{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:03Z","timestamp":1776274503915,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,5]],"date-time":"2019-05-05T00:00:00Z","timestamp":1557014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010700","name":"National Institute of Environmental Research","doi-asserted-by":"publisher","award":["NIER-2019-01-01-027"],"award-info":[{"award-number":["NIER-2019-01-01-027"]}],"id":[{"id":"10.13039\/501100010700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003719","name":"Korea Aerospace Research Institute","doi-asserted-by":"publisher","award":["FR19920"],"award-info":[{"award-number":["FR19920"]}],"id":[{"id":"10.13039\/501100003719","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data.<\/jats:p>","DOI":"10.3390\/s19092082","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"2082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data Provided by the COMS MI Geostationary Satellite: A Case Study in South Korea"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2321-731X","authenticated-orcid":false,"given":"Jong-Min","family":"Yeom","sequence":"first","affiliation":[{"name":"Satellite Application Division, Korea Aerospace Research Institute, 115 Gwahangno Yuseong-gu, Daejeon 34133, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-5779","authenticated-orcid":false,"given":"Seonyoung","family":"Park","sequence":"additional","affiliation":[{"name":"Satellite Application Division, Korea Aerospace Research Institute, 115 Gwahangno Yuseong-gu, Daejeon 34133, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taebyeong","family":"Chae","sequence":"additional","affiliation":[{"name":"Satellite Application Division, Korea Aerospace Research Institute, 115 Gwahangno Yuseong-gu, Daejeon 34133, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"New and Renewable Energy Resource &amp; Policy Center, Korea Institute of Energy Research, 152 Gajeong-ro Yuseong-gu, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Suk","family":"Lee","sequence":"additional","affiliation":[{"name":"Environmental Satellite Center, National Institute of Environmental Research, 42, Hwangyeong-ro, Seogu, Incheon 22689, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0304-3800(81)90011-9","article-title":"Calculating solar radiation for ecological studies","volume":"14","author":"Brock","year":"1981","journal-title":"Ecol. Model."},{"key":"ref_2","first-page":"2321","article-title":"A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.","volume":"12","author":"Zhou","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"433","DOI":"10.2166\/nh.2011.074","article-title":"Synthesis of incoming shortwave radiation for hydrological simulation","volume":"42","author":"Shook","year":"2011","journal-title":"Hydrol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3354\/cr015033","article-title":"Variation among solar radiation data sets for the Eastern US and its effects on predictions of forest production and water yield","volume":"15","author":"Aber","year":"2000","journal-title":"Clim. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.atmosres.2017.09.006","article-title":"Estimation solar radiation using NOAA\/AVHRR and ground measurement data","volume":"199","author":"Fallahi","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4975","DOI":"10.1002\/2015JD023097","article-title":"An efficient physically based parameterization to derived surface solar irradiance based on satellite atmospheric products","volume":"120","author":"Qin","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5389","DOI":"10.1080\/01431160410001705024","article-title":"Application Meteosat derived meteorological information for crop yield prediction in Europe","volume":"25","author":"Roebeling","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.jaridenv.2004.07.016","article-title":"Desert vegetation and timing of solar radiation","volume":"60","author":"Walton","year":"2005","journal-title":"J. Arid Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Paulescu, M., Paulescu, E., Gravila, P., and Badescu, V. (2013). Solar radiation measurements. Weather Modeling and Forecasting of PV Systems Operation. Green Energy and Technology, Springer.","DOI":"10.1007\/978-1-4471-4649-0"},{"key":"ref_10","unstructured":"Beyer, F., Polo, J., Suri, M., Torres, J.L., Lorenz, E., Muller, S., Hoyer-Klick, C., and Ineichen, P. (2019, February 17). Report on Benchmarking of Radiation Products. Available online: https:\/\/www.researchgate.net\/publication\/265362324_Report_on_Benchmarking_of_Radiation_Products."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Badescu, V. (2008). Validation and ranking methodologies for solar radiation models. Modeling Solar Radiation at the Earth\u2019s Surface, Springer.","DOI":"10.1007\/978-3-540-77455-6"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.foreco.2012.03.022","article-title":"Reconciling satellite with ground data to estimate forest productivity at national scales","volume":"276","author":"Hasenauer","year":"2012","journal-title":"Forest Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1016\/j.agrformet.2009.03.005","article-title":"A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain","volume":"149","author":"Alsamamra","year":"2009","journal-title":"Agr. Forest Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.3390\/s7112763","article-title":"Statistical modeling of spatio-temporal variability in monthly average daily solar radiation over Turkey","volume":"7","author":"Evrendilek","year":"2007","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1039\/b914121a","article-title":"Assessment of renewable energy potential through satellite data and numerical models","volume":"2","author":"Tapiador","year":"2009","journal-title":"Energy Environ. Sci."