Statistical and Remote Sensing Tools in Soil Modelling and Monitoring

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

Deadline for manuscript submissions: closed (21 December 2021) | Viewed by 2212

Special Issue Editors


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Guest Editor
Department of Geology and Institute of Science and Aerospatial Technologies (ICTEA), University of Oviedo, Oviedo, Spain
Interests: earth and planetary sciences; quantitative geomorphology; soil modelling; spatial statistics; spectroscopy; remote sensing; GIS
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Guest Editor
Department of Mining Exploitation and Prospecting, Universidad de Oviedo, Oviedo, Spain
Interests: point cloud processing; machine learning; spatial statistics
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Guest Editor
Department of Physics, University of Oviedo, Oviedo, Spain
Interests: remote sensing; albedo; surface temperature
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Special Issue Information

Dear Colleagues,

This special issue welcomes contributions that focus on the application of geostatistical and other mathematical tools as matching learning and neuronal networks to describe and predict spatial variation and perform spatial interpolation of soil data. Special attention will be paid to novel technologies to measure soil properties, such as spectroscopy and proximal and remote sensing systems to map and monitor the soil and  GIS and LiDAR technology to include digital elevation models in soil modelling.

The following are examples of suitable topics: soil data modelling, soil GIS for the computation of algebraic functions using the (semi) variogram to quantify the spatial variation of a regionalized variable, digital soil mapping, application of soil information in specific sectors (e.g. agriculture, natural resource management, soil degradation,  climate change mitigation, frozen soil monitoring (permafrost) or archaeology exploration)

Manuscripts may consider the collection of baseline soil data or more advanced models of soil properties.

Prof. Dr. Susana del Carmen Fernández Menedez
Prof. Celestino Ordóñez Galán
Dr. Javier Fernández Calleja
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 modelling and monitoring
  • digital soil mapping
  • geostatistical
  • maching learning
  • neuronal networks
  • GIS
  • remote sensing
  • spectroscopy
  • LiDAR
  • MDT

Published Papers (1 paper)

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Research

17 pages, 3941 KiB  
Communication
Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India
by Vinod Kumar, Parveen Sihag, Ali Keshavarzi, Shevita Pandita and Andrés Rodríguez-Seijo
Appl. Sci. 2021, 11(18), 8362; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188362 - 09 Sep 2021
Cited by 6 | Viewed by 1600
Abstract
The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a [...] Read more.
The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co. Full article
(This article belongs to the Special Issue Statistical and Remote Sensing Tools in Soil Modelling and Monitoring)
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