Advanced characterization of the diurnal variations of aerosol optical and microphysical properties by SYREMIS/GRASP synergistic retrieval algorithm using observations from multiple spaceborne sensors

Accurate characterization of the diurnal variation of aerosol optical and microphysical properties—including Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and Single Scattering Albedo (SSA)—is fundamental for climate and air quality modeling. However, retrieving high-temporal-resolution, physically consistent aerosol products from multiple satellite instruments remains a challenge. Current multi-satellite approaches often rely on “post-retrieval” merging of independent Level-2 products, a process that frequently introduces physical inconsistencies and artificial temporal oscillations. To address this, building upon the recently established SYREMIS/GRASP synergetic retrieval framework (Litvinov et al., 2025), an inversion approach based on harmonized Level-1 satellite measurements, this study extensively evaluates its performance in capturing aerosol diurnal variability. We evaluate two configurations: (i) LEO + LEO synergy (Sentinel-3 A/B OLCI and Sentinel-5P TROPOMI) and (ii) LEO + GEO synergy (integrating Himawari-8 AHI). By enforcing multi-pixel smoothness constraints within the retrieval, SYREMIS maintains high physical consistency across instruments, effectively mitigating the structural discrepancies and “noise” inherent in standard merging techniques. Our validation against AERONET ground-based data and intercomparison with the XAERDT product suite confirm that SYREMIS provides high retrieval stability and accuracy. Without applying post-retrieval quality filtering, SYREMIS achieves an AOD GCOS fulfillment percentage of up to 70%, an AE RMSE below 0.3, and an SSA “Optimal” requirement fulfillment rate of up to 85% at representative sites. We demonstrate the framework's capability to resolve rapid aerosol dynamics through case studies of biomass-burning events over Thailand and Myanmar, as well as dust pollution over Qatar in 2019. As a three-month proof-of-concept assessment (March–May 2019) , these results highlight the practical capability of the established SYREMIS/GRASP approach to generate high-resolution, physically consistent, and stable aerosol data records from the measurements of multiple satellites.

Authors

Institutions

Publication Details

Journal
Remote Sensing of Environment
Published
2026-10-14
DOI
https://doi.org/10.1016/j.rse.2026.115739
Primary Topic
Atmospheric aerosols and clouds
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced characterization of the diurnal variations of aerosol optical and microphysical properties by SYREMIS/GRASP synergistic retrieval algorithm using observations from multiple spaceborne sensors

Daniele Gasbarra, Christian Matar, Elody Fluck, Christian Retscher et al.
Remote Sensing of Environment
Atmospheric aerosols and clouds
article

Advanced characterization of the diurnal variations of aerosol optical and microphysical properties by SYREMIS/GRASP synergistic retrieval algorithm using observations from multiple spaceborne sensors

Daniele Gasbarra, Christian Matar, Elody Fluck, Christian Retscher, Оleg Dubovik, Alexandru Marius Dandocsi, Arthur Lehner, Siyao Zhai, Pavel Litvinov, Manuel Dornacher, Chong Li, Anton Lopatin, David Fuertes, Tatyana Lapyonok, Goyo Cancio, Cheng Chen
article en

Abstract

Accurate characterization of the diurnal variation of aerosol optical and microphysical properties—including Aerosol Optical Depth (AOD), Angstrom Exponent (AE), and Single Scattering Albedo (SSA)—is fundamental for climate and air quality modeling. However, retrieving high-temporal-resolution, physically consistent aerosol products from multiple satellite instruments remains a challenge. Current multi-satellite approaches often rely on “post-retrieval” merging of independent Level-2 products, a process that frequently introduces physical inconsistencies and artificial temporal oscillations. To address this, building upon the recently established SYREMIS/GRASP synergetic retrieval framework (Litvinov et al., 2025), an inversion approach based on harmonized Level-1 satellite measurements, this study extensively evaluates its performance in capturing aerosol diurnal variability. We evaluate two configurations: (i) LEO + LEO synergy (Sentinel-3 A/B OLCI and Sentinel-5P TROPOMI) and (ii) LEO + GEO synergy (integrating Himawari-8 AHI). By enforcing multi-pixel smoothness constraints within the retrieval, SYREMIS maintains high physical consistency across instruments, effectively mitigating the structural discrepancies and “noise” inherent in standard merging techniques. Our validation against AERONET ground-based data and intercomparison with the XAERDT product suite confirm that SYREMIS provides high retrieval stability and accuracy. Without applying post-retrieval quality filtering, SYREMIS achieves an AOD GCOS fulfillment percentage of up to 70%, an AE RMSE below 0.3, and an SSA “Optimal” requirement fulfillment rate of up to 85% at representative sites. We demonstrate the framework's capability to resolve rapid aerosol dynamics through case studies of biomass-burning events over Thailand and Myanmar, as well as dust pollution over Qatar in 2019. As a three-month proof-of-concept assessment (March–May 2019) , these results highlight the practical capability of the established SYREMIS/GRASP approach to generate high-resolution, physically consistent, and stable aerosol data records from the measurements of multiple satellites.

Remote Sensing of EnvironmentVol. 348
Centre National de la Recherche Scientifique (FR), Chinese Academy of Sciences (CN), Université de Lille (FR), Istituto Nazionale di Fisica Nucleare, Galileo Galilei Institute for Theoretical Physics (IT), European Commission (BE), Hefei Institutes of Physical Science (CN), Laboratoire d'Optique Atmosphérique (FR), Anhui Institute of Optics and Fine Mechanics (CN)
European Space Agency, European Commission
Climate action
Openalex Percentile: Top 17%
Atmospheric aerosols and clouds
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.