{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:20:44Z","timestamp":1771892444657,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["51979233"],"award-info":[{"award-number":["51979233"]}]},{"name":"National Natural Science Foundation of China","award":["2023-YBNY-221"],"award-info":[{"award-number":["2023-YBNY-221"]}]},{"name":"National Natural Science Foundation of China","award":["2022KW-47"],"award-info":[{"award-number":["2022KW-47"]}]},{"name":"Shaanxi Province Key Research and Development Projects","award":["51979233"],"award-info":[{"award-number":["51979233"]}]},{"name":"Shaanxi Province Key Research and Development Projects","award":["2023-YBNY-221"],"award-info":[{"award-number":["2023-YBNY-221"]}]},{"name":"Shaanxi Province Key Research and Development Projects","award":["2022KW-47"],"award-info":[{"award-number":["2022KW-47"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52\u20130.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.<\/jats:p>","DOI":"10.3390\/rs15184400","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:09:50Z","timestamp":1694081390000},"page":"4400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3804-4071","authenticated-orcid":false,"given":"Xin","family":"Cui","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China"}]},{"given":"Yuxin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China"}]},{"given":"Xuedong","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China"}]},{"given":"Weitong","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China"},{"name":"College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China"}]},{"given":"Liyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Shenjin","family":"Huang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1111\/sum.12772","article-title":"Soil salinity: A global threat to sustainable development","volume":"38","author":"Singh","year":"2022","journal-title":"Soil Use Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105854","DOI":"10.1016\/j.catena.2021.105854","article-title":"Modeling salinized wasteland using remote sensing with the integration of decision tree and multiple validation approaches in Hetao irrigation district of China","volume":"209","author":"Sun","year":"2022","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.geoderma.2018.09.046","article-title":"UAV based soil salinity assessment of cropland","volume":"338","author":"Ivushkin","year":"2019","journal-title":"Geoderma"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"121416","DOI":"10.1016\/j.saa.2022.121416","article-title":"Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest","volume":"279","author":"Zhu","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_6","first-page":"102592","article-title":"Improving Unmanned Aerial Vehicle (UAV) remote sensing of rice plant potassium accumulation by fusing spectral and textural information","volume":"104","author":"Lu","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tang, Y., Ma, J., Xu, J., Wu, W., Wang, Y., and Guo, H. (2022). Assessing the Impacts of Tidal Creeks on the Spatial Patterns of Coastal Salt Marsh Vegetation and Its Aboveground Biomass. Remote Sens., 14.","DOI":"10.3390\/rs14081839"},{"key":"ref_8","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":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11769-015-0740-7","article-title":"Mapping Soil Salinity Using a Similarity-based Prediction Approach: A Case Study in Huanghe River Delta, China","volume":"25","author":"Yang","year":"2015","journal-title":"Chin. Geogr. Sci."},{"key":"ref_10","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_11","first-page":"230","article-title":"Soil salinity assessment through satellite thermography for different irrigated and rainfed crops","volume":"68","author":"Ivushkin","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_12","first-page":"102838","article-title":"Remote sensing prediction and characteristic analysis of cultivated land salinization in different seasons and multiple soil layers in the coastal area","volume":"111","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Poblete, T., Ortega-Far\u00edas, S., Moreno, M.A., and Bardeen, M. (2017). Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV). Sensors, 17.","DOI":"10.3390\/s17112488"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1080\/10106049.2013.868040","article-title":"Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network","volume":"29","author":"Phonphan","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Qi, G., Zhao, G., and Xi, X. (2020). Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta. Sensors, 20.","DOI":"10.3390\/s20226521"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108095","DOI":"10.1016\/j.agwat.2022.108095","article-title":"Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia","volume":"277","author":"Cao","year":"2023","journal-title":"Agric. Water Manage."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jher.2019.04.001","article-title":"Impact of changes in water management on hydrology and environment: A case study in North China","volume":"28","author":"Li","year":"2020","journal-title":"J. Hydro-Environ. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, G., Han, W., Dong, Y., Zhai, X., Huang, S., Ma, W., Cui, X., and Wang, Y. (2023). Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China. Remote Sens., 15.","DOI":"10.3390\/rs15040875"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.3389\/fpls.2019.01270","article-title":"Maize Canopy Temperature Extracted from UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1002\/ldr.4445","article-title":"Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on unmanned aerial vehicle remote sensing","volume":"34","author":"Cui","year":"2023","journal-title":"Land Degrad. Dev."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1080\/10106049.2020.1778104","article-title":"Inversion of soil salinity according to different salinization grades using multi-source remote sensing","volume":"37","author":"Wang","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_25","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 Regional."},{"key":"ref_26","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_27","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_28","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_29","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_30","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared l, lnear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"979","article-title":"From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research","volume":"23","author":"Wang","year":"2003","journal-title":"Acta Ecol. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gao, G., and Wang, S. (2012, January 1\u20133). Compare Analysis of Vegetation Cover Change in Jianyang City Based on RVI and NDVI. Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China.","DOI":"10.1109\/RSETE.2012.6260516"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing leaf pigment content and activity with a reflectometer","volume":"143","author":"Gamon","year":"1999","journal-title":"New Phytologist."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1080\/01431160110107806","article-title":"Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction","volume":"23","author":"Gitelson","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","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 Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"147170","DOI":"10.1016\/j.scitotenv.2021.147170","article-title":"Abiotic and biotic factors contribute to CO2 exchange variation at the hourly scale in a semiarid maize cropland","volume":"784","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106174","DOI":"10.1016\/j.compag.2021.106174","article-title":"Evaluating the sensitivity of water stressed maize chlorophyll and structure based on UAV derived vegetation indices","volume":"185","author":"Zhang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112059","DOI":"10.1016\/j.rse.2020.112059","article-title":"A novel approach to quantify soil salinity by simulating the dielectric loss of SAR in three-dimensional density space","volume":"251","author":"Periasamy","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"115656","DOI":"10.1016\/j.geoderma.2021.115656","article-title":"A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network","volume":"409","author":"Wang","year":"2022","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1001\/jamapsychiatry.2019.3671","article-title":"Establishment of Best Practices for Evidence for Prediction: A Review","volume":"77","author":"Poldrack","year":"2020","journal-title":"Jama Psychiat."},{"key":"ref_45","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_46","first-page":"255","article-title":"A Concordance Correlation Coefficient to Evaluate Reproducibility","volume":"1","author":"Lin","year":"1989","journal-title":"Int. Biom. Soc."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1111\/j.1365-2389.2012.01495.x","article-title":"Predicting soil properties from the Australian soil visible-near infrared spectroscopic database","volume":"63","author":"Rossel","year":"2012","journal-title":"Eur. J. Soil Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1002\/cyto.a.20311","article-title":"Spectral Imaging: Principles and Applications","volume":"69A","author":"Garini","year":"2006","journal-title":"Cytom. Part A"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7781","DOI":"10.1016\/j.eswa.2010.04.062","article-title":"A reduced data set method for support vector regression","volume":"37","author":"Shieh","year":"2010","journal-title":"Expert Syst. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4400\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:41Z","timestamp":1760129201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,7]]},"references-count":49,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184400"],"URL":"https:\/\/doi.org\/10.3390\/rs15184400","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,7]]}}}