{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T14:36:48Z","timestamp":1778855808412,"version":"3.51.4"},"reference-count":96,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at     V V     (    \u03c3  v v  \u2218    ) and     V H     (    \u03c3  v h  \u2218    ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the     \u03c3  v v  \u2218     was well correlated with SSM compared to the     \u03c3  v h  \u2218    , which showed more dispersion with correlation coefficients values (r) of about     0.84     and     0.61     for the     V V     and     V H     polarizations, respectively. Afterwards, these values of     \u03c3  v v  \u2218     were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about     \u2212 0.7     dB and     \u2212 1.2     dB and a root mean square (RMSE) of about     1.1     dB and     1.5     dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to     0.9     dB. Then, a classical inversion approach of     \u03c3  v v  \u2218     observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and     \u2212 0.13     vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.<\/jats:p>","DOI":"10.3390\/rs12010072","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T10:28:43Z","timestamp":1577183323000},"page":"72","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-6507","authenticated-orcid":false,"given":"Jamal","family":"Ezzahar","sequence":"first","affiliation":[{"name":"Ecole Nationale des Sciences Appliqu\u00e9es, Universit\u00e9 Cadi Ayyad, Safi B.P. 511-40000, Morocco"},{"name":"Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3203-5278","authenticated-orcid":false,"given":"Nadia","family":"Ouaadi","sequence":"additional","affiliation":[{"name":"Facult\u00e9 des Sciences Semlalia, Universit\u00e9 Cadi Ayyad, Marrakech B.P. 2410, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re, Universit\u00e9 de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamal","family":"Elfarkh","sequence":"additional","affiliation":[{"name":"Facult\u00e9 des Sciences et Techniques, Universit\u00e9 Cadi Ayyad, Marrakech B.P. 2410, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghizlane","family":"Aouade","sequence":"additional","affiliation":[{"name":"Facult\u00e9 des Sciences et Techniques, Universit\u00e9 Cadi Ayyad, Marrakech B.P. 2410, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"Khabba","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco"},{"name":"Facult\u00e9 des Sciences Semlalia, Universit\u00e9 Cadi Ayyad, Marrakech B.P. 2410, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8595-7949","authenticated-orcid":false,"given":"Salah","family":"Er-Raki","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco"},{"name":"Facult\u00e9 des Sciences et Techniques, Universit\u00e9 Cadi Ayyad, Marrakech B.P. 2410, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelghani","family":"Chehbouni","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco"},{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re, Universit\u00e9 de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6542-5793","authenticated-orcid":false,"given":"Lionel","family":"Jarlan","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re, Universit\u00e9 de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zeng, L., Hu, S., Xiang, D., Zhang, X., Li, D., Li, L., and Zhang, T. (2019). Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method. Remote Sens., 11.","DOI":"10.3390\/rs11030284"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture-climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth-Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1089\/ees.2005.22.9","article-title":"A review of soil moisture dynamics: From rainfall infiltration to ecosystem response","volume":"22","author":"Daly","year":"2005","journal-title":"Environ. Eng. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.1100217","article-title":"Regions of strong coupling between soil moisture and precipitation","volume":"305","author":"Koster","year":"2004","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1146\/annurev.earth.30.091201.140434","article-title":"Scaling of soil moisture: A hydrologic perspective","volume":"30","author":"Western","year":"2002","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/JSTARS.2009.2037163","article-title":"Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring","volume":"3","author":"Bolten","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.5194\/hess-18-2343-2014","article-title":"The suitability of remotely sensed soil moisture for improving operational flood forecasting","volume":"18","author":"Wanders","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.5194\/hess-19-1659-2015","article-title":"Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes","volume":"19","author":"Ryu","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.rse.2015.01.016","article-title":"Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables","volume":"160","author":"Wanders","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.5194\/hess-19-4275-2015","article-title":"Correction of real-time satellite pre-cipitation with satellite soil moisture observations","volume":"19","author":"Zhan","year":"2015","journal-title":"Earth Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.advwatres.2013.09.020","article-title":"Optimization of stomatal conductance for maximum carbon gain under dynamic soil moisture","volume":"62","author":"Manzoni","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"L10401","DOI":"10.1029\/2009GL037716","article-title":"Towards a Kalman Filter based soil moisture analysis system for the operational ECMWF Integrated Forecast System","volume":"36","author":"Drusch","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0168-1699(00)00184-8","article-title":"Measurement of soil water content and electrical conductivity by time domain reflectometry: A review","volume":"31","author":"Noborio","year":"2001","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4079","DOI":"10.5194\/hess-16-4079-2012","article-title":"COSMOS: The COsmic-ray Soil Moisture Observing System","volume":"16","author":"Zreda","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.5194\/hess-20-1269-2016","article-title":"Use of cosmic-ray neutron sensors for soil moisture monitoring in forests","volume":"20","author":"Blume","year":"2016","journal-title":"Earth Syst. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/0022-1694(70)90066-1","article-title":"The gravimetric method of soil moisture determination part I: A study of equipment and methodological problems","volume":"11","author":"Reynolds","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1002\/met.197","article-title":"Soil moisture modelling and validation at an agricultural site in Norfolk using the Met Office surface exchange scheme (MOSES)","volume":"18","author":"Kong","year":"2011","journal-title":"Meteorol. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1084669","DOI":"10.1080\/23312041.2015.1084669","article-title":"Present status of soil moisture estimation by microwave remote sensing","volume":"1","author":"Das","year":"2015","journal-title":"Cogent Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1080\/07038992.1996.10874632","article-title":"Potential of Synthetic Aperture Radar for Large-Scale Soil Moisture Monitoring: A Review","volume":"22","author":"Boisvert","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/36.739141","article-title":"Radar Backscatter Inversion Techniques for Estimation of Surface Soil Moisture: EFEDA-Spain and HAPEX-Sahel Case Studies","volume":"37","author":"Hoekman","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advwatres.2017.09.006","article-title":"Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms","volume":"109","author":"Karthikeyan","year":"2017","journal-title":"Adv. Water Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3390\/rs1030210","article-title":"Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques","volume":"1","author":"Barrett","year":"2009","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/JSTARS.2011.2169236","article-title":"A fusion approach to retrieve soil moisture with SAR and optical data","volume":"5","author":"Prakash","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1029\/WR016i006p00961","article-title":"Survey of methods for soil moisture determination","volume":"16","author":"Schmugge","year":"1980","journal-title":"Water Reours. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2018.04.013","article-title":"Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil","volume":"201","author":"Amazirh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.jhydrol.2012.10.044","article-title":"Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications","volume":"476","author":"Kornelsen","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products and future trends","volume":"83","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1109\/TGRS.2002.806994","article-title":"Soil moisture estimation from ERS\/SAR data: Toward an operational methodology","volume":"40","author":"Zribi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/S0034-4257(99)00102-9","article-title":"Estimation of watershed soil moisture index from ERS\/SAR data","volume":"72","author":"Quesney","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/S0034-4257(96)00145-9","article-title":"A comparison of soil moisture retrieval models using SIR-C measurements over the Little Washita River watershed","volume":"59","author":"Wang","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2005.04.005","article-title":"New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion","volume":"96","author":"Zribi","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/36.406677","article-title":"Measuring soil moisture with imaging radars","volume":"33","author":"Dubois","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0168-1923(00)00189-1","article-title":"Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland","volume":"105","author":"Moran","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.3390\/s7102458","article-title":"Operational mapping of soil moisture using synthetic aperture radar data: application to the Touch basin (France)","volume":"7","author":"Baghdadi","year":"2007","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1002\/hyp.6609","article-title":"Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling","volume":"22","author":"Baghdadi","year":"2008","journal-title":"Hydrol. Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.3390\/s8074213","article-title":"On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar","volume":"8","author":"Verhoest","year":"2008","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/36.134086","article-title":"An Empirical Model and an Inversion Technique for Radar Scattering from Bare Soil Surfaces","volume":"30","author":"Oh","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/36.134085","article-title":"Backscattering from a randomly rough dielectric surface","volume":"30","author":"Fung","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"W01418","DOI":"10.1029\/2004WR003905","article-title":"Skirvin, S. Comparison of four models to determine surface soil moisture from C-band radar imagery in a sparsely vegetated semiarid landscape","volume":"42","author":"Thoma","year":"2006","journal-title":"Water Resour. Res."},{"key":"ref_41","unstructured":"Petropoulos, G.P. (2017). Satellite Remote sensing of Surface Soil Moisture. Remote Sensing of Energy Fluxes Soil Moisture Content, Taylor and Francis Group."