Application of Terrestrial Microwave Remote Sensing to Agricultural

  • Slides: 21
Download presentation
Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring Wade Crow USDA ARS

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring 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

Potential Agricultural Users of Satellite Soil Moisture • Potential agricultural users are diverse (irrigation,

Potential Agricultural Users of Satellite Soil Moisture • 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).

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

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.

Baseline USDA FAS Treatment of Soil Moisture Goal: Use global soil moisture products (among

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. 2 -Layer Soil Moisture Model Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models Analysts

Modifications Examined by Project What is the added value of assimilating remotely-sensed soil moisture

Modifications Examined by Project 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? Remotely-Sensed Soil Moisture Data Assimilation “Modern” Land Surface Model Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models Analysts

How do We Evaluate These Modifications? 1) Obtain a multi-year, monthly, 0. 25° root-zone

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].

Global Rank Correlations for Model and Data Assimilation Rank correlation between soil moisture for

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

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

2002 -2010 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

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

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P)

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P) C (A) L (A/P) Resolution: 30 km 45 km 50 km 10 km Dates: 2002 -2011 2010 2008 2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2 -3 days (at midlatitudes)…latency is on the order of 12 -24 hours.

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P)

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P) C (A) L (A/P) Resolution: 30 km 45 km 50 km 10 km Dates: 2002 -2011 2010 2008 2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2 -3 days (at midlatitudes)…latency is on the order of 12 -24 hours.

Current SMOS Data Assimilation Product ESA Soil Moisture Ocean Salinity (SMOS) Mission • L-band

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

Current SMOS Data Assimilation Product Model-only SMOS+ Model (En. KF) Operationally Implemented in Crop

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

Current SMOS Data Assimilation Product

Current SMOS Data Assimilation Product

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P)

Remotely-Sensed Soil Moisture Resources Sensor: AMSR-E SMOS ASCAT SMAP Band: C/X (P) L (P) C (A) L (A/P) Resolution: 30 km 45 km 50 km 10 km Dates: 2002 -2011 2010 2008 2015 - Past/Current/Future Repeat time (for all sensors) on the order of 2 -3 days (at midlatitudes)…latency is on the order of 12 -24 hours.

NASA SMAP Mission Concept • L-band unfocused SAR and radiometer system, offset-fed 6 -m,

NASA SMAP Mission Concept • L-band unfocused SAR and radiometer system, offset-fed 6 -m, light-weight deployable mesh reflector. Shared feed for: Ø 1. 26 GHz HH, VV, HV Radar at 1 -3 km (30% nadir gap) Ø 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 6 am/6 pm orbit (680 km) • Launch: January 29, 2015 • Mission duration 3 years • Improved resolution relative to SMOS (10 -km versus 45 -km).

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

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

Thank you… Bolten, J. D. and W. T. Crow, "Improved prediction of quasi-global vegetation

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, L 19406, 10. 1029/2012 GL 053470, 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, 34513460, 10. 5194/hess-16 -3451 -2012, 2012.

Prototype SMAP-Based Data Assimilation System “SMAP Passive” = SMOS “SMAP Active” = ASCAT ACTIVE

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

Current SMOS Data Assimilation Product Soil Moisture Remote Sensing: • SMOS L 2 (0

Current SMOS Data Assimilation Product Soil Moisture Remote Sensing: • SMOS L 2 (0 -5 cm) surface soil moisture. • ~45 -km spatial resolution. • L 2 to L 3 conversion (0. 25°) by NESDIS SMOPS. • ~2 retrievals every 3 -days. • ~12 -hour latency. Soil Moisture Data Assimilation: el d Mo Model Observation • Modified 2 -Layer Palmer Model at 0. 25°. • AFWA LIS forcing/daily time step. • Assimilate SMOS L 3 into model. • 30 -member Ensemble Kalman filter (En. KF). • Use En. KF to update surface and root-zone. • 3 -day composite delivered at ~4 -day latency (from start of composite period).

Global Rank Correlations for Various Models Rank correlation between soil moisture for month i

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