Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China

Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for add...

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Vydáno v:	Plant and soil Ročník 498; číslo 1-2; s. 451 - 469
Hlavní autoři:	Xiao, Sentian, Nurmemet, Ilyas, Zhao, Jing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.05.2024 Springer Springer Nature B.V
Témata:	Agriculture Algorithms Analysis Arid zones Aridity Biomedical and Life Sciences China Digital Elevation Models Earth resources technology satellites Ecology Electrical conductivity Electrical resistivity Landsat Learning algorithms Life Sciences Machine learning Mapping Oases Plant Physiology Plant Sciences Radar Radar data Radar systems Remote sensing Research Article Root-mean-square errors Salinity Salinity effects Salinization Soil chemistry Soil mapping Soil research Soil salinity soil salinization Soil Science & Conservation Soils Soils, Salts in China Random forest model GF-3 data Soil salinization estimation Polarization decomposition Multi-source data prediction
ISSN:	0032-079X, 1573-5036
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Abstract	Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem. Methods This study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models. Results The best accuracy corresponding to an R 2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity. Conclusion The radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas.
AbstractList	AimsSoil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem.MethodsThis study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models.ResultsThe best accuracy corresponding to an R2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity.ConclusionThe radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas. AIMS: Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem. METHODS: This study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models. RESULTS: The best accuracy corresponding to an R² of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity. CONCLUSION: The radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas. Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem. Methods This study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models. Results The best accuracy corresponding to an R.sup.2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity. Conclusion The radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas. Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source data, combining radar and optical remote sensing data, and applying machine learning-based algorithms to these data could be beneficial for addressing the soil salinization problem. Methods This study combines the environmental covariates extracted from the Gaofen-3 (GF-3) radar data, Landsat-8 multispectral data, and digital elevation model (DEM) data to explore the advantages of radar remote sensing in detecting soil salinity. The soil salinity distribution degree in the Keriya Oasis is mapped using a machine-learning-based method, and the advantages of different sensor images in predicting soil salinity are evaluated. Three soil salinity inversion models are constructed using measured electrical conductivity (EC) data, the random forest (RF), gradient boosting tree (GDBT), and extreme gradient boosting (XGBoost) models. Results The best accuracy corresponding to an R 2 of 0.87, and a root mean square error (RMSE) of 6.02 is achieved by the RF model on the GF-3 + Landsat-8 data. Therefore, the use of multi-source data is a more effective method for mapping soil salinity in the study area. The mapping results of the optimal model demonstrate that natural factors significantly influence the distribution of soil salinity. Conclusion The radar polarization decomposition characteristics are incorporated into the inversion of soil salinity modeling as an environmental covariate, providing an innovative and efficient method for soil salinity estimation in arid areas.
