Development of Physics-Informed Convolutional Neural Network (PI-CNN) Model for Retrieval of High-resolution AOD over Cities of Indo-Gangetic Plain

Conventional satellite-based aerosol optical depth (AOD) products typically offer coarse spatial resolutions, suitable for large-scale atmospheric studies but inadequate for localized applications such as urban air quality assessments. To address this limitation, we developed a Physics-Informed Convolutional Neural Network (PI-CNN) that estimates AOD at 30m resolution using Top-of-Atmosphere (ToA) reflectance from Landsat imagery over the Delhi and Kanpur regions of the Indo-Gangetic Plain (IGP). The architecture incorporates the Radiative Transfer Model (RTM) equations into the CNN structure, ensuring physically consistent retrievals. The model was trained over Kanpur using physics-based AOD estimates as training targets, and fine-tuned to Delhi through transfer learning. Evaluation against AERONET observations yielded correlation coefficients of 0.81 and 0.78 for Kanpur and Delhi, respectively, with corresponding MAE/RMSE values of 0.046/0.21 and 0.066/0.25. Furthermore, PI-CNN was compared with the traditional SEMARA retrieval method, which captured extreme values more effectively. In contrast, PI-CNN provided smoother, more generalized outputs with higher spatial variability than SEMARA. PI-CNN effectively reproduced the spatial distribution of AOD across different land use land cover (LULC), showing strong consistency with SEMARA and demonstrating its reliability in capturing spatial variations. These findings highlight the potential of PI-CNN as a flexible and scalable framework for retrieving high-resolution, physics-consistent AOD datasets across local to global scales.