{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:24:57Z","timestamp":1760149497328,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003702","name":"Korea Institute of Energy Research","doi-asserted-by":"publisher","award":["C3-2412"],"award-info":[{"award-number":["C3-2412"]}],"id":[{"id":"10.13039\/501100003702","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.<\/jats:p>","DOI":"10.3390\/rs15163965","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"3965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Real-Time Terrain Correction of Satellite Imagery-Based Solar Irradiance Maps Using Precomputed Data and Memory Optimization"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3199-0137","authenticated-orcid":false,"given":"Myeongchan","family":"Oh","sequence":"first","affiliation":[{"name":"Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea"}]},{"given":"Chang Ki","family":"Kim","sequence":"additional","affiliation":[{"name":"Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7963-3622","authenticated-orcid":false,"given":"Boyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea"}]},{"given":"Yongheack","family":"Kang","sequence":"additional","affiliation":[{"name":"Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-4157","authenticated-orcid":false,"given":"Hyun-Goo","family":"Kim","sequence":"additional","affiliation":[{"name":"Renewable Energy Big Data Laboratory, Korea Institute of Energy Research, Daejeon 34129, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, C.K., Kim, H.G., Kang, Y.H., Yun, C.Y., and Lee, Y.G. (2020). Intercomparison of satellite-derived solar irradiance from the GEO-KOMSAT-2A and HIMAWARI-8\/9 satellites by the evaluation with ground observations. Remote Sens., 12.","DOI":"10.3390\/rs12132149"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111371","DOI":"10.1016\/j.rse.2019.111371","article-title":"Estimating surface solar irradiance from satellites: Past, present, and future perspectives","volume":"233","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0038-092X(02)00122-6","article-title":"A new operational model for satellite-derived irradiances: Description and validation","volume":"73","author":"Perez","year":"2002","journal-title":"Sol. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/02693799508902046","article-title":"Topographic Solar-Radiation Models for Gis","volume":"9","author":"Dubayah","year":"1995","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/713811744","article-title":"Vectorial algebra algorithms for calculating terrain parameters from dems and solar radiation modelling in mountainous terrain","volume":"17","author":"Corripio","year":"2003","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1016\/j.renene.2010.05.011","article-title":"Solar resources estimation combining digital terrain models and satellite images techniques","volume":"35","author":"Bosch","year":"2010","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1016\/j.solener.2010.06.002","article-title":"Spatial disaggregation of satellite-derived irradiance using a high-resolution digital elevation model","volume":"84","author":"Cebecauer","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111239","DOI":"10.1016\/j.rse.2019.111239","article-title":"Assimilating remote sensing data into GIS-based all sky solar radiation modeling for mountain terrain","volume":"231","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113364","DOI":"10.1016\/j.rse.2022.113364","article-title":"Estimation of fine spatial resolution all-sky surface net shortwave radiation over mountainous terrain from Landsat 8 and Sentinel-2 data","volume":"285","author":"Ma","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.cpc.2008.01.048","article-title":"Fast clear-sky solar irradiation computation for very large digital elevation models","volume":"178","author":"Romero","year":"2008","journal-title":"Comput. Phys. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.renene.2018.03.068","article-title":"A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping","volume":"126","author":"Oh","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.procs.2012.04.039","article-title":"A fast GIS-tool to compute the maximum solar energy on very large terrains","volume":"9","author":"Tabik","year":"2012","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.cageo.2012.10.010","article-title":"GPU-based roofs\u2019 solar potential estimation using LiDAR data","volume":"52","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17212","DOI":"10.3390\/rs71215877","article-title":"Estimating roof solar energy potential in the downtown area using a GPU-accelerated solar radiation model and airborne LiDAR data","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Stendardo, N., Desthieux, G., Abdennadher, N., and Gallinelli, P. (2020). GPU-enabled shadow casting for solar potential estimation in large urban areas. Application to the solar cadaster of Greater Geneva. Appl. Sci., 10.","DOI":"10.3390\/app10155361"},{"key":"ref_16","unstructured":"Geraldi, E., Larosa, S., Romano, F., Cersosimo, A., Cimini, D., Di Paola, F., Gallucci, D., Gentile, S., Nilo, S.T., and Ricciardelli, E. (2017, January 27\u201329). The analysis of static boundary condition in solar resource assessment by satellite: The role of high-resolution Digital Terrain Model in irradiance downscaling process. Proceedings of the 4th International Conference on Energy & Meteorology (ICEM), Bari, Italy."