Related Research Areas
Carbon Cycle & Ecosystems

The upcoming deployment of NASA Soil Moisture Active/Passive (SMAP) mission represents an important milestone in efforts to develop science applications for remote sensing observations. However, the vertical support of SMAP soil moisture retrievals (top 2 to 5 cm of the soil column) are generally considered too shallow for most ecologic applications. Consequently, it is currently unclear what the actual added impact of SMAP surface soil moisture observations will be on ecosystem and agricultural productivity forecasting applications relying on root-zone soil moisture predictions to capture agricultural drought impacts. Productivity monitoring and forecasting applications are critical for a variety of ecosystems. In agricultural systems, crop growth and eventual yield can be predicted based on an accurate assessment of growing season climate and water availability conditions. A critical issue for SMAP is whether its surface soil moisture retrievals can be extrapolated to root-zone depths with sufficient accuracy to add significant skill to end of season yield and productivity forecasts. Examining this issue with agricultural landscapes, with well-developed crop growth models and operational forecasting systems, will provide a critical first test case for other ecosystem types. In such tests, the level of yield forecasting skill available from a non-SMAP system (relying exclusively on unconstrained water balance modeling forced by observed precipitation) represents a critical baseline that must be materially enhanced for SMAP measurements to be considered of value. Our central goal here will be to prepare for the application of SMAP measurements to a specific ecological forecasting activity - yield and productivity prediction for agricultural and rangeland ecosystems. This preparation will be based on a series of synthetic data assimilation experiments designed to clarify whether SMAP surface soil moisture retrievals can be reliably extrapolated to root-zone ap depths with sufficient accuracy to add significant skill to end-of-season yield and productivity forecasts.

Project PI: Wade Crow/USDA ARS

USDA-ARS Hydrology and Remote Sensing Laboratory Bldg. 007, Rm. 104, BARC-West Beltsville, MD 20705-2350 USA

Phone: (301) 504-6847

Fax: (301) 504-8931



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Started: Sep 02, 2010

Last Activity: Mar 18, 2011


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