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Control","award":["SKLHSE\u20132022\u2013IOW11"],"award-info":[{"award-number":["SKLHSE\u20132022\u2013IOW11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson\u2019s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing.<\/jats:p>","DOI":"10.3390\/rs14225639","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T08:52:56Z","timestamp":1667897576000},"page":"5639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Inversion of Different Cultivated Soil Types\u2019 Salinity Using Hyperspectral Data and Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Pingping","family":"Jia","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Geography and Planning, Ningxia University, Yinchuan 750021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3585-5338","authenticated-orcid":false,"given":"Junhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Ecology and Environment, Ningxia University, Yinchuan 750021, China"},{"name":"Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China, Ningxia University, Yinchuan 750021, China"}]},{"given":"Wei","family":"He","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Ding","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9536-9251","authenticated-orcid":false,"given":"Kazem","family":"Zamanian","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Institute of Soil Science, Leibniz University of Hannover, 30419 Hannover, Germany"}]},{"given":"Keli","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Ningxia University, Yinchuan 750021, China"}]},{"given":"Xiaoning","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10661-017-6415-3","article-title":"Assessment, Monitoring soil for sustainable development and land degradation neutrality","volume":"190","author":"Hermann","year":"2018","journal-title":"Environ. 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