Papers
2
Total Citations
37
H-Index
2
About
Zhihao Qin is a researcher whose work bridges atmospheric remote sensing and the emerging application of large language models in engineering education. His most-cited contribution, a 2020 paper on retrieving total precipitable water vapor from FengYun 3D MERSI-2 satellite data, addresses a fundamental challenge in optical remote sensing: correcting atmospheric effects on Earth surface imagery. By developing an algorithm to quantify water vapor—a key atmospheric variable—Qin’s work supports more accurate radiometric transfer modeling and improved satellite data interpretation. This paper has accumulated 35 citations, reflecting its utility in atmospheric and remote sensing communities. More recently, Qin has ventured into educational technology, co-authoring a 2025 study on benchmarking large language models for homework assessment in circuit analysis. This work explores how LLMs like ChatGPT can assist in engineering pedagogy, offering insights into automated grading and personalized learning. While still early in its impact (2 citations), it signals a forward-looking interest in AI’s role in STEM education. Together, Qin’s research demonstrates a versatility spanning physical remote sensing and computational education, contributing both to foundational atmospheric correction methods and to innovative uses of AI in teaching.
Research Focus
Key Achievements
Top Papers
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