Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7

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.

[1]  Yan Zhang,et al.  The impact of land use changes and erosion process on heavy metal distribution in the hilly area of the Loess Plateau, China. , 2020, The Science of the total environment.

[2]  Claudia Notarnicola,et al.  Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..

[3]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Zhou Shi,et al.  Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution , 2017, PloS one.

[5]  B. Efron Computers and the Theory of Statistics: Thinking the Unthinkable , 1979 .

[6]  Bin Dai,et al.  Identifying the origins and spatial distributions of heavy metals in soils of Ju country (Eastern China) using multivariate and geostatistical approach , 2014, Journal of Soils and Sediments.

[7]  W. Farmer Ordinary kriging as a tool to estimate historical daily streamflow records , 2016 .

[8]  C. Micó,et al.  Baseline values for heavy metals in agricultural soils in an European Mediterranean region. , 2007, The Science of the total environment.

[9]  U. Förstner,et al.  Heavy metal accumulation in river sediments: A response to environmental pollution , 1973 .

[10]  E. Smolders,et al.  Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils. , 2014, The Science of the total environment.

[11]  Qihao Weng,et al.  A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery , 2016 .

[12]  C. Micó,et al.  Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis. , 2006, Chemosphere.

[13]  M. Denk,et al.  Prediction of soil parameters using the spectral range between 350 and 15,000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony, Germany , 2018 .

[14]  Xiangnan Liu,et al.  Long-term Landsat monitoring of mining subsidence based on spatiotemporal variations in soil moisture: A case study of Shanxi Province, China , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Rosa Lasaponara,et al.  Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt , 2020, Remote. Sens..

[16]  Guofeng Wu,et al.  Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data—A case study along Le’an River, China , 2011, Geo spatial Inf. Sci..

[17]  Hang Cheng,et al.  Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy , 2019, Geoderma.

[18]  L. Montanarella,et al.  A spatial assessment of mercury content in the European Union topsoil , 2021, The Science of the total environment.

[19]  M. Arias,et al.  Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations. , 2006, Environmental pollution.

[20]  L. Montanarella,et al.  Copper distribution in European topsoils: An assessment based on LUCAS soil survey. , 2018, The Science of the total environment.

[21]  G. Tóth,et al.  Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment. , 2016, The Science of the total environment.

[22]  Qingwei Guo,et al.  Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China. , 2012, Ecotoxicology and environmental safety.

[23]  Manqun Wang,et al.  Heavy Metals and Pesticides Toxicity in Agricultural Soil and Plants: Ecological Risks and Human Health Implications , 2021, Toxics.

[24]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[25]  S. M. Kashefipour,et al.  Numerical Modelling of Heavy Metals Transport Processes in Riverine Basins , 2012 .

[26]  Hamid Reza Matinfar,et al.  Capability of vis-NIR spectroscopy and Landsat 8 spectral data to predict soil heavy metals in polluted agricultural land (Iran) , 2016, Arabian Journal of Geosciences.

[27]  Shi Zhou,et al.  In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy , 2014, PloS one.

[28]  A. Lausch,et al.  Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images. , 2020, The Science of the total environment.

[29]  Thomas Kemper,et al.  Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. , 2002, Environmental science & technology.

[30]  Kurt Hornik,et al.  Support Vector Machines in R , 2006 .

[31]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[32]  J. Qi,et al.  Total carbon mapping in glacial till soils using near-infrared spectroscopy, Landsat imagery and topographical information , 2007 .

[33]  Qian Du,et al.  Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data , 2019, Environmental Monitoring and Assessment.

[34]  Barry Haack,et al.  Visible and Infrared Remote Imaging of Hazardous Waste: A Review , 2010, Remote. Sens..

[35]  Xiaoman He,et al.  Interactions among heavy metal bioaccessibility, soil properties and microbial community in phyto-remediated soils nearby an abandoned realgar mine. , 2021, Chemosphere.

[36]  W. Ahmed,et al.  Soil nutrients and heavy metal availability under long-term combined application of swine manure and synthetic fertilizers in acidic paddy soil , 2020, Journal of Soils and Sediments.

[37]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[38]  G. Ayoko,et al.  The use of reflectance visible-NIR spectroscopy to predict seasonal change of trace metals in suspended solids of Changjiang River , 2013 .

[39]  Alexandros Karatzoglou,et al.  Kernel-based machine learning for fast text mining in R , 2010, Comput. Stat. Data Anal..

[40]  J. Loch,et al.  Evaporation as the transport mechanism of metals in arid regions. , 2014, Chemosphere.

[41]  H. Ramon,et al.  Effect of Wavelength Range on the Measurement Accuracy of Some Selected Soil Constituents Using Visual-Near Infrared Spectroscopy , 2006 .