{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:52:45Z","timestamp":1783133565731,"version":"3.54.6"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T00:00:00Z","timestamp":1649376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51869010"],"award-info":[{"award-number":["51869010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"emonstration of key technologies for green development of industrialized ecological agriculture in Jingtai desert Gobi","award":["20YF8ND141"],"award-info":[{"award-number":["20YF8ND141"]}]},{"name":"Gansu Youth Science and Technology Fund Program","award":["21JR7RA778"],"award-info":[{"award-number":["21JR7RA778"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization severely restricts the development of global industry and agriculture and affects human beings. In the arid area of Northwest China, oasis saline-alkali land threatens the development of agriculture and food security. This paper develops and optimizes an inversion monitoring model for monitoring the soil salt content using unmanned aerial vehicle (UAV) multispectral remote sensing data. Using the multispectral remote sensing data in three research areas, the soil salt inversion models based on the support vector machine regression (SVR), random forest (RF), backpropagation neural network (BPNN), and extreme learning machine (ELM) were constructed. The results show that the four constructed models based on the spectral index can achieve good inversion accuracy, and the red edge band can effectively improve the soil salt inversion accuracy in saline-alkali land with vegetation cover. Based on the obtained results, for bare land, the best model for soil salt inversion is the ELM model, which reaches the determination coefficient (Rv2) of 0.707, the root mean square error RMSEv of 0.290, and the performance deviation ratio (RPD) of 1.852 on the test dataset. However, for agricultural land with vegetation cover, the best model for soil salinity inversion using the vegetation index is the BPNN model, which achieves Rv2 of 0.836, RMSEv of 0.027, and RPD of 2.100 on the test dataset. This study provides technical support for rapid monitoring and inversion of soil salinization and salinization control in irrigation areas.<\/jats:p>","DOI":"10.3390\/rs14081804","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T05:13:08Z","timestamp":1649481188000},"page":"1804","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenju","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changquan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"School of Civil Engineering, Lanzhou College of Information Science and Technology, Lanzhou 730300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China"},{"name":"Baiyin New Material Research Institute of Lanzhou University of Technology, Baiyin 730900, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,8]]},"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","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_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"106869","DOI":"10.1016\/j.ecolind.2020.106869","article-title":"Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China","volume":"119","author":"Wang","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"810","DOI":"10.2136\/sssaj1979.03615995004300040040x","article-title":"Measurement of Apparent Electrical Conductivity of Soils by an Electromagnetic Induction Probe to Aid Salinity Surveys","volume":"43","author":"Ballantyne","year":"1979","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"201","DOI":"10.2136\/sssaj2007.0013","article-title":"Detecting Soil Salinity in Alfalfa Fields using Spatial Modeling and Remote Sensing","volume":"72","author":"Eldeiry","year":"2008","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1080\/01431169608949124","article-title":"Potentiality of Landsat, SPOT and IRS satellite imagery, for recognition of salt affected soils in Indian Arid Zone","volume":"17","author":"Kalra","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/S0034-4257(01)00321-2","article-title":"Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization","volume":"80","author":"Dehaan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159595","DOI":"10.1109\/ACCESS.2020.3020325","article-title":"Spectral Index Fusion for Salinized Soil Salinity Inversion Using Sentinel-2A and UAV Images in a Coastal Area","volume":"8","author":"Ma","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bannari, A., El-Battay, A., Bannari, R., and Rhinane, H. (2018). Sentinel-MSI VNIR and SWIR Bands Sensitivity Analysis for Soil Salinity Discrimination in an Arid Landscape. Remote Sens., 10.","DOI":"10.3390\/rs10060855"},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"105304","DOI":"10.1016\/j.catena.2021.105304","article-title":"Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts","volume":"202","author":"Li","year":"2021","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.catena.2017.12.011","article-title":"Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging","volume":"163","author":"Xu","year":"2018","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1080\/01431161.2015.1129562","article-title":"Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta","volume":"37","author":"An","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_23","first-page":"100258","article-title":"UAVs technology for the development of GUI based application for precision agriculture and environmental research","volume":"16","author":"Srivastava","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2015.12.029","article-title":"UAVs as remote sensing platform in glaciology: Present applications and future prospects","volume":"175","author":"Bhardwaj","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1109\/TPAMI.2014.2382106","article-title":"Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting","volume":"37","author":"Lindner","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3390\/rs70100488","article-title":"Soil Salinity Retrieval from Advanced Multi-Spectral Sensor with Partial Least Square Regression","volume":"7","author":"Fan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.agwat.2016.09.014","article-title":"Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing","volume":"183","author":"Nortes","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.catena.2019.03.011","article-title":"Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification","volume":"178","author":"Kaplan","year":"2019","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Huang, W., Cui, X., Shi, Y., and Liu, L. (2018). New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery. Sensors, 18.","DOI":"10.3390\/s18030868"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/JSTARS.2017.2774807","article-title":"Image Classification Using RapidEye Data: Integration of Spectral and Textual Features in a Random Forest Classifier","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2015.03.031","article-title":"Evaluating temporal consistency of long-term global NDVI datasets for trend analysis","volume":"163","author":"Tian","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.3390\/rs5052113","article-title":"Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology","volume":"5","author":"Forkel","year":"2013","journal-title":"Remote Sens."},{"key":"ref_35","first-page":"102277","article-title":"Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands","volume":"96","author":"Vaudour","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhao, G., Lang, K., Su, B., Chen, X., Xi, X., and Zhang, H. (2019). Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage. Sensors, 19.","DOI":"10.3390\/s19071485"},{"key":"ref_38","unstructured":"Abbas, A., and Khan, S. (2007, January 10\u201313). Using Remote Sensing Techniques for Appraisal of Irrigated Soil Salinity. Proceedings of the International Congress on Modelling and Simulation. (MODSIM 2007), Land, Water & Environmental Management: Integrated Systems for Sustainability, Christchurch, New Zealand."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.still.2015.07.021","article-title":"Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy","volume":"155","author":"Nawar","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.geoderma.2019.01.007","article-title":"Modelling and mapping soil organic carbon stocks in Brazil","volume":"340","author":"Gomes","year":"2019","journal-title":"Geoderma"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e9087","DOI":"10.7717\/peerj.9087","article-title":"Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms","volume":"8","author":"Wei","year":"2020","journal-title":"PeerJ"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/JSTARS.2019.2910558","article-title":"Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties","volume":"12","author":"Lu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1080\/10106049.2017.1303090","article-title":"Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia","volume":"33","author":"Allbed","year":"2018","journal-title":"Geocarto Int."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1804\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:50:31Z","timestamp":1760136631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,8]]},"references-count":46,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081804"],"URL":"https:\/\/doi.org\/10.3390\/rs14081804","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,8]]}}}