{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:22:24Z","timestamp":1777058544749,"version":"3.51.4"},"reference-count":72,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:00:00Z","timestamp":1743120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Base Funding","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"Base Funding","award":["UIDP\/04028\/2020"],"award-info":[{"award-number":["UIDP\/04028\/2020"]}]},{"name":"Portuguese Foundation for Science and Technology (FCT)","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"Portuguese Foundation for Science and Technology (FCT)","award":["UIDP\/04028\/2020"],"award-info":[{"award-number":["UIDP\/04028\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil preservation from pollutants is essential for sustaining human and ecological health. This review explores the application of satellite imagery and machine learning (ML) techniques in detecting soil pollution, addressing recent advancements and key challenges in this field. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search across three major databases yielded 47 articles from an initial pool of 1018 publications spanning the last eight years. Among these, 34 studies focused on direct detection of soil pollutants, while 13 examined relationships between vegetation indicators and soil contaminants. This review evaluates various satellite platforms, highlights limitations of existing spaceborne sensors, and compares the effectiveness of ML models for soil pollution detection. Key challenges include the lack of standardization in datasets and methodologies, variations in evaluation metrics, and differences in algorithmic performance across studies. The findings emphasize the need for standardized frameworks and improved sensor capabilities to enhance detection accuracy. This work provides a foundation for future research, encouraging the integration of advanced ML models and multi-sensor satellite data for comprehensive soil pollution monitoring.<\/jats:p>","DOI":"10.3390\/rs17071207","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T13:36:49Z","timestamp":1743169009000},"page":"1207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Systematic Review of Machine Learning Algorithms for Soil Pollutant Detection Using Satellite Imagery"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7837-4668","authenticated-orcid":false,"given":"Amir","family":"TavallaieNejad","sequence":"first","affiliation":[{"name":"CERENA-FEUP, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5063-5632","authenticated-orcid":false,"given":"Maria Cristina","family":"Vila","sequence":"additional","affiliation":[{"name":"CERENA-FEUP, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9492-7207","authenticated-orcid":false,"given":"Gustavo","family":"Paneiro","sequence":"additional","affiliation":[{"name":"Department of Mineral and Energy Resources Engineering, Centre for Natural Resources and Environment (CERENA), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-5503","authenticated-orcid":false,"given":"Jo\u00e3o Santos","family":"Baptista","sequence":"additional","affiliation":[{"name":"CERENA-FEUP, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1111\/sum.12736","article-title":"Bio-mediated soil improvement: An introspection into processes, materials, characterization and applications","volume":"38","author":"Jiang","year":"2022","journal-title":"Soil Use Manag."},{"key":"ref_2","unstructured":"Havugimana, E., Bhople, B., and Kumar, A. (2024, April 20). Soil Pollution-Major Sources and Types of Soil Pollutants Integrated Nutrient Management View Project Cluster Frontline Demonstration on Pulses View Project. Available online: https:\/\/www.researchgate.net\/publication\/321526846."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"692","DOI":"10.2307\/2668517","article-title":"The Stockholm Convention on Persistent Organic Pollutants","volume":"95","author":"Lallas","year":"2001","journal-title":"Am. J. Int. Law."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.geoderma.2014.12.012","article-title":"Evaluation of soil quality for agricultural production using visible\u2013near-infrared spectroscopy","volume":"243\u2013244","author":"Askari","year":"2015","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/nature20139","article-title":"Water balance creates a threshold in soil pH at the global scale","volume":"540","author":"Slessarev","year":"2016","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hu, B., Chen, S., Hu, J., Xia, F., Xu, J., Li, Y., and Shi, Z. (2017). Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0172438"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.geoderma.2014.11.024","article-title":"Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review","volume":"241\u2013242","author":"Horta","year":"2015","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.scitotenv.2018.03.161","article-title":"Remediation techniques for heavy metal-contaminated soils: Principles and applicability","volume":"633","author":"Liu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115845","DOI":"10.1016\/j.envpol.2020.115845","article-title":"VIRS based detection in combination with machine learning for mapping soil pollution","volume":"268","author":"Jia","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"42609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1109\/TGRS.2018.2872131","article-title":"The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning","volume":"57","author":"Fang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. arXiv, Available online: http:\/\/arxiv.org\/abs\/1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_18","unstructured":"Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv, Available online: http:\/\/arxiv.org\/abs\/1508.01991."