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Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.

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Presentation on theme: "Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds."— Presentation transcript:

1 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

2 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

3 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.

4 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.

5 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?

6 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 2000-2010 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].

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

8 2002-2010 Performance in Data-Poor Regions 6 of the 10 most “food insecure” countries in the world.

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

10 Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km2002-2011 SMOSL (P) 45 km2010 - ASCAT C (A)50 km2008 - SMAP L (A/P)10 km2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of 12 -24 hours.

11 Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km2002-2011 SMOSL (P) 45 km2010 - ASCAT C (A)50 km2008 - SMAP L (A/P)10 km2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of 12 -24 hours.

12 L-band passive Non-scanning - aperture synthesis using a 2-D radiometer array. Launched in October 2009. Planned operations until (at least) mid-2017. ESA Soil Moisture Ocean Salinity (SMOS) Mission Current SMOS Data Assimilation Product

13 Model-only SMOS+ Model (EnKF) Current SMOS Data Assimilation Product Operationally Implemented in Crop Explorer as of April 2014. www.pecad.fas.usda.gov/cropexplorer/

14 Current SMOS Data Assimilation Product

15 Remotely-Sensed Soil Moisture Resources Sensor:Band:Resolution: Dates: AMSR-EC/X (P) 30 km2002-2011 SMOSL (P) 45 km2010 - ASCAT C (A)50 km2008 - SMAP L (A/P)10 km2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2-3 days (at mid- latitudes)…latency is on the order of 12 -24 hours.

16 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

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

18 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, 10.1029/2012GL053470, 2012. 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, 3451- 3460, 10.5194/hess-16-3451-2012, 2012.

19 “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

20 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

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

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