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The Use of Proximal and Remote Sensing Techniques for the Detection and Mapping of Contaminants in Soils

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 15034

Special Issue Editors


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Guest Editor
Precision Soil and Crop Engineering (Precision Scoring), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
Interests: proximal soil sensing; soil and water management; soil dynamics; tillage; traction; compaction; mechanical weeding; soil remediation and management and precision agriculture
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Remote Sensing Unit, Flemish Institute for Technological Research, Boeretang 200, 2400 Mol, Belgium
2. Department of Earth & Environmental Sciences, Faculty of BioScience Engineering, Celestijnenlaan 200E, 3001 Heverlee, Belgium
Interests: water–soil–crop modelling; remote sensing; data mining
Soil and Water Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
Interests: remote sensing; proximal sensing; precision agriculture; machine learning; data fusion
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: soil-landscape relationships; machine learning and AI; legacy soil data utilization; precision agriculture; multi-scale landscape metrics; remote sensing-derived variables
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil contamination with potentially toxic elements (PTEs), petroleum hydrocarbons (PHCs), and microplastics poses a threat to the environment and human health. Proper methods and tools are essential to measure and map soil contaminants for effective management and pollution risk assessment. Proximal and remote sensing (RS) techniques are promising tools for detecting, monitoring, and mapping soil contamination at high spatial resolutions and temporal intervals. Massive amounts of RS (e.g., multispectral and hyperspectral) data become available with different spatial, spectral, and temporal resolutions, which play a potential vital role in the detection and monitoring of soil contaminants at different scales. Proximal soil sensing (PSS) offers high sampling resolution with real-time measurement. Generally, PSS includes spectroscopic methods, such as visible and near infrared (vis–NIRS), mid-infrared (MIRS), X-ray fluorescence (XRFS), gamma ray, and laser-induced breakdown spectroscopy (LIBS) in addition to geophysical methods such as electrical resistivity, electromagnetic induction, and ground penetrating radar. PSS techniques provide rapid and accurate laboratory and/or field measurements that can be optimized and combined with advanced data analytics such as machine learning methods. Besides the sensors, data fusion techniques have greatly advanced the monitoring of soil contaminates that require more extensive temporal and spatial information not available with a single sensor or data source. The integration of multi-sensor and data fusion (e.g., RS, PSS, or RS and PSS) with digital soil mapping techniques will provide a better tool for accurately monitoring and mapping soil contaminates at various spatial and temporal scales. The suggested methods may provide new insights into the pollution process and different options for land management practices on contaminated sites. This emerging field needs to be developed to overcome the environmental factors that impact the accurate quantification of PTEs, PHCs, and microplastics, using better in situ measurements and mapping.

This Special Issue focuses on the potential of RS and PSS technologies and advanced machine learning techniques for modeling and mapping soil contaminates, including PTEs, PHCs, and microplastics, for site-specific land reclamation. We invite papers on both fundamental and applied research related to the use of individual and combined sensing tools for soil contaminant analysis with the capabilities of detecting and monitoring soil contamination for better risk assessment and environmental management. We also invite papers dedicated to new proximal sensors that can be used in PTEs, PHCs, and microplastics analysis that are aimed at better detection and mapping at different scales. In particular, research articles that cover but not limited to the following topics are welcome:

  • Remote sensing technologies for estimating and mapping soil contaminates at topsoil layers.
  • Proximal soil sensing tools, including common (see above-mentioned list of technologies) and emerging techniques for the measuring and mapping of HMs, high salt concentrations, PHCs, and microplastics in soils.
  • Sensors and data fusion techniques for modeling soil contaminates.
  • Digital mapping of soil contaminants using remote sensing technology.
  • The fusion of different combinations of remote and proximal sensing for monitoring and management of soil pollution, including risk assessment.
  • Cloud computing and big data analytics for monitoring environmental pollution.

Prof. Dr. Abdul M. Mouazen
Prof. Dr. Anne Gobin
Dr. Said Nawar
Dr. Yiyun Chen
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. Remote Sensing 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 2700 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

  • potentially toxic elements
  • petroleum hydrocarbons
  • microplastics
  • remote sensing
  • proximal sensing
  • machine learning
  • data Fusion
  • soil analysis

Published Papers (5 papers)

