{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:50:56Z","timestamp":1771523456888,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministerio de Ciencia e Innovaci\u00f3n\u2014Gobierno de Espa\u00f1a","award":["PID2021-127089OB-I00"],"award-info":[{"award-number":["PID2021-127089OB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, spatial modeling is of particular relevance as it enables the understanding of the patterns and spatial variability of an event, the monitoring and prediction of the spatial behavior of a variable, the optimization of resources, and the evaluation of the impacts of a phenomenon of interest. Research carried out recently on variables related to solar energy budgets has been of special relevance due to its applications and developments in machine learning (ML) and deep learning (DL). These algorithms are crucial to improve the efficiency, precision, and applicability of remote sensing, allowing greater decision making with more reliable and timely data. Thus, this work proposes a systematic and rigorous methodology for searching research articles about the latest advances and contributions related to the modeling of radiative energy budgets using novel techniques and algorithms in some of the most relevant international scientific databases (Scopus, ScienceDirect, ResearchGate). Search parameters were applied using tracking methods through various filters, specific classifiers, and Boolean operators. The results allowed for an analysis of search trends and citations in the last 5 years related to the topic of interest and the number of most relevant articles for this research, analyzed through specialized metrics and graphs. Additionally, this methodology was classified into four categories of importance for refined and articulated searches in this evaluation: (i) according to the applied interpolation methods, (ii) according to the remote sensors used, (iii) according to the applications in different fields of knowledge. As a relevant fact and with an essentially prospective purpose, a subchapter of this review was dedicated to the latest advances and developments applied to (iv) spatial modeling of terrestrial radiation through ML, this method being a tool that opens multiple alternatives for data processing and analysis in the development and achievement of objectives in the field of geotechnologies. A quantitative comparison was conducted on the predictive performance results between the classification\/regression algorithms found in the studies explored in this review. The evaluation confirmed the existence of a persistent shortage of studies in recent years within the geotechnologies field, particularly concerning the comparison of spatial distribution modeling techniques developed and implemented through ML for incident solar and terrestrial radiation. Therefore, this work provides a synthesis and analysis of the most used and novel techniques in the modeling of solar energy budgets, their limitations, and biggest challenges.<\/jats:p>","DOI":"10.3390\/rs16162883","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:33:52Z","timestamp":1723030432000},"page":"2883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatial Models of Solar and Terrestrial Radiation Budgets and Machine Learning: A Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4308-9936","authenticated-orcid":false,"given":"Juli\u00e1n Guillermo","family":"Garc\u00eda Pedreros","sequence":"first","affiliation":[{"name":"Doctorate in Geotechnologies Applied to Construction, Energy and Industry, Higher Polytechnic School of Avila, University of Salamanca, C\/Hornos Caleros 50, 05003 \u00c1vila, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9427-3864","authenticated-orcid":false,"given":"Susana","family":"Lag\u00fcela L\u00f3pez","sequence":"additional","affiliation":[{"name":"Department of Cartographic and Land Engineering, Higher Polytechnic School of Avila, University of Salamanca, C\/Hornos Caleros 50, 05003 \u00c1vila, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9285-7834","authenticated-orcid":false,"given":"Manuel","family":"Rodr\u00edguez Mart\u00edn","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Higher Polytechnic School of Zamora, University of Salamanca, Av. Cardenal Cisneros 34, 49029 Zamora, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirkham, M.B. (2023). Solar radiation, black bodies, heat budget, and radiation balance. Principles of Soil and Plant Water Relations, Academic Press.","DOI":"10.1016\/B978-0-323-95641-3.00021-0"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Simons, S. (2016). Concepts of Chemical Engineering for Chemists, The Royal Society of Chemistry. [2nd ed.].","DOI":"10.1039\/9781839168680"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Simon, R.O., and H\u00fclsbergen, K.-J. (2021). Energy Balance and Energy Use Efficiency of Annual Bioenergy Crops in Field Experiments in Southern Germany. Agronomy, 11.","DOI":"10.3390\/agronomy11091835"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s10546-020-00529-6","article-title":"Surface-Energy-Balance Closure over Land: A Review","volume":"177","author":"Mauder","year":"2020","journal-title":"Bound. Layer Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"217","DOI":"10.4067\/S0716-078X2011000200008","article-title":"Modelos de distribuci\u00f3n de especies: Una revisi\u00f3n sint\u00e9tica","volume":"84","author":"Mateo","year":"2011","journal-title":"Rev. Chil. Hist. Nat."},{"key":"ref_6","unstructured":"Vald\u00e9s, J., and Londo\u00f1o, L. (2016). An\u00e1lisis y Modelamiento Espacial, Editorial Bonaventuriana."