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/10106040701538157","article-title":"Retrieval of surface roughness using multi-polarized ENVISAT ASAR data","volume":"23","author":"Srivastava","year":"2008","journal-title":"Geocarto Int."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1029\/2004WR003608","article-title":"Applicability of statistical learning algorithms in groundwater quality modeling","volume":"41","author":"Khalil","year":"2005","journal-title":"Water Resour. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"W00B11","DOI":"10.1029\/2008WR006839","article-title":"A novel method to estimate model uncertainty using machine learning techniques","volume":"45","author":"Solomatine","year":"2009","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.5194\/hess-16-1607-2012","article-title":"Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks","volume":"16","author":"Baghdadi","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mirsoleimani, H.R., Sahebi, M.R., Baghdadi, N., and El Hajj, M. (2019). Bare Soil Surface Moisture Retrieval from Sentinel-1 SAR Data Based on the Calibrated IEM and Dubois Models Using Neural Networks. Sensors, 19.","DOI":"10.3390\/s19143209"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.advwatres.2009.10.008","article-title":"Estimating soil moisture using remote sensing data: A machine learning approach","volume":"33","author":"Ahmad","year":"2010","journal-title":"Adv. Water Resour."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"W08440","DOI":"10.1029\/2009WR007911","article-title":"Effective forecasting of hourly typhoon rainfall using support vector machines","volume":"45","author":"Lin","year":"2009","journal-title":"Water. Resour. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"W03413","DOI":"10.1029\/2008WR006855","article-title":"Using oceanic\u2013atmospheric oscillations for long lead time streamflow forecasting","volume":"45","author":"Kalra","year":"2009","journal-title":"Water. Resour. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/j.1752-1688.2002.tb01544.x","article-title":"Flood stage forecasting with support vector machines","volume":"38","author":"Liong","year":"2002","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jhydrol.2005.06.001","article-title":"Multi-time scale stream flow predictions: the support vector machines approach","volume":"318","author":"Asefa","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1109\/LGRS.2011.2156759","article-title":"Estimating Soil Moisture with the Support Vector Regression Technique","volume":"8","author":"Pasolli","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_56","unstructured":"Kecman, V. (2001). Learning and Soft Computing. A Bradford Book, The MIT Press."},{"key":"ref_57","first-page":"281","article-title":"Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing","volume":"Volume 9","author":"Mozer","year":"1997","journal-title":"Neural Information Processing Systems"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1111\/j.1752-1688.2006.tb04512.x","article-title":"Soil moisture prediction using support vector machines","volume":"42","author":"Gill","year":"2006","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_59","first-page":"401","article-title":"Soil moisture prediction using a support vector regression","volume":"14","author":"Lee","year":"2013","journal-title":"J. Korean Data Inf. Sci. Soc."},{"key":"ref_60","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration\u2014Guidelines for Computing Crop Water Requirements, Irrigation and Drain, FAO. Paper No. 56."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4879","DOI":"10.1080\/01431161.2015.1093198","article-title":"Remote sensing of water resources in semi-arid Mediterranean basins: The Joint International Laboratory TREMA","volume":"36","author":"Jarlan","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/11263500802710036","article-title":"Combining a Large Aperture Scintillometer and estimates of available energy to derive evapotranspiration over several agricultural fields in semi-arid regions","volume":"143","author":"Ezzahar","year":"2009","journal-title":"Plant Biosyst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/36.789635","article-title":"Polarimetric SAR speckle filtering and its implication for classification","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02757259409532206","article-title":"Speckle filtering of synthetic aperture radar images: A review","volume":"8","author":"Lee","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/LGRS.2010.2050054","article-title":"Semiempirical calibration of the integral equation model for SAR data in C-band and cross polarization using radar images and field measurements","volume":"8","author":"Baghdadi","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1080\/01431160600658123","article-title":"Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Choker, M., Baghdadi, N., Zribi, M., El Hajj, M., Paloscia, S., Verhoest, N., Lievens, H., and Mattia, F. (2017). Evaluation of the Oh, Dubois and IEM models using large dataset of SAR signal and experimental soil measurements. Water, 9.","DOI":"10.3390\/w9010038"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2005.01.008","article-title":"Potential of ASAR\/ENVISAT for the characterization of soil surface parameters over bare agricultural fields","volume":"96","author":"Holah","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1080\/01431160500239032","article-title":"Soil moisture estimation using multi-incidence and multi-polarization ASAR SAR data","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"256","DOI":"10.3390\/s8010256","article-title":"Soil Moisture Profile Effect on Radar Signal Measurement","volume":"8","author":"Zribi","year":"2008","journal-title":"Sensors"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1016\/j.rse.2011.02.021","article-title":"Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust","volume":"115","author":"Aubert","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Eweys, O.A., Escorihuela, M.J., Villar, J.M., Er-Raki, S., Amazirh, A., Olivera, L., Jarlan, L., Khabba, S., and Merlin, O. (2017). Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical\/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco. Remote Sens., 9.","DOI":"10.3390\/rs9111155"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, D., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., and Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.20944\/preprints201810.0453.v1"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Baghdadi, N., El Hajj, M., Zribi, M., and Bousbih, S. (2017). Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sens., 9.","DOI":"10.3390\/rs9090969"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., and Mougenot, B. (2017). Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters. Sensors, 17.","DOI":"10.3390\/s17112617"},{"key":"ref_77","first-page":"1","article-title":"Comparative evaluation of the sensitivity of multi-polarised Sar and optical data for various land cover","volume":"4","author":"Chauhan","year":"2016","journal-title":"Int. J. Adv. Remote Sens. GIS Geogr."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., and Baghdadi, N. (2017). Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_79","unstructured":"Karjalainen, M., Harri, K., Hyypp\u00e4, J., Laurila, H., and Kuittinen, R. (2004, January 12\u201323). The use of Envisatalternating polarization Sar images in agricultural monitoring in comparison with Radarsat-1 Sar images. Proceedings of the ISPRS Congress, Istanbul, Turkey."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1080\/014311697219330","article-title":"Effect of surface soil moisture gradients on modelling radar backscattering from bare fields","volume":"18","author":"Boisvert","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"261","DOI":"10.5721\/EuJRS20164915","article-title":"Assessment of Different Backscattering Models for Bare Soil Surface Parameters Estimation from SAR Data in band C, L and P","volume":"49","author":"MirMazloumi","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1080\/01431160310001654392","article-title":"Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements","volume":"25","author":"Baghdadi","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"13626","DOI":"10.3390\/rs71013626","article-title":"Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering","volume":"7","author":"Baghdadi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4966","DOI":"10.1109\/TGRS.2013.2286203","article-title":"Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR","volume":"52","author":"Panciera","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Bai, X., He, B., Li, X., Zeng, J., Wang, X., Wang, Z., Zeng, Y., and Su, Z. (2017). First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau. Remote Sens., 9.","DOI":"10.3390\/rs9070714"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ghafouri, A., Amini, J., Dehmollaian, M., and Kavoosi, M.A. (2017). Better Estimated IEM Input Parameters Using Random Fractal Geometry Applied on Multi-Frequency SAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9050445"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1080\/01431160110107671","article-title":"An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils","volume":"23","author":"Baghdadi","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.1080\/014311600750019994","article-title":"Relationship between profile length and roughness variables for natural surfaces","volume":"21","author":"Baghdadi","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5443","DOI":"10.1080\/01431161.2010.502154","article-title":"Statistical properties of soil moisture images derived from Radarsat-1 SAR data","volume":"32","author":"Merzouki","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1016\/j.jhydrol.2017.10.048","article-title":"Retrieving topsoil moisture using RADARSAT-2 data, a novel approach applied at the east of the Netherlands","volume":"555","author":"Eweys","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2008.2009642","article-title":"Potential of estimating soil moisture under vegetation cover by means of PolSAR","volume":"47","author":"Hajnsek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2010.07.011","article-title":"Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data","volume":"151","author":"Gherboudj","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.rse.2006.10.026","article-title":"Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data","volume":"112","author":"Rahman","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_94","first-page":"2041","article-title":"Soil moisture inversion from ERS and SIR-C imagery at the Zwalm catchment, Belgium","volume":"5","author":"Verhoest","year":"2000","journal-title":"IEEE Proc. Int. Geosci. Remote Sens. Symp."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.01.027","article-title":"Soil moisture retrieval over irrigated grassland using X-band SAR data","volume":"176","author":"Baghdadi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_96","unstructured":"Ait Hssaine, B., Merlin, O., Ezzahar, J., Ojha, N., Er-Raki, S., and Khabba, S. An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data. Hydrol. Earth Syst. Sci., (in revision)."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/72\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:19Z","timestamp":1760190319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"references-count":96,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010072"],"URL":"https:\/\/doi.org\/10.3390\/rs12010072","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]}}}