{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:03:56Z","timestamp":1787029436220,"version":"3.56.0"},"reference-count":80,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5\u20132 mg\/kg\u22121 class) where its distribution increased by ~25% from 1988 to 2016\u2014similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.<\/jats:p>","DOI":"10.3390\/rs13091698","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:18:20Z","timestamp":1619558300000},"page":"1698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6624","authenticated-orcid":false,"given":"Ruhollah","family":"Taghizadeh-Mehrjardi","sequence":"first","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"Sonderforschungsbereich 1070 Ressourcen Kulturen (SFB1070 ResourceCultures), University of T\u00fcbingen, 72074 T\u00fcbingen, Germany"},{"name":"Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan 8951656767, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Fathizad","sequence":"additional","affiliation":[{"name":"Department of Arid and Desert Regions Management, School of Natural Resources &amp; Desert Studies, Yazd University, Yazd 89195741, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ali Hakimzadeh Ardakani","sequence":"additional","affiliation":[{"name":"Department of Arid and Desert Regions Management, School of Natural Resources &amp; Desert Studies, Yazd University, Yazd 89195741, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Sodaiezadeh","sequence":"additional","affiliation":[{"name":"Department of Arid and Desert Regions Management, School of Natural Resources &amp; Desert Studies, Yazd University, Yazd 89195741, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Kerry","sequence":"additional","affiliation":[{"name":"Department of Geography, Brigham Young University, Provo, UT 84602, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon","family":"Heung","sequence":"additional","affiliation":[{"name":"Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Halifax, NS B3H 4R2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2602","authenticated-orcid":false,"given":"Thomas","family":"Scholten","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"Sonderforschungsbereich 1070 Ressourcen Kulturen (SFB1070 ResourceCultures), University of T\u00fcbingen, 72074 T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00254-002-0523-1","article-title":"Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals","volume":"42","author":"Lin","year":"2002","journal-title":"Environ. Geol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/j.chemosphere.2006.03.016","article-title":"Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis","volume":"65","author":"Peris","year":"2006","journal-title":"Chemosphere"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"He, J., Yang, Y., Christakos, G., Liu, Y., and Yang, X. (2019). Assessment of soil heavy metal pollution using stochastic site indicators. Geoderma, 337.","DOI":"10.1016\/j.geoderma.2018.09.038"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.scitotenv.2013.08.090","article-title":"A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment","volume":"468\u2013469","author":"Li","year":"2014","journal-title":"Sci. Total Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123288","DOI":"10.1016\/j.jhazmat.2020.123288","article-title":"Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning","volume":"401","author":"Tan","year":"2021","journal-title":"J. Hazard. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/05704928.2018.1442346","article-title":"Proximal and remote sensing techniques for mapping of soil contamination with heavy metals","volume":"53","author":"Shi","year":"2018","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6803","DOI":"10.1016\/j.atmosenv.2004.09.011","article-title":"Heavy metal distribution in dust, street dust and soils from the work place in Karak Industrial Estate, Jordan","volume":"38","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.scitotenv.2013.02.009","article-title":"Multivariate methods and artificial neural networks in the assessment of the response of infaunal assemblages to sediment metal contamination and organic enrichment","volume":"450\u2013451","author":"Subida","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.jenvman.2010.11.011","article-title":"Removal of heavy metal ions from wastewaters: A review","volume":"92","author":"Fu","year":"2011","journal-title":"J. Environ. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s10661-012-2612-2","article-title":"Distribution and source analysis of aluminum in rivers near Xi\u2019an City, China","volume":"185","author":"Wang","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.scitotenv.2018.06.068","article-title":"A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment","volume":"642","author":"Yang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1976","DOI":"10.2134\/jeq2001.1976","article-title":"A Stochastic Empirical Model for Regional Heavy-Metal Balances in Agroecosystems","volume":"30","author":"Keller","year":"2001","journal-title":"J. Environ. Qual."