Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals

Abstract

We propose a method for merging soil moisture retrievals from spaceborne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from using triple collocation analysis (TCA). In regions where TCA is deemed unreliable, we use correlation significance levels ( $p$ -values) as indicator for retrieval quality to decide whether to use active data only, passive data only, or an unweighted average. We apply the proposed merging scheme to active retrievals from advanced scatterometer and passive retrievals from the Advanced Microwave Scanning Radiometer—Earth Observing System using Global Land Data Assimilation System-Noah to complement the triplet required for TCA. The merged time series is evaluated against soil moisture estimates from ERA-Interim/Land and in situ measurements from the International Soil Moisture Network using the European Space Agency’s (ESA’s) current Climate Change Initiative—Soil Moisture (ESA CCI SM) product version v02.3 as benchmark merging scheme. Results show that the $p$ -value classification provides a robust basis for decisions regarding using either active or passive data alone, or an unweighted average in cases where relative weights cannot be estimated reliably, and that the weights estimated from TCA in almost all cases outperform the ternary decision upon which the ESA CCI SM v02.3 is based. The proposed method forms the basis for the new ESA CCI SM product version v03.x and higher.