In this class, we will learn the physical principles behind observing reflected and emitted light from planetary bodies. We will cover different types of optical remote sensing instrumentation, such as cameras, UV- visible- near- and shortwave infrared imaging spectrometers, and thermal infrared imaging spectrometers. We will explore how scientific questions, instrument hardware design, and resulting data products are linked and integrated in spacecraft mission design as well as how these datasets are analyzed and how uncertainties, errors, and accuracies are quantified for these data types. We will walk through and discuss spacecraft instrument papers to understand how a data pipeline is constructed and how an instrument is designed, built, and tested. The course will focus on offering in-depth hands-on experience in computational techniques for analyzing real optical remote sensing spacecraft data for geological and planetary applications during a 3-week mini project (equivalent to 3 weeklong homework problems). The class will also provide a shorter experience with remote sensing techniques in the laboratory through one 2-week homework problem and an optional in-field ground-truthing weekend (TBD Memorial Day weekend for the field trip; however, a make-up assignment can be done instead of the field trip if there are date conflicts). Finally, each student will select a scientific question of interest in the planetary sciences or geology and address it by either proposing an instrument design (engineering student-targeted option) or performing an analysis on a real spacecraft dataset for their final project (science student-targeted option). At the end of the class, you will have an opportunity to also discuss what you have learned with an industry professional, who has worked on instrumentation pipelines and machine learning applications in remote sensing at NASA, Bay Area/El Segundo start-up, and the DOD. Instructor consent is required for undergraduate student enrollment.
3 units · Letter or Credit/No Credit
In this class, we will learn the physical principles behind observing reflected and emitted light from planetary bodies. We will cover different types of optical remote sensing instrumentation, such as cameras, UV- visible- near- and shortwave infrared imaging spectrometers, and thermal infrared imaging spectrometers. We will explore how scientific questions, instrument hardware design, and resulting data products are linked and integrated in spacecraft mission design as well as how these datasets are analyzed and how uncertainties, errors, and accuracies are quantified for these data types. We will walk through and discuss spacecraft instrument papers to understand how a data pipeline is constructed and how an instrument is designed, built, and tested. The course will focus on offering in-depth hands-on experience in computational techniques for analyzing real optical remote sensing spacecraft data for geological and planetary applications during a 3-week mini project (equivalent to 3 weeklong homework problems). The class will also provide a shorter experience with remote sensing techniques in the laboratory through one 2-week homework problem and an optional in-field ground-truthing weekend (TBD Memorial Day weekend for the field trip; however, a make-up assignment can be done instead of the field trip if there are date conflicts). Finally, each student will select a scientific question of interest in the planetary sciences or geology and address it by either proposing an instrument design (engineering student-targeted option) or performing an analysis on a real spacecraft dataset for their final project (science student-targeted option). At the end of the class, you will have an opportunity to also discuss what you have learned with an industry professional, who has worked on instrumentation pipelines and machine learning applications in remote sensing at NASA, Bay Area/El Segundo start-up, and the DOD. Instructor consent is required for undergraduate student enrollment.
Offered in Spring 2027 at Stanford University.