Soil Moisture Presented by Xiwu Zhan Center for

Soil Moisture Presented by Xiwu Zhan Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000

Requirement, Science, and Benefit Requirements/Objectives • Weather & Water: – Increase lead time and accuracy for weather and water warnings and forecasts – Increase development, application, and transition of advanced science and technology to operations and services – Reduce uncertainty associated with weather and water decision tools and assessments Science • How can accuracy in satellite-derived soil moisture estimates be improved and uncertainty be reduced? • What are optimal techniques and algorithms by which multi-platform and multi-spectral satellite observations can be integrated with models and in situ observations to provide reliably global soil moisture data? Benefit • • Enable better drought monitoring to benefit agriculture and water resources management Enable better forecasts of flashflood to reduce life and property loss from disasters Provide better information on ground conditions to help military mobility decision support Provide better input to numerical weather prediction models to improve weather forecasts Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2

Challenges and Path Forward • Science Challenges – – Increase retrieval algorithm accuracy Increase data product spatial resolution Improve root zone soil moisture estimates In situ soil moisture measurements for validation • Next Steps – Refine algorithm for SMOPS and future data from SMOS, Aquarius, & SMAP – Refine data merging approaches for generating higher resolution soil moisture data by combining MW radiometer and MW radar or TIR observations – Improve and refine data assimilation algorithm for deeper layer soil moisture • Transition Paths – – – Continue the SMOPS development collaborating with OSDPD via SPSRB Assimilate SM observations into NWP models via JCSDA Apply SM data in drought monitoring and flash flood forecasts via CPO Integrate SM observations and model output for crop forecasts via NASA Cross-calibrate MW sensors and generate long term SM data record (? ) Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 3

Soil Moisture Remote Sensing Science Microwave (MW): Observed MW brightness temperature depends on soil dielectric constant that is related to soil moisture: – Strength: higher reliability based on direct physical relationships – Weakness: antenna technology limits spatial resolution • Thermal Infrared (TIR): Observed surface temperature changes result from surface energy balance that is dependent on soil moisture: – Strength: TIR sensor could have higher spatial resolution – Weakness: relies on land surface energy balance model that is prone to input data errors. Sensitivity (Delta TB / Delta Vol SM) • 4. 0 0 BARE 3. 0 1 VEGETATION WATER CONTENT (kg/m 2) 2. 0 2 4 1. 0 0 5 SSM/I Two-Source Model (ALEXI) Two ways to retrieve soil moisture from satellites: Microwave Sensitivity By Wavelength and Vegetation Density TMI 10 AMSR / MIS Wavelength (cm) 15 20 25 SMOS / SMAP ABL Ta Ra H = Hc + H s Hc T ac R soil Ts Rx Tc Hs Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 TRAD fc 5 5 km km 4

Soil Moisture Research at STAR • Various MW satellite soil moisture products are evaluated against in situ measurements collaborating with USDA and NASA colleagues AMSR-E SM Data from STAR (blue) show better temporal and spatial variations than NASA baseline product (red) against in situ measurements (black) • An improved single-channel retrieval algorithm is used to generate MW satellite soil moisture products from TMI, AMSR-E, Wind. Sat and posted through the STAR Soil Moisture Data Portal • ALEXI model is being tested and will be implemented within STAR to generate GOES-based ET and soil moisture data products for use in EMC’s Noah LSM validation and CPC’s drought monitoring Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 5

Soil Moisture Research at STAR • Observations from microwave satellite sensors are found to have significant calibration differences with the Simultaneous Conicalscan Overpass (SCO) method • Single-Channel Retrieval (SCR) algorithm is less sensitive to calibration difference while the Multi-Channel Inversion (MCI) algorithm may fail for large calibration errors • A new algorithm combining the SCR and MCI algorithms is being tested and will be used in NESDIS Soil Moisture Operational Product System (SMOPS) Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6

Soil Moisture Operational Product System SMOPS will • Generate global soil moisture data sets from the best available microwave satellite sensors (AMSR-E, ASCAT, SMOS, etc) based on the sensor crosscalibration technique and the combined algorithm developed at STAR • Serve as a friendly provider of global climatologically consistent soil moisture data provider for NCEP NWP soil moisture data needs • Disseminate data to users via NOAA’s Comprehensive Large Array-data Stewardship System (CLASS) Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7

Soil Moisture Data Assimilation • An Ensemble Kalman Filter (En. KF) data assimilation (DA) algorithm was implemented and examined to assimilate AMSR-E global soil moisture data into Noah land surface model • Soil moisture estimates after assimilating satellite data demonstrate better agreement with in situ measurements Original AMSR-E SM Original Noah SM after DA DA Processed AMSR-E SM • En. KF data assimilation also improves deeper layer soil moisture estimates that will be used for crop production and drought monitoring Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 8

Challenges and Path Forward • Science Challenges – – Increase retrieval algorithm accuracy Increase data product spatial resolution Improve root zone soil moisture estimates In situ soil moisture measurements for validation • Next Steps – Refine algorithm for SMOPS and future data from SMOS, Aquarius, & SMAP – Refine data merging approaches for generating higher resolution soil moisture data by combining MW radiometer and MW radar or TIR observations – Improve and refine data assimilation algorithm for deeper layer soil moisture • Transition Paths – – – Continue the SMOPS development collaborating with OSDPD via SPSRB Assimilate SM observations into NWP models via JCSDA Apply SM data in drought monitoring and flash flood forecasts via CPO Integrate SM observations and model output for crop forecasts via NASA Cross-calibrate MW sensors and generate long term SM data record (? ) Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 9