Audience	Academic
Author	Xiao, Sentian Zhao, Jing Nurmemet, Ilyas
Author_xml	– sequence: 1 givenname: Sentian surname: Xiao fullname: Xiao, Sentian organization: College of Geography and Remote Sensing Sciences, Xinjiang University, Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University – sequence: 2 givenname: Ilyas orcidid: 0000-0002-4731-1098 surname: Nurmemet fullname: Nurmemet, Ilyas email: ilyas@xju.edu.cn organization: College of Geography and Remote Sensing Sciences, Xinjiang University, Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University – sequence: 3 givenname: Jing surname: Zhao fullname: Zhao, Jing organization: College of Geography and Remote Sensing Sciences, Xinjiang University, Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University
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CitedBy_id	crossref_primary_10_1016_j_agee_2024_109225 crossref_primary_10_3390_s25082512 crossref_primary_10_1080_01431161_2024_2412804 crossref_primary_10_5194_nhess_25_3505_2025 crossref_primary_10_1016_j_catena_2025_109461 crossref_primary_10_3390_land13111941 crossref_primary_10_1038_s41598_025_07944_0 crossref_primary_10_3390_agronomy15071590 crossref_primary_10_3390_rs16244812 crossref_primary_10_3390_land14030627 crossref_primary_10_3390_land14030649 crossref_primary_10_1002_ldr_5635 crossref_primary_10_5817_CPR2025_1_2 crossref_primary_10_1016_j_catena_2025_109116
Cites_doi	10.1016/j.comcom.2020.02.078 10.1016/j.geoderma.2014.09.011 10.1016/j.isprsjprs.2023.04.018 10.18637/jss.v036.i11 10.3390/rs14020363 10.1080/22797254.2019.1596756 10.1016/j.geoderma.2018.08.006 10.1016/j.ecolind.2016.11.043 10.3390/rs10040598 10.3390/app112311145 10.3390/rs14030512 10.3390/rs10121929 10.1109/JSTARS.2014.2333535 10.3390/s19030589 10.1023/A:1010933404324 10.1007/s00500-021-06095-4 10.1073/pnas.2013771117 10.3390/su10030656 10.3233/FI-2010-288 10.3390/s18030807 10.1016/j.petrol.2018.11.067 10.3390/rs12162601 10.1109/TGRS.2003.813499 10.1016/S0034-4257(98)00028-5 10.1016/j.jenvman.2020.111383 10.3390/rs12244118 10.1016/j.isprsjprs.2010.09.001 10.1007/s11769-014-0718-x 10.1016/j.agsy.2021.103220 10.1109/TGRS.2010.2041242 10.1016/j.geoderma.2014.03.025 10.1016/bs.agron.2021.03.001 10.1016/S0167-9473(01)00065-2 10.1016/j.asr.2021.10.024 10.1145/2939672.2939785 10.1007/978-981-15-8897-6 10.1016/j.asej.2022.101876 10.1214/aos/1013203451 10.1109/TGRS.2008.916220 10.1016/j.scitotenv.2016.08.177 10.1016/j.scitotenv.2019.134235 10.1007/s11430-012-4444-x 10.1021/acs.est.9b03334 10.1109/ACCESS.2020.2995458 10.3390/su10030799 10.1111/jvs.12693 10.1007/s12583-011-0227-0 10.1049/ip-rsn:20030566 10.3390/s18020611 10.1007/s002540100388 10.1016/j.geoderma.2020.114858 10.1016/j.scitotenv.2021.145865 10.3390/s22197226 10.1186/s13059-020-02052-w
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References	Dan (CR16) 1998; 65 Mamat, Yimit, Lv (CR46) 2013; 44 Ma, Ding, Han, Zhang (CR45) 2020; 36 Emamgholizadeh, Bazoobandi, Mohammadi, Ghorbani, Sadeghi (CR21) 2023; 14 Li, Zhang, Huang, Jia (CR40) 2018; 10 CR37 Breiman (CR10) 2001; 45 Ge, Ding, Teng, Wang, Huo, Jin, Han (CR24) 2022; 212 CR33 Wu, Muhaimeed, Al-Shafie, Al-Quraishi (CR63) 2019; 1 Kursa, Rudnicki (CR35) 2010; 36 Huang, Ding, Zou, Liu, Zhang, Chen (CR31) 2019; 19 Ao, Li, Zhu, Ali, Yang (CR7) 2019; 174 Li, Ding, Sun, Wang, Wang (CR67) 2015; 22 Shaheen, Iqbal (CR54) 2018; 10 Shi, Du, Du, Jiang, Chai, Mao, Xu, Ni, Xiong, Liu (CR55) 2012; 55 Wang, Ding, Wu (CR57) 2010; 26 Nurmemet, Sagan, Ding, Halik, Abliz, Yakup (CR51) 2018; 10 Aldabaa, Weindorf, Chakraborty, Sharma, Li (CR4) 2015; 239 Dong, Na (CR19) 2021; 11 Ling, Zhang, Shi, Xu (CR41) 2011; 22 CR47 Aksoy, Yildirim, Gorji, Hamzehpour, Tanik, Sertel (CR2) 2022; 69 CR43 Wang, Yang, Yang, Yang, Ding (CR59) 2019; 52 Peng, Biswas, Jiang, Zhao, Hu, Hu, Shi (CR52) 2019; 337 Jiang, Qiu, Han, Hu (CR32) 2018; 18 