{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:59:29Z","timestamp":1783443569379,"version":"3.54.6"},"reference-count":76,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T00:00:00Z","timestamp":1547164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was supported by the project VT-UD.03\/16-20: \u201cStudying, assessing, and zoning soil salinity intrusion by using multi-temporal satellite imagery \u2013 A case study at Ben Tre province\u201d, which belongs to the national program on Space Science and T","award":["VT-UD.03\/16-20"],"award-info":[{"award-number":["VT-UD.03\/16-20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4\u20136 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.<\/jats:p>","DOI":"10.3390\/rs11020128","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T11:36:42Z","timestamp":1547206602000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)"],"prefix":"10.3390","volume":"11","author":[{"given":"Pham Viet","family":"Hoa","sequence":"first","affiliation":[{"name":"Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6989-2778","authenticated-orcid":false,"given":"Nguyen Vu","family":"Giang","sequence":"additional","affiliation":[{"name":"Space Technology Institute, Vietnam Academy of Science and Technology, Hoang Quoc Viet 18, Cau Giay, Hanoi 10000, Vietnam"},{"name":"Division of Forest, Nature and Landscape, Department of Earth and Environmental Sciences, KU Leuven, 3000 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nguyen An","family":"Binh","sequence":"additional","affiliation":[{"name":"Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le Vu Hong","family":"Hai","sequence":"additional","affiliation":[{"name":"Institute of Techniques for Special Engineering (ITSE), Military Technical Academy, Hoang Quoc Viet 236, Cau Giay, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6422-2847","authenticated-orcid":false,"given":"Tien-Dat","family":"Pham","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, the RIKEN Center for Advanced Intelligence Project (AIP), Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-4475","authenticated-orcid":false,"given":"Mahdi","family":"Hasanlou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dieu","family":"Tien Bui","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 B\u00f8 i Telemark, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Metternicht, G., and Zinck, A. (2008). Remote Sensing of Soil Salinization: Impact on Land Management, CRC Press.","DOI":"10.1201\/9781420065039"},{"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","unstructured":"FAO (2018, November 15). FAO Soils Portal. Available online: http:\/\/www.fao.org\/soils-portal\/soil-management\/management-of-some-problem-soils\/salt-affected-soils\/more-information-on-salt-affected-soils\/en\/."},{"key":"ref_4","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_5","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_6","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_7","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_8","first-page":"64","article-title":"Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices","volume":"50","author":"Lhissou","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/S1002-0160(12)60049-6","article-title":"Model-Based Integrated Methods for Quantitative Estimation of Soil Salinity from Hyperspectral Remote Sensing Data: A Case Study of Selected South African Soils","volume":"22","author":"Mashimbye","year":"2012","journal-title":"Pedosphere"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/S1002-0160(10)60027-6","article-title":"A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data","volume":"20","author":"Weng","year":"2010","journal-title":"Pedosphere"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3823","DOI":"10.1109\/JSTARS.2014.2333535","article-title":"Soil salinity characterization using polarimetric InSAR coherence: Case studies in Tunisia and Morocco","volume":"8","author":"Barbouchi","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/TGRS.2003.813499","article-title":"Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image","volume":"41","author":"Shao","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0034-4257(91)90013-V","article-title":"Applications of microwave remote sensing of soil moisture for water resources and agriculture","volume":"35","author":"Engman","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Horikoshi, S., Schiffmann, R.F., Fukushima, J., and Serpone, N. (2018). Microwave Chemical and Materials Processing, Springer.","DOI":"10.1007\/978-981-10-6466-1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1109\/TGRS.2008.916220","article-title":"Effect of salinity on the dielectric properties of geological materials: Implication for soil moisture detection by means of radar remote sensing","volume":"46","author":"Lasne","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Bell, D., Menges, C., Bartolo, R., Ahmad, W., and VanZyl, J. (2001, January 9\u201313). A multistaged approach to mapping soil salinity in a tropical coastal environment using airborne SAR and Landsat TM data. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium, IGARSS\u201901, Sydney, NSW, Australia."