Print Email Facebook Twitter Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach Title Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach Author Yuan, Zihao (NWO-I/SRON Netherlands Institute for Space Research; Universiteit Leiden) Fu, Guangliang (NWO-I/SRON Netherlands Institute for Space Research) van Diedenhoven, Bastiaan (NWO-I/SRON Netherlands Institute for Space Research) Lin, H.X. (TU Delft Mathematical Physics; Universiteit Leiden) Erisman, Jan Willem (Universiteit Leiden) Hasekamp, Otto P. (NWO-I/SRON Netherlands Institute for Space Research) Date 2024 Abstract This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager. To reference this document use: http://resolver.tudelft.nl/uuid:0ca860dd-f7e4-4458-9514-638116824368 DOI https://doi.org/10.5194/amt-17-2595-2024 ISSN 1867-1381 Source Atmospheric Measurement Techniques, 17 (9), 2595–2610 Part of collection Institutional Repository Document type journal article Rights © 2024 Zihao Yuan, Guangliang Fu, Bastiaan van Diedenhoven, H.X. Lin, Jan Willem Erisman, Otto P. Hasekamp Files PDF amt-17-2595-2024.pdf 6.34 MB Close viewer /islandora/object/uuid:0ca860dd-f7e4-4458-9514-638116824368/datastream/OBJ/view