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About

Charles K. Gatebe is a leading figure in atmospheric and cryospheric remote sensing, whose work bridges the critical gap between satellite observations and ground-based measurements. His primary research focuses on aerosol retrieval, surface reflectance, and the complex bidirectional reflectance distribution function (BRDF) of snow and ice. Gatebe’s most impactful contribution is his pioneering use of machine learning for atmospheric correction, as demonstrated in his highly cited 2017 paper on using multilayer neural networks over coastal waters (153 citations). He also developed a novel method for simultaneously retrieving aerosol and surface optical properties by combining airborne and ground-based radiometric data, a technique that has advanced the capabilities of the Aerosol Robotic Network (AERONET). A key highlight of his career is his involvement in the ARCTAS Spring-2008 campaign, a major International Polar Year initiative, where his detailed analysis of snow bidirectional reflectance provided crucial data for understanding Arctic physical and chemical processes. With a citation record that underscores the practical importance of his work, Gatebe’s research is essential for improving satellite-based climate monitoring, particularly in the rapidly changing polar regions.