{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:43:47Z","timestamp":1776415427838,"version":"3.51.2"},"reference-count":78,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41877109"],"award-info":[{"award-number":["41877109"]}],"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":["42050410320"],"award-info":[{"award-number":["42050410320"]}],"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":["42067003"],"award-info":[{"award-number":["42067003"]}],"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":["42061047"],"award-info":[{"award-number":["42061047"]}],"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":["R2020T29"],"award-info":[{"award-number":["R2020T29"]}],"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":["2021BEG03002"],"award-info":[{"award-number":["2021BEG03002"]}],"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":["2021YFD1900602"],"award-info":[{"award-number":["2021YFD1900602"]}],"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":["Y772121"],"award-info":[{"award-number":["Y772121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["41877109"],"award-info":[{"award-number":["41877109"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["42050410320"],"award-info":[{"award-number":["42050410320"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["42067003"],"award-info":[{"award-number":["42067003"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["42061047"],"award-info":[{"award-number":["42061047"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["R2020T29"],"award-info":[{"award-number":["R2020T29"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["2021BEG03002"],"award-info":[{"award-number":["2021BEG03002"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["2021YFD1900602"],"award-info":[{"award-number":["2021YFD1900602"]}]},{"name":"Jiangsu Specially-Appointed Professor Project","award":["Y772121"],"award-info":[{"award-number":["Y772121"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["41877109"],"award-info":[{"award-number":["41877109"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["42050410320"],"award-info":[{"award-number":["42050410320"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["42067003"],"award-info":[{"award-number":["42067003"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["42061047"],"award-info":[{"award-number":["42061047"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["R2020T29"],"award-info":[{"award-number":["R2020T29"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["2021BEG03002"],"award-info":[{"award-number":["2021BEG03002"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["2021YFD1900602"],"award-info":[{"award-number":["2021YFD1900602"]}]},{"name":"Key R&amp;D Project of Ningxia, China","award":["Y772121"],"award-info":[{"award-number":["Y772121"]}]},{"name":"National Key R&amp;D Program of China","award":["41877109"],"award-info":[{"award-number":["41877109"]}]},{"name":"National Key R&amp;D Program of China","award":["42050410320"],"award-info":[{"award-number":["42050410320"]}]},{"name":"National Key R&amp;D Program of China","award":["42067003"],"award-info":[{"award-number":["42067003"]}]},{"name":"National Key R&amp;D Program of China","award":["42061047"],"award-info":[{"award-number":["42061047"]}]},{"name":"National Key R&amp;D Program of China","award":["R2020T29"],"award-info":[{"award-number":["R2020T29"]}]},{"name":"National Key R&amp;D Program of China","award":["2021BEG03002"],"award-info":[{"award-number":["2021BEG03002"]}]},{"name":"National Key R&amp;D Program of China","award":["2021YFD1900602"],"award-info":[{"award-number":["2021YFD1900602"]}]},{"name":"National Key R&amp;D Program of China","award":["Y772121"],"award-info":[{"award-number":["Y772121"]}]},{"name":"Thousand Young Talents Program, China","award":["41877109"],"award-info":[{"award-number":["41877109"]}]},{"name":"Thousand Young Talents Program, China","award":["42050410320"],"award-info":[{"award-number":["42050410320"]}]},{"name":"Thousand Young Talents Program, China","award":["42067003"],"award-info":[{"award-number":["42067003"]}]},{"name":"Thousand Young Talents Program, China","award":["42061047"],"award-info":[{"award-number":["42061047"]}]},{"name":"Thousand Young Talents Program, China","award":["R2020T29"],"award-info":[{"award-number":["R2020T29"]}]},{"name":"Thousand Young Talents Program, China","award":["2021BEG03002"],"award-info":[{"award-number":["2021BEG03002"]}]},{"name":"Thousand Young Talents Program, China","award":["2021YFD1900602"],"award-info":[{"award-number":["2021YFD1900602"]}]},{"name":"Thousand Young Talents Program, China","award":["Y772121"],"award-info":[{"award-number":["Y772121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An accurate estimation of soil electrical conductivity (EC) using hyperspectral techniques is of great significance for understanding the spatial distribution of solutes and soil salinization. Although spectral transformation has been widely used in data pre-processing, the performance of different pre-processing techniques (or combination methods) on different models of the same data set is still ambiguous. Moreover, extremely randomized trees (ERT) and light gradient boosting machine (LightGBM) models are new learning algorithms with good generalization performance (soil moisture and above-ground biomass), but are less studied in estimating soil salinity in the visible and near-infrared spectra. In this study, 130 soil EC data, soil measured hyperspectral data, topographic factors, conventional salinity indices such as Salinity Index 1, and two-band (2D) salinity indices such as ratio indices, were introduced. The five spectral pre-processing methods of standard normal variate (SNV), standard normal variate and detrend (SNV-DT), inverse (1\/OR) (OR is original spectrum), inverse-log (Log(1\/OR) and fractional order derivative (FOD) (range 