Qiming Qin

Papers

Total Citations

120

H-Index

About

Dr. Qiming Qin is a leading figure in thermal infrared remote sensing, with a primary focus on atmospheric correction and land surface temperature retrieval. His most impactful contribution is the development of a modified split-window covariance-variance ratio (SWCVR) method for estimating atmospheric water vapor from Landsat 8 TIRS imagery, detailed in his highly cited 2015 paper (120 citations). This work directly addresses a critical bottleneck in remote sensing: accurate water vapor retrieval is essential for correcting thermal data and deriving reliable land surface temperatures. By refining the SWCVR algorithm, Dr. Qin provided the remote sensing community with a more robust, physically-based tool for atmospheric correction, enabling higher-quality analyses of urban heat islands, agricultural water stress, and climate dynamics. His research bridges the gap between sensor physics and practical environmental monitoring, making advanced thermal data more accessible for global change studies. Dr. Qin’s work remains a cornerstone for researchers and students working with Landsat thermal data, demonstrating how methodical algorithm development can unlock the full potential of Earth observation satellites.