{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T08:38:32Z","timestamp":1770712712916,"version":"3.49.0"},"reference-count":79,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR + Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR + Landsat data), and then combined with the model inversion plots, we found that PALSAR + Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.<\/jats:p>","DOI":"10.3390\/rs14030512","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"512","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China"],"prefix":"10.3390","volume":"14","author":[{"given":"Qinyu","family":"Wei","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory of Oasis Ecology, Collage of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4731-1098","authenticated-orcid":false,"given":"Ilyas","family":"Nurmemet","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Oasis Ecology, Collage of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Minhua","family":"Gao","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Oasis Ecology, Collage of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Boqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Oasis Ecology, Collage of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/07900627.2018.1443059","article-title":"Conjunctive use of groundwater and surface water to reduce soil salinization in the Yinchuan Plain, North-West China","volume":"34","author":"Li","year":"2018","journal-title":"Int. J. Water Resour. Develop."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, F., Shi, Z., Biswas, A., Yang, S., and Ding, J. (2020). Multi-algorithm comparison for predicting soil salinity. Geoderma, 365.","DOI":"10.1016\/j.geoderma.2020.114211"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1126\/science.1168572","article-title":"Crops for a salinized world","volume":"322","author":"Rozema","year":"2008","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.ecolind.2015.04.027","article-title":"Soil salinization and waterlogging: A threat to environment and agricultural sustainability","volume":"57","author":"Singh","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.1021\/acs.est.9b03334","article-title":"A Regionalised Life Cycle Assessment Model to Globally Assess the Environmental Implications of Soil Salinization in Irrigated Agriculture","volume":"54","author":"Nunez","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.compag.2018.12.005","article-title":"Modelling long-term soil salinity dynamics using SaltMod in Hetao Irrigation District, China","volume":"156","author":"Chang","year":"2019","journal-title":"Comp. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, Y., and Zhang, F. (2019). Evaluation of comprehensive improvement for mild and moderate soil salinization in arid zone. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224790"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.scitotenv.2019.05.037","article-title":"Characterising dryland salinity in three dimensions","volume":"682","author":"Jiang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Szoboszlay, M., Nather, A., Liu, B., Carrillo, A., Castellanos, T., Smalla, K., Jia, Z., and Tebbe, C.C. (2019). Contrasting microbial community responses to salinization and straw amendment in a semiarid bare soil and its wheat rhizosphere. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-46070-6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, Z., Shao, T., Lv, Z., Yue, Y., Liu, A., Long, X., Zhou, Z., Gao, X., and Rengel, Z. (2020). The mechanisms of improving coastal saline soils by planting rice. Sci. Total Environ., 703.","DOI":"10.1016\/j.scitotenv.2019.135529"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.jaridenv.2006.03.010","article-title":"An integrated methodology for assessing soil salinization, a pre-condition for land desertification","volume":"67","author":"Amezketa","year":"2006","journal-title":"J. Arid Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Singh, A. (2021). Soil salinization management for sustainable development: A review. J. Environ. Manag., 277.","DOI":"10.1016\/j.jenvman.2020.111383"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.scitotenv.2019.06.105","article-title":"High-throughput absolute quantification sequencing reveals the effect of different fertilizer applications on bacterial community in a tomato cultivated coastal saline soil","volume":"687","author":"Jiang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.ecoleng.2015.04.032","article-title":"Ameliorants improve saline\u2013alkaline soils on a large scale in northern Jiangsu Province, China","volume":"81","author":"Ya","year":"2015","journal-title":"Ecol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1071\/SR15169","article-title":"Prediction of salt transport in different soil textures under drip irrigation in an arid zone using the SWAGMAN Destiny model","volume":"54","author":"Yang","year":"2016","journal-title":"Soil Res."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","unstructured":"Ivushkin, K., Bartholomeus, H., Bregt, A.K., Pulatov, A., Kempen, B., and de Sousa, L. (2019). Global mapping of soil salinity change. Remote Sens. Environ., 231.","DOI":"10.1016\/j.rse.2019.111260"},{"key":"ref_19","first-page":"64","article-title":"Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices","volume":"50","author":"Lhissou","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiang, H., Shu, H., Lei, L., and Xu, J. (2017). Estimating soil salt components and salinity using hyperspectral remote sensing data in an arid area of China. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.016043"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.pce.2019.01.004","article-title":"Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance","volume":"110","author":"Khadim","year":"2019","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, F., Zhang, X., Chan, N.W., Kung, H.