Nabiollahi, Taghizadeh-Mehrjardi, Shahabi, Heung, Amirian-Chakan, Davari, Scholten (CR49) 2021; 385 Chen, Guestrin (CR12) 2016 Li (CR38) 2013; 50 Kursa, Jankowski, Rudnicki (CR36) 2010; 101 Hopmans, Qureshi, Kisekka, Munns, Grattan, Rengasamy, Ben-Gal, Assouline, Javaux, Minhas (CR30) 2021; 169 Muhetaer, Nurmemet, Abulaiti, Xiao, Zhao (CR48) 2022; 22 Cui, Wang, Zhao, Zhang (CR14) 2020; 155 Yang (CR65) 2001; 41 Emamgholizadeh, Mohammadi (CR20) 2021; 25 Nunez, Finkbeiner (CR50) 2020; 54 CR56 Koch (CR34) 2010; 65 Yi-Jie, Jia-Shan (CR13) 2019 Gorji, Sertel, Tanik (CR25) 2017; 74 Yang, Dai, Tan, Liu, Yang, Li (CR66) 2021 Seydehmet, Lv, Nurmemet, Aishan, Abliz, Sawut, Eziz (CR53) 2018; 10 Li, Song (CR39) 2022; 324 Ma, Wang, Li, Li (CR44) 2015; 31 Aa, Ra (CR1) 2019; 13 Wang, Chen, Luo, Han (CR58) 2015; 25 Han, Ding, Ge, He, Wang, Xie, Zhang (CR27) 2022; 111 Ling, Xu, Liu, Zhang, Fu, Bai (CR42) 2012; 23 Das, Bhattacharya, Setia, Jayasree, Das (CR17) 2023; 200 Akter, Bishop, Vervoort (CR3) 2021; 776 CR28 Ding, Yang, Shi, Wei, Wang (CR18) 2020; 12 Allbed, Kumar, Aldakheel (CR5) 2014; 230 CR68 CR22 Guo, Zang, Zhang (CR26) 2020; 8 Hassani, Azapagic, Shokri (CR29) 2020; 117 CR62 Friedman (CR23) 2002; 38 CR61 CR60 An, Cui, Yang (CR6) 2010; 48 Yanbing, Dunpeng, Fangfang, Zhefeng, Junling, Jian, Yue (CR64) 2008; 27 Boerner (CR9) 2003; 150 Chaieb, Abdelly, Michalet (CR11) 2019; 30 Daliakopoulos, Tsanis, Koutroulis, Kourgialas, Varouchakis, Karatzas, Ritsema (CR15) 2016; 573 Yun, Hu, Guo, Yuan, Han (CR69) 2003; 41 Barbouchi, Abdelfattah, Chokmani, Ben Aissa, Lhissou, El Harti (CR8) 2015; 8 J Cui (6446_CR14) 2020; 155 S Emamgholizadeh (6446_CR21) 2023; 14 M Nunez (6446_CR50) 2020; 54 A Allbed (6446_CR5) 2014; 230 MB Kursa (6446_CR35) 2010; 36 JH Friedman (6446_CR23) 2002; 38 I Nurmemet (6446_CR51) 2018; 10 Y Ao (6446_CR7) 2019; 174 J Peng (6446_CR52) 2019; 337 C Yi-Jie (6446_CR13) 2019 K Nabiollahi (6446_CR49) 2021; 385 J Shi (6446_CR55) 2012; 55 T Gorji (6446_CR25) 2017; 74 J Seydehmet (6446_CR53) 2018; 10 6446_CR56 6446_CR47 S Emamgholizadeh (6446_CR20) 2021; 25 H Ling (6446_CR42) 2012; 23 G Ma (6446_CR45) 2020; 36 WM Boerner (6446_CR9) 2003; 150 T Chen (6446_CR12) 2016 A Aa (6446_CR1) 2019; 13 F Akter (6446_CR3) 2021; 776 G Chaieb (6446_CR11) 2019; 30 A Hassani (6446_CR29) 2020; 117 X Yang (6446_CR65) 2001; 41 A Das (6446_CR17) 2023; 200 S Huang (6446_CR31) 2019; 19 Y Li (6446_CR67) 2015; 22 L Breiman (6446_CR10) 2001; 45 JW Hopmans (6446_CR30) 2021; 169 B Koch (6446_CR34) 2010; 65 6446_CR43 R Dong (6446_CR19) 2021; 11 F Wang (6446_CR58) 2015; 25 J Ding (6446_CR18) 2020; 12 6446_CR37 B Guo (6446_CR26) 2020; 8 A Shaheen (6446_CR54) 2018; 10 Z Mamat (6446_CR46) 2013; 44 F Wang (6446_CR59) 2019; 52 S Aksoy (6446_CR2) 2022; 69 QF Li (6446_CR39) 2022; 324 L Han (6446_CR27) 2022; 111 PAN Yanbing (6446_CR64) 2008; 27 MB Kursa (6446_CR36) 2010; 101 S Yun (6446_CR69) 2003; 41 T Ma (6446_CR44) 2015; 31 XM Li (6446_CR40) 2018; 10 N Muhetaer (6446_CR48) 2022; 22 R Yang (6446_CR66) 2021 6446_CR33 6446_CR68 AAA Aldabaa (6446_CR4) 2015; 239 X Ge (6446_CR24) 2022; 212 6446_CR28 W An (6446_CR6) 2010; 48 GB Dan (6446_CR16) 1998; 65 F Wang (6446_CR57) 2010; 26 H Ling (6446_CR41) 2011; 22 M Barbouchi (6446_CR8) 2015; 8 W Wu (6446_CR63) 2019; 1 I Daliakopoulos (6446_CR15) 2016; 573 6446_CR60 6446_CR61 6446_CR62 S Jiang (6446_CR32) 2018; 18 6446_CR22 X-H Li (6446_CR38) 2013; 50
