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Special Issue "Proximal Soil Sensing"

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

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editors

Guest Editor
Dr. Raphael Viscarra Rossel

Professor of Digital Soil Science & Agriculture, School of Molecular and Life Sciences, Curtin University, Kent St, Bentley WA 6102, Australia
Website | E-Mail
Interests: pedometrics; soil sensing; soil spectroscopy; data fusion; spatial modelling; digital soil mapping
Guest Editor
Dr. Craig R. Lobsey

The University of Southern Queensland, Toowoomba, QLD, Australia
Website | E-Mail
Interests: proximal sensing; spectroscopy; precision agriculture; spatial modelling; software development; instrumentation and electronic engineering

Special Issue Information

Dear Colleagues,

Our current understanding of dynamic soil processes is constrained by a lack of efficient measurement capabilities for key soil properties (e.g. nutrients, carbon, biology, water, pH) across space and in time. The development of proximal soil sensing is essential for the dynamic characterisation of soil to help advance our current understanding of such processes and for monitoring them. Recent technological advances in miniaturised, low-power, sensors that are also wireless show considerable promise. Thus, for this special issue we welcome reviews and original research articles on the following topics:

  1. New soil sensor technologies for sensing biological, physical, and chemical soil properties;
  2. Development of integrated multi-sensor systems for monitoring soil condition and function (or soil health);
  3. Subterranean wireless sensor systems used for monitoring biological, physical, and chemical soil properties;
  4. Sensor data analytics, including signal processing, sampling, multivariate calibration, machine learning, Bayesian modelling, multi-sensor data fusion;
  5. Novel applications of proximal soil sensing in environmental, agronomic, engineering, robotic, archaeologic, remote sensing and space applications;
  6. Use of proximal soil sensing data in processed-based models at different spatial and temporal scales.

Proximal soil sensing refers to the development and use of sensors in the field, which obtain signals from soil when the sensor’s detector is in contact with or close to (within 2 m) the soil (Viscarra Rossel et al. 2011: Advances in Agronomy, Vol. 113). This definition precludes remote sensing and laboratory measurements of soil properties with benchtop instruments, although, we acknowledge that combining proximal with remote sensing may be advantageous in some applications; and that the development of many proximal sensors begins in the laboratory.

Dr. Raphael Viscarra Rossel
Dr. Craig R. Lobsey
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 1800 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
  • Sensing with electromagnetic frequencies: gamma, x-ray, ultraviolet, visible, infrared, terahertz, microwave and radio 
  • Electrochemical and mechanical sensing 
  • Microelectromechanical systems (MEMS) 
  • Multi-sensor systems 
  • Spectroscopy 
  • Signal processing 
  • Multivariate and Bayesian statistics 
  • Sensor fusion 
  • Environmental monitoring
  • Precision agriculture 
  • Sensor networks
  • Wireless sensors
  • Subterranean sensing
  • Soil biology

Published Papers (4 papers)

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Research

Open AccessArticle
Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe
Sensors 2019, 19(5), 1011; https://doi.org/10.3390/s19051011
Received: 13 December 2018 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 27 February 2019
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Abstract
Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on [...] Read more.
Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties. Full article
(This article belongs to the Special Issue Proximal Soil Sensing)
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Open AccessArticle
Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
Sensors 2019, 19(2), 263; https://doi.org/10.3390/s19020263
Received: 8 October 2018 / Revised: 24 December 2018 / Accepted: 7 January 2019 / Published: 11 January 2019
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Abstract
Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) [...] Read more.
Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain. Full article
(This article belongs to the Special Issue Proximal Soil Sensing)
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Open AccessArticle
Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization
Sensors 2018, 18(11), 3869; https://doi.org/10.3390/s18113869
Received: 29 September 2018 / Revised: 26 October 2018 / Accepted: 9 November 2018 / Published: 10 November 2018
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Abstract
In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of [...] Read more.
In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (<2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4–11% across soil properties, and the categorical covariate reduced RMSEP by another 2–9%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties. Full article
(This article belongs to the Special Issue Proximal Soil Sensing)
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Open AccessArticle
Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling
Sensors 2018, 18(10), 3408; https://doi.org/10.3390/s18103408
Received: 23 September 2018 / Revised: 7 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
Cited by 3 | PDF Full-text (2216 KB) | HTML Full-text | XML Full-text
Abstract
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are [...] Read more.
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system. Full article
(This article belongs to the Special Issue Proximal Soil Sensing)
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