Conference Agenda

Tropospheric nitrogen dioxide (NO2) is a crucial trace gas for air quality assessment, yet satellite observations often suffer from spatial gaps (e.g., cloud cover) and temporal limitations. While the geostationary satellite GEMS provides hourly data over Asia, its short historical record and missing data restrict long-term studies. Therefore, a physics-guided adapted normalization network (PhysNorm-Net) is designed to reconstruct a gapless, hourly, and high-resolution (0.05°) tropospheric NO2 dataset over Asia from 2019 to 2024.

The model features an asymmetric U-Net architecture. It handles irregular data gaps using Partial Convolution with a dynamic mask and extracts spatiotemporal representations from meteorological and chemical priors. A novel Physics-Aware Normalization (PhysNorm) module bridges the modality gap by dynamically modulating satellite feature maps using physical backgrounds, ensuring adherence to atmospheric diffusion laws.

Extensive evaluations show that PhysNorm-Net achieves high prediction accuracy (R2 = 0.886). It robustly recovers spatial morphologies and pollution plumes even under extreme missing data scenarios. The generated 2019-2024 dataset accurately captures complex diurnal variations and localized hotspots, providing valuable insights into human activities and pollution policies in Asia.

Accurate estimation of nitrogen oxide (NOx) emissions is essential for understanding their role in atmospheric chemistry and managing air pollution. This study presents a novel approach using Physics-Informed Neural Networks (PINNs) to invert NOx emissions from TROPOspheric Monitoring Instrument (TROPOMI) satellite data. By coupling the physical laws of atmospheric processes, effectively bridging traditional data assimilation techniques with the computational efficiency of deep learning. Unlike purely data-driven models, it directly integrates physical constraints from atmospheric mass continuity equation into the model training process, eliminating the need for inputs or outputs from computationally intensive chemical transport models. Application to the Yangtze River Delta region of China (2018–2023) revealed detailed spatiotemporal NOx emission trends, including the impacts of the COVID-19 pandemic and subsequent recovery. Uncertainty quantification through Monte Carlo dropout provides robust error estimates. This physics-informed approach demonstrates strong potential for efficient NOx emission inversion and offers a versatile foundation for broader quantitative remote sensing applications.

With improvements in the spatial resolution of satellite spectrometers such as TROPOMI, Sentinel-4 and Sentinel-5, more homogeneous cloudy scenes can be resolved at the pixel scale. Therefore, it is worthwhile to use a scattering cloud model in cloud retrieval algorithms. DISAMAR (Determining Instrument Specifications and Analysing Methods for Atmospheric Retrieval) is a computer model developed to simulate the retrieval of atmospheric trace gases, aerosols, clouds, and land-surface properties from passive remote-sensing observations in the 270–2400 nm wavelength range. As a line-by-line radiative transfer model, DISAMAR provides accurate simulations but is computationally expensive. Machine learning techniques can improve the speed of cloud retrieval, because a neural network trained with detailed radiative transfer calculations for scattering clouds can replace the most time-consuming part of the retrieval algorithm.

In this study, we plan to build a cloud retrieval algorithm based on DISAMAR and accelerate it using neural network methods. The algorithm uses TROPOMI observations in the O₂-A band and supports the joint retrieval of cloud optical thickness (COT) and cloud-top pressure (CTP). The neural network models are trained offline using a large, high-resolution spectral data set in the O₂-A band generated by the DISAMAR forward model. All neural networks share the same set of input features but predict different targets, including reflectance and the derivatives of reflectance with respect to cloud pressure and cloud optical thickness. These predictions are then used within an optimal estimation framework to retrieve the cloud parameters.

This study involves remote sensing and artificial intelligence technologies. The study proposed a deep learning-based algorithm to generate the nighttime visible bands for Advanced Himawari Imager geostationary satellite.

Atmospheric aerosols play a vital role in regulating air quality, ecosystems, and climate. Owing to their short atmospheric lifetime, aerosols exhibit strong spatial and temporal variability. Accurate global and regional monitoring of aerosol properties is essential for ecological processes, and radiative forcing. Satellite remote sensing has become a key tool for monitoring aerosol optical thickness (AOT) because of its broad spatial coverage. Traditional physical approaches rely on radiative transfer models (RTMs) to simulate top-of-atmosphere radiances. However, RTMs simplify the real atmosphere, and their accuracy depends strongly on assumed aerosol optical properties and surface reflectance, leading to major uncertainties and inter-algorithm discrepancies. In recent years, data-driven methods have rapidly advanced, driven by developments in machine learning and the increasing availability of collocated satellite and ground-based AOT datasets. The data-driven methods exclusively rely on the data pairs of satellite observations and ground-measured aerosol properties. It learns empirical relationships between satellite observations and measured aerosol properties, and it is more flexible to incorporate more diverse information. However, the AERONET ground stations, commonly used for training, are unevenly distributed and concentrated in urban regions, leaving other surface types such as forests and barren lands underrepresented. Besides, extreme pollution events (e.g., dust storms) are often misclassified as clouds and masked out in AERONET records, introducing bias into training datasets. To mitigate these limitations, this study proposes integrating simulated RTM data into the inversion framework to enhance the robustness and generalization of data-driven AOT retrieval models.

Short annotation如下

Reliable satellite-based estimation of near-surface air pollutants increasingly relies on data-driven models, yet their credibility is hindered by biased generalization assessment and unverified uncertainty estimates. Spatially sparse and unevenly distributed monitoring networks together with strong spatial autocorrelation cause conventional cross-validation approaches to substantially overestimate predictive skill, especially in regions lacking in situ observations. At the same time, although many models produce pixel-level uncertainty estimates, the degree to which these uncertainties reflect true prediction error remains largely unexplored.

This study introduces a controlled, model-agnostic evaluation framework to rigorously examine both spatial generalization and uncertainty reliability in air-quality remote sensing models. A chemical transport model provides a continuous, full-coverage nitrogen dioxide field that serves as an idealized truth. Sampling this field at actual monitoring locations reproduces real observational sparsity while preserving an unbiased reference for domain-wide evaluation. Multiple machine learning models are assessed using sample-based, site-based, and spatially optimized cross-validation to quantify evaluation bias and its dependence on spatial structure.

A dual-path uncertainty strategy is implemented to separately characterize aleatoric and epistemic components, complemented by diagnostic metrics assessing calibration, interval coverage, and sharpness. The framework provides a rigorous pathway for diagnosing reliability in data-driven atmospheric estimation models and supports the development of robust, trustworthy applications in quantitative remote sensing.