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S1161-0301(03)00075-3","article-title":"Modelling regional-scale mass balances of phosphorus, cadmium and zinc fluxes on arable and dairy farms","volume":"20","author":"Keller","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/S1002-0160(06)60087-8","article-title":"Spatial Prediction of Heavy Metal Pollution for Soils in Peri-Urban Beijing, China Based on Fuzzy Set Theory1 1 Project supported by the National Natural Science Foundation of China (Nos. 40571065 and 40235054) and the National Key Basic Research Support","volume":"16","author":"Tan","year":"2006","journal-title":"Pedosphere"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, H., Yilihamu, Q., Yuan, M., Bai, H., Xu, H., and Wu, J. (2020). Prediction models of soil heavy metal(loid)s concentration for agricultural land in Dongli: A comparison of regression and random forest. Ecol. Indic., 119.","DOI":"10.1016\/j.ecolind.2020.106801"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"129497","DOI":"10.1109\/ACCESS.2020.3009248","article-title":"A Collaborative Compound Neural Network Model for Soil Heavy Metal Content Prediction","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/S1002-0160(07)60006-X","article-title":"Spatial Distribution of Heavy Metals in Agricultural Soils of an Industry-Based Peri-Urban Area in Wuxi, China1 1 Project supported by the RURBIFARM (Sustainable Farming at the Rural-Urban Interface) project of the European Union (No. ICA4-CT-2002-10021)","volume":"17","author":"Zhao","year":"2007","journal-title":"Pedosphere"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"42584","DOI":"10.1109\/ACCESS.2020.2976902","article-title":"Improved Mapping of Soil Heavy Metals Using a Vis-NIR Spectroscopy Index in an Agricultural Area of Eastern China","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110406","DOI":"10.1016\/j.ecoenv.2020.110406","article-title":"Spatial characteristics of heavy metal contamination and potential human health risk assessment of urban soils: A case study from an urban region of South India","volume":"194","author":"Adimalla","year":"2020","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jhydrol.2010.01.023","article-title":"An improved statistical approach to merge satellite rainfall estimates and raingauge data","volume":"385","author":"Li","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.atmosenv.2011.11.041","article-title":"Multivariate and geostatistical analyzes of metals in urban soil of Weinan industrial areas, Northwest of China","volume":"47","author":"Li","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126908","DOI":"10.1016\/j.chemosphere.2020.126908","article-title":"Spatiotemporal modeling of soil heavy metals and early warnings from scenarios-based prediction","volume":"255","author":"He","year":"2020","journal-title":"Chemosphere"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.envpol.2006.01.045","article-title":"Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations","volume":"144","author":"Arias","year":"2006","journal-title":"Environ. Pollut."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.envpol.2003.07.002","article-title":"Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland","volume":"127","author":"McGrath","year":"2004","journal-title":"Environ. Pollut."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114961","DOI":"10.1016\/j.envpol.2020.114961","article-title":"Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level","volume":"266","author":"Hu","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, A.H., and Zhao, Y. (2011, January 24\u201326). Evaluation on the soil pollution degree on two sides of highway with the fuzzy mathematics method. Proceedings of the 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE, Nanjing, China.","DOI":"10.1109\/RSETE.2011.5965202"},{"key":"ref_27","first-page":"353","article-title":"Chapter 25 A Comparison of Data-Mining Techniques in Predictive Soil Mapping","volume":"31","author":"Behrens","year":"2006","journal-title":"Dev. Soil Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lagacherie, P. (2008). Digital soil mapping: A state of the art. Digital Soil Mapping with Limited Data, Springer.","DOI":"10.1007\/978-1-4020-8592-5_1"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/j.ecolind.2019.02.026","article-title":"Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation","volume":"101","author":"Veronesi","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.geoderma.2017.12.011","article-title":"Modeling soil organic carbon with Quantile Regression: Dissecting predictors\u2019 effects on carbon stocks","volume":"318","author":"Lombardo","year":"2018","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.scitotenv.2017.05.239","article-title":"Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling","volume":"601\u2013602","author":"Schillaci","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.geoderma.2008.05.010","article-title":"Instance