Presenter Information

Adyn Miles, University of Toronto Aerospace TeamFollow
David Maranto, University of Toronto Aerospace Team
Shiqi Xu, University of Toronto Aerospace Team
Rosalind Liang, University of Toronto Aerospace Team
Yong Da Li, University of Toronto Aerospace Team
Jason Rock, University of Toronto Aerospace Team
Amreen Imrit, University of Toronto Aerospace Team
Maggie Kou, University of Toronto Aerospace Team
Abeer Fatima, University of Toronto Aerospace Team
Allen Kasum, University of Toronto Aerospace Team
Amy Saranchuk, University of Toronto Aerospace Team
Ana-Maria Zamrii, University of Toronto Aerospace Team
Andy Gong, University of Toronto Aerospace Team
Angelina Hui, University of Toronto Aerospace Team
Anthony DiMaggio, University of Toronto Aerospace Team
Bejamin Nero, University of Toronto Aerospace Team
Bettina Oghinan, University of Toronto Aerospace Team
Bruno Almeida, University of Toronto Aerospace Team
Brytni Richards, University of Toronto Aerospace Team
Cameron Rodriguez, University of Toronto Aerospace Team
Dhruv Sirohi, University of Toronto Aerospace Team
Eman Shayeb, University of Toronto Aerospace Team
Emma Belhadfa, University of Toronto Aerospace Team
Eren Cimentepe, University of Toronto Aerospace Team
Ginny Guo, University of Toronto Aerospace Team
Harshit Sohaney, University of Toronto Aerospace Team
Hiba Al-Falahi, University of Toronto Aerospace Team
Ian Vyse, University of Toronto Aerospace Team
Iliya Shofman, University of Toronto Aerospace Team
Jasnoor Guliani, University of Toronto Aerospace Team
Jennifer Zhang, University of Toronto Aerospace Team
Kanver Bhandal, University of Toronto Aerospace Team
Kejsi Gjerazi, University of Toronto Aerospace Team
Ketan Vasudeva, University of Toronto Aerospace Team
Khalil Damouni, University of Toronto Aerospace Team
Kimberley Orna, University of Toronto Aerospace Team
Konstantinos Papaspyridis, University of Toronto Aerospace Team
Kyoka Collina Stone, University of Toronto Aerospace Team
Maggie Fen Wang, University of Toronto Aerospace Team
Mary Cheng, University of Toronto Aerospace Team
Maxime Michet, University of Toronto Aerospace Team
Mingde Yin, University of Toronto Aerospace Team
Mirai Shinjo, University of Toronto Aerospace Team
Nicholas Glenn, University of Toronto Aerospace Team
Novera Ahmed, University of Toronto Aerospace Team
Omar Farag, University of Toronto Aerospace Team
Prachi K. Sukhnani, University of Toronto Aerospace Team
Punyaphat Sukcharoenchaikul, University of Toronto Aerospace Team
Rajvi Rana, University of Toronto Aerospace Team
Rediet Yohannes, University of Toronto Aerospace Team
Selena Liu, University of Toronto Aerospace Team
Shuhan Zheng, University of Toronto Aerospace Team
Stephanie Yi Fei Lu, University of Toronto Aerospace Team
Tianyi Zhang, University of Toronto Aerospace Team
Tobias Rozario, University of Toronto Aerospace Team
Wanda Janaeska, University of Toronto Aerospace Team
Yifan (Julia) Ye, University of Toronto Aerospace Team
Ziyu Chen, University of Toronto Aerospace Team

Session

Weekend Session 7: Science/Mission Payloads - Research & Academia II

Location

Utah State University, Logan, UT

Abstract

Satellite remote sensing missions have grown in popularity over the past fifteen years due to their ability to cover large swaths of land at regular time intervals, making them suitable for monitoring environmental trends such as greenhouse gas emissions and agricultural practices. As environmental monitoring becomes central in global efforts to combat climate change, accessible platforms for contributing to this research are critical. Many remote sensing missions demand high performance of payloads, restricting research and development to organizations with sufficient resources to address these challenges. Atmospheric remote sensing missions, for example, require extremely high spatial and spectral resolutions to generate scientifically useful results. As an undergraduate-led design team, the University of Toronto Aerospace Team’s Space Systems Division has performed an extensive mission selection process to find a feasible and impactful mission focusing on crop residue mapping. This mission profile provides the data needed to improve crop residue retention practices and reduce greenhouse gas emissions from soil, while relaxing performance requirements relative to many active atmospheric sensing missions. This is accompanied by the design of FINCH, a 3U CubeSat with a hyperspectral camera composed of custom and commercial off-the-shelf components. The team’s custom composite payload, the FINCH Eye, strives to advance performance achieved at this form factor by leveraging novel technologies while keeping design feasibility for a student team a priority. Optical and mechanical design decisions and performance are detailed, as well as assembly, integration, and testing considerations. Beyond its design, the FINCH Eye is examined from operational, timeline, and financial perspectives, and a discussion of the supporting firmware, data processing, and attitude control systems is included. Insight is provided into open-source tools that the team has developed to aid in the design process, including a linear error analysis tool for assessing scientific performance, an optical system tradeoff analysis tool, and data processing algorithms. Ultimately, the team presents a comprehensive case study of an accessible and impactful satellite optical payload design process, in hopes of serving as a blueprint for future design teams seeking to contribute to remote sensing research.

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Aug 7th, 2:30 PM

FINCH: A Blueprint for Accessible and Scientifically Valuable Remote Sensing Satellite Missions

Utah State University, Logan, UT

Satellite remote sensing missions have grown in popularity over the past fifteen years due to their ability to cover large swaths of land at regular time intervals, making them suitable for monitoring environmental trends such as greenhouse gas emissions and agricultural practices. As environmental monitoring becomes central in global efforts to combat climate change, accessible platforms for contributing to this research are critical. Many remote sensing missions demand high performance of payloads, restricting research and development to organizations with sufficient resources to address these challenges. Atmospheric remote sensing missions, for example, require extremely high spatial and spectral resolutions to generate scientifically useful results. As an undergraduate-led design team, the University of Toronto Aerospace Team’s Space Systems Division has performed an extensive mission selection process to find a feasible and impactful mission focusing on crop residue mapping. This mission profile provides the data needed to improve crop residue retention practices and reduce greenhouse gas emissions from soil, while relaxing performance requirements relative to many active atmospheric sensing missions. This is accompanied by the design of FINCH, a 3U CubeSat with a hyperspectral camera composed of custom and commercial off-the-shelf components. The team’s custom composite payload, the FINCH Eye, strives to advance performance achieved at this form factor by leveraging novel technologies while keeping design feasibility for a student team a priority. Optical and mechanical design decisions and performance are detailed, as well as assembly, integration, and testing considerations. Beyond its design, the FINCH Eye is examined from operational, timeline, and financial perspectives, and a discussion of the supporting firmware, data processing, and attitude control systems is included. Insight is provided into open-source tools that the team has developed to aid in the design process, including a linear error analysis tool for assessing scientific performance, an optical system tradeoff analysis tool, and data processing algorithms. Ultimately, the team presents a comprehensive case study of an accessible and impactful satellite optical payload design process, in hopes of serving as a blueprint for future design teams seeking to contribute to remote sensing research.