0\u20132, with intervals of 0.25) were performed. A gradient boosting machine (GBM) was used to select sensitive spectral parameters. Models (extreme gradient boosting (XGBoost), LightGBM, random forest (RF), ERT, classification and regression tree (CART), and ridge regression (RR)) were used for inversion soil EC and model validation. The results reveal that the two-dimensional correlation coefficient highlighted EC more effectively than the one-dimensional. Under SNV and the second order derivative, the two-dimensional correlation coefficient increased by 0.286 and 0.258 compared to the one-dimension, respectively. The 13 characteristic factors of slope, NDI, SI-T, RI, profile curvature, DOA, plane curvature, SI (conventional), elevation, Int2, aspect, S1 and TWI provided 90% of the cumulative importance for EC using GBM. Among the six machine models, the ERT model performed the best for simulation (R2 = 0.98) and validation (R2 = 0.96). The ERT model showed the best performance among the EC estimation models from the reference data. The kriging map based on the ERT simulation showed a close relationship with the measured data. Our study selected the effective pre-processing methods (SNV and the 2 order derivative) using one- and two-dimensional correlation, 13 important factors and the ERT model for EC hyperspectral inversion. This provides a theoretical support for the quantitative monitoring of soil salinization on a larger scale using remote sensing techniques.<\/jats:p>","DOI":"10.3390\/rs14112602","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"2602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity"],"prefix":"10.3390","volume":"14","author":[{"given":"Pingping","family":"Jia","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Geography and Planning, Ningxia University, Yinchuan 750021, China"}]},{"given":"Junhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Ecology and Environment, Ningxia University, Yinchuan 750021, China"},{"name":"Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China"}]},{"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Rong","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9536-9251","authenticated-orcid":false,"given":"Kazem","family":"Zamanian","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Institute of Soil Science, Leibniz University of Hannover, 30419 Hannover, Germany"}]},{"given":"Keli","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Ningxia University, Yinchuan 750021, China"}]},{"given":"Xiaoning","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00188-8","article-title":"Remote sensing of soil salinity: Potentials and constraints","volume":"85","author":"Metternicht","year":"2003","journal-title":"Remote Sens Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.ecolind.2015.01.004","article-title":"Detecting soil salinity with MODIS time series VI data","volume":"52","author":"Zhang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10661-017-6415-3","article-title":"Monitoring soil for sustainable development and land degradation neutrality","volume":"190","author":"Hermann","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105207","DOI":"10.1016\/j.catena.2021.105207","article-title":"The effect of temperature on decomposition of the different parts of maize residues in a Solonchak","volume":"201","author":"Huang","year":"2021","journal-title":"Catena"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.3390\/w10081030","article-title":"Cropland soil salinization and associated hydrology: Trends, processes and examples","volume":"10","author":"Nachshon","year":"2018","journal-title":"Water"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zaman, M., Shahid, S.A., and Heng, L. (2018). Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques, Springer.","DOI":"10.1007\/978-3-319-96190-3"},{"key":"ref_7","first-page":"748","article-title":"Progress and perspectives on agricultural remote sensing research and applications in China","volume":"20","author":"Chen","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3905","DOI":"10.1002\/ldr.4009","article-title":"A Simplified Sub-Surface Soil Salinity estimation using Synergy of Sentinel-1 SAR and Sentinel-2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India","volume":"32","author":"Tripathi","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_9","first-page":"510","article-title":"Comparative study on hyperspectral inversion accuracy of soil salt content and electrical conductivity","volume":"34","author":"Peng","year":"2014","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geoderma.2019.06.040","article-title":"Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region. Xinjiang, China","volume":"353","author":"Wang","year":"2019","journal-title":"Geoderma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1111\/plb.12552","article-title":"Stoichiometric variation of halophytes in response to changes in soil salinity","volume":"19","author":"Sun","year":"2017","journal-title":"Plant Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.ecolind.2016.11.043","article-title":"Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey","volume":"74","author":"Gorji","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Behrens","year":"2016","journal-title":"Earth Sci. