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Rethinking Satellite Data Merging: From Averaging to SNR Optimization

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posted on 23.03.2021, 14:35 by Seokhyeon Kim, Ashish Sharma, Yi Y. Liu, Sean I. Young
Merging of multiple satellite datasets is a simple yet effective way to reduce prediction error. However, most merging methods for satellite data today are based on weighted averaging first proposed in 1969 for economic forecasting, which does not provide optimal outcomes when applied to satellite data. If our aim is to produce a merged data product that minimizes the prediction errors against a prediction target, there is no reason to insist that the merged product be an average of the parent datasets. A more disciplined approach based on mathematical optimization would be to minimize prediction errors. However, formulating merging as an optimization problem is insufficient by itself as the statistics needed for optimization, e.g. signal-to-noise ratio (SNR) of parent products, are often unavailable in practice and must be estimated jointly. In this paper, we address both of these problems for data merging. We first formulate optimal merging of satellite data as a SNR optimization (SNR-opt), and propose an estimation method to jointly estimate the required SNRs. This SNR-based approach has a natural interpretation as a multi-input single-output Wiener filter. Through extensive experimental validation on three global- scale satellite-derived soil moisture and land surface temperature products, we demonstrate that our SNR optimization significantly improves merging results over weighted averaging schemes.

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Email Address of Submitting Author

sean0@stanford.edu

Submitting Author's Institution

Stanford University

Submitting Author's Country

United States of America

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