-t., Zhou, X., and Wang, Y. (2020). Quantitative Evaluation of Spatial and Temporal Variation of Soil Salinization Risk Using GIS-Based Geostatistical Method. Remote Sens., 12.","DOI":"10.3390\/rs12152405"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saha, S. (2011). Microwave remote sensing in soil quality assessment. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 38.","DOI":"10.5194\/isprsarchives-XXXVIII-8-W20-34-2011"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Romanov, A., Khvostov, I., and Sukovatova, A.Y. (2017, January 22\u201325). Seasonal variations of microwave radiation of saline soils in the Kulunda steppe on evidence derived from SMOS. Proceedings of the 2017 Progress In Electromagnetics Research Symposium-Spring (PIERS), St. Petersburg, Russia.","DOI":"10.1109\/PIERS.2017.8262274"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nurmemet, I., Sagan, V., Ding, J.-L., Halik, \u00dc., Abliz, A., and Yakup, Z. (2018). A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10040598"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1002\/ldr.3148","article-title":"Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq","volume":"29","author":"Wu","year":"2018","journal-title":"Land Degrad. Develop."},{"key":"ref_27","unstructured":"Li, Y.-Y., Zhao, K., Ding, Y.-L., Ren, J.-H., and Li, Y. (2013, January 26\u201328). An empirical method for soil salinity and moisture inversion in west of Jilin. Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Nanjing, China."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.5589\/m13-004","article-title":"Modeling the dielectric behavior of saline soil at microwave frequencies","volume":"39","author":"Gong","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1002\/ldr.2661","article-title":"Multispectral and Microwave Remote Sensing Models to Survey Soil Moisture and Salinity","volume":"28","author":"Periasamy","year":"2017","journal-title":"Land Degrad. Develop."},{"key":"ref_30","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":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1134\/S0001433819090445","article-title":"On the Validation of Satellite Microwave Remote Sensing Data under Soil Salinity Conditions","volume":"55","author":"Romanov","year":"2020","journal-title":"Izv. Atmos. Ocean. Phys."},{"key":"ref_32","first-page":"1","article-title":"An Improved Model for Estimating the Dielectric Constant of Saline Soil in C-Band","volume":"19","author":"Dong","year":"2021","journal-title":"IEEE GeoSci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.rse.2015.08.026","article-title":"Regional-scale soil salinity assessment using Landsat ETM + canopy reflectance","volume":"169","author":"Scudiero","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"250","article-title":"Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia","volume":"17","author":"Asfaw","year":"2018","journal-title":"J. Saudi Soc. Agric. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1007\/s40808-020-01015-1","article-title":"Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: Sharif Abad Plain, Central Iran","volume":"7","author":"Habibi","year":"2020","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pan, X., Chen, Y., Cui, J., Peng, Z., Fu, X., Wang, Y., and Men, M. (2021). Accuracy analysis of remote sensing index enhancement for SVM salt inversion model. Geocarto Int., 1\u201318.","DOI":"10.1080\/10106049.2020.1822925"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/S1002-0160(12)60049-6","article-title":"Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data: A Case Study of Selected South African Soils","volume":"22","author":"Mashimbye","year":"2012","journal-title":"Pedosphere"},{"key":"ref_38","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. Develop."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.3390\/rs6021137","article-title":"Mapping and Modelling Spatial Variation in Soil Salinity in the Al Hassa Oasis Based on Remote Sensing Indicators and Regression Techniques","volume":"6","author":"Allbed","year":"2014","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.geoderma.2014.09.011","article-title":"Combination of proximal and remote sensing methods for rapid soil salinity quantification","volume":"239\u2013240","author":"Aldabaa","year":"2015","journal-title":"Geoderma"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Huang, B. (2019). Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-41470-0"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s002540100388","article-title":"The oases along the Keriya River in the Taklamakan Desert, China, and their evolution since the end of the last glaciation","volume":"41","author":"Yang","year":"2001","journal-title":"Environ. Geol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Seydehmet, J., Lv, G., Nurmemet, I., Aishan, T., Abliz, A., Sawut, M., Abliz, A., and Eziz, M. (2018). Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China. Sustainability, 10.","DOI":"10.3390\/su10030656"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Meynart, R., Suzuki, S., Neeck, S.P., Kankaku, Y., Osawa, Y., and Shimoda, H. (2011, January 19\u201322). Development status of PALSAR-2 onboard ALOS-2. Proceedings of the Sensors, Systems, and Next-Generation Satellites XV, Prague, Czech Republic.","DOI":"10.1117\/12.897705"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Arikawa, Y., Saruwatari, H., Hatooka, Y., and Suzuki, S. (2014, January 13\u201318). ALOS-2 launch and early orbit operation result. Proceedings of the 2014 IEEE geoscience and remote sensing symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947212"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, Y., Wang, M., Zhao, Y., and Li, J. (2019). SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions. Remote Sens., 11.","DOI":"10.3390\/rs11080967"},{"key":"ref_50","unstructured":"Bao, S. (2000). Soil Agrochemical Analysis, China Agricultural Press."