{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:32:01Z","timestamp":1760236321411,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002491","name":"Hansung University","doi-asserted-by":"publisher","award":["2021020244"],"award-info":[{"award-number":["2021020244"]}],"id":[{"id":"10.13039\/501100002491","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT))","award":["2021R1F1A1045923"],"award-info":[{"award-number":["2021R1F1A1045923"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The majority of cloud applications are created or delivered to provide users with access to system resources or prebuilt processing algorithms for efficient data storage, management, and production. The number of cases linking cloud computing to the use of global observation satellite data continues to rise, owing to the benefits of cloud computing. This study aims to develop a cloud software as a service (SaaS) that yields reflectance products in high-resolution Korea Multi-Purpose Satellite (KOMPSAT)-3\/3A satellite images. The SaaS model was designed as three subsystems: a Calibration Processing System (CPS), a Request System for CPS supporting RESTful application programming interface (API), and a Web Interface Application System. Open-source components, libraries, and frameworks were used in this study\u2019s SaaS, including an OpenStack for infrastructure as a service. An absolute atmospheric correction scheme based on a Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code with atmospheric variable inputs was used to generate the top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The SaaS implemented in this study provides users with the absolute atmospheric calibration functionality to apply their KOMPSAT-3\/3A satellite image set through a web browser and obtain output directly from this service. According to experiments to check the total performance time for images, bundled with four bands of red, green, blue, and near-infrared, it took approximately 4.88 min on average for the execution time to obtain all reflectance results since satellite images were registered into the SaaS. The SaaS model proposed and implemented in this study can be used as a reference model for the production system to generate reflectance products from other optical sensor images. In the future, SaaS, which offers professional analysis functions based on open source, is expected to grow and expand into new application fields for public users and communities.<\/jats:p>","DOI":"10.3390\/rs13224550","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4550","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Implementation of Open Source-Based Software as a Service (SaaS) to Produce TOA and TOC Reflectance of High-Resolution KOMPSAT-3\/3A Satellite Image"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9136-7275","authenticated-orcid":false,"given":"Kwangseob","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8586-4750","authenticated-orcid":false,"given":"Kiwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","unstructured":"(2021, June 26). Different Types of Cloud Computing Service Models. Available online: https:\/\/www.bluepiit.com\/blog\/different-types-of-cloud-computing-service-models\/."},{"key":"ref_2","unstructured":"(2021, June 26). Choosing the Right Cloud Service: IaaS, PaaS, or SaaS. Available online: https:\/\/rubygarage.org\/blog\/iaas-vs-paas-vs-saas."},{"key":"ref_3","unstructured":"(2021, June 26). Advantages of Cloud Computing in Remote Sensing Applications. Available online: https:\/\/www.l3harrisgeospatial.com\/Learn\/Blogs\/Blog-Details\/ArtMID\/10198\/ArticleID\/15934\/Advantages-of-Cloud-Computing-in-Remote-Sensing-Applications."},{"key":"ref_4","unstructured":"(2021, June 26). ENVI in the Cloud. Available online: https:\/\/www.l3harrisgeospatial.com\/Learn\/Whitepapers\/Whitepaper-Detail\/ArtMID\/17811\/ArticleID\/15764\/ENVI-in-the-Cloud."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, C., and Huang, Q. (2014). Spatial Cloud Computing: A Practical Approach, CRC Press.","DOI":"10.1201\/b16106"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, L., Yan, J., and Ma, Y. (2020). Cloud Computing in Remote Sensing, CRC Press.","DOI":"10.1201\/9780429488764"},{"key":"ref_7","unstructured":"(2021, June 26). A Planetary-Scale Platform for Earth Science Data & Analysis. Available online: https:\/\/earthengine.google.com\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1080\/17538947.2012.748847","article-title":"Geoprocessing in cloud computing platforms\u2014A comparative analysis","volume":"6","author":"Yue","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1016\/j.future.2013.05.002","article-title":"Rapid processing of remote sensing images based on cloud computing","volume":"29","author":"Wang","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.future.2017.02.044","article-title":"A cloud-based remote sensing data production system","volume":"86","author":"Yan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kopp, S., Becker, P., Doshi, A., Wright, D.J., Zhang, K., and Xu, H. (2019). Achieving the Full Vision of Earth Observation Data Cubes. Data, 4.","DOI":"10.3390\/data4030094"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, G., Xia, J., Ben, J., Cao, Q., Zhao, L., Ma, Y., Zhang, L., and Zhu, D. (2020). Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12010062"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gomes, V.C.F., Queiroz, G.R., and Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12081253"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schramm, M., Pebesma, E., Milutin Milenkovi\u2019c, M., Luca Foresta, L., Jeroen Dries, J., Jacob, A., Wagner, W., Mohr, M., Neteler, M., and Kadunc, M. (2021). The openEO API\u2013Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities. Remote Sens., 13.","DOI":"10.3390\/rs13061125"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhou, J., and Zhang, D. (2021). On-the-Fly Fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes. Sensors, 21.","DOI":"10.3390\/s21092971"},{"key":"ref_17","unstructured":"Kline, K. (2018, June 26). USGS Landsat Migration to the Cloud, Presentation Material in CEOS WGISS-49 Meeting. Available online: https:\/\/ceos.org\/meetings\/wgiss-51\/."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.ecoleng.2018.06.024","article-title":"Assessing the effectiveness of riparian restoration projects using Landsat and precipitation data from the cloud-computing application ClimateEngine.org","volume":"120","author":"Hausner","year":"2018","journal-title":"Ecol. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7459","DOI":"10.1080\/01431161.2019.1597311","article-title":"Mapping the spatial distribution and changes of oil palm land cover using an open source cloud-based mapping platform","volume":"40","author":"Shaharum","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1080\/17538947.2018.1494761","article-title":"A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine","volume":"12","author":"Yang","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1080\/15481603.2018.1538621","article-title":"Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification","volume":"56","author":"Antunes","year":"2019","journal-title":"Gisci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring in-land water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1080\/17538947.2020.1772893","article-title":"Mapping summer soybean and corn with remote sensing on Google Earth Engine cloud computing in Parana state\u2014Brazil","volume":"13","author":"Paludo","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Amani, M., Kakooei, M., Moghimi, A., Ghorbanian, A., Ranjgar, B., Sahel Mahdavi, S., Davidson, A., Thierry Fisette, T., Rollin, P., and Brisco, B. (2020). Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada. Remote Sens., 12.","DOI":"10.3390\/rs12213561"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zou, Q., Li, G., and Yu, W. (2020). Cloud Computing Based on Computational Characteristics for Disaster Monitoring. Appl. Sci., 10.","DOI":"10.3390\/app10196676"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ferreira, K.R., Queiroz, G.R., Vinhas, L., Marujo, R.F.B., Simoes, R.E.O., Picoli, M.C.A., Camara, G., Cartaxo, R., Gomes, V.C.F., and Santos, L.A. (2020). Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sens., 12.","DOI":"10.3390\/rs12244033"},{"key":"ref_27","first-page":"100489","article-title":"Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing","volume":"22","author":"Singh","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"118","DOI":"10.3390\/agriengineering3010008","article-title":"Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery","volume":"3","author":"Hardy","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_29","unstructured":"Cloudeo (2021, June 26). Available online: https:\/\/www.cloudeo.group\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1080\/17538947.2016.1196505","article-title":"Free and open source software for geospatial applications (FOSS4G) to support Future Earth","volume":"10","author":"Brovellia","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"H\u00fctt, C., Waldhoff, G., and Bareth, G. (2020). Fusion of Sentinel-1 with Official Topographic and Cadastral Geodata for Crop-Type Enriched LULC Mapping Using FOSS and Open Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020120"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Coetzee, S., Iv\u00e1nov\u00e1, I., Mitasova, H., and Brovelli, M.A. (2020). Open Geospatial Software and Data: A Review of the Current State and a Perspective into the Future. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020090"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"206","DOI":"10.3390\/geomatics1020013","article-title":"GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature","volume":"1","author":"Duarte","year":"2021","journal-title":"Geomatics"},{"key":"ref_34","unstructured":"Keighan, E., Pross, B., and Caumont, H. (2014). Testbed 10 Performance of OGC\u00ae Services in the Cloud: The WMS, WMTS, and WPS Cases, Open Geospatial Consortium Inc.. OGC 14-028r1."},{"key":"ref_35","unstructured":"Percivall, G. (2021, July 26). The Role of Geospatial in Edge-Fog-Cloud Computing\u2014An OGC White Paper, OGC 18-004r1. Available online: https:\/\/docs.opengeospatial.org\/wp\/18-004r1\/18-004r1.html."},{"key":"ref_36","unstructured":"(2021, July 26). OGC Testbed-14: Federated Clouds Engineering Report, OGC 18-090r1. Available online: http:\/\/docs.opengeospatial.org\/per\/18-090r1.html."},{"key":"ref_37","unstructured":"(2021, July 26). The Most Widely Deployed Open Source Cloud Software in the World. Available online: https:\/\/www.openstack.org\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11108\/kagis.2013.16.4.141","article-title":"Testing Implementation of Remote Sensing Image Analysis Processing Service on OpenStack of Open Source Cloud Platform","volume":"16","author":"Kang","year":"2013","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kang, S., and Lee, K. (2016). Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment. Remote Sens., 8.","DOI":"10.3390\/rs8080662"},{"key":"ref_40","first-page":"339","article-title":"Cloud-based Satellite Image Processing Service by Open Source Stack: A KARI Case","volume":"33","author":"Lee","year":"2017","journal-title":"Korean J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yoon, G., Kim, K., and Lee, K. (2017). Linkage of OGC WPS 2.0 to the e-Government Standard Framework in Korea: An Implementation Case for Geo-Spatial Image Processing. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6010025"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lee, K., and Kim, K. (2018). A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack. Remote Sens., 10.","DOI":"10.3390\/rs10081274"},{"key":"ref_43","unstructured":"Orfeo ToolBox (2021, July 26). Open Source Processing of Remote Sensing Images. Available online: https:\/\/www.orfeo-toolbox.org\/."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Duarte, L., Silva, P., and Teodoro, A.C. (2018). Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030109"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"De Luca, G., Silva, J.M.N., Cerasoli, S., Ara\u00fajo, J., Campos, J., Di Fazio, S., and Modica, G. (2019). Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox. Remote Sens., 11.","DOI":"10.3390\/rs11101238"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lee, K., Kim, K., Lee, S., and Kim, Y. (2020). Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy(TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension. ISPRS Int. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040257"},{"key":"ref_47","first-page":"167","article-title":"Validation of Surface Reflectance Product of KOMPSAT-3A Image Data Using RadCalNet Data","volume":"36","author":"Lee","year":"2020","journal-title":"Korean J. Remote Sens."},{"key":"ref_48","first-page":"1327","article-title":"An Experiment for Surface Reflectance Image Generation of KOMPSAT 3A Image Data by Open Source Implementation","volume":"35","author":"Lee","year":"2019","journal-title":"Korean J. Remote Sens."},{"key":"ref_49","first-page":"1509","article-title":"Validation of Surface Reflectance Product of KOMPSAT-3A Image Data Application of RadCalNet Bao-tou (BTCN) Data","volume":"36","author":"Kim","year":"2020","journal-title":"Korean J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kim, K., and Lee, K. (2020). A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing. Remote Sens., 12.","DOI":"10.3390\/rs12233971"},{"key":"ref_51","unstructured":"Satellite Agriculture & Land Surface Applications (2021, July 26). Available online: https:\/\/salsa.umd.edu\/6spage.html."},{"key":"ref_52","unstructured":"OpenStreetMap (2021, July 26). Available online: https:\/\/www.openstreetmap.org."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4550\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:29:29Z","timestamp":1760167769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":52,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224550"],"URL":"https:\/\/doi.org\/10.3390\/rs13224550","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,11,12]]}}}