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tricco, A.C., Lillie, E., Zarin, W., O\u2019brien, K., Colquhoun, H., Kastner, M., Levac, D., Ng, C., Sharpe, J.P., and Wilson, K. (2016). A scoping review on the conduct and reporting of scoping reviews. BMC Med. Res. Methodol., 16.","DOI":"10.1186\/s12874-016-0116-4"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.envpol.2021.118397","article-title":"A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics","volume":"292","author":"Mendes","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112985","DOI":"10.1016\/j.rse.2022.112985","article-title":"Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model","volume":"273","author":"Ma","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111588","DOI":"10.1016\/j.foodres.2022.111588","article-title":"Regional prediction of multi-mycotoxin contamination of wheat in Europe using machine learning","volume":"159","author":"Wang","year":"2022","journal-title":"Food Res. Int."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fang, Y., Xu, L., Wong, A., and Clausi, D.A. (2022). Multi-Temporal Landsat-8 Images for Retrieval and Broad Scale Mapping of Soil Copper Concentration Using Empirical Models. Remote Sens., 14.","DOI":"10.3390\/rs14102311"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113424","DOI":"10.1016\/j.jenvman.2021.113424","article-title":"Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan","volume":"298","author":"Stieglitz","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neunet.2018.05.019","article-title":"Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks","volume":"105","author":"Sharma","year":"2018","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"152972","DOI":"10.1016\/j.scitotenv.2022.152972","article-title":"Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence","volume":"817","author":"Veloso","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rado\u010daj, D., Juri\u0161i\u0107, M., Ga\u0161parovi\u0107, M., Pla\u0161\u010dak, I., and Antoni\u0107, O. (2021). Cropland suitability assessment using satellite-based biophysical vegetation properties and machine learning. Agronomy, 11.","DOI":"10.3390\/agronomy11081620"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pech-May, F., Aquino-Santos, R., Rios-Toledo, G., and Posadas-Dur\u00e1n, J.P.F. (2022). Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine. Sensors, 22.","DOI":"10.3390\/s22134729"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"115118","DOI":"10.1016\/j.geoderma.2021.115118","article-title":"Mapping soil organic carbon stock by hyperspectral and time-series multispectral remote sensing images in low-relief agricultural areas","volume":"398","author":"Guo","year":"2021","journal-title":"Geoderma"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xing, C., Chen, N., Zhang, X., and Gong, J. (2017). A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sens., 9.","DOI":"10.3390\/rs9050484"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"159387","DOI":"10.1016\/j.scitotenv.2022.159387","article-title":"Mapping soil arsenic pollution at a brownfield site using satellite hyperspectral imagery and machine learning","volume":"857","author":"Jia","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116600","DOI":"10.1016\/j.envpol.2021.116600","article-title":"Microplastics pollution in the soil mulched by dust-proof nets: A case study in Beijing, China","volume":"275","author":"Chen","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.scitotenv.2018.11.230","article-title":"A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution","volume":"655","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_36","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."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109330","DOI":"10.1016\/j.ecolind.2022.109330","article-title":"Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning","volume":"143","author":"Lin","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"132922","DOI":"10.1016\/j.jclepro.2022.132922","article-title":"Exploring the potential of multispectral satellite images for estimating the contents of cadmium and lead in cropland: The effect of the dimidiate pixel model and random forest","volume":"367","author":"Wang","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113263","DOI":"10.1016\/j.rse.2022.113263","article-title":"Identifying distinct plastics in hyperspectral experimental lab-, aircraft-, and satellite data using machine\/deep learning methods trained with synthetically mixed spectral data","volume":"281","author":"Zhou","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101867","DOI":"10.1016\/j.ecoinf.2022.101867","article-title":"Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm","volume":"72","author":"Papi","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101777","DOI":"10.1016\/j.ecoinf.2022.101777","article-title":"Modelling and evaluation of land use changes through satellite images in a multifunctional catchment: Social, economic and environmental implications","volume":"71","author":"Novo","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106921","DOI":"10.1016\/j.gexplo.2021.106921","article-title":"Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran","volume":"233","author":"Azizi","year":"2022","journal-title":"J. Geochem. Explor."