{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:59:39Z","timestamp":1783443579013,"version":"3.54.6"},"reference-count":104,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFE0119100"],"award-info":[{"award-number":["2017YFE0119100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold\u2013dry Qaidam Basin (QB\u2013G) and Gaotai\u2013Minghua in the relatively warm\u2013dry Hexi Corridor (HC\u2013GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm\u2013Elman (SCA\u2013Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB\u2013G) and 86 (HC\u2013GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA\u2013Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB\u2013G was higher than that in HC\u2013GM. The soils of QB\u2013G are mainly the chloride type and those of HC\u2013GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA\u2013Elman and DELM models in QB\u2013G (the highest MAEv, RMSEv, and Rv2 were 0.09, 0.12 and 0.75, respectively) were higher than those in HC\u2013GM (the highest MAEv, RMSEv, and Rv2 were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB\u2013G had higher correlation coefficients with EC due to the regular altitude change and cold\u2013dry climate. (3) Most of the SCA\u2013Elman results (the mean Rv2 in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean Rv2 in HC\u2013GM and QB\u2013G were 0.51 and 0.49, respectively). Therefore, SCA\u2013Elman was more suitable for the soil salinity prediction in HC\u2013GM and QB\u2013G. This can provide a reference for soil salinization monitoring and model selection in the future.<\/jats:p>","DOI":"10.3390\/rs14020347","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T23:17:07Z","timestamp":1642029427000},"page":"347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiaofang","family":"Jiang","sequence":"first","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5407-1475","authenticated-orcid":false,"given":"Hanchen","family":"Duan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Drylands Salinization Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Drylands Salinization Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinglin","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3413-6531","authenticated-orcid":false,"given":"Cuihua","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Drylands Salinization Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1795-8656","authenticated-orcid":false,"given":"Xian","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Drylands Salinization Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","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_2","unstructured":"FAO (2021). Status of the world\u2019s soil resources. Agric. Compr. Dev. China, 10, 64."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/0034-4257(93)90068-9","article-title":"Spectral band selection for the characterization of salinity status of soils","volume":"43","author":"Csillag","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1080\/014311698215883","article-title":"Image transforms as a tool for the study of soil salinity and alkalinity dynamics","volume":"19","author":"Dwivedi","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2016.02.028","article-title":"Identification of WorldView-2 spectral and spatial factors in detecting salt accumulation in cultivated fields","volume":"273","author":"Muller","year":"2016","journal-title":"Geoderma"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.eswa.2015.10.039","article-title":"Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization","volume":"47","author":"Mirjalili","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A sine cosine algorithm for solving optimization problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.eswa.2019.03.002","article-title":"Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation","volume":"127","author":"Li","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., and Panahi, S. (2019). Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Appl. Sci., 9.","DOI":"10.3390\/app9183755"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"196425","DOI":"10.1109\/ACCESS.2020.3034053","article-title":"Analysis of multi-phase flow through porous media for imbibition phenomena by using the LeNN-WOA-NM algorithm","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","unstructured":"Cheng, L., and Tang, X.F. (2021). Improved sine cosine algorithm optimizing feature selection and data classification. J. Comput. Appl., 1\u201311. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/detail.aspx?FileName=JSJY20210926007&DbName=CAPJ2021."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2007.02.005","article-title":"Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)","volume":"110","author":"Farifteh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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":"Fourati","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_16","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning Publications."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1002\/ldr.3737","article-title":"Estimating soil salinity with different fractional vegetation cover using remote sensing","volume":"32","author":"Zhang","year":"2020","journal-title":"Land Degrad. Dev."