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Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling

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: 15 May 2025 | Viewed by 3545

Special Issue Editors

Dr. Bifeng Hu

E-Mail Website
Guest Editor
School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
Interests: proximal soil sensing; remote sensing; digital soil mapping; pedometrics; spatio-temporal variation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Asim Biswas

E-Mail Website
Guest Editor
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; vadose zone hydrology; precision agriculture; proximal soil sensing; digital soil mapping; soil spatial variability
Special Issues, Collections and Topics in MDPI journals
Dr. Wenjun Ji

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: sensor-data fusion; soil spectroscopy; proximal soil sensing; digital soil mapping; sustainable agriculture
Special Issues, Collections and Topics in MDPI journals
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

Special Issue Information

Dear Colleagues,

Remote and proximal sensing have emerged as the most promising and widely used techniques for the acquisition of information about an object or any phenomenon without physical contact with the object. Remote sensing is widely tied to the utilization of satellite, airborne, or UAV platforms using multi- or hyperspectral imagery. With regard to proximal sensing, the sensor is closer to the object (usually within 2 m) and is installed on platforms ranging from handheld, fixed installations to robotics or tractor-embedded sensors. The types of sensors range from simple RGB or grey-level cameras to multispectral and hyperspectral high-resolution imaging systems or even thermographic cameras.

Traditionally, collecting soil information is labor-intensive, takes a long time, and has a high economic cost, which hinders the acquisition of soil information on a large spatial scale or in hard-to-access locations. With input from remote sensing and proximal sensing technology, we can obtain soil information on a higher scale in a more efficient and intact way, which is critical in mapping soil properties.

For this Special Issue, we welcome the submission of papers on both fundamental and applied research relating to remote, areal and proximal sensing for the measurement and spatial modelling of soil. We also invite papers dedicated to new sensors that can be used in soil measurement and mapping.

We invite researchers to contribute original research articles, reviews, and case studies focusing on proximal soil sensing for the measurement and spatial modelling of soil. Topics of interest include, but are not limited to, the following:

  • The monitoring or measurement of soil properties using remote sensing and proximal soil sensing techniques (such as Vis-NIR, MIR, PXRF, or LIBS);
  • The development of novel remote-sensing- or proximal-soil-sensing-based soil monitoring frameworks or technologies;
  • Mapping soil properties using data collected through remote sensing and proximal soil sensing techniques;
  • New methods or models used for monitoring soil properties utilizing remote sensing and proximal soil sensing techniques.

Dr. Bifeng Hu
Prof. Dr. Asim Biswas
Dr. Wenjun Ji
Dr. Yongsheng Hong
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

  • remote sensing
  • proximal soil sensing
  • UAV
  • soil spectroscopy
  • digital soil mapping
  • geostatistics
  • soil properties
  • machine learning

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Published Papers (5 papers)

