Identification of Bias in Satellite Measurements Using its Geospatial Properties

Identification of bias in satellite retrievals is a challenging task as the bias, by definition, is the difference between the average of measurements made on the same object and its true value. Given so, the identification of bias requires knowledge of the true value, which is sometimes impossible to obtain except through actual measurements. Two common types of approaches are deployed to avoid this circularity: 1) either measurements are compared with a secondary measurement platform, which often only partially overlaps with primary measurements in space-time, or 2) by examining the internal properties of the measurements as different sorts of inconsistencies under specific circumstances can point to a bias. In this letter, we use the recent advances in space and space-time modeling and show that inconsistencies in interpolated satellite retrievals using spatial-only and spatio-temporal kriging point to possible bias in the measurements due to specific spatio-temporal properties of the field of bias, which often mimics the spatio-temporal properties of the causal phenomena. We suggest a new data quality ranking system based on the absence of this inconsistency. We demonstrate the method using the Global Ozone Monitoring Experiment-2 (GOME-2) satellite retrievals of chlorophyll-induced fluorescence (SIF) and Greenhouse Gases Observing Satellite (GOSAT) retrievals of XCO2.

Automated summary

Identifying bias in satellite retrievals is challenging, with two common approaches to avoid circularity: comparing measurements with a secondary platform or examining internal properties of measurements. Inconsistencies in interpolated satellite retrievals can point to possible bias, and a new data quality ranking system is suggested based on the absence of this inconsistency.