A Data-Mining Approach for the Validation of Aerosol Retrievals

Operational algorithms for retrieval of aerosols from satellite observations are typically created manually based on the domain knowledge. Validation studies, where the retrievals are compared to the available ground-truth data, are periodically performed with the goal of understanding how to further improve the quality of the retrieval algorithms. This letter describes a data-mining approach aimed to facilitate this highly labor-intensive process. It is based on training a neural network for retrieval and comparing its performance with that of the operational algorithm. The situations, where a neural network is more accurate, point to the weaknesses of the operational algorithm that could be corrected. Use of decision trees is proposed to provide easily interpretable descriptions of such situations. The approach was applied on 3646 collocated Moderate Resolution Imaging Spectroradiometer and AERONET observations over the continental U.S. related to the retrieval of aerosol optical thickness. The experiments showed that the approach is feasible and that it can be a valuable tool for the domain scientists working on the development of retrieval algorithms.