[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120497-en":3,"doc-seo-120497-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120497,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Increasing Chlorophyll-A Spatial Resolution Using Machine Learning - Research Report","This paper studied satellite-based measurement of chlorophyll-a concentration in the Mediterranean Sea and addresses the limitations of low spatial resolution observations (e.g., GCOM-C/SGLI and Sentinel-3 OLCI). The work proposes a machine-learning information technology for a pilot area near Cyprus that fuses low-resolution satellite data, ground-based chlorophyll-a measurements, and high-resolution Sentinel-2 data. Correlation analysis identifies the most informative features affecting chlorophyll-a levels. Random Forest and Multilayer Perceptron models enable chlorophyll-a mapping at 10-meter spatial resolution, achieving determination R² of 0.36 and correlation of 0.6 with ground measurements. Results support monitoring aquatic ecosystem status.","Increasing Chlorophyll-A Spatial Resolution Using  \nMachine Learning  \nBohdan Yailymov Department of Space Information Systems and Technologies Space Research Institute National Academy of Science of Ukraine and State Space Agency of Ukraine Kyiv, Ukraine [yailymov@gmail.com](yailymov@gmail.com)  \nPavlo Henitsoi  \nDepartment of Mathematical Modelling and Data Analysis National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv, Ukraine [pavge-ipt21@lll.kpi.ua](pavge-ipt21@lll.kpi.ua)  \nAndrii Shelestov  \nDepartment of Mathematical Modelling and Data Analysis National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv, Ukraine [andrii.shelestov@gmail.com](andrii.shelestov@gmail.com)  \nNataliia Kussul  \nDepartment of Mathematical Modelling and Data Analysis National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» Kyiv, Ukraine [nataliia.kussul@gmail.com](nataliia.kussul@gmail.com)  \nAbstract—This paper studied the question of measuring the concentration of chlorophyll-a in the Mediterranean Sea using satellite data. Low spatial resolution satellite data such as GCOM-C/SGLI and Sentinel-3 OLCI allow measurements of chlorophyll-a concentrations at the sea surface. However, these data have limited accuracy and spatial resolution, which creates challenges for monitoring local changes in coastal zones and small water areas. To increase the spatial resolution and accuracy of chlorophyll-a measurement, this paper proposes an information technology based on machine learning for a pilot area in the Mediterranean Sea near Cyprus. This technology combines low-resolution satellite data with ground-based measurements of chlorophyll-a and high-resolution data from the Sentinel-2 satellite. A comparative analysis of correlations between various satellite and ground data was carried out to determine the most informative features affecting the level of chlorophyll-a. Using the Random Forest and Multilayer Perceptron machine learning algorithms, an information technology was developed to improve the spatial resolution of chlorophyll-a concentration based on high-resolution satellite data. The developed technology makes it possible to create chlorophyll-a maps with a spatial resolution of 10 meters. The obtained results show a coefficient of determination of 0.36 anda correlation of 0.6 with ground measurements. The proposed approach is promising for monitoring the state of aquatic ecosystems.  \nKeywords— machine learning, satellite data, chlorophyll-a, cloud technologies, information technology, iMERMAID  \nI. INTRODUCTION  \nMeasuring chlorophyll-a is an important way to assess water quality because chlorophyll-a reflects the photosynthetic activity of phytoplankton, which is the main producer of organic matter in aquatic ecosystems. However, measuring chlorophyll-a using traditional methods, such as water sample collection and laboratory analysis, is timeconsuming, expensive, and limited in spatial and temporal coverage. Therefore, satellite observations have become an alternative and effective tool for monitoring chlorophyll-a over large areas.  \nSatellite monitoring of chlorophyll-a is based on the measurement of reflected sunlight from the water surface in various spectral ranges. Chlorophyll-a absorbs light in the blue  \nand red ranges and reflects in the green range. Thus, the concentration of chlorophyll-a can be determined using special algorithms that use signal ratios in different ranges. The higher the spatial resolution of the data, the stronger is the effect of internal scattering, which depends on the optical properties of water, such as transparency, color, turbidity, concentration of dissolved and suspended substances. These factors affect the spectral shape of the signal coming from the water and can mask or alter the chlorophyll-a signal. To account for these factors, complex optical water models are required, which may be unavailable or inaccura","cbCaiowS7rw3i6Oa","https://ap.wps.com/l/cbCaiowS7rw3i6Oa","pdf",877184,1,5,"English","en",105,"# Introduction\n## Chlorophyll-a as a water quality indicator\n## Satellite observation principles\n## Challenges of low-resolution satellite chlorophyll-a retrievals\n## Ground measurement limitations\n## Related work and algorithms","[{\"question\":\"Why is chlorophyll-a important for assessing water quality?\",\"answer\":\"Chlorophyll-a reflects the photosynthetic activity of phytoplankton, which is the main producer of organic matter in aquatic ecosystems, making it a key indicator of water quality.\"},{\"question\":\"What problem does the paper address with existing satellite measurements?\",\"answer\":\"Low spatial resolution satellite data limit both accuracy and spatial detail, making it difficult to monitor local changes in coastal zones and small water areas.\"},{\"question\":\"How does the proposed method improve chlorophyll-a spatial resolution?\",\"answer\":\"It combines low-resolution satellite observations, ground-based chlorophyll-a measurements, and high-resolution Sentinel-2 data, then trains Random Forest and Multilayer Perceptron models to generate chlorophyll-a maps at 10-meter resolution.\"}]","Increasing Chlorophyll-A Spatial Resolution Using Machine Learning - Research Report | PDF",1785730368,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"increasing-chlorophyll-a-spatial-resolution-using-machine-learning-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/increasing-chlorophyll-a-spatial-resolution-using-machine-learning-research-report/120497/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is chlorophyll-a important for assessing water quality?","Question",{"text":75,"@type":76},"Chlorophyll-a reflects the photosynthetic activity of phytoplankton, which is the main producer of organic matter in aquatic ecosystems, making it a key indicator of water quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper address with existing satellite measurements?",{"text":80,"@type":76},"Low spatial resolution satellite data limit both accuracy and spatial detail, making it difficult to monitor local changes in coastal zones and small water areas.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve chlorophyll-a spatial resolution?",{"text":84,"@type":76},"It combines low-resolution satellite observations, ground-based chlorophyll-a measurements, and high-resolution Sentinel-2 data, then trains Random Forest and Multilayer Perceptron models to generate chlorophyll-a maps at 10-meter resolution.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]