References_xml	– ident: CR22 – volume: 25 start-page: 13451 year: 2021 end-page: 13464 ident: CR20 article-title: New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity publication-title: Soft Comput – ident: CR68 – volume: 65 start-page: 204 year: 1998 end-page: 216 ident: CR16 article-title: Remote sensing of desert dune forms by Polarimetric synthetic aperture radar (SAR) publication-title: Remote Sens Environ – volume: 10 start-page: 799 year: 2018 ident: CR54 article-title: Spatial distribution and mobility assessment of carcinogenic heavy metals in soil profiles using geostatistics and random forest, boruta algorithm publication-title: Sustainability – volume: 12 start-page: 2601 issue: 16 year: 2020 ident: CR18 article-title: Using apparent electrical conductivity as indicator for investigating potential spatial variation of soil salinity across seven oases along Tarim River in Southern Xinjiang, China publication-title: Remote Sens – ident: CR61 – volume: 18 start-page: 807 issue: 3 year: 2018 ident: CR32 article-title: A quality assessment method based on common distributed targets for GF-3 polarimetric SAR data publication-title: Sensors – volume: 19 start-page: 589 issue: 3 year: 2019 ident: CR31 article-title: Soil moisture retrival based on sentinel-1 imagery under sparse vegetation coverage publication-title: Sensors – volume: 230 start-page: 1 year: 2014 end-page: 8 ident: CR5 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 publication-title: Geoderma – volume: 36 start-page: 124 year: 2020 end-page: 131 ident: CR45 article-title: Digital mapping of soil salinization in arid area wetland based on variable optimized selection and machine learning publication-title: Trans Chin Soc Agric Eng – volume: 52 start-page: 256 year: 2019 end-page: 276 ident: CR59 article-title: Comparison of machine learning algorithms for soil salinity predictions in three dryland oases located in Xinjiang Uyghur autonomous region (XJUAR) of China publication-title: Eur J Remote Sens – volume: 54 start-page: 3082 year: 2020 end-page: 3090 ident: CR50 article-title: A regionalised life cycle assessment model to globally assess the environmental implications of soil salinization in irrigated agriculture publication-title: Environ Sci Technol – volume: 55 start-page: 1052 year: 2012 end-page: 1078 ident: CR55 article-title: Progresses on microwave remote sensing of land surface parameters publication-title: Sci China-Earth Sci – volume: 8 start-page: 94394 year: 2020 end-page: 94403 ident: CR26 article-title: Soil Salizanation information in the Yellow River Delta based on feature surface models using Landsat 8 OLI data publication-title: Ieee Access – volume: 337 start-page: 1309 year: 2019 end-page: 1319 ident: CR52 article-title: Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China publication-title: Geoderma – volume: 573 start-page: 727 year: 2016 end-page: 739 ident: CR15 article-title: The threat of soil salinity: a European scale review publication-title: Sci Total Environ – volume: 150 start-page: 113 year: 2003 end-page: 124 ident: CR9 article-title: Recent advances in extra-wide-band polarimetry, interferometry and polarimetric interferometry in synthetic aperture remote sensing and its applications publication-title: Iee Proc-Radar Sonar Navig – volume: 324 year: 2022 ident: CR39 article-title: High-performance concrete strength prediction based on ensemble learning publication-title: Constr Build Mater – volume: 27 start-page: 814 year: 2008 end-page: 822 ident: CR64 article-title: Geomorphological features of the Keriya River valley and the early-middle Pleistocene great lake of the Tarim basin publication-title: Geol Bull China – volume: 10 start-page: 598 issue: 4 year: 2018 ident: CR51 article-title: A WFS-SVM model for soil salinity mapping in Keriya Oasis, northwestern China using polarimetric decomposition and fully PolSAR data publication-title: Remote Sens – volume: 776 start-page: 145865 year: 2021 ident: CR3 article-title: Space-time modelling of groundwater level and salinity publication-title: Sci Total Environ – year: 2021 ident: CR66 publication-title: Polarimetric microwave imaging – ident: CR60 – volume: 69 start-page: 1072 year: 2022 end-page: 1086 ident: CR2 article-title: Assessing the performance of machine learning algorithms for soil salinity mapping in Google earth engine platform using sentinel-2A and Landsat-8 OLI data publication-title: Adv Space Res – volume: 41 start-page: 1879 year: 2003 