{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:25:27Z","timestamp":1783578327106,"version":"3.55.0"},"reference-count":86,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42461042"],"award-info":[{"award-number":["42461042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41561051"],"award-info":[{"award-number":["41561051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region, China","award":["2023D01A44"],"award-info":[{"award-number":["2023D01A44"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Soil salinization poses a serious threat to global soil health and agricultural productivity, especially in arid and semi-arid regions, making the accurate assessment of its extent and severity crucial. This study employs hyperspectral remote sensing data to estimate soil salinity content (SSC) in the Weigan-Kuqa River Oasis in Xinjiang, China. To address hyperspectral dimensionality and noise challenges, multiple spectral transformation methods are systematically introduced and compared, including mathematical transformations, continuous wavelet transformation (CWT), discrete wavelet transformation (DWT), and their combined approaches. By incorporating multiple machine learning algorithms\u2014including random forest (RF), support vector machine (SVM), gradient boosting decision tree (GBDT), and deep forest (DF)\u2014a novel integrated framework that combines multi-transformation with multi-model algorithms for estimating SSC was developed. Results revealed that\n                    <jats:italic>R<\/jats:italic>\n                    -DWT showed the strongest correlation with SSC (|\n                    <jats:italic>r<\/jats:italic>\n                    |\n                    <jats:sub>max<\/jats:sub>\n                    \u2009=\u20090.621). SSC-sensitive bands are primarily distributed across the absorption regions of 1633\u2009nm (clay minerals), 1809\u20131810\u2009nm and 1951\u20131955\u2009nm (hydrated ions), 1969\u20131971\u2009nm and 1987\u20131989\u2009nm (crystalline water and hydroxyl groups), and 2001\u20132041\u2009nm (soluble salts). Among the spectral transformations, (1\/\n                    <jats:italic>R<\/jats:italic>\n                    )\u2032-CWT-2\n                    <jats:sup>7<\/jats:sup>\n                    yielded relatively high prediction accuracy. At the modeling algorithm level, the DF algorithm exhibited superior overall performance compared with the other algorithms. Among all models, the\n                    <jats:italic>R<\/jats:italic>\n                    -DWT-H7-DF model achieved the best overall performance, with\n                    <jats:italic>R<\/jats:italic>\n                    \u00b2 values of 0.87 for the training set and 0.67 for the test set. Research demonstrates that integrating appropriate spectral transformations with modeling methods can enhance the accuracy of SSC estimation, providing a feasible technical pathway and methodological support for monitoring soil salinization in arid regions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0347859","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:27:46Z","timestamp":1777570066000},"page":"e0347859","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimation of soil salt content in the oasis tillage layer based on hyperspectral transformation and model combination"],"prefix":"10.1371","volume":"21","author":[{"given":"Yanping","family":"Guo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5830-8575","authenticated-orcid":true,"given":"Xuemei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"pone.0347859.ref001","doi-asserted-by":"crossref","first-page":"115656","DOI":"10.1016\/j.geoderma.2021.115656","article-title":"A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network","volume":"409","author":"N Wang","year":"2022","journal-title":"Geoderma"},{"key":"pone.0347859.ref002","doi-asserted-by":"crossref","first-page":"111666","DOI":"10.1016\/j.microc.2024.111666","article-title":"Effects of salt content and particle size on spectral reflectance and model accuracy: estimating soil salt content in arid, saline-alkali lands","volume":"207","author":"MY Sun","year":"2024","journal-title":"Microchem J"},{"issue":"11","key":"pone.0347859.ref003","doi-asserted-by":"crossref","first-page":"5305","DOI":"10.1109\/JSTARS.2015.2478490","article-title":"Predicting Soil Salt Content Over Partially Vegetated Surfaces Using Non-Negative Matrix Factorization","volume":"8","author":"Y Liu","year":"2015","journal-title":"IEEE J Sel Top Appl Earth Observations Remote 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Chen","year":"2025","journal-title":"Geoderma"},{"issue":"4","key":"pone.0347859.ref007","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3390\/rs17040678","article-title":"High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1\/2 Radar and Optical Time-Series Data with Super Ensemble Model","volume":"17","author":"Z Cui","year":"2025","journal-title":"Remote Sensing"},{"key":"pone.0347859.ref008","doi-asserted-by":"crossref","first-page":"108970","DOI":"10.1016\/j.agwat.2024.108970","article-title":"Inversion of salinization in multilayer soils and prediction of water demand for salt regulation in coastal region","volume":"301","author":"Y Song","year":"2024","journal-title":"Agricultural Water Management"},{"key":"pone.0347859.ref009","doi-asserted-by":"crossref","first-page":"106031","DOI":"10.1016\/j.compag.2021.106031","article-title":"Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and 