},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1590\/0103-9016-2015-0131","article-title":"Digital soil mapping using reference area and artificial neural networks","volume":"73","author":"Arruda","year":"2016","journal-title":"Scientia Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., and Zhang, H. (2021). Soil salinity mapping using machine learning algorithms with the Sentinel-2 MSI in arid areas, China. Remote Sens., 13.","DOI":"10.3390\/rs13020305"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s42452-019-1873-6","article-title":"Intelligent routing between capsules empowered with deep extreme machine learning technique","volume":"2","author":"Naz","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3013129","article-title":"NOx measurements in vehicle exhaust using advanced deep ELM networks","volume":"70","author":"Ouyang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bas, E., Egrioglu, E., and Karahasan, O. (2021). A Pi-Sigma artificial neural network based on sine cosine optimization algorithm. Granular Comput.","DOI":"10.1007\/s41066-021-00297-9"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"142030","DOI":"10.1016\/j.scitotenv.2020.142030","article-title":"Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China","volume":"754","author":"Wang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"499","DOI":"10.5194\/soil-6-499-2020","article-title":"Assessing soil salinity dynamics using time-lapse electromagnetic conductivity imaging","volume":"6","author":"Paz","year":"2020","journal-title":"Soil"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/02757259309532180","article-title":"Remote sensing of salt affected soils","volume":"7","author":"Mougenot","year":"1993","journal-title":"Remote Sens. Rev."},{"key":"ref_28","first-page":"51","article-title":"Distinguishing saline from nonsaline rangelands with Skylab imagery","volume":"6","author":"Everitt","year":"1977","journal-title":"Remote Sens. Earth Resour."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/0034-4257(89)90089-8","article-title":"Spectral properties of halite-rich mineral mixtures: Implications for middle infrared remote sensing of highly saline environments","volume":"27","author":"Eastes","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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_31","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_32","unstructured":"Abbas, A., and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. International Congress on Modelling and Simulation (MODSIM 2007)\u2014Land, Water & Environmental Management: Integrated Systems for Sustainability, Christchurch, New Zealand, 10\u201313 December 2007, Modelling and Simulation Society of Australia and New Zealand."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1080\/00103620802432717","article-title":"Characterization of slightly and moderately saline and sodic soils in irrigated agricultural land using simulated data of advanced land imaging (EO-1) sensor","volume":"39","author":"Bannari","year":"2008","journal-title":"Commun. Soil Sci. Plan."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.proeng.2012.01.1193","article-title":"Remote Sensing Techniques for salt affected soil mapping: Application to the Oran Region of Algeria","volume":"33","author":"Dehni","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_35","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS, Texas A&M University."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/rs6021211","article-title":"The generalized difference vegetation index (GDVI) for dryland characterization","volume":"6","author":"Wu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1080\/2150704X.2019.1597298","article-title":"Monitoring vegetation dynamics using the universal normalized vegetation index (UNVI): An optimized vegetation index-VIUPD","volume":"10","author":"Zhang","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1590\/S0100-69162013000300009","article-title":"Spatial autocorrelation of NDVI and GVI indices derived from Landsat\/TM images for soybean crops in the western of the state of Paran\u00e1 in 2004\/2005 crop season","volume":"33","author":"Dalposso","year":"2013","journal-title":"Engenharia Agr\u00edcola"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_46","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_47","unstructured":"Bannari, A., Asalhi, H., and Teillet, P.M. (2005). Transformed difference vegetation index (TDVI) for vegetation cover mapping. IEEE International Geoscience and Remote Sensing Symposium, IEEE."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.geodrs.2014.10.004","article-title":"Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA","volume":"2","author":"Scudiero","year":"2014","journal-title":"Geoderma Reg."},{"key":"ref_49","first-page":"589","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"5","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00254-006-0544-2","article-title":"Designing of the perpendicular drought index","volume":"52","author":"Ghulam","year":"2007","journal-title":"Environ. Geol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.isprsjprs.2007.03.002","article-title":"Modified perpendicular drought index (MPDI): A real-time drought monitoring method","volume":"62","author":"Ghulam","year":"2007","journal-title":"ISPRS J. Photogramm."