Accounting for seasonal retrieval errors in the merging of multi-sensor satellite soil moisture products

Abstract

ESA CCI soil moisture (SM) merges satellite microwave remote sensing datasets by means of their inverse-uncertainty weighted average. Estimates of uncertainty are produced with Triple Collocation Analysis (TCA) and assume a constant level of noise for the entire sensor period. However, errors in soil moisture retrievals vary throughout the year, since many impacting environmental parameters are characterized by a seasonality of their own. Here, we attempt to quantify this seasonal component and assess the impact of time-variant uncertainty estimates on the quality of merged soil moisture. We derive a long-term error variance estimate for three satellite products (from ASCAT, AMSR2, and SMAP) per day of year using a sliding window of 90 days. Merging weights climatologies are subsequently obtained as the inverse of such uncertainty. We analyse the impact of the modified approach by comparison with the merging based on stationary uncertainties/weights. The two key findings are that (i) the merged soil moisture estimates do not differ significantly between the stationary and the seasonal merging because seasonal uncertainty variations, e.g. caused by vegetation cover, usually affect all satellite missions in a similar way and thus cause only marginal changes in their relative weighting; yet, (ii) an evaluation against in situ data suggests that the estimated uncertainties of the new merged product are more representative of their seasonal behaviour. Based on these findings, we conclude that using a seasonal TCA approach can add value to merged products such as the ESA CCI SM by providing a more realistic characterization of dataset uncertainty – in particular its temporal variation.