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AgriEngineering
Special Issues
Geotechnologies for Agriculture and Soil & Food Security
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Geotechnologies for Agriculture and Soil & Food Security

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (1 June 2023) | Viewed by 4920

Special Issue Editors

Prof. Dr. Raul Roberto Poppiel

E-Mail Website
Guest Editor
Institute of Geoscience, University of Brasília, Brasília 70.910-900, Brazil
Interests: pedometrics; digital soil mapping; soil spectroscopy; machine learning; soil monitoring; soil quality
Dr. Ruhollah Taghizadeh-Mehrjardi

E-Mail Website
Guest Editor
Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany
Interests: digital soil mapping; machine learning; pedology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global agricultural land areas comprise nearly 50 million ha (or 38% of the world's land area). These surfaces are usually used as cropland (33%) and pastureland (66%) (available at: https://ourworldindata.org/grapher/agricultural-area-per-capita?tab=map&time=2018).

As the world's population continues to grow, the demand for food is also increasing with the same intensity. The pressure on soil, which is a limited resource, is also growing. Global cropland area per capita decreased continuously over the period between 1961 and 2018, from nearly 0.45 ha/person in 1961 to 0.21 ha/person in 2018. This means that agricultural land per person halved and the pressure on the soil twice (available at: https://ourworldindata.org/grapher/agricultural-area-per-capita).

Given that soil plays an important role in food production and climate regulation, we need solutions to preserve and/or restore its quality. These technologies must view soils as a complex body that functions with various constituents that vary over space and time. The n-dimensional products generated by these geotechnologies should be made available to end-users to support their decisions and policy making.

In the last decade, remote and proximal sensed soil data coupled with pedometric techniques including data science and cloud computing, provide new ways to produce and make available n-dimensional soil information at several cartographic scales or spatial resolutions and geographical extents. The adoption of these technological products has proven to be useful in supporting several global goals, especially for increasing sustainable food production and water conservation. However, to meet the food demands of the present and near future, scientists must also turn their attention to the subsoil.

Therefore, we propose the following topics for this Special Issue:

  • Remote monitoring of bare soils and natural surfaces for its security;
  • Surface reflectance of soils and vegetation to estimate top/subsoil water and productivity;
  • Land surface temperature of soils and vegetation to estimate top/subsoil water and productivity;
  • Pedometric mapping of top/subsoils and its relationships with productivity;
  • Precise maps of top/subsoil attributes: physical, chemical, biological, mineralogical and color;
  • Strategies for n-dimensional mapping of soil quality;
  • Strategies for pedometric mapping of soil depth;
  • Historical, present and future soil carbon storage maps;
  • Soil productivity mapping based on soil maps and crop growth simulation models;
  • Monitoring soil degradation (e.g., erosion, compaction, pollution) in agricultural lands;
  • Web services and geoportals of soil maps and geospatial products for end-users.

We are confident that your contribution of high scientific quality will be a solid reference for the scientific and professional community to produce accurate and useful geospatial products for global agriculture.

Prof. Dr. Raul Roberto Poppiel
Dr. Ruhollah Taghizadeh-Mehrjardi
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • artificial intelligence
  • big data analytics
  • cloud computing
  • deep learning
  • digital agriculture
  • GIS applications
  • n-dimensional maps
  • pedometrics
  • soil productivity
  • soil quality
  • subsoil
  • unmanned aerial systems
  • web services
  • yield prediction

Published Papers (2 papers)

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Research

17 pages, 7105 KiB  
Article
Spectral Mixture Modeling of an ASTER Bare Soil Synthetic Image Using a Representative Spectral Library to Map Soils in Central-Brazil
by Jean J. Novais, Raul R. Poppiel, Marilusa P. C. Lacerda, Manuel P. Oliveira, Jr. and José A. M. Demattê
AgriEngineering 2023, 5(1), 156-172; https://doi.org/10.3390/agriengineering5010011 - 19 Jan 2023
Cited by 1 | Viewed by 1609
Abstract
Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a [...] Read more.
Pedological maps in suitable scales are scarce in most countries due to the high costs involved in soil surveying. Therefore, methods for surveying and mapping must be developed to overpass the cartographic material obtention. In this sense, this work aims at assessing a digital soil map (DSM) built by multispectral data extrapolation from a source area to a target area using the ASTER time series modeling technique. For that process, eight representative toposequences were established in two contiguous micro-watersheds, with a total of 42 soil profiles for analyses and classification. We found Ferralsols, Plinthosols, Regosols, and a few Cambisols, Arenosols, Gleisols, and Histosols, typical of tropical regions. In the laboratory, surface soil samples were submitted to spectral readings from 0.40 µm to 2.50 µm. The soil spectra were morphologically interpreted, identifying shapes and main features typical of tropical soils. Soil texture grouped the curves by cluster analysis, forming a spectral library (SL). In parallel, an ASTER time series (2001, 2004, and 2006) was processed, generating a bare soil synthetic soil image (SySI) covering 39.7% of the target area. Multiple Endmember Spectral Mixture Analysis modeled the SL on the SySI generating DSM with 73% of Kappa index, in which identified about 77% is covered by rhodic Ferralsols. Besides the overestimation, the DSM represented the study area’s pedodiversity. Given the discussion raised, we consider including subsoil data and other features using other sensors in operations modeled by machine learning algorithms to improve results. Full article
(This article belongs to the Special Issue Geotechnologies for Agriculture and Soil & Food Security)
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25 pages, 3505 KiB  
Article
Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data
by Sina Mallah, Bahareh Delsouz Khaki, Naser Davatgar, Raul Roberto Poppiel and José A. M. Demattê
AgriEngineering 2023, 5(1), 40-64; https://doi.org/10.3390/agriengineering5010004 - 28 Dec 2022
Viewed by 2331
Abstract
The demand for high quality and low-cost spatial distribution information of soil texture classes (STCs) is of great necessity in developing countries. This paper explored digital mapping of topsoil STCs using soil fractions, terrain attributes and artificial neural network (ANN) algorithms. The 4493 [...] Read more.
The demand for high quality and low-cost spatial distribution information of soil texture classes (STCs) is of great necessity in developing countries. This paper explored digital mapping of topsoil STCs using soil fractions, terrain attributes and artificial neural network (ANN) algorithms. The 4493 soil samples covering 10 out of 12 STCs were collected from the rice fields of the Guilan Province of Northern Iran. Nearly 75% of the dataset was used to train the ANN algorithm and the remaining 25% to apply a repeated 10-fold cross-validation. Spatial prediction of soil texture fractions was carried out via geostatistics and then a pixel-based approach with an ANN algorithm was performed to predict STCs. The ANN presented reasonable accuracy in estimating USDA STCs with a kappa coefficient of 0.38 and pixel classification accuracy percentage of 52%. Hybridizing soil particles with relief covariates yielded better estimates for coarse- and medium-STCs. The results also showed that clay particle and terrain attributes are more important covariates than plant indices in areas under single crop cultivation. However, it is recommended to examine the approach in areas with diverse vegetation cover. Full article
(This article belongs to the Special Issue Geotechnologies for Agriculture and Soil & Food Security)
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