{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T20:32:34Z","timestamp":1780518754824,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, P.R. China","award":["2020ZJUGP001"],"award-info":[{"award-number":["2020ZJUGP001"]}]},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, P.R. China","award":["42001048"],"award-info":[{"award-number":["42001048"]}]},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, P.R. China","award":["2020"],"award-info":[{"award-number":["2020"]}]},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, P.R. China","award":["2020TC205"],"award-info":[{"award-number":["2020TC205"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020ZJUGP001"],"award-info":[{"award-number":["2020ZJUGP001"]}],"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":["42001048"],"award-info":[{"award-number":["42001048"]}],"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":["2020"],"award-info":[{"award-number":["2020"]}],"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":["2020TC205"],"award-info":[{"award-number":["2020TC205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["2020ZJUGP001"],"award-info":[{"award-number":["2020ZJUGP001"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["42001048"],"award-info":[{"award-number":["42001048"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["2020"],"award-info":[{"award-number":["2020"]}]},{"name":"State Key Laboratory of Resources and Environmental Information System","award":["2020TC205"],"award-info":[{"award-number":["2020TC205"]}]},{"name":"Chinese Universities Scientific Fund","award":["2020ZJUGP001"],"award-info":[{"award-number":["2020ZJUGP001"]}]},{"name":"Chinese Universities Scientific Fund","award":["42001048"],"award-info":[{"award-number":["42001048"]}]},{"name":"Chinese Universities Scientific Fund","award":["2020"],"award-info":[{"award-number":["2020"]}]},{"name":"Chinese Universities Scientific Fund","award":["2020TC205"],"award-info":[{"award-number":["2020TC205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate updating of soil salination and alkalization maps based on remote sensing images and machining learning methods plays an essential role in food security, biodiversity, and desertification. However, there is still a lack of research on using machine learning, especially one-dimensional convolutional neural networks (CNN)s, and soil-forming factors to classify the salinization and alkalization degree. As a case study, the study estimated the soil salination and alkalization by Random forests (RF) and CNN based on the 88 observations and 16 environmental covariates in Da\u2019an city, China. The results show that: the RF model (accuracy = 0.67, precision = 0.67 for soil salination) with the synthetic minority oversampling technique performed better than CNN. Salinity and vegetation spectral indexes played the most crucial roles in soil salinization and alkalinization estimation in Songnen Plain. The spatial distribution derived from the RF model shows that from the 1980s to 2021, soil salinization and alkalization areas increased at an annual rate of 1.40% and 0.86%, respectively, and the size of very high salinization and alkalization was expanding. The degree and change rate of soil salinization and alkalization under various land-use types followed mash &gt; salinate soil &gt; grassland &gt; dry land and forest. This study provides a reference for rapid mapping, evaluating, and managing soil salinization and alkalization in arid areas.<\/jats:p>","DOI":"10.3390\/rs14133020","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"3020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Yan","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kader","family":"Kayem","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-5402","authenticated-orcid":false,"given":"Zhou","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Plant Science, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Plant Science, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zuo","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4996-3177","authenticated-orcid":false,"given":"Wenjun","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoguo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"Key Laboratory of Agricultural Land Quality, Ministry of Natural Resources, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Atri, N. (2020). Impact of salinity stress in crop plants and mitigation strategies. New Frontiers in Stress Management for Durable Agriculture, Springer.","DOI":"10.1007\/978-981-15-1322-0_4"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.scitotenv.2016.08.177","article-title":"The threat of soil salinity: A European scale review","volume":"573","author":"Daliakopoulos","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.geoderma.2014.01.027","article-title":"The use of electromagnetic induction techniques in soils studies","volume":"223","author":"Doolittle","year":"2014","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1002\/ldr.751","article-title":"Sodicity-induced land degradation and its sustainable management: Problems and prospects","volume":"17","author":"Qadir","year":"2006","journal-title":"Land Degrad. Dev."