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/S0304-3800(98)00095-7","article-title":"Fuzzy classification of JERS-1 SAR data: An evaluation of its performance for soil salinity mapping","volume":"111","author":"Metternicht","year":"1998","journal-title":"Ecol. Model."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2007.02.005","article-title":"Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)","volume":"110","author":"Farifteh","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8803","DOI":"10.3390\/rs70708803","article-title":"Monitoring soil salinization in Keriya River Basin, Northwestern China using passive reflective and active microwave remote sensing data","volume":"7","author":"Nurmemet","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nurmemet, I., Sagan, V., Ding, J.-L., Halik, \u00dc., Abliz, A., and Yakup, Z. (2018). A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10040598"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taghadosi, M.M., Hasanlou, M., and Eftekhari, K. (2018). Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. Int. J. Remote Sens., 1\u201316.","DOI":"10.1080\/01431161.2018.1512767"},{"key":"ref_23","first-page":"287","article-title":"Rapid integrated and ecosystem-based assessment of climate change vulnerability and adaptation for Ben Tre Province, Viet Nam","volume":"52","author":"Le","year":"2014","journal-title":"J. Sci. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1080\/00380768.2017.1413926","article-title":"Methane emission from rice cultivation in different agro-ecological zones of the Mekong river delta: Seasonal patterns and emission factors for baseline water management","volume":"64","author":"Vo","year":"2018","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10584-014-1113-4","article-title":"Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre Province, Mekong Delta","volume":"133","author":"Renaud","year":"2015","journal-title":"Clim. Chang."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.scitotenv.2016.09.182","article-title":"Dissolved reactive phosphorus played a limited role in phosphorus transport via runoff, throughflow and leaching on contrasting cropping soils from southwest Australia","volume":"577","author":"Sharma","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_28","unstructured":"ESA (2018, October 14). SENTINEL-1 SAR User Guide Introduction. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-1-sar."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2011.09.030","article-title":"Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements","volume":"120","author":"Rucci","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.asr.2017.05.034","article-title":"Sentinel-1A\u2014First precise orbit determination results","volume":"60","author":"Peter","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiang, H., Rusuli, Y., Amuti, T., and He, Q. (2018). Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network. Int. J. Remote Sens., 1\u201323.","DOI":"10.1080\/01431161.2018.1513180"},{"key":"ref_32","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. Inc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5991","DOI":"10.1109\/TGRS.2015.2430845","article-title":"Soil moisture retrieval using neural networks: Application to SMOS","volume":"53","author":"Aires","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_35","unstructured":"Haykin, S. (1998). Neural Networks: A Comprehensive Foundation, Prentice Hall. [2nd ed.]."},{"key":"ref_36","unstructured":"Witten, I.H., Frank, E., and Mark, A.H. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/JSTARS.2016.2641583","article-title":"Gaussian process sensitivity analysis for oceanic chlorophyll estimation","volume":"10","author":"Blix","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4899","DOI":"10.1109\/TGRS.2017.2687421","article-title":"Soil Moisture Estimation by SAR in Alpine Fields Using Gaussian Process Regressor Trained by Model Simulations","volume":"55","author":"Stamenkovic","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Tien Bui, D. (2018). Improving accuracy estimation of forest aboveground biomass based on incorporation of ALOS-2 PALSAR-2 and sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E. (2004). Gaussian processes in machine learning. Advanced Lectures on Machine Learning, Springer.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2016.10.009","article-title":"Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring","volume":"187","author":"Nutini","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_43","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley-Interscience."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1002\/2015JG003315","article-title":"Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data","volume":"122","author":"Garcia","year":"2017","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.measurement.2013.09.019","article-title":"A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm","volume":"47","author":"Zhu","year":"2014","journal-title":"Measurement"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1162\/089976602760128081","article-title":"Training v-support vector regression: Theory and algorithms","volume":"14","author":"Chang","year":"2002","journal-title":"Neural Comput."},{"key":"ref_50","unstructured":"Platt, J. (2019, January 10). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Available online: https:\/\/www.microsoft.com\/en-us\/research\/publication\/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines\/."