end-page: 1888 ident: CR69 article-title: Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image publication-title: IEEE Trans Geosci Remote Sens – year: 2019 ident: CR13 publication-title: Application of improved boruta algorithm in feature selection – volume: 1 issue: 8 year: 2019 ident: CR63 article-title: Using L-band radar data for soil salinity mapping—a case study in Central Iraq publication-title: Environ Res Commun – volume: 26 start-page: 168 year: 2010 ident: CR57 article-title: Remote sensing monitoring models of soil salinization based on NDVI-SI feature space publication-title: Trans Chin Soc Agric Eng – volume: 200 start-page: 191 year: 2023 end-page: 212 ident: CR17 article-title: A novel method for detecting soil salinity using AVIRIS-NG imaging spectroscopy and ensemble machine learning publication-title: ISPRS J Photogramm Remote Sens – volume: 50 start-page: 1190 year: 2013 end-page: 1197 ident: CR38 article-title: Using “random forest” for classification and regression publication-title: Chin J Appl Entomol – volume: 117 start-page: 33017 year: 2020 end-page: 33027 ident: CR29 article-title: Predicting long-term dynamics of soil salinity and sodicity on a global scale publication-title: Proc Natl Acad Sci U S A – start-page: 785 year: 2016 end-page: 794 ident: CR12 article-title: Xgboost: A scalable tree boosting system publication-title: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining – volume: 22 start-page: 780 year: 2011 end-page: 791 ident: CR41 article-title: Runoff variation law and its response to climate change in the headstream area of the Keriya River basin, Xinjiang publication-title: J Earth Sci – ident: CR43 – volume: 31 start-page: 259 year: 2015 end-page: 265 ident: CR44 article-title: Land cover/land use classification based on polarimetric target decomposition of microwave remote sensing publication-title: Trans Chin Soc Agric Eng – ident: CR47 – volume: 22 start-page: 113 issue: 4 year: 2015 end-page: 117 ident: CR67 article-title: Remote sensing monitoring models of soil salinization based on the three dimensional feature space of MSAVI-WI-SI publication-title: Res Soil Water Conservation – volume: 30 start-page: 312 year: 2019 end-page: 321 ident: CR11 article-title: Interactive effects of climate and topography on soil salinity and vegetation zonation in north-African continental saline depressions publication-title: J Veg Sci – ident: CR37 – volume: 111 year: 2022 ident: CR27 article-title: Using spatiotemporal fusion algorithms to fill in potentially absent satellite images for calculating soil salinity: A feasibility study publication-title: Int J Appl Earth Obs Geoinf – volume: 44 start-page: 1314 year: 2013 end-page: 1320 ident: CR46 article-title: Spatial distributing pattern of salinized soils and their salinity in typical area of Yutian Oasis publication-title: J Soil Sci – volume: 101 start-page: 271 year: 2010 end-page: 285 ident: CR36 article-title: Boruta - a system for feature selection publication-title: Fundame Inform – ident: CR33 – volume: 174 start-page: 776 year: 2019 end-page: 789 ident: CR7 article-title: The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling publication-title: J Pet Sci Eng – ident: CR56 – volume: 212 year: 2022 ident: CR24 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 publication-title: Catena – volume: 25 start-page: 321 year: 2015 end-page: 336 ident: CR58 article-title: Mapping of regional soil salinities in Xinjiang and strategies for amelioration and management publication-title: Chin Geogr Sci – volume: 8 start-page: 3823 year: 2015 end-page: 3832 ident: CR8 article-title: Soil salinity characterization using Polarimetric InSAR coherence: case studies in Tunisia and Morocco publication-title: Ieee J Sel Topics Appl Earth Observ Remote Sens – volume: 38 start-page: 367 year: 2002 end-page: 378 ident: CR23 article-title: Stochastic gradient boosting publication-title: