SMAP and Agricultural Productivity Applications

Wade T. Crow

USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD

Thanks: John Bolten (NASA GSFC), Xiwu Zhan (NOAA NESDIS) and Curt Reynolds (USDA FAS IPAD)

ISDWG SMAP Applications Workshop March 3, 2011

Funded by the NASA Applied Sciences Program (joint with NASA HSB, NESDIS STAR, USDA ARS, and USDA FAS, PI: Wade Crow, Program Manager: Brad Doorn)

SMAP Mission Concept

• L-band unfocused SAR and radiometer system with offset-fed 6-m light-weight deployable mesh reflector rotating about nadir axis (14.6 rpm) Single feed (dual-pol radar and polarimetric radiometer)

Conical scan, fixed incidence angle across swath

Contiguous 1000 km swath

Radar resolution: 1-3 km (degrades over center 30%)

Radiometer resolution: 40 km

• Sun-synchronous dawn/dusk orbit

• Mission Ops duration 3 years

The USDA Foreign Agricultural Service (FAS) International Production Assessment Division (IPAD):

• Monthly global production estimates for commodity crops.

• 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

Past USDA FAS/IPAD Treatment of Soil Moisture

Time/space attributes:

- 12-km resolution.

- Global coverage within agriculturally relevant areas.

- Monthly reporting cycle (< weekly latency).

Crop Stress (Alarm) Models

Crop Models

2-Layer Soil Moisture Model Analysts

Global Rain and Met Forcing Data

Remotely-Sensed Soil Moisture

Data Assimilation

Current 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)

Crop Stress (Alarm) Models

Crop Models

2-Layer Soil Moisture Model Analysts

Global Rain and Met Forcing Data

Remotely-Sensed Soil Moisture

Data Assimilation

What is the added value of integrating remotely-sensed soil moisture information?

Data Denial Methodology

Can the poor-precipitation open loop case be corrected to resemble the good-precipitation benchmark case through the assimilation of remotely-sensed soil moisture (EnKF case)?

Example Time Series Results

Improvement in Root-Zone RMSE Fit to Benchmark

Bolten, J.D., W.T. Crow, T.J. Jackson, X. Zhan and C.A. Reynolds, IEEE Journal of Selected Topics in Applied Earth

Observations and Remote Sensing, 3, 57-66, 10.1109/JSTARS.2009.2037163, 2010.

Improvement in Lagged Correlation with EVIPreliminary 2002 to 2008 results in United States Southern Great Plains

Summary:

• X-band AMSR-E soil moisture retrievals can add value to both global-scale root-zone soil moisture monitoring and vegetation status forecasting activities.

• Added skill will be enhanced by improved L-band SMAP retrieval accuracy (versus existing X-band AMSR-E).

• Evaluate added skill all the way to agricultural yield and production statistics (the central goal of USDA FAS IPAD).

Thank you….questions?