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Research

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19 pages, 5753 KiB  
Article
Potential of EnMAP Hyperspectral Imagery for Regional-Scale Soil Organic Matter Mapping
by Yassine Bouslihim and Abdelkrim Bouasria
Remote Sens. 2025, 17(9), 1600; https://doi.org/10.3390/rs17091600 (registering DOI) - 30 Apr 2025
Abstract
The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (SOM) prediction and mapping using actual spectral data from 282 soil [...] Read more.
The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (SOM) prediction and mapping using actual spectral data from 282 soil samples. Different spectral preprocessing techniques, including Savitzky–Golay (SG) smoothing, the second derivative of SG, and Standard Normal Variate (SNV) transformation, were evaluated in combination with embedded feature selection to identify the most relevant wavelengths for SOM prediction. Partial Least Squares Regression (PLSR) models were developed under different pre-treatment scenarios. The best performance was obtained using SNV preprocessing with the top 30 EnMAP bands (wavelengths) selected, giving R2 = 0.68, RMSE = 0.34%, and RPIQ = 1.75. The combination of SNV with feature selection successfully identified significant wavelengths for SOM prediction, particularly around 550 nm in the Vis–NIR region, 1570–1630 nm, and 1600 nm and 2200 nm in the SWIR region. The resulting SOM predictions exhibited spatially consistent patterns that corresponded with known soil–landscape relationships, highlighting the potential of EnMAP hyperspectral data for mapping soil properties despite its limited geographical availability. While these results are promising, this study identified limitations in the ability of PLSR to extrapolate predictions beyond the sampled areas, suggesting the need to explore non-linear modeling approaches. Future research should focus on evaluating EnMAP’s performance using advanced machine learning techniques and comparing it to other available hyperspectral products to establish robust protocols for satellite-based soil monitoring. Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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26 pages, 9530 KiB  
Article
Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning
by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang and Fan Wang
Remote Sens. 2025, 17(7), 1164; https://doi.org/10.3390/rs17071164 - 25 Mar 2025
Viewed by 210
Abstract
Soil vanadium contamination poses a significant threat to ecosystems. Hyperspectral remote sensing plays a critical role in extracting spectral features of heavy metal contamination, mapping its spatial distribution, and monitoring its trends over time. This study targets a vanadium-contaminated area in Panzhihua City, [...] Read more.
Soil vanadium contamination poses a significant threat to ecosystems. Hyperspectral remote sensing plays a critical role in extracting spectral features of heavy metal contamination, mapping its spatial distribution, and monitoring its trends over time. This study targets a vanadium-contaminated area in Panzhihua City, Sichuan Province. Soil sampling and spectral measurements occurred in the laboratory. Hyperspectral (Gaofen-5, GF-5) and multispectral (Gaofen-2, GF-2; Sentinel-2) images were acquired and preprocessed, and feature bands were extracted by combining laboratory spectral data. A dual-branch convolutional neural network (DB-CNN) fused hyperspectral and multispectral images and confirmed the fusion’s effectiveness. Six prevalent machine learning models were adopted, and a unified learning framework leveraged a Random Forest (RF) as a second-layer model to enhance the predictive performance of these base models. Both the base models and the ensemble learning model were evaluated based on predictive accuracy. The fusion process enhanced the predictive performance of the base models, improving R2 values for vanadium (V) and pentavalent vanadium (V5+) from 0.54 and 0.3 to 0.58 and 0.39, respectively, at a 4 m resolution. Further optimization using RF as a second-layer model to refine Extreme Trees (ETs) significantly increased R2 values to 0.83 and 0.75 for V and V5+, respectively, at this scale. The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models. Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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19 pages, 24741 KiB  
Article
Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China
by Jiaxiang Zhai, Nan Wang, Bifeng Hu, Jianwen Han, Chunhui Feng, Jie Peng, Defang Luo and Zhou Shi
Remote Sens. 2024, 16(19), 3671; https://doi.org/10.3390/rs16193671 - 1 Oct 2024
Viewed by 1550
Abstract
Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content [...] Read more.
Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content (SSC) that combines spectral and texture information from unmanned aerial vehicle (UAV) images. Reflectance, spectral index, and one-dimensional (OD) texture features were extracted from UAV images. Building on the one-dimensional texture features, we constructed two-dimensional (TD) and three-dimensional (THD) texture indices. The technique of Recursive Feature Elimination (RFE) was used for feature selection. Models for soil salinity estimation were built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN). Spatial distribution maps of soil salinity were then generated for each model. The effectiveness of the proposed method is confirmed through the utilization of 240 surface soil samples gathered from an arid region in northwest China, specifically in Xinjiang, characterized by sparse vegetation. Among all texture indices, TDTeI1 has the highest correlation with SSC (|r| = 0.86). After adding multidimensional texture information, the R2 of the RF model increased from 0.76 to 0.90, with an improvement of 18%. Among the three models, the RF model outperforms PLSR and CNN. The RF model, which combines spectral and texture information (SOTT), achieves an R2 of 0.90, RMSE of 5.13 g kg−1, and RPD of 3.12. Texture information contributes 44.8% to the soil salinity prediction, with the contributions of TD and THD texture indices of 19.3% and 20.2%, respectively. This study confirms the great potential of introducing texture information for monitoring soil salinity in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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19 pages, 4714 KiB  
Article
The Use of Vis-NIR-SWIR Spectroscopy and X-ray Fluorescence in the Development of Predictive Models: A Step forward in the Quantification of Nitrogen, Total Organic Carbon and Humic Fractions in Ferralsols
by Bruna Coelho de Lima, José A. M. Demattê, Carlos H. dos Santos, Carlos S. Tiritan, Raul R. Poppiel, Marcos R. Nanni, Renan Falcioni, Caio A. de Oliveira, Nicole G. Vedana, Guilherme Zimmermann and Amanda S. Reis
Remote Sens. 2024, 16(16), 3009; https://doi.org/10.3390/rs16163009 - 16 Aug 2024
Cited by 1 | Viewed by 1022
Abstract
The objective was to verify the performance of spectral techniques as well as validation models in the prediction of nitrogen, total organic carbon, and humic fractions under different cultivation conditions. Chemical analyses for the determination of nitrate, total nitrogen, total organic carbon, and [...] Read more.
The objective was to verify the performance of spectral techniques as well as validation models in the prediction of nitrogen, total organic carbon, and humic fractions under different cultivation conditions. Chemical analyses for the determination of nitrate, total nitrogen, total organic carbon, and the chemical fractionation of soil organic matter were performed, as well as spectral analyses by Vis-NIR-SWIR and X-ray fluorescence. The results of the spectroscopy were processed using RStudio v. 4.1.3, and PLSR and support vector machine learning algorithms were applied to validate the models. The Vis-NIR-SWIR and XRF spectroscopic techniques showed high performance and are indicated for the prediction of nitrogen, total organic carbon, and humic fractions in Ferralsols of medium sandy texture. However, it is important to highlight that each technique has its own characteristic mechanism of action: Vis-NIR-SWIR detects the element based on harmonic tones, while XRF is based on the atomic number of the element or elemental association. The PLSR and SVM models showed excellent validation results, allowing them to fit the experimental data, emphasizing that they are different statistical methods. Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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Review