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K., Welp, G., and Thiel, M. (2017). High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: A comparison of machine learning and multiple linear regression models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2018.04.023","article-title":"Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy","volume":"211","author":"Ferreira","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.scitotenv.2018.02.204","article-title":"High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia","volume":"630","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_56","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_57","unstructured":"Stewart, C. (2016, January 5\u20139). Exercise Sentinel-1 Processing, Course Materials. Proceedings of the 8th ESA Training Course on Radar and Optical Remote Sensing, Cesis, Latvia."},{"key":"ref_58","unstructured":"Foumelis, M. (2015, January 14\u201318). ESA Sentinel-1 Toolbox Generation of SAR Backscattering Mosaics, Course Materials. Proceedings of the 6th ESA Advanced Training Course on Land Remote Sensing, Bucharest, Romania."},{"key":"ref_59","first-page":"506","article-title":"Multi-temporal multi-spectral and radar remote sensing for agricultural monitoring in the Braila Plain","volume":"6","author":"Poenaru","year":"2015","journal-title":"Agric. Agric. Sci. Procedia"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening gamma: Radiometric terrain correction for SAR imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"12262","DOI":"10.1002\/2017GL075547","article-title":"Winter Sentinel-1 Backscatter as a Predictor of Spring Arctic Sea Ice Melt Pond Fraction","volume":"44","author":"Scharien","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Rizzoli, P., Bello, J.L.B., Pulella, A., Sica, F., and Zink, M. (2018, January 22\u201327). A Novel Approach to Monitor Deforestation in the Amazon Rainforest by Means of Sentinel-1 and Tandem-X Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518483"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bioresita, F., Puissant, A., Stumpf, A., and Malet, J.-P. (2018). A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020217"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.patcog.2013.04.001","article-title":"Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means","volume":"47","author":"Torres","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_65","unstructured":"Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D.B., Oimoen, M.J., Zhang, Z., Danielson, J.J., Krieger, T., Curtis, B., and Haase, J. (2011). ASTER Global Digital Elevation Model Version 2\u2014Summary of Validation Results."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1016\/j.mcm.2009.10.037","article-title":"A method of salt-affected soil information extraction based on a support vector machine with texture features","volume":"51","author":"Cai","year":"2010","journal-title":"Math. Comput. Model."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.geoderma.2015.08.039","article-title":"Study of an on-line measurement method for the salt parameters of soda-saline soils based on the texture features of cracks","volume":"263","author":"Ren","year":"2016","journal-title":"Geoderma"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.asoc.2017.06.030","article-title":"Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest","volume":"70","author":"Matin","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1198\/tast.2009.08199","article-title":"Variable importance assessment in regression: Linear regression versus random forest","volume":"63","year":"2009","journal-title":"Am. Stat."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ecolmodel.2007.05.011","article-title":"Random forests as a tool for ecohydrological distribution modelling","volume":"207","author":"Peters","year":"2007","journal-title":"Ecol. Model."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/LGRS.2017.2745049","article-title":"A systematic approach for variable selection with Random Forests: Achieving stable variable importance values","volume":"14","author":"Behnamian","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"10813","DOI":"10.3390\/rs61110813","article-title":"Modeling and mapping of soil salinity with reflectance spectroscopy and landsat data using two quantitative methods (PLSR and MARS)","volume":"6","author":"Nawar","year":"2014","journal-title":"Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.agsy.2014.09.002","article-title":"Land health surveillance and response: A framework for evidence-informed land management","volume":"132","author":"Shepherd","year":"2015","journal-title":"Agric. Syst."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bui, K.-T.T., Tien Bui, D., Zou, J., Van Doan, C., and Revhaug, I. (2016). A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput. Appl., 1\u201312.","DOI":"10.1007\/s00521-016-2666-0"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:25:16Z","timestamp":1760185516000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,11]]},"references-count":76,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020128"],"URL":"https:\/\/doi.org\/10.3390\/rs11020128","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,11]]}}}