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.scitotenv.2017.10.323","article-title":"Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using Vis-NIR spectroscopy and regression techniques","volume":"616","author":"Douglas","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.geoderma.2014.07.028","article-title":"Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan\u2013Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments","volume":"235\u2013236","author":"Ding","year":"2014","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5127","DOI":"10.1007\/s12517-014-1580-y","article-title":"Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: A case study from Egypt","volume":"8","author":"Nawar","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118553","DOI":"10.1016\/j.saa.2020.118553","article-title":"Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra","volume":"240","author":"Zhang","year":"2020","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0003-2670(03)00331-3","article-title":"The potential of field spectroscopy for the assessment of sediment properties in river floodplains","volume":"484","author":"Kooistra","year":"2003","journal-title":"Anal. Chim. Acta"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e6310","DOI":"10.7717\/peerj.6310","article-title":"Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP","volume":"7","author":"Wang","year":"2019","journal-title":"PeerJ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.catena.2015.09.024","article-title":"Evaluation of MLSR and PLSR for estimating soil element contents using visible\/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula","volume":"137","author":"Yu","year":"2016","journal-title":"Catena"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.geoderma.2018.10.025","article-title":"Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy","volume":"337","author":"Hong","year":"2019","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.scitotenv.2018.06.319","article-title":"Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine","volume":"644","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"3103","DOI":"10.3390\/w7063103","article-title":"Water Use Efficiency in Saline Soils under Cotton Cultivation in the Tarim River Basin","volume":"7","author":"Zhao","year":"2015","journal-title":"Water"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2017.03.013","article-title":"Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates","volume":"299","author":"Vermeulen","year":"2017","journal-title":"Geoderma"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"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":"Zou","year":"2010","journal-title":"Anal. Chim. Acta"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.geoderma.2017.11.006","article-title":"A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra","volume":"314","author":"Dotto","year":"2018","journal-title":"Geoderma"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1016\/j.scitotenv.2018.01.122","article-title":"Evaluation of Vis-NIR reflectance spectroscopy sensitivity to weathering for enhanced assessment of oil contaminated soils","volume":"626","author":"Douglas","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.scitotenv.2017.10.025","article-title":"Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices","volume":"615","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"114211","DOI":"10.1016\/j.geoderma.2020.114211","article-title":"Multi-algorithm comparison for predicting soil salinity","volume":"365","author":"Wang","year":"2020","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11175","DOI":"10.1007\/s12517-015-2004-3","article-title":"Modeling of soil salinity within a semi-arid region using spectral analysis","volume":"8","author":"Bouaziz","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis-NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"117983","DOI":"10.1016\/j.saa.2019.117983","article-title":"Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice","volume":"229","author":"Das","year":"2019","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_38","unstructured":"Ke, G.L., Meng, Q., Finley, T., Wang, T.F., Chen, W., Ma, W.D., Ye, Q.W., and Liu, T.Y. (2017). Light GBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.15541\/jim20200748","article-title":"Training a model for predicting adsorption energy of metal ions based on machine learning","volume":"36","author":"Zhang","year":"2021","journal-title":"J. Inorg. Mater."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"941","DOI":"10.11834\/jrs.20219396","article-title":"Soil moisture retrieval using extremely randomized trees over the Shandian river basin","volume":"25","author":"Cheng","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Y.Z., Ma, J., Liang, S.L., Li, X.S., and Li, M.Y. (2020). An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products. Remote Sens., 12.","DOI":"10.3390\/rs12244015"},{"key":"ref_42","first-page":"922","article-title":"Effect of different spectra types on the accuracy and correction of soil salt content inversion in Yinchuan Plain, China","volume":"33","author":"Chen","year":"2021","journal-title":"J. Appl. Ecol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e00399","DOI":"10.1016\/j.geodrs.2021.e00399","article-title":"Inversion of soil pH during the dry and wet seasons in the Yinbei region of Ningxia, China, based on multi-source remote sensing data","volume":"25","author":"Jia","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_44","unstructured":"Lu, R.K. (1999). Soil Argrochemistry Analysis Protocols, China Agriculture Science Press."},{"key":"ref_45","unstructured":"Li, B.G., and Xu, J.M. (2019). The Nature and Properties of Soils, Science Press. [14th ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ."},{"key":"ref_47","first-page":"3277","article-title":"Prediction of soil organic matter using Visible-Short Near-Infrared imaging spectroscopy","volume":"40","author":"Jiao","year":"2020","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2014.03.025","article-title":"Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region","volume":"230\u2013231","author":"Allbed","year":"2014","journal-title":"Geoderma"},{"key":"ref_49","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_50","unstructured":"Abbas, A., and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. Proceedings of the Advances and Applications for Management and Decision Making Land, Water and Environmental Management: Integrated Systems for Sustainability MODSIM07, Modelling and Simulation Society of Australia and New Zealand."