Special Issue "Integrating GIS and Remote Sensing in Soil Mapping and Modeling"

Special Issue Editors

Dr. Dimitris Triantakonstantis
E-Mail Website
Guest Editor
Department of Soil Science of Athens, Institute of Soil and Water Resources, Hellenic Agricultural Organization "DEMETER", 1 S. Venizelou Str., 14123, Lycovrisi, Attiki, Greece
Interests: GIS; remote sensing; soil science; spatial modeling; climate change
Dr. Panagiotis Tziachris
E-Mail Website
Guest Editor
Department of Soil Science of Thermi-Thessaloniki, Institute of Soil and Water Resources, Hellenic Agricultural Organization "DEMETER", Leoforos Georgikis Sxolis, 57001, Thermi, Thessaloniki, Greece
Interests: spatial analysis; geostatistics; GIS; machine learning; soil modeling; remote sensing

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to publish original contributions or review articles that evaluate the integration of GIS and remote sensing in agricultural practice by improving soil quality and environmental health. The complexity of spatial data and modeling methods in soil science imposes the need for combined integrated approaches of robust methods, leading to more accurate and reliable outcomes toward sustainable soil management. More specifically, we are interested in studies that investigate the impact of widely applied geographical approaches in everyday soil research and activities. This Special Issue addresses many aspects, including soil mapping and spatial modeling of soil characteristics, precision agriculture, geostatistics, machine learning, and development of software tools for data collection and processing. Works that directly address the response of anthropogenic intervention to ecosystems and climate change are particularly welcome. Theoretical approaches and lab and/or field experimentation cases are equally welcome to this Special Issue on “Integrating GIS and Remote Sensing in Soil Mapping and Modeling”.

The following topics are welcome (though the Special Issue is not limited to these):

  • Mapping and spatial modeling of soil properties using GIS and remote sensing;
  • New GIS and remote sensing approaches in agricultural applications that make use of trending techniques such as machine and deep learning algorithms;
  • Carbon farming calculation tools for estimating greenhouse gas emissions;
  • How sustainable soil management could enhance climate change mitigation and adaptation;
  • Technologies provided by remote sensing, geographic information systems (GIS), and global positioning systems (GPS) for maximizing the economic and environmental benefits of precision agriculture.

Dr. Dimitris Triantakonstantis
Dr. Panagiotis Tziachris
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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1400 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

  • GIS
  • remote sensing
  • soil science
  • spatial modeling
  • climate change
  • precision agriculture

Published Papers (3 papers)

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Research

Article
The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes
ISPRS Int. J. Geo-Inf. 2021, 10(4), 243; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040243 - 07 Apr 2021
Viewed by 545
Abstract
Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness [...] Read more.
Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes. Full article
(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)
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Article
Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements
ISPRS Int. J. Geo-Inf. 2021, 10(1), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010042 - 19 Jan 2021
Cited by 1 | Viewed by 829
Abstract
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble [...] Read more.
Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study. Full article
(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)
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Article
Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining
ISPRS Int. J. Geo-Inf. 2020, 9(12), 739; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120739 - 10 Dec 2020
Viewed by 566
Abstract
In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform [...] Read more.
In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform pollution management strategies. Here we combined geochemistry and remote sensing techniques to offer a preliminary soil pollution assessment of a vast abandoned spoil heap in the surroundings of La Soterraña mining site (Asturias, Spain). To study the soil distribution of the PTEs over time, twenty-seven soil samples were randomly collected downstream of and around the main spoil heap. Furthermore, the area was covered by an unmanned aerial vehicle (UAV) carrying a high-resolution multispectral camera with four bands (red, green, red-edge and near infrared). Multielement analysis revealed mercury and arsenic as principal pollutants. Two indices (from a database containing up to 55 indices) offered a proper correlation with the concentration of PTEs. These were: CARI2, presenting a Pearson Coefficient (PC) of 0.89 for concentrations >200 mg/kg of As; and NDVIg, PC of −0.67 for >40 mg/kg of Hg. The combined approach helps prediction of those areas susceptible to greatest pollution, thus reducing the costs of geochemical campaigns. Full article
(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)
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