Comput Stat Data Anal – volume: 13 start-page: 415 year: 2019 end-page: 425 ident: CR1 article-title: Mapping soil salinity in arid and semi-arid regions using Landsat 8 OLI satellite data - ScienceDirect publication-title: Remote Sens Appl: Soc Environ – volume: 169 start-page: 1 year: 2021 end-page: 191 ident: CR30 article-title: Critical knowledge gaps and research priorities in global soil salinity publication-title: Adv Agron – volume: 36 start-page: 1 year: 2010 end-page: 13 ident: CR35 article-title: Feature selection with the Boruta package publication-title: J Stat Softw – volume: 10 start-page: 1929 issue: 12 year: 2018 ident: CR40 article-title: Capabilities of Chinese Gaofen-3 synthetic aperture radar in selected topics for coastal and ocean observations publication-title: Remote Sens – volume: 48 start-page: 2732 issue: 6 year: 2010 end-page: 2739 ident: CR6 article-title: Three-component model-based decomposition for polarimetric SAR data publication-title: IEEE Trans Geosci Remote Sens – volume: 10 start-page: 656 issue: 3 year: 2018 ident: CR53 article-title: Model prediction of secondary soil salinization in the Keriya Oasis, Northwest China publication-title: Sustainability – volume: 11 start-page: 11145 issue: 23 year: 2021 ident: CR19 article-title: Quantitative retrieval of soil salinity using landsat 8 OLI imagery publication-title: Appl Sci – volume: 239 start-page: 34 year: 2015 end-page: 46 ident: CR4 article-title: Combination of proximal and remote sensing methods for rapid soil salinity quantification publication-title: Geoderma – volume: 74 start-page: 384 year: 2017 end-page: 391 ident: CR25 article-title: Monitoring soil salinity via remote sensing technology under data scarce conditions: a case study from Turkey publication-title: Ecol Indic – volume: 65 start-page: 581 year: 2010 end-page: 590 ident: CR34 article-title: Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment publication-title: ISPRS J Photogramm Remote Sens – volume: 385 year: 2021 ident: CR49 article-title: Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests publication-title: Geoderma – volume: 155 start-page: 125 year: 2020 end-page: 131 ident: CR14 article-title: Towards predictive analysis of android vulnerability using statistical codes and machine learning for IoT applications publication-title: Comput Commun – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: CR10 article-title: Random forest publication-title: Mach Learn – ident: CR28 – volume: 23 start-page: 563 year: 2012 end-page: 568 ident: CR42 article-title: Suitable scale of oasis in Keriya River basin, Xinjiang publication-title: Adv Water Sci – ident: CR62 – volume: 14 issue: 2 year: 2023 ident: CR21 article-title: Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea publication-title: Ain Shams Eng J – volume: 41 start-page: 314 year: 2001 end-page: 320 ident: CR65 article-title: The oases along the Keriya River in the Taklamakan Desert, China, and their evolution since the end of the last glaciation publication-title: Environ Geol – volume: 22 start-page: 7226 year: 2022 ident: CR48 article-title: An efficient approach for inverting the soil salinity in Keriya Oasis, northwestern China, based on the optical-radar feature-space model publication-title: Sensors – volume: 155 start-page: 125 year: 2020 ident: 6446_CR14 publication-title: Comput Commun doi: 10.1016/j.comcom.2020.02.078 – volume: 239 start-page: 34 year: 2015 ident: 6446_CR4 publication-title: Geoderma doi: 10.1016/j.geoderma.2014.09.011 – volume: 111 year: 2022 ident: 6446_CR27 publication-title: Int J Appl Earth Obs Geoinf – volume: 200 start-page: 191 year: 2023 ident: 6446_CR17 publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2023.04.018 – volume: 36 start-page: 1 year: 2010 ident: 6446_CR35 publication-title: J Stat Softw doi: 10.18637/jss.v036.i11 – ident: 6446_CR47 doi: 10.3390/rs14020363 – volume: 52 start-page: 256 