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Special Issue "Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation II"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 1763

Special Issue Editors

Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Interests: proximal and remote soil sensing; agricultural geophysics; ground penetrating radar and electromagnetic induction; unmanned aerial vehicles; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
Department of Environment, Ghent University, Coupure links 653, 9000 Gent, Belgium
Interests: urban and industrial soils; contaminated soil assessment and remediation; digital soil mapping; environmental geophysics; geostatistical methods; environmental engineering
Special Issues, Collections and Topics in MDPI journals
Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Interests: hydrology; soil physics; proximal and remote soil sensing; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
USDA/ARS Soil Drainage Research Unit, 590 Woody Hayes Drive, Room 234, Columbus, OH 43210, USA
Interests: hydrology; agricultural/geotechnical engineering; soil science; proximal and remote soil sensing; geophysics; geology
Special Issues, Collections and Topics in MDPI journals
Dr. John Triantifilis
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Guest Editor
Portfolio Leader, Land and Water at Manaaki Whenua - Landcare Research, Lincoln, Canterbury, New Zealand
Interests: soil science; digital soil mapping; environmental geophysics; salinity assessment and management; geophysical methods; geostatistical methods; soil use and management

Special Issue Information

Dear Colleagues,

The increasing global population is placing enormous pressure on soil resources. To maintain productivity while ensuring sustainable use, information about the soil status is required. Recent technological advances in proximal and remote sensors have made their usage feasible, providing more affordable ways to map soil physical and chemical properties. Moreover, the use of these digital soil maps is increasingly helping us to find and improve their applications in precision agriculture; archeological reconstruction; soil health assessment; environmental, industrial, and urban soil exploration; and the remediation of contaminated sites.

In terms of proximal sensors, this includes the use of nondestructive smart sensing technologies such as direct current resistivity, electromagnetic induction, ground-penetrating radar, magnetometry, as well as gamma-ray and visible near-infrared (vis–NIR) spectroscopy. In some instances, these sensors are also used remotely, available on UAVs, or from airborne and satellites in different wavelength bands. Their use has proven to be a rapid and cost-effective augmentation to the labor-intensive, time-consuming, and cumbersome traditional methods that typically provide only localized and discrete measurements for various soil properties.

In this Special Issue, we invite manuscripts that show cutting-edge research and recent developments on the use of soil sensor data for mapping and monitoring different soil physical and chemical properties at various spatial and temporal scales. We would like to include contributions on applications of novel technologies and methodologies (e.g., mathematical modeling of soil and sensor data) for soil mapping and monitoring, particularly in multisensor data fusion, about the integration of proximal and/or remote sensor data to derive comprehensive soil information.

We look forward to receiving manuscripts from you and your colleagues. Should you require further information, please do not hesitate to contact us.

Dr. Triven Koganti
Dr. Ellen Van De Vijver
Dr. Bo Vangsø Iversen
Dr. Barry J. Allred
Dr. John Triantifilis
Guest Editors

Manuscript Submission Information

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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. Sensors 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 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

  • Proximal soil sensing
  • Remote soil sensing
  • Digital soil mapping and monitoring
  • Multisensor data fusion
  • Multisensor systems
  • Nondestructive techniques

Published Papers (2 papers)

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Research

Article
Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City
Sensors 2022, 22(24), 9895; https://doi.org/10.3390/s22249895 - 15 Dec 2022
Viewed by 530
Abstract
With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study [...] Read more.
With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries. Full article
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Article
Delineation of Nitrate Reduction Hotspots in Artificially Drained Areas through Assessment of Small-Scale Spatial Variability of Electrical Conductivity Data
Sensors 2022, 22(4), 1508; https://doi.org/10.3390/s22041508 - 15 Feb 2022
Cited by 1 | Viewed by 853
Abstract
Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by [...] Read more.
Identification of nitrate reduction hotspots (NRH) can be instrumental in implementing targeted strategies for reducing nitrate loading from agriculture. In this study, we aimed to delineate possible NRH areas from soil depths of 80 to 180 cm in an artificially drained catchment by utilizing electrical conductivity (EC) values derived by the inversion of apparent electrical conductivity data measured by an electromagnetic induction instrument. The NRH areas were derived from the subzones generated from clustering the EC values via two methods, unsupervised ISODATA clustering and the Optimized Hot Spot Analysis, that highly complement each other. The clustering of EC values generated three classes, wherein the classes with high EC values correspond to NRH areas as indicated by their low redox potential values and nitrate (NO3) concentrations. Nitrate concentrations in the NRH were equal to 13 to 17% of the concentrations in non-NRH areas and occupied 26% of the total area of the drainage catchments in the study. It is likely that, with the identification of NRH areas, the degree of nitrogen reduction in the vadose zone may be higher than initially estimated at the subcatchment scale. Full article
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