Ville Kolehmainen

Papers

Total Citations

H-Index

About

Ville Kolehmainen is a leading researcher in atmospheric remote sensing, specializing in Bayesian statistical methods and machine learning for aerosol retrieval from satellite data. His major contributions center on developing advanced algorithms that significantly improve the accuracy and spatial resolution of aerosol optical depth (AOD) estimates over land. Notably, his Bayesian Aerosol Retrieval (BAR) algorithm for MODIS, published in 2018 with 43 citations, pioneered a simultaneous pixel-retrieval approach that leverages spatial correlations to enhance retrieval performance. This work was preceded by his Bayesian Dark Target (BDT) algorithm, which laid the groundwork for probabilistic aerosol inversion. More recently, Kolehmainen has applied deep learning to correct aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product (2022, 30 citations), addressing persistent biases in operational satellite data. His research bridges classical Bayesian inversion with modern neural networks, offering robust solutions for climate modeling, air quality monitoring, and atmospheric correction. Through these innovations, Kolehmainen has established himself as a key figure in advancing satellite-based aerosol science, with his work directly impacting global environmental monitoring capabilities.