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Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in...
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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: 28 November 2024 | Viewed by 1885

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 (2 papers)

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Research

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 858
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
Viewed by 634
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

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. 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.

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 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.

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