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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Research Project #426847

Research Project: Using Triple Collocation to Leverage Sparse Ground-Based Observations for SMAP Calibration/Validation Activities

Location: Hydrology and Remote Sensing Laboratory

Project Number: 8042-13610-029-75-I
Project Type: Interagency Reimbursable Agreement

Start Date: May 19, 2014
End Date: May 18, 2019

This project seeks to implement an upscaling procedure to link ground-based observations of surface soil moisture (obtained at a point-scale) to footprint-scale (~402 km2) retrievals obtained from the NASA Soil Moisture Active/Passive Satellite mission. This upscaling procedure is needed to validate retrievals used against soil moisture networks (operated by USDA and NOAA) within the contiguous United States.

The project will address the soil moisture point-to-footprint upscaling problem by applying a triple collocation strategy to estimate and subsequently correct for the impact of random sampling error on comparisons between (satellite-based) SMAP soil moisture retrievals and point-scale ground observations. Triple collocation is a statistical technique commonly applied in the geosciences whereby the root-mean-square uncertainty of a single geophysical product is estimated through cross-comparisons against estimates of the same product acquired via two other independent means. Here the primary focus will be validating SMAP L2/3 soil moisture products using: 1) ground-based soil moisture observations obtained from a sparse ground-based network, and 2) gridded surface soil moisture products obtained independently from a land surface model. The analysis will be used to estimate the impact of spatial sampling uncertainty associated with using a single sparse ground-based observation to validate a time series of coarse-scale SMAP soil moisture retrievals. As a result, it will effectively broaden SMAP validation activities to fully leverage substantial investment in sparse ground-based instrument networks.