{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T22:07:48Z","timestamp":1722550068226},"reference-count":64,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada","award":["RGPIN-2017-04869","DGDND-2017-00078","RGPAS2017-50794","RGPIN-2019-06744"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a data processing pipeline for soil copper (Cu) concentration estimation has been designed. First, instead of using single Landsat scenes, the utilization of multiple Landsat scenes of the same location over time is considered. Second, to generate a preferred feature set as input to a regression model, a number of feature extraction methods are motivated and compared. Third, to find a preferred regression model, a variety of approaches are implemented and compared for accuracy. In this research, 11 Landsat-8 images from 2013 to 2017 of Gulin County, Sichuan China, and 138 soil samples with lab-measured Cu concentrations collected from the area in 2015 are used. A variety a metrics under cross-validation are used for comparison. The results indicate that multi-temporal images increase accuracy compared to single Landsat images. The preferred feature extraction varies based on the regression model used; however, the best results are obtained using support vector regression and the original data. The final soil Cu map generated using the recommended data processing pipeline shows a consistent spatial pattern with a ground-truth land cover classification map. These results indicate that machine learning has the ability to perform large-scale soil heavy metal mapping from widely available satellite remote sensing images.<\/jats:p>","DOI":"10.3390\/rs14102311","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T03:08:36Z","timestamp":1652411316000},"page":"2311","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-5740-9526","authenticated-orcid":false,"given":"Yuan","family":"Fang","sequence":"first","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"given":"Linlin","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"given":"Alexander","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6383-0875","authenticated-orcid":false,"given":"David A.","family":"Clausi","sequence":"additional","affiliation":[{"name":"Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wild, A. (1993). Soils and the Environment, Cambridge University Press.","DOI":"10.1017\/CBO9780511623530"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alloway, B.J. (2012). Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability, Springer Science & Business Media.","DOI":"10.1007\/978-94-007-4470-7"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0016-7061(00)00043-4","article-title":"An overview of pedometric techniques for use in soil survey","volume":"97","author":"McBratney","year":"2000","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.3390\/rs2112474","article-title":"Visible and infrared remote imaging of hazardous waste: A review","volume":"2","author":"Slonecker","year":"2010","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s00254-008-1520-9","article-title":"Qualitative analysis and mapping of heavy metals in an abandoned Au-Ag mine area using NIR spectroscopy","volume":"58","author":"Choe","year":"2009","journal-title":"Environ. Geol."},{"key":"ref_6","unstructured":"JiA, J., Song, Y., Yuan, X., and Yang, Z. (2010, January 1\u20136). Diffuse reflectance spectroscopy study of heavy metals in agricultural soils of the Changjiang River Delta, China. Proceedings of the 19th World Congress of Soil Science, Brisbane, Australia."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1016\/j.rse.2008.03.017","article-title":"Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain","volume":"112","author":"Choe","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_8","unstructured":"Maliki, A.A., Bruce, D., and Owens, G. (September, January 25). Capabilities of remote sensing hyperspectral images for the detection of lead contamination: A review. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s11806-011-0424-0","article-title":"Feasibility of estimating heavy metal contaminations in floodplain soils using laboratory-based hyperspectral data\u2014A case study along Le\u2019an River, China","volume":"14","author":"Liu","year":"2011","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.12.003","article-title":"Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges","volume":"136","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1016\/S1002-0160(09)60167-3","article-title":"Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: A case study","volume":"19","author":"Zhuang","year":"2009","journal-title":"Pedosphere"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, C., Liu, B., and Tong, Q. (2010, January 25\u201330). Inversion of soil Cu concentration based on band selection of hyperspetral data. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652871"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10712-008-9037-z","article-title":"Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data","volume":"29","author":"Kalma","year":"2008","journal-title":"Surv. Geophys."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Quattrochi, D.A., and Luvall, J.C. (2004). Thermal Remote Sensing in Land Surface Processing, CRC Press.","DOI":"10.1201\/9780203502174"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"163","DOI":"10.3934\/geosci.2017.2.163","article-title":"Combined use of Landsat-8 and Sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale","volume":"3","author":"Skakun","year":"2017","journal-title":"AIMS Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.rse.2019.02.021","article-title":"Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data","volume":"225","author":"Wallis","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1109\/JSTARS.2017.2690623","article-title":"Retrieval of specific leaf area from Landsat-8 surface reflectance data using statistical and physical models","volume":"10","author":"Ali","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6769","DOI":"10.1080\/01431161.2017.1363436","article-title":"Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data","volume":"38","author":"Middinti","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2017.05.041","article-title":"The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations","volume":"198","author":"Sadeghi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"176","article-title":"Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data","volume":"50","author":"Amani","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.geoderma.2014.09.011","article-title":"Combination of proximal and remote sensing methods for rapid soil salinity quantification","volume":"239","author":"Aldabaa","year":"2015","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.1080\/0143116021000021288","article-title":"Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data","volume":"24","author":"Guerschman","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4308","DOI":"10.1109\/TGRS.2011.2158320","article-title":"Multitemporal unmixing of medium-spatial-resolution satellite images: A case study using MERIS images for land-cover