Estimating regional ground-level PM2.5 directly from satellite top-of-atmosphere reflectance using deep learning

TL;DR

This study demonstrates that deep learning can directly estimate ground-level PM2.5 from satellite top-of-atmosphere reflectance, bypassing traditional aerosol optical depth retrieval methods, with high accuracy and finer spatial resolution.

Contribution

It introduces a novel deep learning approach for direct PM2.5 estimation from TOA reflectance, eliminating the need for physical radiative transfer models.

Findings

Achieved high correlation with ground measurements (R2=0.87).

Produced PM2.5 maps with finer spatial resolution.

Outperformed traditional AOD-based models.

Abstract

Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground-level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work are achieved based on a machine learning model. Specifically, we establish the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref-PM modeling). Taking the Wuhan Urban Agglomeration (WUA) as a case study, the results demonstrate that compared with the AOD-PM modeling, the Ref-PM modeling obtains a competitive performance, with out-of-sample cross-validated R2 and RMSE values of 0.87 and 9.89 ug/m3 respectively. Also, the TOA-reflectance-derived PM2.5 have a finer resolution and larger spatial coverage than the AOD-derived PM2.5. This work updates the traditional cognition of remote sensing PM2.5 estimation and has the potential to promote the application in atmospheric environmental monitoring.