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47 pages, 3987 KiB  
Review
Estimating Soil Attributes for Yield Gap Reduction in Africa Using Hyperspectral Remote Sensing Data with Artificial Intelligence Methods: An Extensive Review and Synthesis
by Nadir El Bouanani, Ahmed Laamrani, Hicham Hajji, Mohamed Bourriz, Francois Bourzeix, Hamd Ait Abdelali, Ali El-Battay, Abdelhakim Amazirh and Abdelghani Chehbouni
Remote Sens. 2025, 17(9), 1597; https://doi.org/10.3390/rs17091597 (registering DOI) - 30 Apr 2025
Abstract
Africa’s rapidly growing population is driving unprecedented demands on agricultural production systems. However, agricultural yields in Africa are far below their potential. One of the challenges leading to low productivity is Africa‘s poor soil quality. Effective soil fertility management is an essential key [...] Read more.
Africa’s rapidly growing population is driving unprecedented demands on agricultural production systems. However, agricultural yields in Africa are far below their potential. One of the challenges leading to low productivity is Africa‘s poor soil quality. Effective soil fertility management is an essential key factor for optimizing agricultural productivity while ensuring environmental sustainability. Key soil fertility properties—such as soil organic carbon (SOC), nutrient levels (i.e., nitrogen (N), phosphorus (P), potassium (K), moisture retention (MR) or moisture content (MC), and soil texture (clay, sand, and loam fractions)—are critical factors influencing crop yield. In this context, this study conducts an extensive literature review on the use of hyperspectral remote sensing technologies, with a particular focus on freely accessible hyperspectral remote sensing data (e.g., PRISMA, EnMAP), as well as an evaluation of advanced Artificial Intelligence (AI) models for analyzing and processing spectral data to map soil attributes. More specifically, the study examined progress in applying hyperspectral remote sensing technologies for monitoring and mapping soil properties in Africa over the last 15 years (2008–2024). Our results demonstrated that (i) only very few studies have explored high-resolution remote sensing sensors (i.e., hyperspectral satellite sensors) for soil property mapping in Africa; (ii) there is a considerable value in AI approaches for estimating and mapping soil attributes, with a strong recommendation to further explore the potential of deep learning techniques; (iii) despite advancements in AI-based methodologies and the availability of hyperspectral sensors, their combined application remains underexplored in the African context. To our knowledge, no studies have yet integrated these technologies for soil property mapping in Africa. This review also highlights the potential of adopting hyperspectral data (i.e., encompassing both imaging and spectroscopy) integrated with advanced AI models to enhance the accurate mapping of soil fertility properties in Africa, thereby constituting a base for addressing the question of yield gap. Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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