, and soil-heavy metals. It aims to gather cutting-edge research studies that highlight multi-source data, innovative approaches, case studies, and review discussions. Soil property maps and crop spatial distributions are also suggested to be used conjunctively to assess the linkages between crop types and soil properties, supporting precision planting management and crop distribution optimization.

The research areas may include (but are not limited to) the following:

  1. Innovative remote/proximal sensing techniques for the retrieval of soil properties (soil organic matter, soil moisture, soil salinity, soil-heavy metals, etc.).
  2. Methods for optimizing soil sampling locations to improve spatial representativeness and minimize fieldwork costs.
  3. Spatial monitoring of soil properties in soil profiles.
  4. Approaches for integrating multi-source or multi-platform remote sensing to improve soil property mapping accuracy.
  5. The impacts of soil properties variability on crop planting structure, agricultural management practices, and agricultural productivity.
  6. Tracking long- and short-term trends in soil properties and crop type responses.
  7. Case studies showcasing the role of soil property mapping in precision agriculture, quality evaluation of cultivated land, and land reclamation.
  8. Monitoring crop growth and yield under soil quality limitations.

Dr. Miao Lu
Dr. Shangrong Wu
Dr. Feng Liu
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.

Keywords

  • soil quality
  • soil properties
  • soil mapping
  • crop responses
  • crop growth/yield

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Published Papers (3 papers)

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Research

25 pages, 5633 KiB  
Article
A Hybrid Framework for Soil Property Estimation from Hyperspectral Imaging
by Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona and Oscar Mendez
Remote Sens. 2025, 17(15), 2568; https://doi.org/10.3390/rs17152568 - 24 Jul 2025
Viewed by 127
Abstract
Accurate estimation of soil properties is crucial for optimizing agricultural practices and promoting sustainable resource management. Hyperspectral imaging provides a non-invasive means of quantifying key soil parameters, but effectively utilizing the high-dimensional hyperspectral data presents significant challenges. In this paper, we introduce HyperSoilNet, [...] Read more.
Accurate estimation of soil properties is crucial for optimizing agricultural practices and promoting sustainable resource management. Hyperspectral imaging provides a non-invasive means of quantifying key soil parameters, but effectively utilizing the high-dimensional hyperspectral data presents significant challenges. In this paper, we introduce HyperSoilNet, a hybrid deep learning framework for estimating soil properties from hyperspectral imagery. HyperSoilNet leverages a pretrained hyperspectral-native CNN backbone and integrates it with a carefully optimized machine learning (ML) ensemble to combine the strengths of deep representation learning with traditional ML techniques. We evaluate our framework on the Hyperview challenge dataset, focusing on four critical soil properties: potassium oxide, phosphorus pentoxide, magnesium, and soil pH. Comprehensive experiments demonstrate that HyperSoilNet surpasses state-of-the-art models, achieving a score of 0.762 on the challenge leaderboard. Through detailed ablation studies and spectral analysis, we provide insights on the components of the framework, and their contribution to performance, showcasing its potential for advancing precision agriculture and sustainable soil management practices. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Soil Property Mapping)
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25 pages, 10637 KiB  
Article
Rubber Plantation Expansion Leads to Increase in Soil Erosion in the Middle Lancang-Mekong River Basin During the Period 2003–2022
by Hongfeng Xu, Tien Dat Pham, Qingquan Wu, Peng Chai, Dengsheng Lu, Dengqiu Li and Yaoliang Chen
Remote Sens. 2025, 17(13), 2220; https://doi.org/10.3390/rs17132220 - 28 Jun 2025
Viewed by 466
Abstract
The booming nature rubber industry has contributed to the extensive expansion of rubber plantations in the Lancang-Mekong River Basin over recent decades. To date, limited research has focused on the assessment of soil erosion caused by this expansion, resulting in a knowledge gap [...] Read more.
The booming nature rubber industry has contributed to the extensive expansion of rubber plantations in the Lancang-Mekong River Basin over recent decades. To date, limited research has focused on the assessment of soil erosion caused by this expansion, resulting in a knowledge gap in the systematic and quantitative understanding of its ecological and hydrological impacts. This study evaluates soil erosion within rubber plantations and changes associated with their expansion by modifying the Revised Universal Soil Loss Equation (RUSLE) model in the middle section of the Lancang-Mekong River Basin from 2003 to 2022. The results show that: (1) rubber plantations have expanded rapidly, reaching a total area of 70.391 × 104 ha; (2) over the 20-year period, soil erosion trends within rubber plantations show both slight aggravation (affecting 45.377% of the area) and slight mitigation (affecting 35.859% of the area); (3) soil erosion in rubber plantations shows a pattern of decreasing, then increasing, and then decreasing again with stand age, with the lowest erosion (0.693 t·ha−1·yr−1) observed in plantations aged 10–15 years and the highest (1.017 t·ha−1·yr−1) in those aged 15–20 years; (4) rubber plantation expansion led to a fivefold increase in soil erosion with an average soil loss of 0.148 t·ha−1·yr−1 in the non-expansion areas and 0.902 t·ha−1·yr−1 in expansion areas; and (5) slope had the most significant impact on soil erosion. Interactions between slope and other factors —especially slope and soil type (Q > 0.777)—consistently demonstrated strong explanatory power. This research provides valuable insights for the assessment and management of soil erosion in rubber plantations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Soil Property Mapping)
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26 pages, 3626 KiB  
Article
Spatiotemporal Patterns of Cropland Sustainability in Black Soil Zones Based on Multi-Source Remote Sensing: A Case Study of Heilongjiang, China
by Jing Yang, Li Wang, Jinqiu Zou, Lingling Fan and Yan Zha
Remote Sens. 2025, 17(12), 2044; https://doi.org/10.3390/rs17122044 - 13 Jun 2025
Viewed by 335
Abstract
Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks [...] Read more.
Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks in China’s black soil zones, we developed a comprehensive evaluation system with 13 indicators from four dimensions: the soil capacity, the natural capacity, the management level, and crop productivity. With this system and the entropy weight method, we systematically analyzed the spatiotemporal patterns of cropland sustainability in the selected black soil regions from 2010 to 2020. Additionally, a diagnostic model was applied to identify the key limiting factors constraining improvements in cropland sustainability. The results revealed that cropland sustainability in Heilongjiang Province has increased by 7% over the past decade, largely in the central and northeastern regions of the study area, with notable gains in soil capacity (+15.6%), crop productivity (+22.4%), and the management level (+4.8%). While the natural geographical characteristics show no obvious improvement in the overall score, they display significant spatial heterogeneity (with better conditions in the central/eastern regions than in the west). Sustainability increased the most in sloping dry farmland and paddy fields, followed by plain dry farmland and arid windy farmland areas. The soil organic carbon content and effective irrigation amount were the main obstacles affecting improvements in cropland sustainability in black soil regions. Promoting the implementation of technical models, strengthening investment in cropland infrastructure, and enhancing farmer engagement in black soil conservation are essential in ensuring long-term cropland sustainability. These findings provide a solid foundation for sustainable agricultural development, contributing to global food security and aligning with SDG 2 (zero hunger). Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Soil Property Mapping)
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