Bayesian denoising of satellite images using co-registered NO2 images

Abstract

Accurate emission tracking (e.g., locating and quantifying hot spots) using satellite images requires a good signal-to-noise ratio (SNR) of total column images. Achieving this SNR is challenging for satellite-based trace gas imagers, especially when enhancements are small relative to the background or small relative to retrieval uncertainty. Therefore, some satellites carry additional trace gas imagers with high SNR, such as NO2, which is co-emitted with the trace gas of interest. While NO2 is frequently used qualitatively for plume detection or plume fitting, its potential for quantitative noise reduction remains largely untapped. This paper presents two methods to enhance the SNR of total column images using co-registered NO2 images through minimum mean square error (MMSE) Bayesian denoising, which are a simple form of a Kalman filter or maximum a posteriori estimate. The first “joint MMSE” method relies on the presence of plumes in both the low- and co-registered high-SNR NO2 images. The second “self-similar MMSE” method utilizes image self-similarity and is based on an existing technique called BM3D. The methods are evaluated using a synthetic dataset (SMARTCARB) of atmospheric CO2 and NO2 concentrations, achieving over +40 dB improvement in peak SNR (i.e., an over 10(40/10) increase in SNR). Additionally, the methods are applied to TROPOMI SO2 and NO2 data over South Africa and used to compute a divergence image, demonstrating that an estimated 40 %–80 % noise reduction is possible. By enhancing the SNR of total column images, these techniques improve the detectability of subtle emission signals, which could benefit atmospheric monitoring applications.