},{"key":"ref_7","first-page":"1","article-title":"Spatial distribution of hydrogen sulfite and ammonia emissions from a wastewater treatment plant in Costa Rica, using the AERMOD air pollution dispersion model","volume":"37","year":"2023","journal-title":"Uniciencia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e15220","DOI":"10.7717\/peerj.15220","article-title":"Spatial weed distribution models under climate change: A short review","volume":"11","author":"Tirado","year":"2023","journal-title":"PeerJ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s10661-022-10813-2","article-title":"Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils","volume":"195","author":"Budak","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.rser.2017.09.092","article-title":"Performance comparison of two global solar radiation models for spatial interpolation purposes","volume":"82","author":"Loghmari","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.solener.2017.09.057","article-title":"Spatiotemporal Interpolation and Forecast of Irradiance Data Using Kriging","volume":"158","author":"Jamaly","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/ce\/zkad075","article-title":"A hybrid machine-learning model for solar irradiance forecasting","volume":"8","author":"Almarzooqi","year":"2024","journal-title":"Clean Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"169992","DOI":"10.1016\/j.scitotenv.2024.169992","article-title":"A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations","volume":"914","author":"Han","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, G., Song, L., Zhao, L., and Tao, S. (2024). Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Estimated by Thermal-Infrared Remote Sensing. Remote. Sens., 16.","DOI":"10.20944\/preprints202401.0644.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"119868","DOI":"10.1016\/j.renene.2023.119868","article-title":"High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning","volume":"222","author":"Mongus","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111583","DOI":"10.1016\/j.ecolind.2024.111583","article-title":"Combining biodiversity and geodiversity on landscape scale: A novel approach using rare earth elements and spatial distribution models in an agricultural Mediterranean landscape","volume":"158","author":"Pelacani","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.envsoft.2016.06.005","article-title":"Importance of spatially distributed hydrologic variables for land use change modeling","volume":"83","author":"Wagner","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106347","DOI":"10.1016\/j.marenvres.2024.106347","article-title":"Spatial patterns of \u03b2-diversity under cumulative pressures in the Western Mediterranean Sea","volume":"195","author":"Pennino","year":"2024","journal-title":"Mar. Environ. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"127380","DOI":"10.1016\/j.jhydrol.2021.127380","article-title":"Spatial distribution of meteorological factors controlling stable isotopes in precipitation in Northern Chile","volume":"605","author":"Valdivielso","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103108","DOI":"10.1016\/j.healthplace.2023.103108","article-title":"Quality appraisal of spatial epidemiology and health geography research: A scoping review of systematic reviews","volume":"83","author":"Wood","year":"2023","journal-title":"Health Place"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107627","DOI":"10.1016\/j.jqsrt.2021.107627","article-title":"Characterization of temporal and spatial variability of aerosols from ground-based climatology: Towards evaluation of satellite mission requirements","volume":"268","author":"Chen","year":"2021","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119833","DOI":"10.1016\/j.jenvman.2023.119833","article-title":"For whom the bell tolls. A spatial analysis of the renewable energy transition determinants in Europe in light of the Russia-Ukraine war","volume":"352","author":"Gatto","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111378","DOI":"10.1016\/j.enpol.2020.111378","article-title":"The spatial economics of energy justice: Modelling the trade impacts of increased transport costs in a low carbon transition and the implications for UK regional inequality","volume":"140","author":"Olner","year":"2020","journal-title":"Energy Policy"},{"key":"ref_24","unstructured":"Reichelt, J., Henning, V., and Foeckler, P. (2024, August 06). Mendeley Reference Manager, Versi\u00f3n 2.120.0 [Computer Software]. Available online: https:\/\/www.mendeley.com."},{"key":"ref_25","first-page":"103196","article-title":"Upscaling of longwave downward radiation from instantaneous to any temporal scale: Algorithms, validation, and comparison","volume":"117","author":"Du","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2020EA001527","DOI":"10.1029\/2020EA001527","article-title":"A Machine Learning Technique for Spatial Interpolation of Solar Radiation Observations","volume":"8","author":"Leirvik","year":"2021","journal-title":"Earth Space Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s40808-020-00878-8","article-title":"Interpolation methods applied to the spatialization of monthly solar irradiation in a region of complex terrain in the state of Rio de Janeiro in the southeast of Brazil","volume":"7","author":"Pessanha","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_28","first-page":"195","article-title":"El riesgo de contaminaci\u00f3n por ozono en dos ciudades espa\u00f1olas (Madrid y Sevilla). Un estudio realizado con t\u00e9cnicas de modelado espacial y SIG","volume":"73","year":"2021","journal-title":"Geographicalia"},{"key":"ref_29","first-page":"102","article-title":"Interpolacion Regnie Para Lluvia y Temperatura En Las Regiones Andina, Caribe y Pac\u00edfica de Colombia","volume":"21","author":"Carrillo","year":"2018","journal-title":"Colomb. For."