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Research

23 pages, 1961 KiB  
Article
Visible Near-Infrared Spectroscopy and Pedotransfer Function Well Predict Soil Sorption Coefficient of Glyphosate
by Sonia Akter, Lis Wollesen de Jonge, Per Møldrup, Mogens Humlekrog Greve, Trine Nørgaard, Peter Lystbæk Weber, Cecilie Hermansen, Abdul Mounem Mouazen and Maria Knadel
Remote Sens. 2023, 15(6), 1712; https://doi.org/10.3390/rs15061712 - 22 Mar 2023
Cited by 1 | Viewed by 1429
Abstract
The soil sorption coefficient (Kd) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of Kd is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method [...] Read more.
The soil sorption coefficient (Kd) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of Kd is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method for glyphosate Kd estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of Kd values (37–2409 L kg−1) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict Kd: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis–NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Feox, and Pox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis–NIRS obtained higher predictive ability for Kd than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of Kd on the DK and GR samples. However, the differences in prediction accuracy between vis–NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis–NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate Kd. Full article
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16 pages, 5857 KiB  
Article
Oil-Contaminated Soil Modeling and Remediation Monitoring in Arid Areas Using Remote Sensing
by Gordana Kaplan, Hakan Oktay Aydinli, Andrea Pietrelli, Fabien Mieyeville and Vincenzo Ferrara
Remote Sens. 2022, 14(10), 2500; https://doi.org/10.3390/rs14102500 - 23 May 2022
Cited by 8 | Viewed by 3345
Abstract
Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models oil-contaminated soils using one of the world’s greatest environmental disasters, the onshore oil spill in the desert of Kuwait [...] Read more.
Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models oil-contaminated soils using one of the world’s greatest environmental disasters, the onshore oil spill in the desert of Kuwait in 1991. This work uses state-of-art remote sensing technologies and machine learning to investigate the oil spills during the first Gulf War. We were able to identify oil-contaminated and clear locations in Kuwait using unsupervised classification over pre- and post-oil spill data. The research area’s pre-war and post-war circumstances, in terms of oil spills, were discovered by developing spectral signatures with different wavelengths and several spectral indices utilized for oil-contamination detection. Following that, we use this data for sampling and training to model various oil-contaminated soil levels. In addition, we analyze two separate datasets and used three modeling methodologies, Random Tree (RT), Support Vector Machine (SVM) and Random Forest (RF). The results show that the suggested approach is effective in detecting oil-contaminated soil. As a result, the location and degree of contamination may be established. The results of this analysis can be a valid support to the studies of an appropriate remediation. Full article
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19 pages, 9035 KiB  
Article
Soil Moisture Influence on the FTIR Spectrum of Salt-Affected Soils
by Le Thi Thu Hien, Anne Gobin, Duong Thi Lim, Dang Tran Quan, Nguyen Thi Hue, Nguyen Ngoc Thang, Nguyen Thanh Binh, Vu Thi Kim Dung and Pham Ha Linh
Remote Sens. 2022, 14(10), 2380; https://doi.org/10.3390/rs14102380 - 15 May 2022
Cited by 3 | Viewed by 2536
Abstract
Soil salinity has a major impact on agricultural production. In a changing climate with rising sea-levels, low-lying coastal areas are increasingly inundated whereby saltwater gradually contaminates the soil. Drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with [...] Read more.
Soil salinity has a major impact on agricultural production. In a changing climate with rising sea-levels, low-lying coastal areas are increasingly inundated whereby saltwater gradually contaminates the soil. Drought prone areas may suffer from salinity due to high evapotranspiration rates in combination with the use of saline irrigation water. Salinity is difficult to monitor because soil moisture affects the soil’s spectral signature. We conducted Fourier-transform infrared spectroscopy on alluvial and sandy soil samples in the coastal estuary of the Red River Delta. The soils are contaminated with NaCl, Na2CO3 and Na2SO4 salts. In an experiment of salt contamination, we established that three ranges of the spectrum were strongly influenced by both salt and moisture content in the soil, at wavenumbers 3200–3400 cm−1 (2.9–3.1 µm); 1600–1700 cm−1 (5.9–6.3 µm); 900–1100 cm−1 (9.1–11.1 µm). The Na2CO3 contaminated soil and the spectral value had a linear relationship between wavelengths 6.9 and 7.4 µm. At wavelength 6.99 µm, there was no relationship between absorbance and soil moisture, but the absorbance was proportional to the salt content (R2 = 0.85; RMSE = 0.68 g) and electrical conductivity (R2 = 0.50; RMSE = 3.8 dS/m). The relationship between soil moisture and spectral absorbance value was high at wavelengths below 6.7 µm, resulting in a quadratic relation between soil moisture and absorbance at wavelength 6.13 µm (R2 = 0.80; RMSE = 5.2%). The spectral signatures and equations might be useful for mapping salt-affected soils, particularly in difficult to access locations. Technological advances in thermal satellite sensors may offer possibilities for monitoring soil salinity. Full article
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23 pages, 34411 KiB  
Article
Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7
by Abdul M. Mouazen, Felix Nyarko, Muhammad Qaswar, Gergely Tóth, Anne Gobin and Dimitrios Moshou
Remote Sens. 2021, 13(22), 4615; https://doi.org/10.3390/rs13224615 - 16 Nov 2021
Cited by 5 | Viewed by 2959
Abstract
Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use [...] Read more.
Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years. Full article
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17 pages, 5403 KiB  
Article
A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction
by Yulong Tu, Bin Zou, Huihui Feng, Mo Zhou, Zhihui Yang and Ying Xiong
Remote Sens. 2021, 13(14), 2657; https://doi.org/10.3390/rs13142657 - 6 Jul 2021
Cited by 7 | Viewed by 2521
Abstract
Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed [...] Read more.
Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed in order to to reveal the soil cadmium (Cd) spectral response characteristics and predict its concentration. NSSCd were produced by adding the quantitative Cd solution into background soil. Then, prior spectral bands (i.e., the bands with higher variable importance in projection (VIP) score in NSSCd spectra) were used for predicting Cd concentration in soil samples collected from the Hengyang mining area and Baoding agriculture area. The partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS) were used for validation. Compared to using entire VNIR spectral ranges, the new modeling strategy performed very well, with the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) showing an improvement from 0.63 and 1.72 to 0.71 and 1.95 in Hengyang and from 0.54 and 1.57 to 0.76 and 2.19 in Baoding. These results suggest that NSS prior spectral bands are critical for soil HM prediction. Our results represent an exciting finding for the future design of remote sensing sensors for soil HM detection. Full article
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