},{"key":"ref_51","first-page":"1399","article-title":"Extraction and modeling of regional soil salinization based on data from GF-1 satellite","volume":"53","author":"Cao","year":"2016","journal-title":"Acta Pedol. Sin."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.agrformet.2017.05.018","article-title":"Comparison of different satellite bands and vegetation indices for estimation of soil organic matter based on simulated spectral configuration","volume":"244\u2013245","author":"Jin","year":"2017","journal-title":"Agric. Meteorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113875","DOI":"10.1016\/j.geoderma.2019.07.033","article-title":"Cadmium concentration estimation in peri-urban agricultural soils: Using reflectance spectroscopy, soil auxiliary information, or a combination of both?","volume":"354","author":"Hong","year":"2019","journal-title":"Geoderma"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.biosystemseng.2020.01.017","article-title":"Narrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field grown tomato crops","volume":"192","author":"Ihuoma","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_56","first-page":"14","article-title":"Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area","volume":"73","author":"Rukeya","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0169-7439(98)00167-1","article-title":"Validation and verification of regression in small data sets","volume":"44","author":"Martens","year":"1998","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"ref_58","unstructured":"Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_60","first-page":"253","article-title":"Combination of Fractional order differential and machine learning algorithm for spectral estimation of soil organic carbon content","volume":"57","author":"Zhao","year":"2020","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_61","unstructured":"Shi, Z. (2014). Principle and Method of Soil Surface Hyperspectral Remote Sensing, Science Press."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Peon, J., Recondo, C., Fernandez, S., Calleja, J.F., De Miguel, E., and Carretero, L. (2017). Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121211"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.geoderma.2018.08.006","article-title":"Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China","volume":"337","author":"Peng","year":"2018","journal-title":"Geoderma"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106173","DOI":"10.1016\/j.ecolind.2020.106173","article-title":"Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements","volume":"112","author":"Gorji","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"114086","DOI":"10.1016\/j.geoderma.2019.114086","article-title":"Prediction of soil salinity and sodicity using electromagnetic conductivity imaging","volume":"361","author":"Paz","year":"2020","journal-title":"Geoderma"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Habibi, V., Ahmadi, H., Jafari, M., and Moeini, A. (2021). Mapping soil salinity using a combined spectral and topographical indices with artificial neural network. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0228494"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.geoderma.2008.08.004","article-title":"Identifying Optimal Spectral Bands to Assess Soil Properties with VNIR Radiometry in Semi-Arid Soils","volume":"147","author":"Koch","year":"2008","journal-title":"Geoderma"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"546","DOI":"10.2136\/sssaj2013.06.0241","article-title":"Quantitative Model Based on Field-Derived Spectral Characteristics to Estimate Soil Salinity in Minqin County, China","volume":"78","author":"Pang","year":"2014","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes, D.J.J., Heuvelink, G.B., Ruiperez, G.M., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauermarschallinger, B. (2017). Soil Grids 250 m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jenvman.2018.03.089","article-title":"Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping","volume":"217","author":"Valavi","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_74","first-page":"1305","article-title":"Effects of salt and drought stresses on rhizosphere soil bacterial community structure and peanut yield","volume":"31","author":"Xu","year":"2020","journal-title":"J. Appl. Ecol."},{"key":"ref_75","first-page":"1033","article-title":"Effects of Combined Amendment on Improvement of Salinized Soil and Plant Growth","volume":"53","author":"Tang","year":"2021","journal-title":"Soils"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, Y., Pan, X.Z., Wang, C.K., Li, Y.L., and Shi, R.J. (2015). Predicting soil salinity with Vis-NIR spectra after removing the effects of soil moisture using external parameter orthogonalization. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0140688"},{"key":"ref_77","first-page":"5007","article-title":"Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images: Applications in different oases in Xinjiang, China","volume":"37","author":"Wang","year":"2017","journal-title":"Acta Ecol. Sin."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.geoderma.2016.05.005","article-title":"An assessment of model averaging to improve predictive power of portable vis-NIR and XRF for the determination of agronomic soil properties","volume":"279","author":"Stockmann","year":"2016","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2602\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:20:42Z","timestamp":1760138442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,28]]},"references-count":78,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112602"],"URL":"https:\/\/doi.org\/10.3390\/rs14112602","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,28]]}}}