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Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach

Handschuh, Jana and Erbertseder, Thilo and Baier, Frank (2023) Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach. Remote Sensing, 15 (8), pp. 2064-2083. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs15082064. ISSN 2072-4292.

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Official URL: https://dx.doi.org/10.3390/rs15082064


The latest epidemiological studies have revealed that the adverse health effects of PM2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM2.5 distributions. Although the accuracy and reliability of satellite-based PM2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R2 values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI.

Item URL in elib:https://elib.dlr.de/196360/
Document Type:Article
Title:Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Handschuh, JanaUNSPECIFIEDhttps://orcid.org/0009-0006-9806-496XUNSPECIFIED
Erbertseder, ThiloUNSPECIFIEDhttps://orcid.org/0000-0003-4888-1065UNSPECIFIED
Baier, FrankUNSPECIFIEDhttps://orcid.org/0000-0002-3425-6309UNSPECIFIED
Date:13 April 2023
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 2064-2083
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:satellite AOD; PM2.5; random forest; feature importance; Germany
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Atmosphere
Deposited By: Erbertseder, Thilo
Deposited On:18 Sep 2023 09:30
Last Modified:19 Sep 2023 08:25

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