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_54","unstructured":"Oldham, K.B., and Spanier, J. (1974). The Fractional Calculus, Academic Press."},{"key":"ref_55","unstructured":"Podlubny, I. (1998). Fractional Differential Equations: An Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of Their Solution and Some of Their Applications, Academic Press."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","article-title":"A survey on ensemble learning","volume":"14","author":"Dong","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_57","unstructured":"Huang, G., Zhu, Q., and Siew, C. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, Hungary."},{"key":"ref_58","unstructured":"Wang, X.C., Shi, F., Yu, L., and Li, Y. (2013). Analysis of 43 Cases of Neural Network in MATLAB, Beihang University Press."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2015.03.110","article-title":"Deep extreme learning machines: Supervised autoencoding architecture for classification","volume":"174","author":"Tissera","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, Z., Liang, Z., Zhu, S., Correia, J.A.F.O., and De Jesus, A.M.P. (2019). PSO-BP neural network-based strain prediction of wind turbine blades. Materials, 12.","DOI":"10.3390\/ma12121889"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?\u2014Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_62","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_63","unstructured":"Dai, C.D., Jiang, X.G., and Tang, L.L. (2004). Remote Sensing Image Application Processing and Analysis, Tsinghua University Press."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.geoderma.2011.10.015","article-title":"Modeling salinity effects on soil reflectance under various moisture conditions and its inverse application: A laboratory experiment","volume":"170","author":"Wang","year":"2012","journal-title":"Geoderma"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"978","DOI":"10.2136\/sssaj1972.03615995003600060045x","article-title":"Spectrophotometric determination of soil-water content","volume":"36","author":"Bowers","year":"1972","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_66","first-page":"1","article-title":"Reflectance properties of soils","volume":"38","author":"Baumgardner","year":"1985","journal-title":"Adv. Agron."},{"key":"ref_67","first-page":"485","article-title":"Diffuse reflectance spectra of several clay-minerals","volume":"57","author":"Lindberg","year":"1972","journal-title":"Am. Miner."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.2136\/sssaj1981.03615995004500060031x","article-title":"Characteristic variations in reflectance of surface soils","volume":"45","author":"Stoner","year":"1981","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_69","first-page":"226","article-title":"Study of soils from spectral composition of reflected radiation","volume":"2","author":"Karmanov","year":"1970","journal-title":"Soviet Soil Science-Ussr"},{"key":"ref_70","unstructured":"Wen, Z.W. (1963). Discussion on the classification of saline soil in Xinjiang Province.  Xinjiang Agric. Sci., 463\u2013469. Available online: https:\/\/kns.cnki.net\/kcms\/detail\/detail.aspx?dbcode=CJFD&dbname=CJFD7984&filename=XJNX196312000&uniplatform=NZKPT&v=y4lGY2wIESlvvhpmFrpQ1a4rzvrukV5FeP-WT2plO6W2tANIbB838fafPUV7vzZD."},{"key":"ref_71","first-page":"43","article-title":"Salt-affected soils type and saline-geochemical features in Qaidam Basin","volume":"27","author":"Li","year":"1990","journal-title":"Acta Pedol Sin."},{"key":"ref_72","unstructured":"Yu, R.P., Wang, Z.Q., and Zhu, S.Q. (1993). Chinese Saline Soil, Science Press."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.geoderma.2008.03.011","article-title":"Spectral characteristics of salt-affected soils: A laboratory experiment","volume":"145","author":"Farifteh","year":"2008","journal-title":"Geoderma"},{"key":"ref_74","first-page":"121","article-title":"Visible and near-infrared spectra of minerals and rocks: V. Halides, phosphates, arsenates, vanadates and borates","volume":"3","author":"Hunt","year":"1972","journal-title":"Mod. Geol."},{"key":"ref_75","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_76","doi-asserted-by":"crossref","first-page":"105256","DOI":"10.1016\/j.atmosres.2020.105256","article-title":"Potential linkages of extreme climate events with vegetation and large-scale circulation indices in an endorheic river basin in northwest China","volume":"247","author":"Cheng","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"An, Q., He, H., Gao, J., Nie, Q., Cui, Y., Wei, C., and Xie, X. (2020). Analysis of temporal-spatial variation characteristics of drought: A case study from Xinjiang, China. Water, 12.","DOI":"10.3390\/w12030741"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"780","DOI":"10.2134\/jeq2005.0327","article-title":"Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh","volume":"36","author":"Tilley","year":"2007","journal-title":"J. Environ. Qual."