year: 2019 ident: 6446_CR59 publication-title: Eur J Remote Sens doi: 10.1080/22797254.2019.1596756 – volume: 337 start-page: 1309 year: 2019 ident: 6446_CR52 publication-title: Geoderma doi: 10.1016/j.geoderma.2018.08.006 – volume: 27 start-page: 814 year: 2008 ident: 6446_CR64 publication-title: Geol Bull China – volume: 74 start-page: 384 year: 2017 ident: 6446_CR25 publication-title: Ecol Indic doi: 10.1016/j.ecolind.2016.11.043 – volume: 10 start-page: 598 issue: 4 year: 2018 ident: 6446_CR51 publication-title: Remote Sens doi: 10.3390/rs10040598 – volume: 11 start-page: 11145 issue: 23 year: 2021 ident: 6446_CR19 publication-title: Appl Sci doi: 10.3390/app112311145 – ident: 6446_CR62 doi: 10.3390/rs14030512 – volume: 10 start-page: 1929 issue: 12 year: 2018 ident: 6446_CR40 publication-title: Remote Sens doi: 10.3390/rs10121929 – volume: 8 start-page: 3823 year: 2015 ident: 6446_CR8 publication-title: Ieee J Sel Topics Appl Earth Observ Remote Sens doi: 10.1109/JSTARS.2014.2333535 – volume: 44 start-page: 1314 year: 2013 ident: 6446_CR46 publication-title: J Soil Sci – volume: 19 start-page: 589 issue: 3 year: 2019 ident: 6446_CR31 publication-title: Sensors doi: 10.3390/s19030589 – volume: 45 start-page: 5 year: 2001 ident: 6446_CR10 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 25 start-page: 13451 year: 2021 ident: 6446_CR20 publication-title: Soft Comput doi: 10.1007/s00500-021-06095-4 – volume: 117 start-page: 33017 year: 2020 ident: 6446_CR29 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.2013771117 – volume: 36 start-page: 124 year: 2020 ident: 6446_CR45 publication-title: Trans Chin Soc Agric Eng – volume: 10 start-page: 656 issue: 3 year: 2018 ident: 6446_CR53 publication-title: Sustainability doi: 10.3390/su10030656 – volume: 101 start-page: 271 year: 2010 ident: 6446_CR36 publication-title: Fundame Inform doi: 10.3233/FI-2010-288 – volume: 22 start-page: 113 issue: 4 year: 2015 ident: 6446_CR67 publication-title: Res Soil Water Conservation – volume: 18 start-page: 807 issue: 3 year: 2018 ident: 6446_CR32 publication-title: Sensors doi: 10.3390/s18030807 – volume: 174 start-page: 776 year: 2019 ident: 6446_CR7 publication-title: J Pet Sci Eng doi: 10.1016/j.petrol.2018.11.067 – volume: 12 start-page: 2601 issue: 16 year: 2020 ident: 6446_CR18 publication-title: Remote Sens doi: 10.3390/rs12162601 – volume: 41 start-page: 1879 year: 2003 ident: 6446_CR69 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2003.813499 – volume: 65 start-page: 204 year: 1998 ident: 6446_CR16 publication-title: Remote Sens Environ doi: 10.1016/S0034-4257(98)00028-5 – ident: 6446_CR56 doi: 10.1016/j.jenvman.2020.111383 – ident: 6446_CR60 doi: 10.3390/rs12244118 – volume: 65 start-page: 581 year: 2010 ident: 6446_CR34 publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2010.09.001 – volume: 25 start-page: 321 year: 2015 ident: 6446_CR58 publication-title: Chin Geogr Sci doi: 10.1007/s11769-014-0718-x – volume: 23 start-page: 563 year: 2012 ident: 6446_CR42 publication-title: Adv Water Sci – ident: 6446_CR61 doi: 10.1016/j.agsy.2021.103220 – volume: 48 start-page: 2732 issue: 6 year: 2010 ident: 6446_CR6 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2010.2041242 – volume: 230 start-page: 1 year: 2014 ident: 6446_CR5 publication-title: Geoderma doi: 10.1016/j.geoderma.2014.03.025 – volume: 324 year: 2022 ident: 6446_CR39 publication-title: Constr Build Mater – volume: 169 start-page: 1 year: 2021 ident: 6446_CR30 publication-title: Adv Agron doi: 10.1016/bs.agron.2021.03.001 – volume: 50 start-page: 1190 year: 2013 ident: 6446_CR38 publication-title: Chin J Appl Entomol – volume: 38 start-page: 367 year: 2002 ident: 6446_CR23 publication-title: Comput Stat Data Anal doi: 10.1016/S0167-9473(01)00065-2 – volume: 69 start-page: 1072 year: 2022 ident: 6446_CR2 publication-title: Adv Space Res doi: 10.1016/j.asr.2021.10.024 – volume: 1 issue: 8 year: 2019 ident: 