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Tian","year":"2026","journal-title":"Measurement"},{"key":"pone.0347859.ref023","doi-asserted-by":"crossref","first-page":"108905","DOI":"10.1016\/j.compag.2024.108905","article-title":"Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices","volume":"220","author":"J Geng","year":"2024","journal-title":"Computers and Electronics in Agriculture"},{"issue":"14","key":"pone.0347859.ref024","first-page":"1923","article-title":"Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters","volume":"13","author":"J Zhuang","year":"2024","journal-title":"Plants (Basel)"},{"issue":"09","key":"pone.0347859.ref025","first-page":"2296","article-title":"Inversion of soil salinity in Yinchuan Plain based on fractional-order differential spectral index","volume":"42","author":"RH Chen","year":"2023","journal-title":"Chinese J Ecology"},{"issue":"22","key":"pone.0347859.ref026","doi-asserted-by":"crossref","first-page":"4643","DOI":"10.3390\/rs13224643","article-title":"Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis","volume":"13","author":"J Liu","year":"2021","journal-title":"Remote Sensing"},{"issue":"4","key":"pone.0347859.ref027","doi-asserted-by":"crossref","first-page":"600","DOI":"10.3390\/rs17040600","article-title":"Research on Hyperspectral Inversion of Soil Organic Carbon in Agricultural Fields of the Southern Shaanxi Mountain Area","volume":"17","author":"Y Han","year":"2025","journal-title":"Remote Sensing"},{"issue":"22","key":"pone.0347859.ref028","doi-asserted-by":"crossref","first-page":"5294","DOI":"10.3390\/rs15225294","article-title":"Improving the Accuracy of Soil Organic Carbon Estimation: CWT-Random Frog-XGBoost as a 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Sensing"},{"key":"pone.0347859.ref034","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.agrformet.2018.08.019","article-title":"Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China","volume":"263","author":"J Fan","year":"2018","journal-title":"Agricultural and Forest Meteorology"},{"key":"pone.0347859.ref035","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.ins.2017.08.060","article-title":"Oblique random forest ensemble via Least Square Estimation for time series forecasting","volume":"420","author":"X Qiu","year":"2017","journal-title":"Information Sciences"},{"key":"pone.0347859.ref036","doi-asserted-by":"crossref","first-page":"113642","DOI":"10.1016\/j.ecolind.2025.113642","article-title":"Optimal time-window for assessing soil salinity via Sentinel-2 multitemporal synthetic data in the arid agricultural regions of China","volume":"176","author":"J Xiong","year":"2025","journal-title":"Ecol Indics"},{"key":"pone.0347859.ref037","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ins.2021.12.111","article-title":"Implementing local-explainability in Gradient Boosting Trees: Feature Contribution","volume":"589","author":"\u00c1 Delgado-Panadero","year":"2022","journal-title":"Information Sciences"},{"key":"pone.0347859.ref038","doi-asserted-by":"crossref","first-page":"107472","DOI":"10.1016\/j.asoc.2021.107472","article-title":"Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees","volume":"109","author":"Q Gu","year":"2021","journal-title":"Applied Soft Computing"},{"key":"pone.0347859.ref039","doi-asserted-by":"crossref","first-page":"116589","DOI":"10.1016\/j.geoderma.2023.116589","article-title":"Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China","volume":"437","author":"Z Bai","year":"2023","journal-title":"Geoderma"},{"key":"pone.0347859.ref040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSC.2021.3133673","article-title":"Gradient Boosted Neural Decision Forest","author":"M Dong","year":"2021","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"pone.0347859.ref041","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/rse2.365","article-title":"Deep learning\u2010based training data augmentation combined with post\u2010classification improves the classification accuracy for dominant and scattered invasive forest tree species","volume":"10","author":"SB Lik\u00f3","year":"2023","journal-title":"Remote Sens Ecol Conserv"},{"issue":"10","key":"pone.0347859.ref042","doi-asserted-by":"crossref","first-page":"8169","DOI":"10.1109\/TGRS.2019.2918587","article-title":"Deep Multigrained Cascade Forest for Hyperspectral Image Classification","volume":"57","author":"X Liu","year":"2019","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"9","key":"pone.0347859.ref043","doi-asserted-by":"crossref","first-page":"4298","DOI":"10.1109\/TKDE.2020.3038799","article-title":"Improving deep forest by screening","volume":"34","author":"M Pang","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"pone.0347859.ref044","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1093\/nsr\/nwy108","article-title":"Deep forest","volume":"6","author":"Z-H Zhou","year":"2019","journal-title":"Natl Sci Rev"},{"issue":"8","key":"pone.0347859.ref045","first-page":"2638","article-title":"Performance evaluation of the deep forest 2021 (DF21) model in retrieving soil cadmium concentration using hyperspectral data","volume":"43","author":"Z Zi-hao","year":"2023","journal-title":"Spectrosc Spect Anal"},{"issue":"5","key":"pone.0347859.ref046","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa393","article-title":"Identification of haploinsufficient genes from epigenomic data using deep forest","volume":"22","author":"Y Yang","year":"2021","journal-title":"Brief Bioinform"},{"key":"pone.0347859.ref047","doi-asserted-by":"crossref","first-page":"18129","DOI":"10.1109\/JSTARS.2024.3462480","article-title":"Infrared Precipitation Retrieval Method Based on Residual Deep Forest","volume":"17","author":"C