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1016\/j.ecolind.2011.03.025","article-title":"Using hyperspectral vegetation indices as a proxy to monitor soil salinity","volume":"11","author":"Zhang","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2011.04.001","article-title":"Environmental factors of spatial distribution of soil salinity on flat irrigated terrain","volume":"163","author":"Akramkhanov","year":"2011","journal-title":"Geoderma"},{"key":"ref_82","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_83","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1080\/01431169508954546","article-title":"Spectral behavior of salt-affected soils","volume":"16","author":"Rao","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/0924-2716(94)90045-0","article-title":"Spectral reflectance properties of different types of soil surfaces","volume":"49","author":"Singh","year":"1994","journal-title":"ISPRS J. Photogramm."},{"key":"ref_85","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","author":"Allbed","year":"2014","journal-title":"Geoderma"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jsames.2010.07.005","article-title":"Temporal variations of natural soil salinity in an arid environment using satellite images","volume":"30","author":"Gutierrez","year":"2010","journal-title":"J. S. Am. Earth Sci."},{"key":"ref_87","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_88","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_89","doi-asserted-by":"crossref","first-page":"8952","DOI":"10.1080\/01431161.2021.1978579","article-title":"Effect of spring irrigation on soil salinity monitoring with UAV-borne multispectral sensor","volume":"42","author":"Yang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neunet.2007.11.001","article-title":"Relation between weight size and degree of over-fitting in neural network regression","volume":"21","author":"Hagiwara","year":"2008","journal-title":"Neural Netw."},{"key":"ref_91","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":"2019","journal-title":"Geoderma"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"105517","DOI":"10.1016\/j.ecolind.2019.105517","article-title":"Mapping coastal wetland soil salinity in different seasons using an improved comprehensive land surface factor system","volume":"107","author":"Chi","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, W., Hu, Y., Tian, S., and Liu, D. (2021). Soil moisture and salinity inversion based on new remote sensing index and neural network at a salina-alkaline wetland. Water, 13.","DOI":"10.3390\/w13192762"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2016.2616418","article-title":"Advanced spectral classifiers for hyperspectral images: A review","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens.Mag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1080\/01431161.2019.1654142","article-title":"Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model","volume":"41","author":"Wang","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_96","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_97","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engappai.2017.05.003","article-title":"Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction","volume":"63","author":"Wang","year":"2017","journal-title":"Eng. Appl. Artif. Intel."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2017.01.004","article-title":"Kernel fusion based extreme learning machine for cross-location activity recognition","volume":"37","author":"Wang","year":"2017","journal-title":"Inform. Fusion."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2015.01.097","article-title":"Incremental regularized extreme learning machine and it\u05f3 s enhancement","volume":"174","author":"Xu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"114858","DOI":"10.1016\/j.geoderma.2020.114858","article-title":"Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests","volume":"385","author":"Nabiollahi","year":"2021","journal-title":"Geoderma"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/01431161.2018.1512767","article-title":"Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery","volume":"40","author":"Taghadosi","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_102","first-page":"1","article-title":"Retrieval of the soil salinity from Sentinel-1 Dual-Polarized SAR data based on deep neural network regression","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Hoa, P., Giang, N., Binh, N., Hai, L., Pham, T., Hasanlou, M., and Tien Bui, D. (2019). Soil salinity mapping using SAR Sentinel-1 data and advanced machine learning algorithms: A case study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sens., 11.","DOI":"10.3390\/rs11020128"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Shi, H., Hellwich, O., Luo, G., Chen, C., He, H., Ochege, F.U., Van de Voorde, T., Kurban, A., and de Maeyer, P. (2021). A global meta-analysis of soil salinity prediction integrating satellite remote sensing, soil sampling, and machine learning. IEEE Trans. Geosci. Remote Sens., 1\u201315. Available online: https:\/\/ieeexplore.ieee.org\/document\/9538387\/.","DOI":"10.1109\/TGRS.2021.3109819"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:02:11Z","timestamp":1760364131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":104,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020347"],"URL":"https:\/\/doi.org\/10.3390\/rs14020347","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}