6446_CR63 publication-title: Environ Res Commun – start-page: 785 volume-title: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining year: 2016 ident: 6446_CR12 doi: 10.1145/2939672.2939785 – volume: 212 year: 2022 ident: 6446_CR24 publication-title: Catena – volume-title: Polarimetric microwave imaging year: 2021 ident: 6446_CR66 doi: 10.1007/978-981-15-8897-6 – volume: 14 issue: 2 year: 2023 ident: 6446_CR21 publication-title: Ain Shams Eng J doi: 10.1016/j.asej.2022.101876 – ident: 6446_CR22 doi: 10.1214/aos/1013203451 – ident: 6446_CR37 doi: 10.1109/TGRS.2008.916220 – volume: 573 start-page: 727 year: 2016 ident: 6446_CR15 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2016.08.177 – ident: 6446_CR43 doi: 10.1016/j.scitotenv.2019.134235 – volume: 55 start-page: 1052 year: 2012 ident: 6446_CR55 publication-title: Sci China-Earth Sci doi: 10.1007/s11430-012-4444-x – volume: 54 start-page: 3082 year: 2020 ident: 6446_CR50 publication-title: Environ Sci Technol doi: 10.1021/acs.est.9b03334 – volume: 8 start-page: 94394 year: 2020 ident: 6446_CR26 publication-title: Ieee Access doi: 10.1109/ACCESS.2020.2995458 – volume: 31 start-page: 259 year: 2015 ident: 6446_CR44 publication-title: Trans Chin Soc Agric Eng – volume: 10 start-page: 799 year: 2018 ident: 6446_CR54 publication-title: Sustainability doi: 10.3390/su10030799 – volume: 30 start-page: 312 year: 2019 ident: 6446_CR11 publication-title: J Veg Sci doi: 10.1111/jvs.12693 – volume: 22 start-page: 780 year: 2011 ident: 6446_CR41 publication-title: J Earth Sci doi: 10.1007/s12583-011-0227-0 – volume: 150 start-page: 113 year: 2003 ident: 6446_CR9 publication-title: Iee Proc-Radar Sonar Navig doi: 10.1049/ip-rsn:20030566 – volume-title: Application of improved boruta algorithm in feature selection year: 2019 ident: 6446_CR13 – ident: 6446_CR28 doi: 10.3390/s18020611 – volume: 41 start-page: 314 year: 2001 ident: 6446_CR65 publication-title: Environ Geol doi: 10.1007/s002540100388 – volume: 13 start-page: 415 year: 2019 ident: 6446_CR1 publication-title: Remote Sens Appl: Soc Environ – volume: 385 year: 2021 ident: 6446_CR49 publication-title: Geoderma doi: 10.1016/j.geoderma.2020.114858 – volume: 776 start-page: 145865 year: 2021 ident: 6446_CR3 publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2021.145865 – volume: 22 start-page: 7226 year: 2022 ident: 6446_CR48 publication-title: Sensors doi: 10.3390/s22197226 – ident: 6446_CR33 – ident: 6446_CR68 doi: 10.1186/s13059-020-02052-w – volume: 26 start-page: 168 year: 2010 ident: 6446_CR57 publication-title: Trans Chin Soc Agric Eng
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Snippet	Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source... Aims Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source... AimsSoil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source... AIMS: Soil salinization has been an important environmental problem globally, particularly in oasis areas in arid zones. The advantages of using multi-source...
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SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	451
SubjectTerms	Agriculture Algorithms Analysis Arid zones Aridity Biomedical and Life Sciences China Digital Elevation Models Earth resources technology satellites Ecology Electrical conductivity Electrical resistivity Landsat Learning algorithms Life Sciences Machine learning Mapping Oases Plant Physiology Plant Sciences Radar Radar data Radar systems Remote sensing Research Article Root-mean-square errors Salinity Salinity effects Salinization Soil chemistry Soil mapping Soil research Soil salinity soil salinization Soil Science & Conservation Soils Soils, Salts in
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Title	Soil salinity estimation based on machine learning using the GF-3 radar and Landsat-8 data in the Keriya Oasis, Southern Xinjiang, China
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