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Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study\u2019s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and \u22120.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.<\/jats:p>","DOI":"10.3390\/rs15041066","type":"journal-article","created":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T01:36:52Z","timestamp":1676511412000},"page":"1066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5851-7569","authenticated-orcid":false,"given":"Jiao","family":"Tan","sequence":"first","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianli","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijing","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7855-6525","authenticated-orcid":false,"given":"Xiangyu","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruimei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9228-0617","authenticated-orcid":false,"given":"Shaofeng","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"},{"name":"Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7059-368X","authenticated-orcid":false,"given":"Yongkang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 800017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.ecolind.2016.11.043","article-title":"Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey","volume":"74","author":"Gorji","year":"2017","journal-title":"Ecol. Indic."},{"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":"134235","DOI":"10.1016\/j.scitotenv.2019.134235","article-title":"Curing the earth: A review of anthropogenic soil salinization and plant-based strategies for sustainable mitigation","volume":"698","author":"Litalien","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1038\/s41467-021-26907-3","article-title":"Global predictions of primary soil salinization under changing climate in the 21st century","volume":"12","author":"Hassani","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7035","DOI":"10.3390\/s8117035","article-title":"Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China","volume":"8","author":"Wu","year":"2008","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, S., Zhuang, Q., Jin, X., Bian, Z., Zhou, M., Meng, Z., Han, C., Guo, X., and Jin, W. (2022). A Review on Carbon Source and Sink in Arable Land Ecosystems. Land, 11.","DOI":"10.3390\/land11040580"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"136092","DOI":"10.1016\/j.scitotenv.2019.136092","article-title":"Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI","volume":"707","author":"Wang","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1007\/s40333-015-0053-9","article-title":"Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis","volume":"7","author":"Yahiaoui","year":"2015","journal-title":"J. Arid. Land"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106054","DOI":"10.1016\/j.catena.2022.106054","article-title":"Updated soil salinity with fine spatial resolution and high accuracy: The synergy of Sentinel-2 MSI, environmental covariates and hybrid machine learning approaches","volume":"212","author":"Ge","year":"2022","journal-title":"Catena"},{"key":"ref_10","unstructured":"Zinck, A. (2008). Remote Sensing of Soil Salinization, CRC Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114870","DOI":"10.1016\/j.envres.2022.114870","article-title":"Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features","volume":"217","author":"Yang","year":"2023","journal-title":"Environ. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103230","DOI":"10.1016\/j.pce.2022.103230","article-title":"Soil salinity prediction models constructed by different remote sensors","volume":"128","author":"Avdan","year":"2022","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_13","first-page":"4505815","article-title":"A Global Meta-Analysis of Soil Salinity Prediction Integrating Satellite Remote Sensing, Soil Sampling, and Machine Learning","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.2134\/jeq2002.1453","article-title":"Spectral Properties of Salt Crusts Formed on Saline Soils","volume":"31","author":"Howari","year":"2002","journal-title":"J. Environ. Qual."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11175","DOI":"10.1007\/s12517-015-2004-3","article-title":"Modeling of soil salinity within a semi-arid region using spectral analysis","volume":"8","author":"Fourati","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106173","DOI":"10.1016\/j.ecolind.2020.106173","article-title":"Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements","volume":"112","author":"Gorji","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115935","DOI":"10.1016\/j.geoderma.2022.115935","article-title":"Improving remote sensing of salinity on topsoil with crop residues using novel indices of optical and microwave bands","volume":"422","author":"Wang","year":"2022","journal-title":"Geoderma"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s12665-021-09752-x","article-title":"Soil salinity inversion based on novel spectral index","volume":"80","author":"Zhou","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Yengoh, G.T., Dent, D., Olsson, L., Tengberg, A.E., and Tucker, C.J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations, Springer.","DOI":"10.1007\/978-3-319-24112-8"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"112586","DOI":"10.1016\/j.rse.2021.112586","article-title":"A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery","volume":"264","author":"Roy","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Kerr, R.B., Lupafya, E., and Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"113136","DOI":"10.1016\/j.rse.2022.113136","article-title":"A new object-class based gap-filling method for PlanetScope satellite image time series","volume":"280","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"578","DOI":"10.17516\/1999-494X-0247","article-title":"Spatial Distribution of NDVI Seeds of Cereal Crops with Different Levels of Weediness According to PlanetScope Satellite Data","volume":"13","author":"Pisman","year":"2020","journal-title":"J. Sib. Fed. Univ. Eng. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.isprsjprs.2021.02.008","article-title":"Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning","volume":"174","author":"Sagan","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/22797254.2020.1806734","article-title":"Near-real time forest change detection using PlanetScope imagery","volume":"53","author":"Francini","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qayyum, N., Ghuffar, S., Ahmad, H.M., Yousaf, A., and Shahid, I. (2020). Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100560"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mansaray, A., Dzialowski, A., Martin, M., Wagner, K., Gholizadeh, H., and Stoodley, S. (2021). Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds. Remote Sens., 13.","DOI":"10.3390\/rs13091847"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pickering, J., Tyukavina, A., Khan, A., Potapov, P., Adusei, B., Hansen, M., and Lima, A. (2021). Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation. Remote Sens., 13.","DOI":"10.3390\/rs13112191"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113310","DOI":"10.1016\/j.rse.2022.113310","article-title":"Evaluating fine-scale phenology from PlanetScope satellites with ground observations across temperate forests in eastern North America","volume":"283","author":"Zhao","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112763","DOI":"10.1016\/j.rse.2021.112763","article-title":"NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales","volume":"268","author":"Dechant","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108878","DOI":"10.1016\/j.agrformet.2022.108878","article-title":"Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates","volume":"316","author":"Kong","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.proeps.2015.08.062","article-title":"Soil Salinity Prediction, Monitoring and Mapping Using Modern Technologies","volume":"15","author":"Gorji","year":"2015","journal-title":"Procedia Earth Planet Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2016.02.028","article-title":"Identification of WorldView-2 spectral and spatial factors in detecting salt accumulation in cultivated fields","volume":"273","author":"Muller","year":"2016","journal-title":"Geoderma"},{"key":"ref_37","first-page":"102839","article-title":"Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study","volume":"111","author":"Han","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","unstructured":"Khan, S., and Abbas, A. (2007, January 10\u201313). Using remote sensing techniques for appraisal of irrigated soil salinity. Proceedings of the MODSIM07 International Congress on Modelling and Simulation: Land, Water & Environmental Management: Integrated Systems for Sustain, Christchurch, New Zealand."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.tree.2005.05.011","article-title":"Using the satellite-derived NDVI to assess ecological responses to environmental change","volume":"20","author":"Pettorelli","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_40","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. Plant Anal."},{"key":"ref_41","first-page":"792","article-title":"Partial least square regression (PLS regression)","volume":"6","author":"Abdi","year":"2003","journal-title":"Encycl. Res. Methods Soc. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"145807","DOI":"10.1016\/j.scitotenv.2021.145807","article-title":"Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index","volume":"775","author":"Wang","year":"2021","journal-title":"Sci. Total. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.geoderma.2018.07.005","article-title":"A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia","volume":"332","author":"Zovko","year":"2018","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gopalakrishnan, T., and Kumar, L. (2020). Modeling and Mapping of Soil Salinity and Its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka. Sustainability, 12.","DOI":"10.3390\/su12208317"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115399","DOI":"10.1016\/j.geoderma.2021.115399","article-title":"Assessing toxic metal chromium in the soil in coal mining areas via proximal sensing: Prerequisites for land rehabilitation and sustainable development","volume":"405","author":"Wang","year":"2022","journal-title":"Geoderma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.agwat.2018.09.009","article-title":"Evaluation of wavelengths and spectral reflectance indices for high-throughput assessment of growth, water relations and ion contents of wheat irrigated with saline water","volume":"212","author":"Hassan","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xie, B., Ding, J., Ge, X., Li, X., Han, L., and Wang, Z. (2022). Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms. Sensors, 22.","DOI":"10.3390\/s22072685"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1002\/ldr.3811","article-title":"Development. Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models","volume":"32","author":"Abedi","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_52","first-page":"177","article-title":"Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms","volume":"2","author":"Ma","year":"2021","journal-title":"Reg. Sustain."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.1002\/ldr.4287","article-title":"Soil salinity inversion in coastal cotton growing areas: An integration method using satellite-ground spectral fusion and satellite-UAV collaboration","volume":"33","author":"Qi","year":"2022","journal-title":"Land Degrad. Dev."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kearns, M., and Ron, D. (1997, January 6\u20139). Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Proceedings of the Tenth Annual Conference on Computational Learning Theory, Nashville, TN, USA.","DOI":"10.1145\/267460.267491"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_56","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_57","unstructured":"Davis, E. (2018). Comparison of Sentinel-2 and Landsat 8 OLI in the Mapping of Soil Salinity in Hyde County, North Carolina. [Ph.D Thesis, University of South Carolina]."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"307","DOI":"10.5194\/isprs-archives-XLII-3-W6-307-2019","article-title":"Hanumesh sentinel 2 and landsat-8 bands sensitivity analysis for mapping of alkaline soil in northern dry zone of Karnataka, India","volume":"42","author":"Meti","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/22797254.2019.1571870","article-title":"Retrieval of soil salinity from Sentinel-2 multispectral imagery","volume":"52","author":"Taghadosi","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1002\/ldr.752","article-title":"Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand","volume":"17","author":"Shrestha","year":"2006","journal-title":"Land Degrad. Dev."},{"key":"ref_63","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_64","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_65","first-page":"102969","article-title":"Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks","volume":"112","author":"Ge","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","unstructured":"Abood, S., Maclean, A., and Falkowski, M. (2011). Soil Salinity Detection in the Mesopotamian Agricultural Plain Utilizing WorldView-2 imagery. [Ph.D Thesis, Michigan Technological University Houghton]."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/10106049.2016.1250826","article-title":"Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece","volume":"33","author":"Alexakis","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Yu, H., Liu, M., Du, B., Wang, Z., Hu, L., and Zhang, B. (2018). Mapping Soil Salinity\/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China. Sensors, 18.","DOI":"10.3390\/s18041048"},{"key":"ref_70","first-page":"156","article-title":"Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra","volume":"26","author":"Sidike","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xie, M., Hu, B., Jiang, Q., Shi, Z., He, Y., and Peng, J. (2022). Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China. Remote Sens., 14.","DOI":"10.3390\/rs14194962"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1002\/ldr.3737","article-title":"Estimating soil salinity with different fractional vegetation cover using remote sensing","volume":"32","author":"Zhang","year":"2020","journal-title":"Land Degrad. Dev."},{"key":"ref_74","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 Part A Mol. Biomol. Spectrosc."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.pce.2010.12.004","article-title":"Characterizing soil salinity in irrigated agriculture using a remote sensing approach","volume":"55\u201357","author":"Abbas","year":"2013","journal-title":"Phys. Chem. 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