Bao","year":"2024","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sensing"},{"key":"pone.0347859.ref048","doi-asserted-by":"crossref","first-page":"109622","DOI":"10.1016\/j.agwat.2025.109622","article-title":"Soil moisture retrieval and spatiotemporal variation analysis based on deep learning","volume":"317","author":"Z Zhang","year":"2025","journal-title":"Agricultural Water Management"},{"issue":"3","key":"pone.0347859.ref049","first-page":"83","article-title":"Hyperspectral inversion of the RF model for soil salinity in oasis tillage layer based on optimal mathematics and wavelet transform","volume":"41","author":"YP Guo","year":"2025","journal-title":"Trans Chin Soc Agric Eng"},{"key":"pone.0347859.ref050","doi-asserted-by":"crossref","first-page":"110114","DOI":"10.1016\/j.compag.2025.110114","article-title":"Thresholding and continuous wavelet transform (CWT) analysis of ground penetrating radar (GPR) data for estimation of potato biomass","volume":"232","author":"H Ruiz-Guzman","year":"2025","journal-title":"Comput Electron Agric"},{"issue":"6","key":"pone.0347859.ref051","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.3390\/agronomy14061309","article-title":"Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index","volume":"14","author":"X Liu","year":"2024","journal-title":"Agronomy"},{"issue":"1","key":"pone.0347859.ref052","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3390\/agronomy14010175","article-title":"Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland","volume":"14","author":"H Jin","year":"2024","journal-title":"Agronomy"},{"issue":"11","key":"pone.0347859.ref053","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.3390\/rs11111331","article-title":"Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis","volume":"11","author":"F Li","year":"2019","journal-title":"Remote Sensing"},{"issue":"19","key":"pone.0347859.ref054","first-page":"188","article-title":"Classification of convolutional autoencoder motor imagery EEG signals based on discrete wavelet transform","volume":"46","author":"YX Guo","year":"2024","journal-title":"Electr Meas Technol"},{"key":"pone.0347859.ref055","doi-asserted-by":"crossref","first-page":"104453","DOI":"10.1016\/j.jag.2025.104453","article-title":"A novel hyperspectral remote sensing estimation model for surface soil texture using AHSI\/ZY1-02D satellite image","volume":"138","author":"Q Shen","year":"2025","journal-title":"International J Applied Earth Observation and Geoinformation"},{"issue":"22","key":"pone.0347859.ref056","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.3390\/rs11222605","article-title":"Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data","volume":"11","author":"S Wang","year":"2019","journal-title":"Remote Sensing"},{"issue":"10","key":"pone.0347859.ref057","first-page":"2993","article-title":"Estimation of chlorophyll content in spartina alterniflora leaves based on continuous wavelet transformation and random forest algorithm","volume":"44","author":"C Guan","year":"2024","journal-title":"Spectrosc Spect Anal"},{"key":"pone.0347859.ref058","doi-asserted-by":"crossref","first-page":"112264","DOI":"10.1016\/j.ecolind.2024.112264","article-title":"Data driven forest fire susceptibility mapping in Bangladesh","volume":"166","author":"M Haydar","year":"2024","journal-title":"Ecol Indic"},{"issue":"2","key":"pone.0347859.ref059","article-title":"A multiple spectral important feature fusion method for wood species identification","volume":"59","author":"Y He","year":"2025","journal-title":"Wood Sci Technol"},{"key":"pone.0347859.ref060","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.enggeo.2010.05.005","article-title":"A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs","volume":"114","author":"A Al-Anazi","year":"2010","journal-title":"Eng Geol"},{"key":"pone.0347859.ref061","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.knosys.2016.10.031","article-title":"An efficient instance selection algorithm to reconstruct training set for support vector machine","volume":"116","author":"C Liu","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"7","key":"pone.0347859.ref062","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1080\/15481603.2021.1965399","article-title":"Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data","volume":"58","author":"A Jamali","year":"2021","journal-title":"GISci Remote Sensing"},{"key":"pone.0347859.ref063","doi-asserted-by":"crossref","first-page":"112978","DOI":"10.1016\/j.eswa.2019.112978","article-title":"An imprecise deep forest for classification","volume":"141","author":"LV Utkin","year":"2020","journal-title":"Expert Systems with 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for high-precision estimation of soil organic matter using two-dimensional correlation spectroscopy\u2014to support collaborative use of global open soil spectral libraries","volume":"445","author":"K Xia","year":"2024","journal-title":"Geoderma"},{"issue":"1","key":"pone.0347859.ref068","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11119-009-9123-3","article-title":"Estimating soil organic carbon from soil reflectance: a review","volume":"11","author":"M Ladoni","year":"2010","journal-title":"Precis Agric"},{"issue":"2","key":"pone.0347859.ref069","doi-asserted-by":"crossref","first-page":"503","DOI":"10.3390\/app15020503","article-title":"Enhanced hyperspectral forest soil organic matter prediction using a black-winged kite algorithm-optimized convolutional neural network and support vector machine","volume":"15","author":"Y Deng","year":"2025","journal-title":"Appl Sci 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