},{"key":"ref_51","first-page":"415","article-title":"Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data","volume":"13","author":"Abuelgasim","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, L., Lv, X., Chen, Q., Sun, G., and Yao, J. (2020). Estimation of Surface Soil Moisture during Corn Growth Stage from SAR and Optical Data Using a Combined Scattering Model. Remote Sens., 12.","DOI":"10.3390\/rs12111844"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sekertekin, A., Marangoz, A.M., and Abdikan, S. (2020). ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields. Comp. Electron. Agric., 171.","DOI":"10.1016\/j.compag.2020.105303"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S0034-4257(00)00200-5","article-title":"Parameterization of vegetation backscatter in radar-based, soil moisture estimation","volume":"76","author":"Bindlish","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, Y., Qiu, Y., Zhang, Z., Zhang, J., Chen, C., Han, J., and Liu, D. (2020). Estimating salt content of vegetated soil at different depths with Sentinel-2 data. PeerJ, 8.","DOI":"10.7717\/peerj.10585"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compag.2017.11.037","article-title":"A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China","volume":"144","author":"Wu","year":"2018","journal-title":"Comp. Electron. Agric."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, P., Liu, Z., Hu, Y., Shi, Z., Pan, Y., Wang, L., and Wang, G. (2019). Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data. Sustainability, 11.","DOI":"10.3390\/su11020419"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, Y., Shang, X., Chen, Z., Mei, K., Wang, Z., Dahlgren, R.A., Zhang, M., and Ji, X. (2021). A support vector regression model to predict nitrate-nitrogen isotopic composition using hydro-chemical variables. J. Environ. Manag., 290.","DOI":"10.1016\/j.jenvman.2021.112674"},{"key":"ref_61","unstructured":"Eberhart, R., and Kennedy, J. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.still.2015.11.004","article-title":"Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods","volume":"157","author":"Hosseini","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Fu, C., Gan, S., Yuan, X., Xiong, H., and Tian, A. (2018). Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities. Remote Sens., 10.","DOI":"10.3390\/rs10091387"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1007\/s12665-009-0292-1","article-title":"Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks","volume":"60","author":"Yunkai","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_65","first-page":"102","article-title":"Environmental sensitive variable optimization and machine learning algorithm using in soil salt prediction at oasis","volume":"34","author":"Wang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_66","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_67","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_68","doi-asserted-by":"crossref","unstructured":"Minhas, P.S., Ramos, T.B., Ben-Gal, A., and Pereira, L.S. (2020). Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues. Agric. Water Manag., 227.","DOI":"10.1016\/j.agwat.2019.105832"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.jhydrol.2018.11.004","article-title":"Mechanisms and feedbacks for evapotranspiration-induced salt accumulation and precipitation in an arid wetland of China","volume":"568","author":"Liu","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wei, Y., Shi, Z., Biswas, A., Yang, S., Ding, J., and Wang, F. (2020). Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China. Sci. Total Environ., 716.","DOI":"10.1016\/j.scitotenv.2019.135387"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1080\/01431161.2018.1513180","article-title":"Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network","volume":"40","author":"Jiang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, J., Yu, D., Teng, D., He, B., Chen, X., Ge, X., Zhang, Z., Wang, Y., and Yang, X. (2020). Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI. Sci. Total Environ., 707.","DOI":"10.1016\/j.scitotenv.2019.136092"},{"key":"ref_73","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\u20133","author":"Scudiero","year":"2014","journal-title":"Geoderma Reg."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.ecolind.2018.05.069","article-title":"Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA","volume":"93","author":"Whitney","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1080\/22797254.2019.1596756","article-title":"Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur Autonomous Region (XJUAR) of China","volume":"52","author":"Wang","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5925","DOI":"10.1109\/TGRS.2017.2717043","article-title":"Influence of Surface Roughness Measurement Scale on Radar Backscattering in Different Agricultural Soils","volume":"55","author":"Lievens","year":"2017","journal-title":"IEEE Trans. GeoSci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Gharechelou, S., Tateishi, R., and Johnson, B.A. (2018). A Simple Method for the Parameterization of Surface Roughness from Microwave Remote Sensing. Remote Sens., 10.","DOI":"10.3390\/rs10111711"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wu, W., Muhaimeed, A.S., Al-Shafie, W.M., and Al-Quraishi, A.M.F. (2019). Using L-band radar data for soil salinity mapping\u2014a case study in Central Iraq. Environ. Res. Commun., 1.","DOI":"10.1088\/2515-7620\/ab37f0"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hoa, P., Giang, N., Binh, N., Hai, L., Pham, T.-D., 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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/512\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:05:26Z","timestamp":1760133926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/512"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,21]]},"references-count":79,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030512"],"URL":"https:\/\/doi.org\/10.3390\/rs14030512","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,21]]}}}