selection and classification tree analysis for large spatial datasets in digital soil mapping","volume":"146","author":"Schmidt","year":"2008","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2008.05.008","article-title":"Soil organic carbon concentrations and stocks on Barro Colorado Island\u2014Digital soil mapping using Random Forests analysis","volume":"146","author":"Grimm","year":"2008","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.geoderma.2015.11.014","article-title":"An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping","volume":"265","author":"Heung","year":"2016","journal-title":"Geoderma"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.geoderma.2016.12.001","article-title":"Comparing the use of training data derived from legacy soil pits and soil survey polygons for mapping soil classes","volume":"290","author":"Heung","year":"2017","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104289","DOI":"10.1016\/j.still.2019.06.006","article-title":"Some practical aspects of predicting texture data in digital soil mapping","volume":"194","author":"Minasny","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rentschler, T., Werban, U., Ahner, M., Behrens, T., Gries, P., Scholten, T., Teuber, S., and Schmidt, K. (2020). 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning. Vadose Zone J., 19.","DOI":"10.1002\/vzj2.20062"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/B978-0-12-800137-0.00003-0","article-title":"GlobalSoilMap. Toward a Fine-Resolution Global Grid of Soil Properties","volume":"Volume 125","author":"Arrouays","year":"2014","journal-title":"Advances in Agronomy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1560-8115-20-2","article-title":"A systematic review on status of lead pollution and toxicity in Iran; Guidance for preventive measures","volume":"20","author":"Karrari","year":"2012","journal-title":"DARU J. Pharm. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.3390\/ijerph9051874","article-title":"Mapping of Cu and Pb contaminations in soil using combined geochemistry, topography, and remote sensing: A case study in the le\u2019an river floodplain, China","volume":"9","author":"Chen","year":"2012","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3846\/16486897.2016.1184152","article-title":"Mapping of heavy metal contamination in alluvial soils of the Middle Nile Delta of Egypt","volume":"24","author":"Shokr","year":"2016","journal-title":"J. Environ. Eng. Landsc. Manag."},{"key":"ref_43","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_44","first-page":"1726","article-title":"Studies regarding the use of remote sensing satellite data for the identification of heavy metal pollution in agricultural fields","volume":"22","author":"Dana","year":"2011","journal-title":"Ann. DAAAM"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"114552","DOI":"10.1016\/j.geoderma.2020.114552","article-title":"Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran","volume":"376","author":"Mahdianpari","year":"2020","journal-title":"Geoderma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"114233","DOI":"10.1016\/j.geoderma.2020.114233","article-title":"Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran","volume":"365","author":"Fathizad","year":"2020","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106736","DOI":"10.1016\/j.ecolind.2020.106736","article-title":"Spatio-temporal dynamic of soil quality in the central Iranian desert modeled with machine learning and digital soil assessment techniques","volume":"118","author":"Fathizad","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.geoderma.2017.12.024","article-title":"Assessing the effects of slope gradient and land use change on soil quality degradation through digital mapping of soil quality indices and soil loss rate","volume":"318","author":"Nabiollahi","year":"2018","journal-title":"Geoderma"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mahmoudzadeh, H., Matinfar, H.R., Taghizadeh-Mehrjardi, R., and Kerry, R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Reg., 21.","DOI":"10.1016\/j.geodrs.2020.e00260"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Farr, T.G., and Kobrick, M. (2000). The Shuttle Radar Topography Mission. Rev. Geophys.","DOI":"10.1029\/EO081i048p00583"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jafrearsci.2018.04.012","article-title":"Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert","volume":"145","author":"Fathizad","year":"2018","journal-title":"J. African Earth Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.cageo.2005.12.009","article-title":"A conditioned Latin hypercube method for sampling in the presence of ancillary information","volume":"32","author":"Minasny","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114890","DOI":"10.1016\/j.geoderma.2020.114890","article-title":"High resolution middle eastern soil attributes mapping via open data and cloud