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

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 18857

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Special Issue Editors

Dr. Triven Koganti

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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
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Dr. Ellen Van De Vijver

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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
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Dr. Maria Knadel

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Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Interests: vis–NIR spectroscopy; proximal soil sensing; on-the-go spectroscopy; soil characterization and mapping
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Dr. Bo Vangsø Iversen

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Department of Agroecology, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark
Interests: hydrology; soil physics; proximal and remote soil sensing; digital soil mapping
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Dr. Barry J. Allred

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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
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Dr. John Triantifilis

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School of Biological, Earth and Environmental Sciences, UNSW Sydney, Kensington, NSW 2052, Australia
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 of mapping soil physical and chemical properties. Moreover, the use of these digital soil maps is increasingly helping us to find and/or improve their applications in precision agriculture, archeological reconstruction, soil health assessment, environmental, industrial, and urban soil exploration and 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, 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 a manuscript from you and your colleagues. Should you require further information, please do not hesitate to contact us.

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

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Keywords

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

Published Papers (6 papers)

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Research

Open AccessArticle

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

and

Sensors 2021, 21(11), 3919; https://doi.org/10.3390/s21113919 - 06 Jun 2021

Cited by 5 | Viewed by 3290

Abstract

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R² of 0.73 and RPD of 1.91 for SOM, R² of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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Open AccessArticle

Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China

and

Sensors 2021, 21(9), 3220; https://doi.org/10.3390/s21093220 - 06 May 2021

Cited by 9 | Viewed by 1814

Abstract

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R² value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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Open AccessArticle

Mapping of Agricultural Subsurface Drainage Systems Using Unmanned Aerial Vehicle Imagery and Ground Penetrating Radar

and

Sensors 2021, 21(8), 2800; https://doi.org/10.3390/s21082800 - 15 Apr 2021

Cited by 9 | Viewed by 3473

Abstract

Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes’ location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes’ location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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Open AccessArticle

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Tiago Rodrigues Tavares

José Paulo Molin

S. Hamed Javadi

Hudson Wallace Pereira de Carvalho

and

Abdul Mounem Mouazen

Sensors 2021, 21(1), 148; https://doi.org/10.3390/s21010148 - 29 Dec 2020

Cited by 18 | Viewed by 3239

Abstract

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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Open AccessEditor’s ChoiceArticle

Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?

Shree R. S. Dangal

and

Jonathan Sanderman

Sensors 2020, 20(23), 6729; https://doi.org/10.3390/s20236729 - 25 Nov 2020

Cited by 16 | Viewed by 2839

Abstract

Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO₃, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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Open AccessArticle

Dielectric Spectroscopy and Application of Mixing Models Describing Dielectric Dispersion in Clay Minerals and Clayey Soils

Juan D. González-Teruel

Scott B. Jones

Fulgencio Soto-Valles

and

Sensors 2020, 20(22), 6678; https://doi.org/10.3390/s20226678 - 22 Nov 2020

Cited by 13 | Viewed by 3000

Abstract

The number of sensors, ground-based and remote, exploiting the relationship between soil dielectric response and soil water content continues to grow. Empirical expressions for this relationship generally work well in coarse-textured soils but can break down for high-surface area and intricate materials such as clayey soils. Dielectric mixing models are helpful for exploring mechanisms and developing new understanding of the dielectric response in porous media that do not conform to a simple empirical approach, such as clayey soils. Here, we explore the dielectric response of clay minerals and clayey soils using the mixing model approach in the frequency domain. Our modeling focuses on the use of mixing models to explore geometrical effects. New spectroscopic data are presented for clay minerals (talc, kaolinite, illite and montmorillonite) and soils dominated by these clay minerals in the 1 MHz–6 GHz bandwidth. We also present a new typology for the way water is held in soils that we hope will act as a framework for furthering discussion on sensor design. We found that the frequency-domain response can be mostly accounted for by adjusting model structural parameters, which needs to be conducted to describe the Maxwell–Wagner (MW) relaxation effects. The work supports the importance of accounting for soil structural properties to understand and predict soil dielectric response and ultimately to find models that can describe the dielectric–water content relationship in fine-textured soils measured with sensors. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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