Work

Scene-Aware Correction of Scalar-to-Vector Reflectance in the Visible NO2 Retrieval Window

Year: 2026

Type: article

Abstract: Polarization affects forward modeling in the 420–455 nm visible NO2 retrieval window, yet operational retrievals require computational efficiency. We generated 205,200 paired scalar DISORT and vector VLIDORT simulations for a clear atmosphere and four pure-aerosol scenes (sulfate, sea salt, soot, and mineral dust), covering four NO2 vertical column densities, 19 wavelengths, viewing geometries, and surface albedos. Vector Stokes-I reflectance was the reference. We compared scene-specific inverse-distance-weighted look-up tables (IDW-LUTs) with unified and scene-specific backpropagation (BP), histogram-based gradient boosting (HGB), and Transformer models. Test samples were held out by geometry–surface state. The scalar baseline yielded an RRMSE of 3.0620%. IDW-LUT, unified BP, and unified HGB reduced this error by 44.90%, 79.48%, and 82.27%, respectively. The unified Transformer performed best (MARE = 0.1133%; RRMSE = 0.2402%; P95 = 0.3008%; 92.15% RRMSE reduction), at 0.4636 ms per 19-wavelength spectrum and 0.1159 MiB storage. The unified Transformer offers a strong accuracy–storage balance, while unified BP was the fastest among the unified machine-learning models. Conclusions are limited to clear-sky, pure, known-aerosol scenes; mixed aerosols, clouds, profile variability, instrument effects, and end-to-end NO2 retrievals remain to be tested.

Source: Sensors

DOI: 10.3390/s26217084

Authors: Yuqian Zhang, Kun Wu, Yijing Lu

Institutions: Nanjing University of Information Science and Technology, ETH Zurich, Institute for Atmospheric and Climate Science, Cornell University

Language: English

Topic: Atmospheric aerosols and clouds

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137