Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8

For the past two decades, quantitative retrievals of aerosol optical depth (AOD) have been made from both geostationary and polar-orbiting satellites, and the results have been widely used in numerous studies. Despite the progress made in improving the accuracy of AOD retrievals, there are still major challenges, especially over land. A notable one for the so-called Dark-Target (DT) algorithms is building the surface reflectance (SR) relationships (SRR) to derive SR in the visible channels from SR in the short-wave infrared (SWIR) channel, mainly because these relationships are strongly subjected to entangled factors (e.g., viewing geometry, surface type, and vegetation state). In this study, we examine the benefits of a new method for deriving the SRR using deep learning techniques. The SRR constructed by the deep neural network (DNN) considers multiple related inputs, such as the SWIR normalized difference vegetation index (NDVISWIR), viewing geometry, and seasonality, among others. We then incorporate the DNN-constrained SRR into a DT algorithm developed at NOAA/STAR to retrieve AOD from the Advanced Himawari Instrument (AHI) onboard the new generation of geostationary satellites, Himawari-8. The revised DT algorithm with the deep learning technique (DTDL) demonstrates improved performance over the study region (95–125°E, 18–30°N, a portion of the AHI full disk), as attested by significantly reduced random noise, especially for low NDVISWIR and high surface albedo cases. Robust independent tests indicate that this algorithm can be applied to untrained regions, not only to those used in training. The method directly benefits the algorithm development for Himawari-8 and can also be adopted for other geostationary or polar-orbiting satellites. Our study illustrates how artificial intelligence could significantly improve AOD retrievals from multi-spectral satellite observations following this new approach.