Special Issue "Advanced Spectroscopy-Based Technologies in Soil Monitoring"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Dr. Veronika Kopačková-Strnadová
Website
Guest Editor
Czech Geological Survey, Prague 118 21, Czech Republic
Interests: imaging spectroscopy; mineral spectroscopy; environmental monitoring; optical and thermal remote sensing
Special Issues and Collections in MDPI journals
Dr. Mario Marchetti
Website
Guest Editor
Matériaux et Structures (MAST), Université Gustave Eiffel - IFSTTAR, Marne la Vallée 77447, France
Interests: applied spectroscopy; materials science; sustainable materials; materials ageing

Special Issue Information

Dear Colleagues,

Soil contamination is unfortunately a global problem and with an increasing population growth will become one of the biggest challenges to be solved in the future. Due to the fact that soil contaminations include a wide range of natural, synthetic metallic, and organic compounds, and minerals present in a large degree of spatial variations, one of the barriers we face today is the lack of cost-effective and operational approaches to assess soil properties. Using modern distance methods such as proximal remote sensing and imaging spectroscopy is quite possibly the way to go to improve the cost efficiency of currently used methods to determine soil geochemical property as well as concepts used for sampling strategies.

Hence, in this Special Issue, we are looking for novel solutions and approaches that open up the possibilities for quantitative prediction of diverse soil parameters in the lab as well as in a real environment. In particular, we are looking for such solutions allowing synergic use of spectroscopic data covering different spectral regions (e.g., VNIR/SWIR and LWIR) and new techniques achieving higher model accuracy including but not limited to the topics as follows:

  • Synergic use of optical and thermal spectroscopic data for quantitative prediction of soil parameters/constituents;
  • Moving from lab to real environment: spectral and spatial upscaling, using different platforms;
  • New modeling approaches especially those allowing global soil monitoring;
  • New solutions to improve soil sampling strategy;
  • Potential of state-of-the art sensors and satellite systems for soil monitoring;
  • Potential of future sensors and satellite systems for soil monitoring.

Dr. Veronika Kopačková-Strnadová
Dr. Mario Marchetti
Guest Editors

Manuscript Submission Information

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Keywords

  • Soil monitoring
  • Soil geochemical property
  • Soil degradation and contamination
  • Optical and thermal remote sensing
  • Soil property modeling
  • Spectral and spatial upscaling
  • Global soil monitoring
  • Imaging spectroscopy

Published Papers (1 paper)

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Research

Open AccessArticle
Prediction of Soil-Available Potassium Content with Visible Near-Infrared Ray Spectroscopy of Different Pretreatment Transformations by the Boosting Algorithms
Appl. Sci. 2020, 10(4), 1520; https://doi.org/10.3390/app10041520 - 23 Feb 2020
Abstract
The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, [...] Read more.
The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky–Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration. Full article
(This article belongs to the Special Issue Advanced Spectroscopy-Based Technologies in Soil Monitoring)
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