[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128417-en":3,"doc-seo-128417-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128417,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Earth observation and machine learning for advanced crop yield forecasting - Dissertation","673 million people faced hunger in 2024, while climate change and a growing world population increased pressure on global agriculture. With 13% of food production lost in the supply chain, optimized resource management is critical to reduce both food waste and hunger. Crop yield forecasts support better stock management and guide crop import and export decisions. Current forecast models often lack accuracy due to data scarcity and the underestimation of extreme events like droughts and war-related impacts.","DISSERTATION  \nEarth observation and machine learning for advanced crop yield forecasting  \nEine Dissertation zur Er langung des akademischen Grades  \nDoktor der technische n Wissenschaften (Dr.techn.)  \nunter der Betreuung von  \nUniv.Prof. Dr.rer.nat. Wouter Arnoud Dorigo  \nDurchgeführt an der  \nTU Wien  \nFakultät für Mathematik und Geoinformation Forschungsgruppe Klima-und Umweltfernerkundung  \nvon  \nPIET EMANUEL BÜECHI  \nMatrikelnummer: 12238991  \nDecember 9, 2025 Unterschrift: .............. .............. .. .  \nDISSERTATION  \nEarth observation and machine learning for advanced crop yield forecasting  \nA thesis submitted in fulfillment of the academic degree of  \nDoktor der technische n Wissenschaften (Dr.techn.)  \nunder the supervision of  \nUniv.Prof. Dr.rer.nat. Wouter Arnoud Dorigo  \nresearch conducted at  \nTU Wien  \nFaculty of Mathematics and Geoinformation Research Unit of Climate and Environmental Remote Sensing  \nby  \nPIET EMANUEL BÜECHI  \nMatriculation number: 12238991  \nDecember 9, 2025 Signature: .............. .............. ....  \nSupervisor: Univ. Prof. Dr. rer. nat. Wouter Arnoud Dorigo MSc  \nTU Wien  \nDepartment of Geodesy and Geoinformation  \nResearch Unit of Climate and Environmental Remote Sensing Wiedner Hauptstraße 8  \n1040 Vienna, Austria  \nReferee: Prof. Dr. rer. nat. Claas Nendel  \nLeibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e. V. Eberswalder Straße 84  \n15374 Müncheberg, Germany  \n[Univ.Prof. Dr.sc. Ioannis Giannopoulos MSc BSc](Univ.Prof. Dr.sc. Ioannis Giannopoulos MSc BSc)  \nTU Wien  \nDepartment of Geodesy and Geoinformation  \nResearch Unit Geoinformation  \nWiedner Hauptstraße 8  \n1040 Vienna, Austria  \nPiet Emanuel Büechi  \nEarth observation and machine learning for advanced crop yield forecasting Dissertation, December 9, 2025  \nTU Wien  \nDepartment of Geodesy and Geoinformation  \nResearch Unit of Climate and Environmental Remote Sensing Wiedner Hauptstraße 8/E120.8, A-1040 Vienna, Austria  \nErklärung zur Verfassung der Arbeit – Author’s Statement  \nHier mit er kläre ich, dass ich diese Arbeit selbstständig verfasst habe, dass ich die verwendeten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen – die anderen Werken oder dem Internet im Wortlaut oder dem Sinn entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe. Generative künstliche Intelligenz ([Perplexity.ai](Perplexity.ai) [und Claude.ai](und Claude.ai)) wurde ausschliesslich dazu verwendet um die Texte grammatikalisch und stilistisch zu veränder n, für Übersetzungen und zur Unterstützung beim Programmieren. Alle angepassten Textstellen und Programme wurden von mir danach überprüft und ich übernehme volle Verantwortung fürderen Korrektheit.  \nI hereby declare that I independently drafted this manuscript, that all sources and references are correctly cited, and that the respective parts of this manuscript- including tables, maps, and figures-which were included from other manuscripts or the internet either semantically or syntactically are made clearly evident in the text and all respective sources are correctly cited. Generative artificial intelligence ( Perplexity.ai and claude.ai) was used exclusively to modify the texts grammatically and stylistically, for translation, and to assist with programming. All modified text passages and programs were subsequently reviewed by me, and I take full responsibility for their correctness.  \nv  \nAbstract  \n673 million people faced hunger in 2024, while climate change and a growing world population put further pressure on global agriculture. Given that 13% of global food production goes to waste in the supply chain, optimised resource management is essential to reduce both food waste and hunger. Crop yield forecasts enable decision-makers to better manage stocks and guide the import and export of crops. Despite enormous efforts over the past decades to provide reliabl","cbCait0hZ7bywzu1","https://ap.wps.com/l/cbCait0hZ7bywzu1","pdf",4066981,2,1,118,"English","en",105,"# Abstract\n## Research questions\n## Key findings\n## Future steps","[{\"question\":\"Why are current crop yield forecasting models often not accurate enough?\",\"answer\":\"Accuracy is frequently inadequate due to data scarcity and the underestimation of extreme events, such as droughts, on crop yields.\"},{\"question\":\"How does the thesis address forecasting in data-scarce situations?\",\"answer\":\"It uses transfer learning to train and test models across domains, fine-tuning a field-scale crop yield model with only a limited number of field-scale samples.\"},{\"question\":\"What improvements does transfer learning bring during extreme events such as war-related conditions?\",\"answer\":\"Transfer learning improves crop yield forecasts in both average years and during the war, even though yield losses may still be underestimated.\"}]","Earth observation and machine learning for advanced crop yield forecasting - 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