},{"key":"ref_6","first-page":"371","article-title":"Leaching and root zone salinity control","volume":"12","author":"Ayars","year":"2012","journal-title":"Agric. Salin. Assess. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33017","DOI":"10.1073\/pnas.2013771117","article-title":"Predicting long-term dynamics of soil salinity and sodicity on a global scale","volume":"117","author":"Hassani","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.agron.2021.03.001","article-title":"Critical knowledge gaps and research priorities in global soil salinity","volume":"169","author":"Hopmans","year":"2021","journal-title":"Adv. Agron."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"125321","DOI":"10.1016\/j.jhydrol.2020.125321","article-title":"A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer","volume":"591","author":"Sahour","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_11","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_12","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":"Nicolas","year":"2006","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"4442","article-title":"Soil salinity mapping by multiscale remote sensing in Mesopotamia, Iraq","volume":"7","author":"Wu","year":"2014","journal-title":"IEEE J. Stars"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Wang, N., Xue, J., Peng, J., Biswas, A., He, Y., and Shi, Z. (2020). Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China. Remote Sens., 12.","DOI":"10.3390\/rs12244118"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"6134","DOI":"10.1080\/01431161.2019.1587205","article-title":"Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: A case study of agricultural lands in coastal North Carolina","volume":"40","author":"Davis","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J., and Zalidis, G. (2020). Employing a multi-input deep convolutional neural network to derive soil clay content from a synergy of multi-temporal optical and radar imagery data. Remote Sens., 12.","DOI":"10.3390\/rs12091389"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hegazi, E.H., Yang, L., and Huang, J. (2021). A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sens., 13.","DOI":"10.3390\/rs13244964"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yin, Q., Li, J., Ma, F., Xiang, D., and Zhang, F. (2021). Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models. Remote Sens., 13.","DOI":"10.3390\/rs13224503"},{"key":"ref_22","unstructured":"Nachtergaele, F., van Velthuizen, H., Verelst, L., Batjes, N.H., Dijkshoorn, K., van Engelen, V., Fischer, G., Jones, A., and Montanarela, L. (2010, January 1\u20136). The harmonized world soil database. Proceedings of the 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, BNE, Australia."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for automated geoscientific analyses (SAGA) v. 2.1. 4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_24","first-page":"9","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_26","unstructured":"Branco, P., Ribeiro, R.P., and Torgo, L. (2016). UBL: An R package for utility-based learning. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_28","unstructured":"R Core Team (2013). R: A Language and Environment for Statistical Computing, R Core Team."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., and Shi, Z. (2019). Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens., 11.","DOI":"10.3390\/rs11070736"},{"key":"ref_31","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. Dev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zheng, L., Guo, J., and Liu, E. (2011, January 27\u201329). Preliminary investigation of the spatial variability of soil infiltration indexes. Proceedings of the 2011 International Conference on New Technology of Agricultural, Zibo, China.","DOI":"10.1109\/ICAE.2011.5943859"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1111\/ejss.12893","article-title":"Synthetic resampling strategies and machine learning for digital soil mapping in Iran","volume":"71","author":"Schmidt","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.geoderma.2019.05.016","article-title":"Addressing the issue of digital mapping of soil classes with imbalanced class observations","volume":"350","author":"Sharififar","year":"2019","journal-title":"Geoderma"},{"key":"ref_37","unstructured":"Lauron, M.L.C., and Pabico, J.P. (2016). Improved sampling techniques for learning an imbalanced data set. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"146253","DOI":"10.1016\/j.scitotenv.2021.146253","article-title":"An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran","volume":"778","author":"Garajeh","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-5-79-2019","article-title":"Using deep learning for digital soil mapping","volume":"5","author":"Padarian","year":"2019","journal-title":"Soil"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2019.05.012","article-title":"Using deep learning for multivariate mapping of soil with quantified uncertainty","volume":"351","author":"Wadoux","year":"2019","journal-title":"Geoderma"},{"key":"ref_42","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_43","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_44","doi-asserted-by":"crossref","unstructured":"Bai, L., Wang, C., Zang, S., Zhang, Y., Hao, Q., and Wu, Y. (2016). Remote sensing of soil alkalinity and salinity in the Wuyu\u2019er-Shuangyang River Basin, Northeast China. Remote Sens., 8.","DOI":"10.3390\/rs8020163"},{"key":"ref_45","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","author":"Ding","year":"2014","journal-title":"Geoderma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.biosystemseng.2016.06.005","article-title":"Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields","volume":"152","author":"Ji","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1111\/ejss.12239","article-title":"Accounting for the effects of water and the environment on proximally sensed vis\u2013NIR soil spectra and their calibrations","volume":"66","author":"Ji","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_48","first-page":"766","article-title":"Changes in soil organic carbon of terrestrial ecosystems in China: A mini-review","volume":"53","author":"Huang","year":"2010","journal-title":"Soil Sci. China Agric. Press"},{"key":"ref_49","first-page":"113","article-title":"Plant responses to saline and sodic conditions","volume":"71","author":"Epstein","year":"1990","journal-title":"Agric. Salin. Assess. Manag."},{"key":"ref_50","first-page":"1332","article-title":"Study on salinization characteristics of surface soil in western Songnen Plain","volume":"45","author":"Zhang","year":"2013","journal-title":"Soils"},{"key":"ref_51","first-page":"26","article-title":"Characteristics and current situation of salinized soil in Da\u2019an city, Jilin province","volume":"32","author":"Zhang","year":"2001","journal-title":"Chin. J. Soil Sci."},{"key":"ref_52","first-page":"277","article-title":"Dynamic change of land-use patterns in west part of Song Plain","volume":"26","author":"Liu","year":"2006","journal-title":"Sci. Geol. Sin."},{"key":"ref_53","first-page":"268","article-title":"Study on the secondary saline-alkalization of land in Song Plain","volume":"18","author":"Li","year":"1998","journal-title":"Sci. Geol. Sin."},{"key":"ref_54","first-page":"120","article-title":"Development and drives of land salinization in Songnen Plain","volume":"16","author":"Zhang","year":"2007","journal-title":"Geol. Resour."},{"key":"ref_55","first-page":"111","article-title":"Research on land saline-alkalized in the west of Jinlin province","volume":"26","author":"Liu","year":"2004","journal-title":"Resour. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"114961","DOI":"10.1016\/j.envpol.2020.114961","article-title":"Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level","volume":"266","author":"Hu","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.catena.2019.02.015","article-title":"Soil moisture and salinity as main drivers of soil respiration across natural xeromorphic vegetation and agricultural lands in an arid desert region","volume":"177","author":"Yang","year":"2019","journal-title":"CATENA"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1002\/2016GB005374","article-title":"Interactions between land use change and carbon cycle feedbacks","volume":"31","author":"Mahowald","year":"2017","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shahid, S.A., Zaman, M., and Heng, L. (2018). Soil salinity: Historical perspectives and a world overview of the problem. Guideline for Salinity Assessment, Mitigation and adaptation Using Nuclear and Related Techniques, Springer.","DOI":"10.1007\/978-3-319-96190-3_2"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s12665-018-7386-6","article-title":"Impacts of agricultural irrigation on groundwater salinity","volume":"77","author":"Vallejos","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_61","first-page":"407","article-title":"Large scale development to saline-alkali soil and risk control for the Songnen Plain","volume":"38","author":"Sun","year":"2016","journal-title":"Resour. Sci."},{"key":"ref_62","first-page":"443","article-title":"Relationship between salinization and alkalization of sodic soil in Da\u2019an city","volume":"25","author":"Li","year":"2007","journal-title":"Chin. J. Soil Sci."},{"key":"ref_63","unstructured":"Brady, N.C., Weil, R.R., and Weil, R.R. (2008). The Nature and Properties of Soils, Prentice Hall."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"11669","DOI":"10.1029\/2018GL079766","article-title":"Vegetation controls on dryland salinity","volume":"45","author":"Perri","year":"2018","journal-title":"Geophys. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:44Z","timestamp":1760139524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":64,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133020"],"URL":"https:\/\/doi.org\/10.3390\/rs14133020","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}