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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: 8042-13610-029-075-I
Project Type: Interagency Reimbursable Agreement

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

Objective:
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.

Approach:
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.