1. Enhancing vegetation productivity forecasting using remotely-sensed surface soil moisture retrievals Wade T. Crow USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA John D. Bolten NASA Goddard Space Flight Center, Greenbelt, MD, USA Research funded by a grant from the NASA Applied Sciences Program (W.T. Crow PI, Bradley Doorn, program manager)

3. Vital for economic competitiveness, national security and food security applications.

4. Utilizes a wide-range of satellite data sources, input databases, climate data, crop models, and data extraction routines to arrive at yield and areaestimates.

6. Global coverage within agriculturally relevant areas.

7. Monthly reporting cycle (< weekly latency).2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

8. New USDA IPAD Treatment of Soil Moisture Funded by the NASA Applied Sciences Program (joint with NASA HSB, NESDIS STAR, USDA ARS, and USDA FAS, W.T. Crow PI, Brad Doorn, program manager) Remotely-Sensed Soil Moisture Data Assimilation 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

9. What is the added value of integrating remotely-sensed soil moisture information? Remotely-Sensed Soil Moisture Data Assimilation 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

13. Consider both LRPM soil moisture and vegetation opacity retrievals.

15. Model uncertainty (Q) is parameterized as function of average distance from nearest WMO station.

17. 60º S to 60º N

18. Barren and forested areas removed.

19. Monthly daily max. temp. > 5º C

20. LPRM snow cover maskNo data assimilation…yet LPRM soil moisture provides the most forecasting information

21. Soil moisture DA impact on lag = -1 month correlation Water Balance Model Only EnKF (Water Balance Model + LPRM AMSR-E) EnKF – Water Balance Model (Net Impact of LPRM AMSR-E)

22. Soil moisture DA impact on lag = -1 month correlation EnKF – Water Balance Model (Net Impact of LPRM AMSR-E)

23. Variation of added skill between “data-rich” and “data-poor” areas “Data-rich” countries July 20, 2011 - UN declares Somalia famine in Bakool and Lower Shabelle “Data-poor” and food-insecure countries

24. Conclusions 1) The assimilation of surface soil moisture retrievals significantly improves the utility of root-zone soil moisture estimates for vegetation condition/productivity forecasting. 2) Added benefits (relative to model-only) are relatively small in data-rich areas but large in data-poor locations prone to food insecurity. Future Work Apply to SMOS. Bolten, J.D. and W.T. Crow, “Improved forecasting of quasi-global vegetation conditions using remotely-sensed surface soil moisture,” in submission, 2011.