computing","volume":"385","author":"Poppiel","year":"2021","journal-title":"Geoderma"},{"key":"ref_55","first-page":"103","article-title":"Geoinformation modeling of soil pollution processes by lead compounds in highway geosystems","volume":"52","author":"Galagan","year":"2020","journal-title":"Visnyk V.N. Karazin Kharkiv Natl. Univ. Ser. Geol. Geogr. Ecol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"421","DOI":"10.2136\/sssaj1978.03615995004200030009x","article-title":"Development of a DTPA soil test for zinc, iron, manganese, and copper","volume":"42","author":"Lindsay","year":"1978","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1146\/annurev.psych.52.1.653","article-title":"Problems for Judgment and Decision Making","volume":"52","author":"Hastie","year":"2001","journal-title":"Annu. Rev. Psychol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_59","unstructured":"Liaw, A., and Wiener, M. (2002). Classification and Regression by randomForest. R news, 18\u201322."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.gexplo.2014.05.015","article-title":"On the quantitative relationships between environmental parameters and heavy metals pollution in Mediterranean soils using GIS regression-trees: The case study of Lebanon","volume":"147","author":"Shomar","year":"2014","journal-title":"J. Geochem. Explor."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geoderma.2013.07.020","article-title":"Digital mapping of soil salinity in ardakan region, central iran","volume":"213","author":"Minasny","year":"2014","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/17583004.2017.1330593","article-title":"Artificial bee colony feature selection algorithm combined with machine learning algorithms to predict vertical and lateral distribution of soil organic matter in South Dakota, USA","volume":"8","author":"Neupane","year":"2017","journal-title":"Carbon Manag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17583004.2018.1553434","article-title":"Assessing soil organic carbon stocks under land-use change scenarios using random forest models","volume":"10","author":"Nabiollahi","year":"2019","journal-title":"Carbon Manag."},{"key":"ref_64","unstructured":"Heath, P.H., Malcolm, W.S., Dobson, S., World Health Organization, and International Programme on Chemical Safety (2004). Manganese and Its Compounds: Environmental Aspects, WHO."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.envpol.2007.05.020","article-title":"Metals in particle-size fractions of the soils of five European cities","volume":"152","author":"Biasioli","year":"2008","journal-title":"Environ. Pollut."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0269-7491(00)00243-8","article-title":"Multivariate statistical and GIS-based approach to identify heavy metal sources in soils","volume":"114","author":"Facchinelli","year":"2001","journal-title":"Environ. Pollut."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1046\/j.1466-822X.2001.00248.x","article-title":"Mapping the biomass of Bornean tropical rain forest from remotely sensed data","volume":"10","author":"Foody","year":"2001","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2136\/sssaj2006-0049","article-title":"Digitally Mapping Gypsic and Natric Soil Areas Using Landsat ETM Data","volume":"71","author":"Nield","year":"2007","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_72","unstructured":"Arzani, H., and King, G.W. (July, January 29). Application of Remote Sensing (Landsat TM data) for Vegetation Parameters Measurement in Western Division of NSW. Proceedings of the International Grassland Congress, Hohhot, China."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of Leaf-Area Index from Quality of Light on the Forest Floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.agwat.2016.07.007","article-title":"Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients","volume":"179","author":"Kullberg","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.agwat.2004.09.038","article-title":"Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators","volume":"77","author":"Khan","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1080\/01431169008955053","article-title":"A ratio vegetation index adjusted for soil brightness","volume":"11","author":"Major","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.geoderma.2005.10.009","article-title":"Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data","volume":"134","author":"Douaoui","year":"2006","journal-title":"Geoderma"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/0146-664X(80)90054-4","article-title":"Euclidean distance mapping","volume":"14","author":"Danielsson","year":"1980","journal-title":"Comput. Graph. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:53:31Z","timestamp":1760162011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,27]]},"references-count":80,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091698"],"URL":"https:\/\/doi.org\/10.3390\/rs13091698","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,27]]}}}