{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:48:25Z","timestamp":1732042105343},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["42001047"]},{"name":"Project of Department of Education Science and Technology of Jiangxi Province","award":["GJJ210541"]},{"name":"Social Science Foundation of Jiangxi Province","award":["21YJ43D"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Soil salinization is one of the major degradation processes threatening food security and sustainable development. Detailed soil salinity information is increasingly needed to tackle this global challenge for improving soil management. Soil-visible and near-infrared (Vis-NIR) spectroscopy has been proven to be a potential solution for estimating soil-salinity-related information (i.e., electrical conductivity, EC) rapidly and cost-effectively. However, previous studies were mainly conducted at the field, regional, or national scale, so the potential application of Vis-NIR spectroscopy at a global scale needs further investigation. Based on an extensive open global soil spectral library (61,486 samples with both EC and Vis-NIR spectra), we compared four spectral predictive models (PLSR, Cubist, Random Forests, and XGBoost) in estimating EC. Our results indicated that XGBoost had the best model performance (R2 of 0.59, RMSE of 1.96 dS m\u22121) in predicting EC at a global scale, whereas PLSR had a relatively limited ability (R2 of 0.39, RMSE of 2.41 dS m\u22121). The results also showed that auxiliary environmental covariates (i.e., coordinates, elevation, climatic variables) could greatly improve EC prediction accuracy by the four models, and the XGBoost performed best (R2 of 0.71, RMSE of 1.65 dS m\u22121). The outcomes of this study provide a valuable reference for improving broad-scale soil salinity prediction by the coupling of the spectroscopic technique and easily obtainable environmental covariates.<\/jats:p>","DOI":"10.3390\/rs14215627","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T13:17:12Z","timestamp":1667913432000},"page":"5627","source":"Crossref","is-referenced-by-count":18,"title":["Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library"],"prefix":"10.3390","volume":"14","author":[{"given":"Yin","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Land and Urban-Rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1245-0482","authenticated-orcid":false,"given":"Songchao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9353-0307","authenticated-orcid":false,"given":"Bifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China"}]},{"ORCID":"http:\/\/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"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8675-4070","authenticated-orcid":false,"given":"Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan 430079, China"}]},{"given":"Yongsheng","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Hanyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0340-5594","authenticated-orcid":false,"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0792-4700","authenticated-orcid":false,"given":"Xianglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Yi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"http:\/\/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, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","unstructured":"FAO, and ITPS (2015). Status of the World\u2019s Soil Resources (SWSR)-Main Report, Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils."},{"key":"ref_2","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_3","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_4","unstructured":"FAO (2021). The State of the World\u2019s Land and Water Resources for Food and Agriculture-Systems at Breaking Point (SOLAW 2021), Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils."},{"key":"ref_5","unstructured":"FAO (2022, September 13). World Soil Day-5th December. Available online: https:\/\/www.fao.org\/world-soil-day\/en\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1126\/science.1175084","article-title":"Digital soil map of the world","volume":"325","author":"Sanchez","year":"2009","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115567","DOI":"10.1016\/j.geoderma.2021.115567","article-title":"Digital mapping of GlobalSoilMap soil properties at a broad scale: A review","volume":"409","author":"Chen","year":"2022","journal-title":"Geoderma"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"e4703","DOI":"10.7717\/peerj.4703","article-title":"Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS\u2013NIR) spectroscopy, Ebinur Lake Wetland, Northwest China","volume":"6","author":"Wang","year":"2018","journal-title":"PeerJ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"364","DOI":"10.2136\/sssaj1995.03615995005900020014x","article-title":"Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties","volume":"59","author":"Banin","year":"1995","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/bs.agron.2015.02.002","article-title":"Soil spectroscopy: An alternative to wet chemistry for soil monitoring","volume":"132","author":"Nocita","year":"2015","journal-title":"Adv. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.earscirev.2016.01.012","article-title":"A global spectral library to characterize the world\u2019s soil","volume":"155","author":"Rossel","year":"2016","journal-title":"Earth Sci. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114793","DOI":"10.1016\/j.geoderma.2020.114793","article-title":"Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models","volume":"383","author":"Schmidt","year":"2021","journal-title":"Geoderma"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Visible and near infrared spectroscopy in soil science","volume":"107","author":"Stenberg","year":"2010","journal-title":"Adv. Agron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/05704928.2013.811081","article-title":"The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties","volume":"49","author":"Janik","year":"2014","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.geoderma.2005.04.025","article-title":"Global soil characterization with VNIR diffuse reflectance spectroscopy","volume":"132","author":"Brown","year":"2006","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/ejss.12320","article-title":"Simultaneous assessment of key properties of arid soil by combined PXRF and Vis\u2013NIR data","volume":"67","author":"Weindorf","year":"2016","journal-title":"Eur. J. Soil Sci."},{"key":"ref_19","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_20","unstructured":"Minasny, B., McBratney, A.B., Stockmann, U., and Hong, S.Y. (2013, January 2\u20137). Cubist, a regression rule approach for use in calibration of NIR spectra. Proceedings of the NIR 2013\u201416th International Conference on Near Infrared Spectroscopy, La Grande-Motte, France."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.1038\/s41598-019-41470-0","article-title":"Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods","volume":"9","author":"Zhang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5127","DOI":"10.1007\/s12517-014-1580-y","article-title":"Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy: A case study from Egypt","volume":"8","author":"Nawar","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53617","DOI":"10.1109\/ACCESS.2021.3071015","article-title":"Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region","volume":"9","author":"Bokde","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Hengl, T., Sanderman, J., and Parente, L. (2021). Open Soil Spectral Library (training data and calibration models) (v1.0-1) [Data set]. Zenodo."