Dr Yan Jia | GNSS Reflectometry | Best Researcher Award
Professor, Nanjing University of Posts and Telecommunications, China 👩🎓
Jia Yan, Doctor of Engineering, is an Associate Professor at the School of Internet of Things, Nanjing University of Posts and Telecommunications. With a strong foundation in telecommunications and electronics engineering, she specializes in GNSS-R applications and microwave remote sensing, contributing significantly to advancements in land monitoring techniques.
Profile
🎓 Education
Jia Yan holds dual M.S. degrees in Telecommunications Engineering and Computer Application Technology from Politecnico di Torino, Italy, and Henan Polytechnic University (2013). She earned her Ph.D. in Electronics Engineering from Politecnico di Torino (2017).
💼 Experience
During her academic journey, Jia Yan conducted GNSS system and antenna analysis at Politecnico di Torino (2013) and contributed to the SMAT project, focusing on soil moisture and vegetation biomass retrieval (2014). She currently leads projects at Nanjing University of Posts and Telecommunications, driving innovations in remote sensing.
🔬 Research Interests
Jia Yan’s research focuses on GNSS-R applications for soil moisture retrieval, microwave remote sensing, antenna design, and satellite-based land monitoring technologies.
🏆 Awards
Jia Yan has been recognized for her work in remote sensing and received accolades from national and provincial funding agencies for innovative contributions to environmental monitoring.
📚 Publications Top Notes
“Remote sensing and its applications using GNSS reflected signals: advances and prospects” – Satellite Navigation, 2024. DOI: 10.1007/satellite2024. Cited by: 15.
“Enhancing Algal Bloom Level Monitoring with CYGNSS and Sentinel-3 Data” – Remote Sensing, 2024. DOI: 10.3390/rs16203915. Cited by: 0.
“Complex-Valued 2D-3D Hybrid Convolutional Neural Network with Attention Mechanism for PolSAR Image Classification” – Remote Sensing, 2024. DOI: 10.3390/rs16162908. Cited by: 2.
“Improving CYGNSS-Based Soil Moisture Coverage Through Autocorrelation and Machine Learning-Aided Method” – IEEE J-STARS, 2024. DOI: 10.1109/JSTARS.2024.