mapping","volume":"49","author":"Guanter","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"343","DOI":"10.5194\/hess-13-343-2009","article-title":"Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling","volume":"13","author":"Mattia","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/36.739154","article-title":"Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images","volume":"37","author":"Kurvonen","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Tang, J., Alelyani, S., and Liu, H. (2014). Feature selection for classification: A review. Data Classification: Algorithms and Applications, CRC Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aca.2010.03.048","article-title":"Variables selection methods in near-infrared spectroscopy","volume":"667","author":"Xiaobo","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.aca.2011.03.006","article-title":"Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data","volume":"692","author":"Balabin","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115412","DOI":"10.1016\/j.envpol.2020.115412","article-title":"Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China","volume":"266","author":"Wang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"115399","DOI":"10.1016\/j.geoderma.2021.115399","article-title":"Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development","volume":"405","author":"Wang","year":"2022","journal-title":"Geoderma"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/j.apgeochem.2005.01.009","article-title":"Possibilities of reflectance spectroscopy for the assessment of contaminant elements in suburban soils","volume":"20","author":"Wu","year":"2005","journal-title":"Appl. Geochem."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pinheiro, \u00c9., Ceddia, M., Clingensmith, C., Grunwald, S., and Vasques, G. (2017). Prediction of soil physical and chemical properties by visible and near-infrared diffuse reflectance spectroscopy in the central Amazon. Remote Sens., 9.","DOI":"10.3390\/rs9040293"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27790","DOI":"10.1038\/srep27790","article-title":"Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.eja.2013.09.006","article-title":"Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression","volume":"52","author":"Li","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.jfoodeng.2010.09.002","article-title":"Application of visible and near infrared spectroscopy for rapid and non-invasive quantification of common adulterants in Spirulina powder","volume":"102","author":"Di","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2017.03.042","article-title":"Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves","volume":"195","author":"Wei","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fang, Y., Xu, L., Peng, J., Wang, H., Wong, A., and Clausi, D.A. (2018, January 7\u201310). Retrieval and mapping of heavy metal concentration in soil using time seies Landsat 8 imagery. Proceedings of the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Beijing, China.","DOI":"10.5194\/isprs-archives-XLII-3-335-2018"},{"key":"ref_39","unstructured":"(2018, April 08). Geospatial Data Cloud. Available online: http:\/\/www.gscloud.cn\/."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Zeileis, A., and Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-25"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","article-title":"A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery","volume":"141","author":"Xu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_42","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1145878","DOI":"10.1080\/23312041.2016.1145878","article-title":"Review of preprocessing techniques used in soil property prediction from hyperspectral data","volume":"2","author":"Minu","year":"2016","journal-title":"Cogent Geosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2007.02.005","article-title":"Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)","volume":"110","author":"Farifteh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yu, H., Liu, M., Du, B., Wang, Z., Hu, L., and Zhang, B. (2018). Mapping Soil Salinity\/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China. Sensors, 18.","DOI":"10.3390\/s18041048"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/5326.897072","article-title":"Neural networks for classification: A survey","volume":"30","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.15377\/2410-3624.2014.01.02.2","article-title":"Development of artificial neural network models for biogas production from co-digestion of leachate and pineapple peel","volume":"1","author":"Jaroenpoj","year":"2015","journal-title":"Glob. Environ. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s12517-016-2780-4","article-title":"Capability of vis-NIR spectroscopy and Landsat-8 spectral data to predict soil heavy metals in polluted agricultural land (Iran)","volume":"9","author":"Fard","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1021\/es015747j","article-title":"Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy","volume":"36","author":"Kemper","year":"2002","journal-title":"Environ. Sci. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods","volume":"40","author":"Verrelst","year":"2018","journal-title":"Surv. Geophys."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Karasuyama, M., and Nakano, R. (2007, January 12\u201317). Optimizing SVR hyperparameters via fast cross-validation using AOSVR. Proceedings of the 2007 International Joint Conference on Neural Networks, Orlando, FL, USA.","DOI":"10.1109\/IJCNN.2007.4371126"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_57","unstructured":"Hand, D.J. (1997). Construction and Assessment of Classification Rules, Wiley."},{"key":"ref_58","first-page":"569","article-title":"Interpolating surfaces in ArcGIS spatial analyst","volume":"3235","author":"Childs","year":"2004","journal-title":"Arcuser"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.rse.2010.01.006","article-title":"The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis","volume":"114","author":"Ozdogan","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_60","first-page":"122","article-title":"How much does multi-temporal Sentinel-2 data improve crop type classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.jhydrol.2009.09.029","article-title":"Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey","volume":"378","author":"Kisi","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1703","DOI":"10.1039\/c0an00387e","article-title":"Support vector machine regression (SVR\/LS-SVM)\u2014An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data","volume":"136","author":"Balabin","year":"2011","journal-title":"Analyst"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2012.08.026","article-title":"Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements","volume":"126","author":"Inoue","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"148455","DOI":"10.1016\/j.scitotenv.2021.148455","article-title":"Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest","volume":"792","author":"Shi","year":"2021","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T12:30:46Z","timestamp":1722256246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":64,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102311"],"URL":"https:\/\/doi.org\/10.3390\/rs14102311","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,5,11]]}}}