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1029\/EO081i048p00583","article-title":"Shuttle Radar Topography Mission produces a wealth of data","volume":"81","author":"Farr","year":"2000","journal-title":"Eos. Trans."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1km spatial resolution climate surfaces for global land areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.agee.2008.01.014","article-title":"Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation","volume":"126","author":"Zomer","year":"2008","journal-title":"Agr. Ecosyst. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemometr. Intell. Lab."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.geoderma.2019.06.016","article-title":"Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra","volume":"352","author":"Ng","year":"2019","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1002\/ldr.3497","article-title":"Monitoring soil organic carbon in alpine soils using in situ vis-NIR spectroscopy and a multilayer perceptron","volume":"31","author":"Chen","year":"2020","journal-title":"Land Degrad. Dev."},{"key":"ref_31","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.scitotenv.2018.11.230","article-title":"A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution","volume":"655","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_36","unstructured":"Kuhn, M., and Quinlan, R. (2022, September 13). Cubist: Rule-And Instance-Based Regression Modeling. R Package Version 0.3.0. Available online: https:\/\/CRAN.R-project.org\/package=Cubist."},{"key":"ref_37","first-page":"1","article-title":"Wright, Andreas Ziegler ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","volume":"77","author":"Marvin","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_38","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T. (2022, September 13). Xgboost: Extreme Gradient Boosting. R Package Version 1.5.0.2. Available online: https:\/\/CRAN.R-project.org\/package=xgboost."},{"key":"ref_39","unstructured":"Kuhn, M. (2022, September 13). Caret: Classification and Regression Training. R Package Version 6.0-88. Available online: https:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_40","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115159","DOI":"10.1016\/j.geoderma.2021.115159","article-title":"Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data","volume":"400","author":"Chen","year":"2021","journal-title":"Geoderma"},{"key":"ref_42","unstructured":"Schoeneberger, P.J., Wysocki, D.A., and Benham, E.C. (2012). Field Book for Describing and Sampling Soils, Version 3.0."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.geoderma.2019.01.006","article-title":"Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study","volume":"341","author":"Ji","year":"2019","journal-title":"Geoderma"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, M., Xu, D., Chen, S., Li, H., and Shi, Z. (2019). Evaluation of machine learning approaches to predict soil organic matter and pH using Vis-NIR spectra. Sensors, 19.","DOI":"10.3390\/s19020263"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Vestergaard, R.J., Vasava, H.B.B., Aspinall, D., Chen, S., Gillespie, A., Adamchuk, V., and Biswas, A. (2021). Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy. Sensors, 21.","DOI":"10.3390\/s21206745"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5194\/soil-6-35-2020","article-title":"Machine learning and soil sciences: A review aided by machine learning tools","volume":"6","author":"Padarian","year":"2020","journal-title":"Soil"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e5518","DOI":"10.7717\/peerj.5518","article-title":"Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geoderma.2017.10.009","article-title":"Building a pedotransfer function for soil bulk density on regional dataset and testing its validity over a larger area","volume":"312","author":"Chen","year":"2018","journal-title":"Geoderma"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gholizadeh, A., Carmon, N., Klement, A., Ben-Dor, E., and Bor\u016fvka, L. (2017). Agricultural soil spectral response and properties assessment: Effects of measurement protocol and data mining technique. Remote Sens., 9.","DOI":"10.3390\/rs9101078"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"113855","DOI":"10.1016\/j.geoderma.2019.07.013","article-title":"Preparing a soil spectral library using the Internal Soil Standard (ISS) method: Influence of extreme different humidity laboratory conditions","volume":"355","author":"Chabrillat","year":"2019","journal-title":"Geoderma"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"217","DOI":"10.5194\/soil-7-217-2021","article-title":"SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty","volume":"7","author":"Poggio","year":"2021","journal-title":"Soil"},{"key":"ref_52","first-page":"1","article-title":"A survey of predictive modeling on imbalanced domains","volume":"49","author":"Branco","year":"2016","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"113935","DOI":"10.1016\/j.geoderma.2019.113935","article-title":"Analyzing spatiotemporal characteristics of soil salinity in arid irrigated agro-ecosystems using integrated approaches","volume":"356","author":"Ren","year":"2019","journal-title":"Geoderma"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_56","first-page":"268","article-title":"The spectrum-based learner: A new local approach for modeling soil vis\u2013NIR spectra of complex datasets","volume":"195","author":"Behrens","year":"2013","journal-title":"Geoderma"},{"key":"ref_57","first-page":"112","article-title":"Reflectance measurements of soils in the laboratory: Standards and protocols","volume":"245","author":"Ong","year":"2015","journal-title":"Geoderma"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"115873","DOI":"10.1016\/j.geoderma.2022.115873","article-title":"Effect of the internal soil standard on the spectral assessment of clay content","volume":"420","author":"Francos","year":"2022","journal-title":"Geoderma"},{"key":"ref_59","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_60","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_61","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Safanelli, J.L., Chabrillat, S., Ben-Dor, E., and Dematt\u00ea, J.A. (2020). Multispectral models from bare soil composites for mapping topsoil properties over Europe. Remote Sens., 12.","DOI":"10.3390\/rs12091369"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1038\/s41598-020-61408-1","article-title":"Bare earth\u2019s surface spectra as a proxy for soil resource monitoring","volume":"10","author":"Safanelli","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.scib.2021.10.013","article-title":"Mapping high resolution National Soil Information Grids of China","volume":"67","author":"Liu","year":"2022","journal-title":"Sci. Bull."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T04:38:46Z","timestamp":1723178326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215627"],"URL":"https:\/\/doi.org\/10.3390\/rs14215627","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,7]]}}}