K. E. J. Lehtinen

About

K. E. J. Lehtinen is a leading researcher in atmospheric aerosol science, with a focus on satellite remote sensing, radiative transfer, and machine learning applications. Their major contributions include pioneering the retrieval of aerosol optical depth (AOD) from surface solar radiation measurements using machine learning algorithms, non-linear regression, and radiative transfer-based look-up tables—a method that enables reconstruction of historical aerosol levels before dedicated satellite observations began in the 1990s. Lehtinen also advanced satellite aerosol product accuracy through deep-learning-based post-process correction of the high-resolution Sentinel-3 Level-2 Synergy product, addressing critical challenges in climate modeling and air quality monitoring. Their work on quantifying the effect of water vapor on aerosol direct radiative effect (ADRE) using AERONET fluxes has improved understanding of aerosol radiative forcing. With papers accumulating over 75 citations, Lehtinen’s research bridges observational gaps and enhances the reliability of aerosol data for climate science. Their innovative integration of machine learning with traditional radiative transfer methods marks a significant step forward in atmospheric remote sensing, making their work essential reading for students and researchers in aerosol-climate interactions.

Research Focus

Key Achievements

Top Papers

1. 1
   Retrieval of aerosol optical depth from surface solar radiation measurementsusing machine learning algorithms, non-linear regression and a radiativetransfer-based look-up table

   34 citations · 2016
2. 2
   Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product

   30 citations · 2022
3. 3
   Effect of water vapor on the determination of aerosol direct radiative effect based on the AERONET fluxes

   12 citations · 2014

Key Collaborators

Contact & Links