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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 #328373

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

Location: Hydrology and Remote Sensing Laboratory

Title: Precipitation estimation using L-Band and C-Band soil moisture retrievals

item KOSTER, R. - National Aeronautics And Space Administration (NASA)
item BROCCA, LUCA - Collaborator
item Crow, Wade
item BURGIN, M. - Jet Propulsion Laboratory
item DE LANNOY, G. - National Aeronautics And Space Administration (NASA)

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/2/2016
Publication Date: 10/1/2016
Citation: Koster, R., Brocca, L., Crow, W.T., Burgin, M., De Lannoy, G. 2016. Precipitation estimation using L-Band and C-Band soil moisture retrievals. Water Resources Research. 52:7213–7225.

Interpretive Summary: The accurate measurement of daily rainfall accumulations is vital for the global monitoring of agricultural drought. However, such estimates are not typically available for large regions of the world lacking adequate ground-based rain gauge instrumentation (e.g. Africa, Central Asian and areas of South American). Recent rainfall leaves a clear impression on levels of surface soil moisture. Therefore, a potential solution to this problem is the use of remotely-sensed surface soil moisture retrievals to indirectly estimate recent rainfall accumulation. This paper develops this idea and globally applies it to the newest remotely-sensed surface soil moisture products currently available. It demonstrates - for the first time - that the newest soil moisture sensors (based on longer-wavelength portions of the microwave emission spectrum) provide the most information about recent rainfall accumulations. This result will be used by on-going USDA operational efforts to monitor the event, duration and severity of agricultural droughts world-wide.

Technical Abstract: An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS datasets is higher than that obtained with the C-band product, as might be expected given that the deeper penetration of the L-band signal into the ground provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to ~100 km and 5 days) is on average above 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.