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"134755","DOI":"10.1016\/j.jclepro.2022.134755","article-title":"Regional metal pollution risk assessment based on a long short-term memory model: A case study of the South Altai Mountain mining area, China","volume":"379","author":"Cheng","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"155583","DOI":"10.1016\/j.scitotenv.2022.155583","article-title":"Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments","volume":"835","author":"Mirchooli","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"152086","DOI":"10.1016\/j.scitotenv.2021.152086","article-title":"Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline","volume":"808","author":"Alvyar","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"118981","DOI":"10.1016\/j.envpol.2022.118981","article-title":"Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China","volume":"300","author":"Zhang","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s11273-020-09731-2","article-title":"A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation","volume":"28","author":"Simioni","year":"2020","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.envsoft.2015.11.024","article-title":"Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia","volume":"77","author":"Teng","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Agrawal, A., and Petersen, M.R. (2021). Detecting arsenic contamination using satellite imagery and machine learning. Toxics, 9.","DOI":"10.3390\/toxics9120333"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2019.03.002","article-title":"Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations","volume":"225","author":"Wolanin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2018.11.019","article-title":"Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations","volume":"221","author":"Guzinski","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111260","DOI":"10.1016\/j.rse.2019.111260","article-title":"Global mapping of soil salinity change","volume":"231","author":"Ivushkin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Alexakis, D.D., Tapoglou, E., Vozinaki, A.E.K., and Tsanis, I.K. (2019). Integrated use of satellite remote sensing, artificial neural networks, field spectroscopy, and GIS in estimating crucial soil parameters in terms of soil erosion. Remote Sens., 11.","DOI":"10.3390\/rs11091106"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"136092","DOI":"10.1016\/j.scitotenv.2019.136092","article-title":"Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI","volume":"707","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Khosravi, V., Ardejani, F.D., Gholizadeh, A., and Saberioon, M. (2021). Satellite imagery for monitoring and mapping soil chromium pollution in a mine waste dump. Remote Sens., 13.","DOI":"10.31219\/osf.io\/fpv5c"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jclinepi.2021.02.003","article-title":"Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement","volume":"134","author":"Page","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10596-024-10285-y","article-title":"Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods","volume":"28","year":"2024","journal-title":"Comput. Geosci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yadav, A., Saraswat, S., and Faujdar, N. (2022, January 13\u201314). Geological Information Extraction from Satellite Imagery Using Machine Learning. Proceedings of the 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO, Noida, India.","DOI":"10.1109\/ICRITO56286.2022.9964623"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"120564","DOI":"10.1016\/j.jenvman.2024.120564","article-title":"Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning","volume":"356","author":"Ogungbuyi","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"8109","DOI":"10.1021\/acs.analchem.3c05311","article-title":"Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis","volume":"96","author":"Wang","year":"2024","journal-title":"Anal. Chem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107579","DOI":"10.1016\/j.catena.2023.107579","article-title":"Spatial variability of some heavy metals in arid harrats soils: Combining machine learning algorithms and synthetic indexes based-multitemporal Landsat 8\/9 to establish background levels","volume":"234","author":"Sulieman","year":"2024","journal-title":"Catena"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"117570","DOI":"10.1016\/j.envres.2023.117570","article-title":"A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest","volume":"240","author":"Xu","year":"2024","journal-title":"Environ. Res."},{"key":"ref_65","first-page":"49","article-title":"Efficiency of Machine Learning Algorithms in Soil Salinity Detection Using Landsat-8 Oli Imagery","volume":"10","author":"Alamdar","year":"2023","journal-title":"SPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Mahmoud, R., Hassanin, M., Al Feel, H., and Badry, R.M. (2023). Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt. Sustainability, 15.","DOI":"10.3390\/su15129467"},{"key":"ref_67","unstructured":"European Space Agency (2024, April 12). \u2018Sentinel-2.\u2019 Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_68","unstructured":"\u201cUSGS (2023, May 11). \u2018Landsat.\u2019 Core Science Systems, National Land Imaging Program, United States Geological Survey, Available online: https:\/\/www.usgs.gov\/core-science-systems\/nli\/landsat."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"111416","DOI":"10.1016\/j.ecolind.2023.111416","article-title":"Estimating of heavy metal concentration in agricultural soils from hyperspectral satellite sensor imagery: Considering the sources and migration pathways of pollutants","volume":"158","author":"Yao","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Amieva, J.F., Oxoli, D., and Brovelli, M.A. (2023). Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15225385"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tchounwou, P.B., Yedjou, C.G., Patlolla, A.K., and Sutton, D.J. (2012). Heavy Metal Toxicity and the Environment, Springer.","DOI":"10.1007\/978-3-7643-8340-4_6"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wijaya, D., Aditama, I.S., Austen, S.A., Widjaja, C.S., Jabar, B.A., and Irwansyah, E. (2024, May 16). Tree Counting with Deep Forest Algorithm for Kulon Progo District in Yogyakarta, Indonesia Using Pleiades Satellite Imagery. 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