Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds (USDA FAS), Christa Peters-Lidard (NASA GSFC), John Eylander (AFWA) and Sujay Kumar (NASA GSFC/SAIC) Funded by the NASA Applied Sciences Water Resources Application Area Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

Potential agricultural users are diverse (irrigation, crop insurance, yield forecasting, management practice assessment, etc). (Arguably) the most well-devopled agricultural applications is regional-scale crop condition and production estimates [appropriate spatial scales]. Within the United States, this activity is carried out by the USDA Foreign Agricultural Service (FAS) International Production Assessment Division (IPAD). Potential Agricultural Users of Satellite Soil Moisture

The USDA Foreign Agricultural Service (FAS) International Production Assessment Division (IPAD): Monthly global production estimates for commodity crops. Regional/national scales. Vital for economic competitiveness, national security and food security applications. Utilizes a wide-range of satellite data sources, input databases, climate data, crop models, and data extraction routines to arrive at yield and area estimates. Analyst-based decision support system. Characterizing the extent and impact of agricultural drought (i.e. root-zone soil moisture limitations) is critical for monitoring variations in agricultural productivity.

Crop Stress (Alarm) Models Crop Models 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Baseline USDA FAS Treatment of Soil Moisture Goal: Use global soil moisture products (among many other things) to forecast variations in international agricultural productivity and yield. Baseline Approach: Global application of a (simple) soil water balance model.

Crop Stress (Alarm) Models Crop Models “Modern” Land Surface Model Analysts Global Rain and Met Forcing Data Remotely-Sensed Soil Moisture Modifications Examined by Project Data Assimilation What is the added value of assimilating remotely-sensed soil moisture information? What is the added value of applying a more complex land surface model?

How do We Evaluate These Modifications? 1)Obtain a multi-year, monthly, 0.25° root-zone soil moisture (SM) product. 2)Obtain a multi-year, monthly, 0.25° vegetation indices (NDVI) product. 3)Sort both by month-of-year and rank across all years of the multi-year data set. (e.g., count all June’s in that are drier than June 2005). 4) Calculate the cross-correlation of SM/VI ranks. For a 0.25° OK box: Soil Moisture is black NDVI is red. Degree of cross-correlation depends on: 1)Climate (water versus energy limited growth conditions). 2)Accuracy of the NDVI product. 3)Accuracy of the SM product [Peled et al., 2010].

Global Rank Correlations for Model and Data Assimilation Rank correlation between soil moisture for month i versus NDVI for month i+1

Performance in Data-Poor Regions 6 of the 10 most “food insecure” countries in the world.

Seasonality Impacts Rank correlation between soil moisture for month i versus NDVI for month i+1

Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km SMOSL (P) 45 km ASCAT C (A)50 km SMAP L (A/P)10 km Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of hours.

Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km SMOSL (P) 45 km ASCAT C (A)50 km SMAP L (A/P)10 km Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of hours.

L-band passive Non-scanning - aperture synthesis using a 2-D radiometer array. Launched in October Planned operations until (at least) mid ESA Soil Moisture Ocean Salinity (SMOS) Mission Current SMOS Data Assimilation Product

Model-only SMOS+ Model (EnKF) Current SMOS Data Assimilation Product Operationally Implemented in Crop Explorer as of April

Current SMOS Data Assimilation Product

Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km SMOSL (P) 45 km ASCAT C (A)50 km SMAP L (A/P)10 km Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of hours.

L-band unfocused SAR and radiometer system, offset-fed 6-m, light-weight deployable mesh reflector. Shared feed for:  1.26 GHz HH, VV, HV Radar at 1-3 km (30% nadir gap)  1.4 GHz H, V, 3 rd and 4 th Stokes Radiometer at 40 km Conical scan, fixed incidence angle across swath Contiguous 1000 km swath with 2-3 days revisit (less for combined ascending/descending) Sun-synchronous 6am/6pm orbit (680 km) Launch: January 29, 2015 Mission duration 3 years Improved resolution relative to SMOS (10-km versus 45-km). NASA SMAP Mission Concept

Microwave SM Drought Thermal SM VIS/NIR SM Time/Space Scale Considerations Data Fusion Techniques SAR SM

Thank you… Bolten, J.D. and W.T. Crow, "Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture," Geophysical Research Letters, 39, L19406, /2012GL053470, Crow, W.T., S.V. Kumar and J.D. Bolten, "On the utility of land surface models for agricultural drought monitoring," Hydrologic and Earth System Sciences, 16, , /hess , 2012.

“SMAP Passive” = SMOS “SMAP Active” = ASCAT Clear benefits to integrating active and passive soil moisture products! ACTIVE + PASSIVE PASSIVE-ONLY Prototype SMAP-Based Data Assimilation System

SMOS L2 (0-5 cm) surface soil moisture. ~45-km spatial resolution. L2 to L3 conversion (0.25°) by NESDIS SMOPS. ~2 retrievals every 3-days. ~12-hour latency. Modified 2-Layer Palmer Model at 0.25°. AFWA LIS forcing/daily time step. Assimilate SMOS L3 into model. 30-member Ensemble Kalman filter (EnKF). Use EnKF to update surface and root-zone. 3-day composite delivered at ~4-day latency (from start of composite period). Soil Moisture Remote Sensing: Soil Moisture Data Assimilation: Model Observation Model Current SMOS Data Assimilation Product

Global Rank Correlations for Various Models Rank correlation between soil moisture for month i versus NDVI for month i+1 COMPLEX SIMPLE