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About

Adam Lawson is a remote sensing scientist specializing in satellite ocean color retrieval, atmospheric correction, and sensor calibration and validation. His research focuses on improving the accuracy of bio-optical property retrievals — including chlorophyll concentration, absorption, and backscattering coefficients — from satellite imagery of coastal and open ocean environments. Lawson's most influential work, "Impact of Aerosol Model Selection on Water-Leaving Radiance Retrievals from Satellite Ocean Color Imagery" (2012, 15 citations), critically examined how aerosol model choices during atmospheric correction introduce uncertainties in downstream ocean color products, a foundational concern for operational oceanography. He has contributed significantly to multi-sensor validation efforts, including comparative analyses of the Geostationary Ocean Color Imager (GOCI) and calibration studies spanning sensors from the pioneering CZCS to modern VIIRS instruments. Affiliated with the Naval Research Laboratory at Stennis Space Center, Lawson played a central role in developing SAVANT, an automated tool for match-up protocol analysis between satellites and in situ platforms like AERONET-OC. His more recent work applies machine learning to atmospheric correction of nanosatellites, reflecting a forward-looking approach to emerging small-satellite ocean color missions. His body of work represents meaningful, operationally relevant contributions to the Navy's ocean remote sensing capabilities.