{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:11:12Z","timestamp":1774440672441,"version":"3.50.1"},"reference-count":106,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Space Applications Centre (SAC-ISRO)","award":["Hyd-01"],"award-info":[{"award-number":["Hyd-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical\u2013Vertical (VV) and Vertical\u2013Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH\/VV and VH\u2013VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11\u201320 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike\u2019s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.<\/jats:p>","DOI":"10.3390\/rs13193794","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T22:50:48Z","timestamp":1632351048000},"page":"3794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Machine Learning to Estimate Surface Roughness from Satellite Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6270-9355","authenticated-orcid":false,"given":"Abhilash","family":"Singh","sequence":"first","affiliation":[{"name":"Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1636-9622","authenticated-orcid":false,"given":"Kumar","family":"Gaurav","sequence":"additional","affiliation":[{"name":"Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-3188","authenticated-orcid":false,"given":"Atul Kumar","family":"Rai","sequence":"additional","affiliation":[{"name":"Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-3298","authenticated-orcid":false,"given":"Zafar","family":"Beg","sequence":"additional","affiliation":[{"name":"Fluvial Geomorphology and Remote Sensing Laboratory, Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/ngeo2868","article-title":"The global distribution and dynamics of surface soil moisture","volume":"10","author":"McColl","year":"2017","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"243","DOI":"10.2136\/sssaj1998.03615995006200010031x","article-title":"Surface roughness related processes of runoff and soil loss: A flume study","volume":"62","author":"Helming","year":"1998","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2166\/nh.2007.029","article-title":"Operational readiness of microwave remote sensing of soil moisture for hydrologic applications","volume":"38","author":"Wagner","year":"2007","journal-title":"Hydrol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Baghdadi, N., El Hajj, M., Choker, M., Zribi, M., Bazzi, H., Vaudour, E., Gilliot, J.M., and Ebengo, D.M. (2018). Potential of Sentinel-1 images for estimating the soil roughness over bare agricultural soils. Water, 10.","DOI":"10.3390\/w10020131"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alexakis, D.D., Mexis, F.D.K., Vozinaki, A.E.K., Daliakopoulos, I.N., and Tsanis, I.K. (2017). Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors, 17.","DOI":"10.3390\/s17061455"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"429","DOI":"10.13031\/2013.30167","article-title":"Effect of tillage on surface roughness","volume":"29","author":"Romkens","year":"1986","journal-title":"Trans. ASAE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1051\/agro:2000114","article-title":"Soil roughness and overland flow","volume":"20","author":"Govers","year":"2000","journal-title":"Agronomie"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.isprsjprs.2014.07.010","article-title":"Roughness measurements over an agricultural soil surface with Structure from Motion","volume":"96","author":"Snapir","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2013.08.030","article-title":"Estimation of soil surface roughness of agricultural soils using airborne LiDAR","volume":"140","author":"Turner","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hamze, M., Baghdadi, N., El Hajj, M.M., Zribi, M., Bazzi, H., Cheviron, B., and Faour, G. (2021). Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13112102"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"399","DOI":"10.5194\/soil-1-399-2015","article-title":"Soil surface roughness: Comparing old and new measuring methods and application in a soil erosion model","volume":"1","author":"Thomsen","year":"2015","journal-title":"Soil"},{"key":"ref_13","first-page":"527","article-title":"Soil roughness measurement: Chain method","volume":"48","author":"Saleh","year":"1993","journal-title":"J. Soil Water Conserv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1109\/TGRS.2003.810702","article-title":"Inversion of surface parameters from polarimetric SAR","volume":"41","author":"Hajnsek","year":"2003","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/JSTARS.2011.2116769","article-title":"Mapping soil moisture using RADARSAT-2 data and local autocorrelation statistics","volume":"4","author":"Merzouki","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Zheng, X., Li, L., Chen, S., Jiang, T., Li, X., and Zhao, K. (2019). Temporal evolution characteristics and prediction methods of spatial correlation function shape of rough soil surfaces. Soil Tillage Res., 195.","DOI":"10.1016\/j.still.2019.104417"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alijani, Z., Lindsay, J., Chabot, M., Rowlandson, T., and Berg, A. (2021). Sensitivity of C-Band SAR Polarimetric Variables to the Directionality of Surface Roughness Parameters. Remote Sens., 13.","DOI":"10.3390\/rs13112210"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gharechelou, S., Tateishi, R., and A Johnson, B. (2018). A Simple Method for the Parameterization of Surface Roughness from Microwave Remote Sensing. Remote Sens., 10.","DOI":"10.3390\/rs10111711"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.catena.2005.08.005","article-title":"Soil surface roughness measurement\u2014Methods, applicability, and surface representation","volume":"64","author":"Jester","year":"2005","journal-title":"Catena"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M.H., Crow, W.T., Dabrowska-Zielinska, K., Dadson, S., and Davidson, M.W. (2020). A roadmap for high-resolution satellite soil moisture applications\u2014Confronting product characteristics with user requirements. Remote Sens. Environ., 252.","DOI":"10.5194\/egusphere-egu21-10312"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Le Page, M., Jarlan, L., El Hajj, M.M., Zribi, M., Baghdadi, N., and Boone, A. (2020). Potential for the detection of irrigation events on maize plots using sentinel-1 soil moisture products. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-8588"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9364","DOI":"10.1029\/2018WR023337","article-title":"Synergies for soil moisture retrieval across scales from airborne polarimetric SAR, cosmic ray neutron roving, and an in situ sensor network","volume":"54","author":"Fersch","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.isprsjprs.2012.06.005","article-title":"Assessment of soil surface roughness statistics for microwave remote sensing applications using a simple photogrammetric acquisition system","volume":"72","author":"Marzahn","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2009.2013369","article-title":"Improved understanding of soil surface roughness parameterization for L-band passive microwave soil moisture retrieval","volume":"6","author":"Panciera","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","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_27","unstructured":"Oh, Y., Sarabandi, K., and Ulaby, F.T. (1994, January 8\u201312). An inversion algorithm for retrieving soil moisture and surface roughness from polarimetric radar observation. Proceedings of the IGARSS\u201994-1994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA."},{"key":"ref_28","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":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TGRS.2002.800232","article-title":"Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces","volume":"40","author":"Oh","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TGRS.2003.821065","article-title":"Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces","volume":"42","author":"Oh","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS013i002p00357","article-title":"Vegetation modeled as a water cloud","volume":"13","author":"Attema","year":"1978","journal-title":"Radio Sci."},{"key":"ref_32","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_33","unstructured":"Fung, A.K. (1994). Microwave Scattering and Emission Models and Their Applications, Artech House."},{"key":"ref_34","unstructured":"Shi, J., Wang, J., Hsu, A., O\u2019Neili, P., and Engman, E.T. (1995, January 10\u201314). Estimation of soil moisture and surface roughness parameters using L-band SAR measurements. Proceedings of the 1995 International Geoscience and Remote Sensing Symposium, IGARSS\u201995. Quantitative Remote Sensing for Science and Applications, Firenze, Italy."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/36.628792","article-title":"Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data","volume":"35","author":"Shi","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1080\/01431160110110974","article-title":"Potential of ERS and RADARSAT data for surface roughness monitoring over bare agricultural fields: Application to catchments in Northern France","volume":"23","author":"Baghdadi","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/TGRS.2006.873742","article-title":"Analysis of surface roughness heterogeneity and scattering behavior for radar measurements","volume":"44","author":"Zribi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","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_40","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TGRS.2017.2722468","article-title":"Fusing microwave and optical satellite observations to simultaneously retrieve surface soil moisture, vegetation water content, and surface soil roughness","volume":"55","author":"Sawada","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1080\/01431161.2020.1847353","article-title":"New empirical backscattering models for estimating bare soil surface parameters","volume":"42","author":"Mirmazloumi","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/S0034-4257(02)00069-X","article-title":"A new empirical model to retrieve soil moisture and roughness from C-band radar data","volume":"84","author":"Zribi","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/10106040701538157","article-title":"Retrieval of surface roughness using multi-polarized Envisat-1 ASAR data","volume":"23","author":"Srivastava","year":"2008","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ullmann, T., and Stauch, G. (2020). Surface Roughness Estimation in the Orog Nuur Basin (Southern Mongolia) Using Sentinel-1 SAR Time Series and Ground-Based Photogrammetry. Remote Sens., 12.","DOI":"10.3390\/rs12193200"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Azizi, A., Abbaspour-Gilandeh, Y., Mesri-Gundoshmian, T., Farooque, A.A., and Afzaal, H. (2021). Estimation of soil surface roughness using stereo vision approach. Sensors, 21.","DOI":"10.3390\/s21134386"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/36.602532","article-title":"A further study of the IEM surface scattering model","volume":"35","author":"Hsieh","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1029\/1999WR900033","article-title":"Multifrequency radar observations of bare surface soil moisture content: A laboratory experiment","volume":"35","author":"Mancini","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1130\/0091-7613(1987)15<204:SOTKRN>2.0.CO;2","article-title":"Shifting of the Kosi river, northern India","volume":"15","author":"Wells","year":"1987","journal-title":"Geology"},{"key":"ref_49","first-page":"429","article-title":"The great avulsion of Kosi on 18 August 2008","volume":"97","author":"Sinha","year":"2009","journal-title":"Curr. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sinha, R. (2014). The Kosi Megafan: The best-known Himalayan megafan. Landscapes and Landforms of India, Springer.","DOI":"10.1007\/978-94-017-8029-2_14"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"321","DOI":"10.5194\/esurf-3-321-2015","article-title":"Morphology of the Kosi megafan channels","volume":"3","author":"Gaurav","year":"2015","journal-title":"Earth Surface Dyn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.geomorph.2017.07.004","article-title":"A single width\u2014Discharge regime relationship for individual threads of braided and meandering rivers from the Himalayan Foreland","volume":"295","author":"Gaurav","year":"2017","journal-title":"Geomorphology"},{"key":"ref_53","unstructured":"NRSC (2021, March 25). District and Category Wise Distribution of Land Use and Land Cover in Bihar (2015\u20132016), Available online: https:\/\/bhuvan.nrsc.gov.in\/home\/index.php."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"197","DOI":"10.5194\/isprs-annals-IV-5-197-2018","article-title":"Analysis of the effect of incidence angle and moisture content on the penetration depth of L- and S-band SAR signals into the ground surface","volume":"4","author":"Singh","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Singh, A., Meena, G.K., Kumar, S., and Gaurav, K. (2019, January 9\u201315). Evaluation of the Penetration Depth of L-and S-Band (NISAR mission) Microwave SAR Signals into Ground. Proceedings of the 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), New Delhi, India.","DOI":"10.23919\/URSIAP-RASC.2019.8738217"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Singh, A., Gaurav, K., Meena, G.K., and Kumar, S. (2020). Estimation of soil moisture applying modified dubois model to Sentinel-1; a regional study from central India. Remote Sens., 12.","DOI":"10.3390\/rs12142266"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J.W., and Lang, M.W. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sens. Environ., 240.","DOI":"10.1016\/j.rse.2020.111664"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Martins, V.S., Barbosa, C.C.F., De Carvalho, L.A.S., Jorge, D.S.F., Lobo, F.d.L., and Novo, E.M.L.d.M. (2017). Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sens., 9.","DOI":"10.3390\/rs9040322"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D.P. (2017). A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_60","unstructured":"Jensen, J.R. (1996). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall Inc.. [2nd ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1515\/FREQ.2001.55.3-4.75","article-title":"Shuttle radar topography mission (SRTM) mission overview","volume":"55","author":"Werner","year":"2001","journal-title":"Frequenz"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1029\/EO081i048p00583","article-title":"Shuttle Radar Topography Mission produces a wealth of data","volume":"81","author":"Farr","year":"2000","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6218","DOI":"10.1002\/2017GL073661","article-title":"How robust are in situ observations for validating satellite-derived albedo over the dark zone of the Greenland Ice Sheet?","volume":"44","author":"Ryan","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Thakur, K.K., Vanderstichel, R., Barrell, J., Stryhn, H., Patanasatienkul, T., and Revie, C.W. (2018). Comparison of remotely-sensed sea surface temperature and salinity products with in situ measurements from British Columbia, Canada. Front. Mar. Sci., 5.","DOI":"10.3389\/fmars.2018.00121"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"22071","DOI":"10.1073\/pnas.1900654116","article-title":"Definitions, methods, and applications in interpretable machine learning","volume":"116","author":"Murdoch","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.jhydrol.2006.01.021","article-title":"Support vector regression for real-time flood stage forecasting","volume":"328","author":"Yu","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2018.03.008","article-title":"Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data","volume":"210","author":"Xiao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Kuter, S. (2021). Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression. Remote Sens. Environ., 255.","DOI":"10.1016\/j.rse.2021.112294"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.09.019","article-title":"Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China","volume":"218","author":"Ge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1016\/j.rse.2007.09.003","article-title":"Spacebased estimation of moisture transport in marine atmosphere using support vector regression","volume":"112","author":"Xie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2013.06.007","article-title":"Support vector regression and synthetically mixed training data for quantifying urban land cover","volume":"137","author":"Okujeni","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2015.01.001","article-title":"Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurements: A support vector machine approach","volume":"160","author":"Su","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s00703-021-00787-0","article-title":"Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction","volume":"133","author":"Malik","year":"2021","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.patrec.2017.04.013","article-title":"SK-SVR: Sigmoid kernel support vector regression based in-scale single image super-resolution","volume":"94","author":"Jebadurai","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1109\/TIP.2007.896644","article-title":"Image superresolution using support vector regression","volume":"16","author":"Ni","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.eswa.2017.12.004","article-title":"The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression","volume":"97","author":"Peng","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ghanem, K., Aparicio-Navarro, F.J., Kyriakopoulos, K.G., Lambotharan, S., and Chambers, J.A. (2017, January 6\u20137). Support vector machine for network intrusion and cyber-attack detection. Proceedings of the 2017 Sensor Signal Processing for Defence Conference (SSPD), London, UK.","DOI":"10.1109\/SSPD.2017.8233268"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_82","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., and Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, Available online: https:\/\/papers.nips.cc\/paper\/1996\/file\/d38901788c533e8286cb6400b40b386d-Paper.pdf."