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ejrh.2018.02.002","article-title":"Spatial interpolation of climate variables in Northern Germany\u2014Influence of temporal resolution and network density","volume":"15","author":"Berndt","year":"2018","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.renene.2016.11.022","article-title":"A guideline to select an estimation model of daily global solar radiation between geostatistical interpolation and stochastic simulation approaches","volume":"103","author":"Jeong","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.solener.2017.05.024","article-title":"Mathematical interpolation methods for spatial estimation of global horizontal irradiation in Castilla-Le\u00f3n, Spain: A case study","volume":"151","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.proeng.2016.07.595","article-title":"Assessing the Performance of Several Rainfall Interpolation Methods as Evaluated by a Conceptual Hydrological Model","volume":"154","author":"Mendez","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.envsoft.2015.01.011","article-title":"Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka","volume":"67","author":"Plouffe","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1016\/j.egypro.2014.03.243","article-title":"Ground-measurement GHI Map for Qatar","volume":"49","author":"Bachour","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_36","unstructured":"Garcia Cueto, R., Santill\u00e1n-Soto, N., Haro, Z., Bojorquez, G., Ojeda-Benitez, S., and Quintero-Nu\u00f1ez, M. (2014). El Balance de Radiaci\u00f3n y Modelos de Radiaci\u00f3n Neta Para Diferentes Superficies: Estudio Experimental en Mexicali, M\u00e9xico, Spanish Association of Climate."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20382","DOI":"10.3390\/s141120382","article-title":"Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions","volume":"14","author":"Turrado","year":"2014","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wagner, P.D., Fiener, P., Wilken, F., Kumar, S., and Schneider, K. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol. 2012, 464\u2013465, 388\u2013400.","DOI":"10.1016\/j.jhydrol.2012.07.026"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"70","DOI":"10.2307\/26169706","article-title":"Interpolation of daily solar radiation for New Zealand using a satellite derived cloud cover surface","volume":"29","author":"Tait","year":"2009","journal-title":"Weather. Clim."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0038-092X(01)00009-3","article-title":"Spatial interpolation and estimation of solar irradiation by cumulative semivariograms","volume":"71","year":"2001","journal-title":"Sol. Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13717-020-00255-4","article-title":"Current and near-term advances in Earth observation for ecological applications","volume":"10","author":"Ustin","year":"2021","journal-title":"Ecol. Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"399","DOI":"10.3189\/172756402781817545","article-title":"ASTER measurement of supraglacial lakes in the Mount Everest region of the Himalaya","volume":"34","author":"Goudar","year":"2002","journal-title":"Ann. Glaciol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2003.10.018","article-title":"Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems","volume":"89","author":"Stow","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.geog.2021.11.005","article-title":"Consistency analysis of GRACE and GRACE-FO data in the study of global mean sea level change","volume":"13","author":"Chang","year":"2022","journal-title":"Geod. Geodyn."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ulsig, L., Nichol, C.J., Huemmrich, K.F., Landis, D.R., Middleton, E.M., Lyapustin, A.I., Mammarella, I., Levula, J., and Porcar-Castell, A. (2017). Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010049"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2020GL087978","DOI":"10.1029\/2020GL087978","article-title":"Impact of Coronavirus Outbreak on NO2 Pollution Assessed Using TROPOMI and OMI Observations","volume":"47","author":"Bauwens","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.asr.2011.03.009","article-title":"Clouds and Earth Radiant Energy System (CERES), a review: Past, present and future","volume":"48","author":"Smith","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1016\/j.rse.2011.01.013","article-title":"Comparison of two temperature differencing methods to estimate daily evapotranspiration over a Mediterranean vineyard watershed from ASTER data","volume":"115","author":"Galleguillos","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.ecolind.2017.08.014","article-title":"Spatial modelling provides a novel tool for estimating the landscape level distribution of greenhouse gas balances","volume":"83","author":"Parkkari","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.egypro.2016.10.029","article-title":"Spatial Modelling of Extreme Wind Speed Distributions in Switzerland","volume":"97","author":"Laib","year":"2016","journal-title":"Energy Procedia"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2015.10.026","article-title":"Topographic radiation modeling and spatial scaling of clear-sky land surface longwave radiation over rugged terrain","volume":"172","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.apenergy.2013.02.053","article-title":"Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China","volume":"107","author":"Pan","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_53","first-page":"10","article-title":"Elaboraci\u00f3n de un modelo de estimaci\u00f3n de la distribuci\u00f3n espacial de la radiaci\u00f3n solar global mensual para Chile central","volume":"1","author":"Schweitzer","year":"2007","journal-title":"Geography"},{"key":"ref_54","first-page":"171","article-title":"El cambio clim\u00e1tico y la distribuci\u00f3n espacial de las formaciones vegetales en Colombia","volume":"16","year":"2013","journal-title":"Colomb. For."