Abstract
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.