[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128526-en":3,"doc-seo-128526-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},128526,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and HyPlant - Bottomline","A fast, accurate machine-learning simulator predicts at-sensor radiance spectra for solar-induced fluorescence retrieval using DESIS and HyPlant. The approach treats the input features as atmosphere, geometry, surface, and sensor descriptors, while the target output is the at-sensor radiance spectrum. Models such as OLS and polynomial ridge regression with neural networks are trained and evaluated using mean absolute error. Results show 4th-degree polynomials provide high speed and accuracy, enabling rapid generation of large datasets that can be integrated into SIF retrieval methods.","FAST MACHINE LEARNING SIMULATOR OF AT-SENSOR RADIANCES FOR SOLAR-INDUCED FLUORESCENCE RETRIEVAL WITH DESIS AND HYPLANT  \nMiguel Pato 1 , Kevin Alonso 2 , Stefan Auer 1 , Jim Buffat 3 , Emiliano Carmona 1 , Stefan Maier 1 , Rupert Müller 1 , Patrick Rademske 3 , Uwe Rascher 3 and Hanno Scharr4  \n1 German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, Oberpfaffenhofen, Germany  \n2 RHEA Group c/o European Space Agency (ESA), Largo Galileo Galilei, Frascati 00044, Italy  \n3 Forschungszentrum Jülich GmbH, Institute of Bio-and Geosciences, IBG-2: Plant Sciences, Jülich, Germany  \n4 Forschungszentrum Jülich GmbH, Institute of Advanced Simulations, IAS-8: Data Analytics and Machine Learning, Jülich, Germany  \n| Training and evaluation of ML simulator |  |  |  |  | Step 2\u003Cbr>􀜨: ℝ􀯗 → ℝ􀯠\u003Cbr>Regression (P2, P4), Neural Networks (NN) |\n| --- | --- | --- | --- | --- | --- |\n| Regression problem |  |  | features (input): 􀝔 =  atmosphere, geometry, surface, sensor  targets (output): at-sensor radiance spectrum 􀜮 s = 􀜨 (􀝔) ML methods: Ordinary Least Squares (OLS), Polynomial Ridge\u003Cbr>evaluation: mean absolute error (MAE) |  |  |\n|  |  |  |  |  |  |\n| \u003Cbr>at-sensor radiance spectrum at-sensor radiance spectrum\u003Cbr>3 |  |  |  |  |  |\n| \u003Cbr>4\u003Cbr>re lat ive az imuth ang le [ deg ]\u003Cbr>simulator error distribution\u003Cbr>Sun zenith angle – tilt angle [deg] |  |  |  | 1 Polynomials of 4th degree are fast and accurate.\u003Cbr>2 Speed: 107 times faster than the simulation.\u003Cbr>3 Accuracy: errors 10 times below SIF signal.\u003Cbr>4 There is room for improvement.\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr> |  |\n| \u003Cbr>Bottomline: We developed a fast and accurate ML simulator of at-sensor radiances for DESIS and HyPlant. The simulator enables the swift generation of large data sets and can be integrated into SIF retrieval methods. This illustrates how ML and physical modelling can be combined to unlock the full potential of remote sensing data. |  |  |  |  |  |\n\nContact: Miguel Pato  \n( [miguel.figueiredovazpato@dlr.de](miguel.figueiredovazpato@dlr.de) )  \nIEEE IGARSS 2023, Pasadena, 16-21 July 2023","cbCaikQBBkdFAuEN","https://ap.wps.com/l/cbCaikQBBkdFAuEN","pdf",602192,1,"English","en",105,"# Training and evaluation of ML simulator\n## Regression and neural network setup\n## Speed and accuracy results\n## Bottomline","[{\"question\":\"What does the proposed simulator predict?\",\"answer\":\"It generates the at-sensor radiance spectrum needed for solar-induced fluorescence (SIF) retrieval.\"},{\"question\":\"Which inputs and outputs are used for training?\",\"answer\":\"Inputs are atmosphere, geometry, surface, and sensor features; outputs are the at-sensor radiance spectrum.\"},{\"question\":\"How does the simulator perform compared with full simulations?\",\"answer\":\"It is reported to be about 107× faster, with errors around 10× below the SIF signal.\"}]","Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and HyPlant - Bottomline | PDF",1786001553,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"fast-machine-learning-simulator-of-at-sensor-radiances-for-solar-induced-fluorescence-retrieval-with-desis-and-hyplant-bottomline","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/technology/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/fast-machine-learning-simulator-of-at-sensor-radiances-for-solar-induced-fluorescence-retrieval-with-desis-and-hyplant-bottomline/128526/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-06",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What does the proposed simulator predict?","Question",{"text":73,"@type":74},"It generates the at-sensor radiance spectrum needed for solar-induced fluorescence (SIF) retrieval.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which inputs and outputs are used for training?",{"text":78,"@type":74},"Inputs are atmosphere, geometry, surface, and sensor features; outputs are the at-sensor radiance spectrum.",{"name":80,"@type":71,"acceptedAnswer":81},"How does the simulator perform compared with full simulations?",{"text":82,"@type":74},"It is reported to be about 107× faster, with errors around 10× below the SIF signal.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":109,"slug":110},50,"technology",{"id":112,"doc_module":4,"doc_module_name":45,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]