},{"key":"ref_16","unstructured":"Liang, S., Li, X., and Wang, J. (2012). Atmospheric Correction of Optical Imagery. Advanced Remote Sensing: Terrestrial Information Extraction and Application, Academic Press. [1st ed.]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"D18102","DOI":"10.1029\/2009JD013457","article-title":"An algorithm for estimating downward shortwave radiation from GMS 5 visible imagery and its evaluation over China","volume":"115","author":"Lu","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10872-005-0021-7","article-title":"Validation and improvement of satellite-derived surface solar radiation over the northwestern Pacific Ocean","volume":"61","author":"Kawai","year":"2005","journal-title":"J. Oceanogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6193","DOI":"10.1080\/01431160802175421","article-title":"Neural network determination of cloud attenuation to estimate insolation using MTSAT-1R data","volume":"29","author":"Yeom","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u03bcS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1002\/jgrd.50194","article-title":"Retrieval of validation of global, direct, and diffuse irradiance derived from SEVIRI satellite observations","volume":"118","author":"Greuell","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Romano, F., Cimini, D., Cersosimo, A., Paola, F.D., Gallucci, D., Gentle, S., Geraldi, E., Larosa, S., Nilo, S.T., Ricciardelli, E., Ripepi, E., and Viggiano, M. (2018). Improvement in surface solar irradiance estimation using HRV\/MSG data. Remote Sens., 10.","DOI":"10.3390\/rs10081288"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/S0034-4257(00)00183-8","article-title":"A system to distribute satellite incident solar radiation in real-time","volume":"75","author":"Tanahashi","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.enconman.2009.03.035","article-title":"ANN-based modeling and estimation of daily global solar radiation data: A case study","volume":"50","author":"Benghanem","year":"2009","journal-title":"Energy Convers. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14823","DOI":"10.1038\/s41598-017-13761-x","article-title":"A solar radiation database for Chile","volume":"7","author":"Molina","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_26","first-page":"32141","article-title":"Estimating global solar radiation using common meteorological data in Akure, Nigeria","volume":"103","author":"Adaramola","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.rser.2012.12.043","article-title":"Empirical models for estimating global solar radiation: A review and case study","volume":"21","author":"Besharat","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Muneer, T., Gueymard, C., and Kambezidis, H. (2004). Solar Radiation and Daylight Models, Elsevier. [2nd ed.].","DOI":"10.1016\/B978-075065974-1\/50016-4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/j.rser.2017.08.037","article-title":"Comparison of deterministic and data-driven models for solar radiation estimation in China","volume":"81","author":"Qin","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0022-1694(00)00214-6","article-title":"Daily reservoir inflow forecasting using artificial neural networks with stopped training approach","volume":"230","author":"Coulibaly","year":"2000","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.agrformet.2015.10.011","article-title":"Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions","volume":"216","author":"Park","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.egypro.2012.05.064","article-title":"Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria","volume":"18","author":"Hasni","year":"2012","journal-title":"Energy Procedia"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1080\/00207179208934317","article-title":"Neural Networks for Nonlinear Dynamic System Modeling and Identification","volume":"56","author":"Chen","year":"1992","journal-title":"Int. J. Control"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.apenergy.2016.01.130","article-title":"A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset","volume":"168","author":"Deo","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/j.rser.2017.01.114","article-title":"Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland","volume":"72","author":"Deo","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","article-title":"Machine learning methods for solar radiation forecasting: A review","volume":"105","author":"Voyant","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.enconman.2016.03.082","article-title":"Prediction of daily and mean monthly global solar raisdiation using support vector machine in an arid climate","volume":"118","author":"Belaid","year":"2016","journal-title":"Energy Convers. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.ijar.2008.11.006","article-title":"Semantic hashing","volume":"50","author":"Salakhutdinov","year":"2009","journal-title":"Int. J. Approx. Reason."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Huang, W., Hong, Z., and Meng, L. (2017). Upscaling of surface soil moisture using a deep learning model with VIIRS RDR. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050130"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ciresan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE conference on computer vision and pattern recognition (CVPR), Washington, DC, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1109\/TPAMI.2012.272","article-title":"Deep hierarchies in primate visual cortex: What can we learn for computer vision?","volume":"35","author":"Kruger","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mch. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1162\/neco.2010.08-09-1081","article-title":"Deep belief networks are compact universal approximators","volume":"22","author":"LeRoux","year":"2010","journal-title":"Neural Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2629","DOI":"10.1162\/neco.2008.12-07-661","article-title":"Deep, narrow sigmoid belief networks are universal approximators","volume":"20","author":"Sutskever","year":"2008","journal-title":"Neural Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2013.10.026","article-title":"Improving the accuracy of rainfall rates from optical satellite sensors with machine learning\u2013A random forests-based approach applied to MSG SEVIRI","volume":"141","author":"Kuhnlein","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lee, J., Im, J., Kim, K., and Quackenbush, L.J. (2018). Machine learning approaches for estimating forest stand height using plot-based observations and airborne LiDAR data. Forest, 9.","DOI":"10.3390\/f9050268"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1029\/94JD00483","article-title":"A simple hydrologically based model of land surface water and energy fluxes for general circulation models","volume":"99","author":"Liang","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_50","unstructured":"Skamarock, W., Klemp., J., Dudhia, J., Gill, D., Duda, M., Huang, X.-Y., Wang, W., and Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research. Available online: https:\/\/opensky.ucar.edu\/islandora\/object\/technotes%3A500\/datastream\/PDF\/view."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/BF02742448","article-title":"Estimation of insolation over the Pacific Ocean off the Sanriku Coast","volume":"54","author":"Kawamura","year":"1998","journal-title":"J. Oceanogr."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s13143-012-0011-9","article-title":"Evaluation on penetration rate of cloud for incoming solar radiation using geostationary satellite data","volume":"48","author":"Yeom","year":"2012","journal-title":"Asia-Pac. J. Atmos. Sci."},{"key":"ref_53","unstructured":"KMA (2009). Development of Meteorological Data Processing System for Communication, Ocean and Meteorological Satellite (ATBD), Korea Meteorological Agency."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4834579","DOI":"10.1155\/2016\/4834579","article-title":"Solar radiation received by slopes using COMS imagery, a physically based radiation model, and GLOBE","volume":"2016","author":"Yeom","year":"2016","journal-title":"J. Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"32141","DOI":"10.1029\/1998JD200032","article-title":"Discriminating clear sky from clouds with MODIS","volume":"103","author":"Ackerman","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4541","DOI":"10.1080\/01431160310001657533","article-title":"Neural network estimation of air temperatures from AVHRR data","volume":"25","author":"Jang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.cageo.2009.08.012","article-title":"Improved estimation of surface Solar Insolation using neural network and MTSAT-1R data","volume":"36","author":"Yeom","year":"2010","journal-title":"Comput. Geosci. UK"},{"key":"ref_58","unstructured":"Bertsekas, D.P., and Tsitsiklis, J.N. (1996). Neuro-Dynamic Programing, Athena Scientific."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1090\/qam\/10666","article-title":"A method for the solution of certain problems in least squares","volume":"2","author":"Levenberg","year":"1944","journal-title":"Q. Appl. Math."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1137\/0111030","article-title":"An algorithm for least-squares estimation of nonlinear parameters","volume":"11","author":"Marquardt","year":"1963","journal-title":"J. Soc. Ind. Appl. Math."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/S0893-6080(98)00010-0","article-title":"Automatic early stopping using cross validation: Quantifying the criteria","volume":"11","author":"Prechelt","year":"1997","journal-title":"Neural Netw."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.enconman.2014.12.015","article-title":"Support vector regression based prediction of global solar radiation on a horizontal surface","volume":"91","author":"Mohammadi","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.infrared.2014.12.006","article-title":"Prediction of the solar radiation on the Earth using support vector regression technique","volume":"68","author":"Piri","year":"2015","journal-title":"Infrared. Phys. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7763\/IJCTE.2009.V1.9","article-title":"Atmospheric temperature prediction using support vector machines","volume":"1","author":"Radhika","year":"2009","journal-title":"Int. J. Comput. Theor. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.rser.2014.07.108","article-title":"Potential of radial basis function based support vector regression for global solar radiation prediction","volume":"39","author":"Ramedani","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4052","DOI":"10.1016\/j.eswa.2010.09.067","article-title":"Short term wind speed prediction based on evolutionary support vector regression algorithms","volume":"38","author":"Prieto","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Jang, E., Im, J., Park, G.H., and Park, Y.G. (2017). Estimation of fugacity of carbon dioxide in the East Sea using in situ measurements and Geostationary Ocean Color Imager satellite data. Remote Sens., 9.","DOI":"10.3390\/rs9080821"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/15481603.2015.1026049","article-title":"An object-based SVM method incorporating optimal segmentation scale estimation using Bhattacharyya Distance for mapping salt cedar (Tamarisk spp.) with QuickBird imagery","volume":"52","author":"Xun","year":"2015","journal-title":"Gisci. Remote Sens."},{"key":"ref_71","unstructured":"(2019, May 01). MathWorks. Available online: http:\/\/mathworks.com\/help\/stats\/fitsvm.html."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosenv.2016.11.066","article-title":"Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches","volume":"151","author":"Brokamp","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Kayri, M., Kayri, I., and Gencoglu, M.T. (2017, January 1\u20132). The performance comparison of multiple linear regression, random forest and artificial neural network by using photovoltaic and atmospheric data. Proceedings of the 14th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania.","DOI":"10.1109\/EMES.2017.7980368"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Kim, M., Im, J., Park, H., Park, S., Lee, M.I., and Ahn, J.H. (2017). Detection of tropical overshooting cloud tops using Himawari-8 imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070685"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Higgins, M., and Chadwik, K.D. (2014). A tale of two \u201cforests\u201d: Random forest machine learning aids tropical forest carbon mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085993"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Sim, S., Im, J., Park, S., Park, H., Ahn, M.H., and Chan, P.W. (2018). Icing detection over East Asia from geostationary satellite data using machine learning approaches. Remote Sens., 10.","DOI":"10.3390\/rs10040631"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_79","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_80","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","article-title":"Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea","volume":"56","author":"Lee","year":"2019","journal-title":"Gisci. Remote Sens."},{"key":"ref_81","unstructured":"(2019, May 01). H2O AI\/h2o-tutorials. Available online: https:\/\/github.com\/h2oai\/h2o-tutorials."},{"key":"ref_82","unstructured":"Arora, A., Candel, A., Lanford, J., Ledell, E., and Parmar, V. (2015). Deep Learning with H2O, H2O.ai, Inc."},{"key":"ref_83","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing System 25 (NIPS2012), Lake Tahoe, NV, USA."},{"key":"ref_84","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectifier linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_85","unstructured":"Cook, D. (2016). Practical Machine Learning with H2O, O\u2019Reilly."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1021\/ci025626i","article-title":"Assessing model fit by cross-validation","volume":"43","author":"Hawkins","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2013, January 29\u201331). Deep learning of representations: Looking forward. Proceedings of the International Conference on Statistical Language and Speech Processing, Berlin, Germany.","DOI":"10.1007\/978-3-642-39593-2_1"},{"key":"ref_88","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/0034-4257(94)00069-Y","article-title":"A review of satellite methods to derive surface shortwave irradiance","volume":"51","author":"Pinker","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Park, S., Seo, E., Kang, D., Im, J., and Lee, M. (2018). Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia. Remote Sens., 10.","DOI":"10.3390\/rs10111811"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1007\/s12665-016-5917-6","article-title":"Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches","volume":"75","author":"Im","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"MOD4-1","DOI":"10.1029\/2001GL013252","article-title":"MODIS cloud screening for remote sensing of aerosols over oceans using spatial variability","volume":"29","author":"Martins","year":"2002","journal-title":"Geophys. Res. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.rse.2017.09.021","article-title":"MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5 km resolution from 2000","volume":"204","author":"Ryu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.5194\/acp-16-2543-2016","article-title":"Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data","volume":"16","author":"Tang","year":"2016","journal-title":"Atmos. Chem. Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2082\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:49:14Z","timestamp":1760186954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,5]]},"references-count":94,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19092082"],"URL":"https:\/\/doi.org\/10.3390\/s19092082","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,5]]}}}