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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #345327

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Triple collocation based merging of satellite soil moisture retrievals

item GRUBER, A. - Vienna University Of Technology
item DORIGO, W.A. - Vienna University Of Technology
item Crow, Wade
item WAGNER, W. - Vienna University Of Technology

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2017
Publication Date: 12/28/2017
Citation: Gruber, A., Dorigo, W., Crow, W.T., Wagner, W. 2017. Triple collocation based merging of satellite soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing. 55(12):6780-6792.

Interpretive Summary: Within the past decade remotely-sensed surface soil moisture products have been produced for a large number of different satellite-based sensors. These products are of value for important agricultural applications including: drought monitoring, irrigation scheduling, and optimizing fertilizer usage. However, in order to maximize the accuracy and length of available soil moisture data sets, strategies need to be developed to optimally merge concurrent soil moisture products acquired from different satellite sensors. This manuscript presents a novel mathematical strategy for obtaining reliable error statistics for independent soil moisture products acquired from various satellite sensors and using these statistics to merge multiple soil moisture products into a single, optimized estimate. Applying this procedure will eventually improve the quality of soil moisture data products available to inform agricultural water management decisions.

Technical Abstract: We propose a method for merging soil moisture retrievals from space borne 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 un-weighted average. We apply the proposed merging scheme to active retrievals from ASCAT and passive retrievals from AMSR-E using GLDAS-Noah to complement the triplet required for TCA. The merged time series are evaluated against soil moisture estimates from ERA-Interim/Land and in situ measurements from the International Soil Moisture Network using 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 un-weighted 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.