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/14786450512331329556","article-title":"PV-GIS: A web-based solar radiation database for the calculation of PV potential in Europe","volume":"24","author":"Huld","year":"2005","journal-title":"Int. J. Sustain. Energy"},{"key":"ref_18","unstructured":"Suri, M., and Cebecauer, T. (2016). SolarGIS: New Web Based Service Offering Solar Radiation Data and Tools for Europe, North Africa and Middle East, International Solar Energy Society."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s41560-018-0318-6","article-title":"A simplified skyline-based method for estimating the annual solar energy potential in urban environments","volume":"4","author":"Calcabrini","year":"2019","journal-title":"Nat. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101387","DOI":"10.1016\/j.compenvurbsys.2019.101387","article-title":"A machine learning approach to modelling solar irradiation of urban and terrain 3D models","volume":"78","author":"Vartholomaios","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, S., Chen, N., Zhou, Q., Lin, T., and Li, H. (2022). A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span\u2014A Case Study over the Entirety of China. Remote Sens., 14.","DOI":"10.3390\/rs14071753"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.enconman.2015.04.052","article-title":"Solar radiation mapping using sunshine duration-based models and interpolation techniques: Application to Tunisia","volume":"101","author":"Chelbi","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","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_25","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.1007\/s00024-017-1578-y","article-title":"Toward Improved Solar Irradiance Forecasts: Comparison of the Global Horizontal Irradiances Derived from the COMS Satellite Imagery Over the Korean Peninsula","volume":"174","author":"Kim","year":"2017","journal-title":"Pure Appl. Geophys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.58986","article-title":"Rapid Calculation of Terin Parameters for Radiation Modeling From Digital Elevation Data","volume":"28","author":"Dozier","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Fu, P., and Rich, P. (1999, January 26\u201330). Design and implementation of the Solar Analyst: An ArcView extension for modeling solar radiation at landscape scales. Proceedings of the 19th Annual ESRI User Conference, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0038-092X(90)90055-H","article-title":"Modeling daylight availability and irradiance components from direct and global irradiance","volume":"44","author":"Perez","year":"1990","journal-title":"Sol. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Palz, W. (1996). European Solar Radiation Atlas. Volume II, Global and Diffuse Radiation on Vertical and Inclined Surfaces, Verlag T\u00dcV Rheinland GmbH.","DOI":"10.1007\/978-3-642-80237-9_15"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.rser.2014.08.060","article-title":"Modelling solar potential in the urban environment: State-of-the-art review","volume":"41","author":"Freitas","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_31","unstructured":"(2022, February 09). SciPy Documentation. Available online: https:\/\/docs.scipy.org\/doc\/scipy\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Oh, M., and Park, H.D. (2019). Optimization of solar panel orientation considering temporal volatility and scenario-based photovoltaic potential: A case study in Seoul National University. Energies, 12.","DOI":"10.3390\/en12173262"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112407","DOI":"10.1016\/j.rser.2022.112407","article-title":"Energy digital twin technology for industrial energy management: Classification, challenges and future","volume":"161","author":"Yu","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_34","first-page":"125","article-title":"(Max) A systematic review of a digital twin city: A new pattern of urban governance toward smart cities","volume":"6","author":"Deng","year":"2021","journal-title":"J. Manag. Sci. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"04019045","DOI":"10.1061\/(ASCE)ME.1943-5479.0000741","article-title":"Smart City Digital Twin-Enabled Energy Management: Toward Real-Time Urban Building Energy Benchmarking","volume":"36","author":"Francisco","year":"2020","journal-title":"J. Manag. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1109\/TSTE.2018.2858777","article-title":"Probabilistic Solar Irradiance Forecasting by Conditioning Joint Probability Method and Its Application to Electric Power Trading","volume":"10","author":"Kakimoto","year":"2019","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.solener.2023.04.063","article-title":"Reduced real lifetime of PV panels\u2014Economic consequences","volume":"259","author":"Libra","year":"2023","journal-title":"Sol. Energy"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3965\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:30:35Z","timestamp":1760128235000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":37,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15163965"],"URL":"https:\/\/doi.org\/10.3390\/rs15163965","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}