Papers
2
Total Citations
17
H-Index
2
About
Donglai Jiao is a leading researcher in atmospheric remote sensing, with a primary focus on aerosol optical depth (AOD) retrieval and validation. His work addresses critical challenges in monitoring atmospheric aerosols, which are vital for understanding climate change and assessing public health impacts. Jiao’s major contributions include the rigorous evaluation of satellite aerosol products—specifically MODIS DT, DB, and MAIAC algorithms—over complex land cover types in the Yangtze River Delta, a region with diverse surface conditions. His 2023 study on this topic, which has garnered 9 citations, provides essential insights into the accuracy and limitations of these widely used datasets. Additionally, Jiao has pioneered the application of deep learning to satellite remote sensing, developing a novel convolutional neural network (CNN)-based method for retrieving AOD from Sentinel-2 imagery. This innovative approach, cited 8 times, offers a significant advancement over traditional algorithms by improving retrieval accuracy in challenging environments. Through these achievements, Jiao has demonstrated a strong ability to integrate advanced computational techniques with atmospheric science, making his work highly relevant for students and researchers interested in satellite-based environmental monitoring and machine learning applications in Earth observation.
Research Focus
Key Achievements
Top Papers
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