},{"key":"ref_83","unstructured":"Ittner, A., and Schlosser, M. (1996, January 2\u20134). Discovery of Relevant New Features by Generating Non-Linear Decision Trees. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1093\/bioinformatics\/btr300","article-title":"Classification with correlated features: Unreliability of feature ranking and solutions","volume":"27","author":"Lengauer","year":"2011","journal-title":"Bioinformatics"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Reed, R., and MarksII, R.J. (1999). Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, MIT Press.","DOI":"10.7551\/mitpress\/4937.001.0001"},{"key":"ref_86","first-page":"1341","article-title":"Feature selection with ensembles, artificial variables, and redundancy elimination","volume":"10","author":"Tuv","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_87","unstructured":"Kohavi, R., and Sommerfield, D. (2021, July 13). Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology, Available online: https:\/\/www.aaai.org\/Papers\/KDD\/1995\/KDD95-049.pdf."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"John, G.H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. Machine Learning Proceedings 1994, Morgan Kaufmann Publishers.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/TSMCB.2002.804363","article-title":"Orthogonal forward selection and backward elimination algorithms for feature subset selection","volume":"34","author":"Mao","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybernetics)"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Nguyen-Thoi, T., Ly, H.B., Nguyen, M.D., Al-Ansari, N., Tran, V.Q., and Le, T.T. (2020). Extreme learning machine based prediction of soil shear strength: A sensitivity analysis using Monte Carlo simulations and feature backward elimination. Sustainability, 12.","DOI":"10.3390\/su12062339"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1080\/10618600.2014.907095","article-title":"Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation","volume":"24","author":"Goldstein","year":"2015","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Ly, H.B., Le, T.T., Vu, H.L.T., Tran, V.Q., Le, L.M., and Pham, B.T. (2020). Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability, 12.","DOI":"10.3390\/su12072709"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"208253","DOI":"10.1109\/ACCESS.2020.3038645","article-title":"A machine learning approach to predict the average localization error with applications to wireless sensor networks","volume":"8","author":"Singh","year":"2020","journal-title":"IEEE Access"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Singh, A., Nagar, J., Sharma, S., and Kotiyal, V. (2021). A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Syst. Appl., 172.","DOI":"10.1016\/j.eswa.2021.114603"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"He, X., Zhao, K., and Chu, X. (2021). AutoML: A Survey of the State-of-the-Art. Knowl. Based Syst., 212.","DOI":"10.1016\/j.knosys.2020.106622"},{"key":"ref_98","unstructured":"Neill, S.P., and Hashemi, M.R. (2018). Fundamentals of Ocean Renewable Energy: Generating Electricity from the Sea, Elsevier Ltd.. Available online: https:\/\/www.elsevier.com\/books\/fundamentals-of-ocean-renewable-energy\/neill\/978-0-12-810448-4."},{"key":"ref_99","unstructured":"Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. arXiv."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.apsb.2018.09.010","article-title":"Deep learning for in vitro prediction of pharmaceutical formulations","volume":"9","author":"Yang","year":"2019","journal-title":"Acta Pharm. Sin. B"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Pham, H. (2019). A new criterion for model selection. Mathematics, 7.","DOI":"10.3390\/math7121215"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Vrieze, S.I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods, 17.","DOI":"10.1037\/a0027127"},{"key":"ref_103","unstructured":"Claeskens, G., and Hjort, N.L. (2008). Model Selection and Model Averaging, Cambridge Books, Cambridge University Press. Available online: https:\/\/ideas.repec.org\/b\/cup\/cbooks\/9780521852258.html."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s12524-013-0274-3","article-title":"Modified Dubois model for estimating soil moisture with dual polarized SAR data","volume":"41","author":"Rao","year":"2013","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Choker, M., Baghdadi, N., Zribi, M., El Hajj, M., Paloscia, S., Verhoest, N.E., Lievens, H., and Mattia, F. (2017). Evaluation of the Oh, Dubois and IEM backscatter models using a large dataset of SAR data and experimental soil measurements. Water, 9.","DOI":"10.3390\/w9010038"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2019.02.027","article-title":"Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery","volume":"225","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:03:09Z","timestamp":1760166189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,22]]},"references-count":106,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193794"],"URL":"https:\/\/doi.org\/10.3390\/rs13193794","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,22]]}}}