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Edgar, T.W., and Manz, D.O. (2017). Machine Learning. Research Methods for Cyber Security, Elsevier.","DOI":"10.1016\/B978-0-12-805349-2.00006-6"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gr.2022.03.015","article-title":"Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge","volume":"109","author":"Zhang","year":"2022","journal-title":"Gondwana Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yeom, J.-M., Park, S., Chae, T., Kim, J.-Y., and Lee, C.S. (2019). 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. Sensors, 19.","DOI":"10.3390\/s19092082"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.renene.2018.10.066","article-title":"A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques","volume":"133","author":"Koo","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3216","DOI":"10.1016\/j.ifacol.2020.12.1092","article-title":"Spatial Estimation of Solar Radiation Using Geostatistics and Machine Learning Techniques","volume":"53","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s41651-020-00048-5","article-title":"Advances of Four Machine Learning Methods for Spatial Data Handling: A Review","volume":"4","author":"Du","year":"2020","journal-title":"J. Geovisualiz. Spat. Anal."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"67115","DOI":"10.1007\/s11356-022-20572-9","article-title":"Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models","volume":"29","author":"Kartal","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_63","first-page":"102323","article-title":"Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China","volume":"52","author":"Liao","year":"2022","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"109424","DOI":"10.1016\/j.agrformet.2023.109424","article-title":"Construction of a spatially gridded heat flux map based on airborne flux Measurements using remote sensing and machine learning methods","volume":"334","author":"Sun","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"160269","DOI":"10.1016\/j.scitotenv.2022.160269","article-title":"Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques","volume":"859","author":"Lu","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"113105","DOI":"10.1016\/j.rser.2022.113105","article-title":"Predicting surface solar radiation using a hybrid radiative Transfer\u2013Machine learning model","volume":"173","author":"Lu","year":"2023","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"101556","DOI":"10.1016\/j.uclim.2023.101556","article-title":"Evaluation of interpolation methods for the prediction of urban methane concentrations","volume":"49","author":"Stadler","year":"2023","journal-title":"Urban Clim."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"111","DOI":"10.4067\/S0718-07642016000400012","article-title":"Comparaci\u00f3n entre Interpoladores Espaciales en el Estudio de Distribuci\u00f3n de Part\u00edculas Sedimentables Insolubles en la Cuenca Atmosf\u00e9rica de Lima y Callao","volume":"27","author":"Chirinos","year":"2016","journal-title":"Inf. Tecnol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"100066","DOI":"10.1016\/j.solcom.2023.100066","article-title":"Machine learning for monitoring and classification in inverters from solar photovoltaic energy plants","volume":"9","author":"Pereira","year":"2024","journal-title":"Sol. Compass"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"104653","DOI":"10.1016\/j.scs.2023.104653","article-title":"Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework","volume":"96","author":"Li","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"112125","DOI":"10.1016\/j.rse.2020.112125","article-title":"Estimating heat storage in urban areas using multispectral satellite data and machine learning","volume":"252","author":"Hrisko","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_72","first-page":"103364","article-title":"Spatial+: A new cross-validation method to evaluate geospatial machine learning models","volume":"121","author":"Wang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"107653","DOI":"10.1016\/j.catena.2023.107653","article-title":"High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data","volume":"235","author":"Sharma","year":"2024","journal-title":"Catena"},{"key":"ref_74","first-page":"100943","article-title":"Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm","volume":"41","author":"Manivannan","year":"2024","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e20297","DOI":"10.1016\/j.heliyon.2023.e20297","article-title":"A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning","volume":"9","author":"Ajibade","year":"2023","journal-title":"Heliyon"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2883\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:29Z","timestamp":1760110289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2883"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":75,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16162883"],"URL":"https:\/\/doi.org\/10.3390\/rs16162883","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]}}}