Scalable Uncertainty Quantification for Multisource Remote-Sensing Data Products

A wide array of data products from many Earth-observing satellite platforms are providing valuable insights on a range of geophysical processes at fine spatial and temporal resolution. Leveraging complementary information from multiple remote-sensing instruments, along with geophysical models, can provide substantial utility for science and applications. The added utility from these derived science data products often comes with added complexity and computational cost for uncertainty quantification (UQ) due to variable fidelity of input uncertainty and nonstationary spatio-temporal correlation. This work outlines a framework for UQ for these derived products that combines generative flexibility of transport maps (TMs) with scalable spatial statistical methods for dense geophysical fields. The presentation will illustrate the approach for estimates of atmospheric aerosols for NASA's upcoming Multi-Angle Imager for Aerosols (MAIA) mission. These aerosol estimates will be delivered for 1-km footprints for multiple mission target areas across the globe, providing information for air quality and health applications.

Uncertainty quantification

Remote sensing

Generative model

Transport map

Air quality 

Section on Statistics and the Environment