Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #326220

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Application of triple collocation in ground-based validation of soil moisture active/passive (SMAP) level 2 data products

Author
item CHEN, F. - Science Systems, Inc
item Crow, Wade
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item Cosh, Michael
item Jackson, Thomas
item BINDLISH, R. - Science Systems, Inc
item REICHLE, R. - National Aeronautics And Space Administration (NASA)
item CHAN, S. - Jet Propulsion Laboratory
item Starks, Patrick - Pat
item Goodrich, David - Dave
item Seyfried, Mark

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/6/2016
Publication Date: 2/1/2016
Citation: Chen, F., Crow, W.T., Colliander, A., Cosh, M.H., Jackson, T.J., Bindlish, R., Reichle, R., Chan, S., Starks, P.J., Goodrich, D.C., Seyfried, M.S. 2016. Application of triple collocation in ground-based validation of soil moisture active/passive (SMAP) level 2 data products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 99:1-14.

Interpretive Summary: Satellite-derived surface soil moisture estimates can potentially be used for a wide range of agricultural applications including: drought forecasting, yield monitoring and flood forecasting. Recently, NASA has launched the Soil Moisture Active/Passive (SMAP) mission. This mission represents that best soil moisture sensor ever launched into space and is expected to offer a significant step forward in our ability to monitor water resources within agricultural landscapes. However, prior to this, soil moisture products derived from the SMAP mission must first be validated via comparisons against ground-based soil moisture observations. This type of comparison can be challenging due to the sharp contrast between the coarse-scale nature of SMAP soil moisture retrievals (with resolutions > 10 km) and the point-scale nature of ground-based soil moisture measurements. This paper presents and evaluates a new mathematical strategy for dealing with this stark difference in resolution. It will be applied during the validation phase of the SMAP mission in order to provide a more conclusive and credible evaluation of SMAP soil moisture estimates. This, in turn, will help motivate the full use of SMAP data products in critical agricultural applications.

Technical Abstract: The validation of the soil moisture retrievals from the recently-launched NASA Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely-sensed soil moisture products to densely-instrumented sites covering an area approximating the satellite ground footprint. However, by leveraging independent soil moisture information obtained from land surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such errors on the evaluation of remotely-sensed soil moisture products. Here we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per satellite footprint) to obtain an unbiased correlation validation metric for satellite surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.