Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight...
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Transcript of Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight...
![Page 1: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/1.jpg)
Wade CrowUSDA ARS Hydrology and Remote Sensing Laboratory
John BoltenNASA 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
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• 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
![Page 3: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/3.jpg)
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.
![Page 4: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/4.jpg)
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.
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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?
![Page 6: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/6.jpg)
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 blackNDVI 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].
![Page 7: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/7.jpg)
Global Rank Correlations for Model and Data Assimilation
Rank correlation between soil moisture for month i versus NDVI for month i+1
![Page 8: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/8.jpg)
2002-2010 Performance in Data-Poor Regions
6 of the 10 most “food insecure” countries in
the world.
![Page 9: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/9.jpg)
Seasonality Impacts
Rank correlation between soil moisture for month i versus NDVI for month i+1
![Page 10: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/10.jpg)
Remotely-Sensed Soil Moisture Resources
Sensor: Band: Resolution: Dates:AMSR-E C/X (P) 30 km 2002-2011SMOS L (P) 45 km 2010 -ASCAT C (A) 50 km 2008 - SMAP L (A/P) 10 km 2015 -
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.
![Page 11: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/11.jpg)
Remotely-Sensed Soil Moisture Resources
Sensor: Band: Resolution: Dates:AMSR-E C/X (P) 30 km 2002-2011SMOS L (P) 45 km 2010 - ASCAT C (A) 50 km 2008 - SMAP L (A/P) 10 km 2015 -
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.
![Page 12: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/12.jpg)
• 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
![Page 13: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/13.jpg)
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/
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Current SMOS Data Assimilation Product
![Page 15: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/15.jpg)
Remotely-Sensed Soil Moisture Resources
Sensor: Band: Resolution: Dates:AMSR-E C/X (P) 30 km 2002-2011SMOS L (P) 45 km 2010 - ASCAT C (A) 50 km 2008 - SMAP L (A/P) 10 km 2015 -
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.
![Page 16: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/16.jpg)
• 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, 3rd and 4th 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
![Page 17: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/17.jpg)
Microwave SM
Drought
Thermal SM
VIS/NIR SM
Time/Space Scale Considerations
Data Fusion Techniques
SAR SM
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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.
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“SMAP Passive” = SMOS
“SMAP Active” = ASCAT
Clear benefits to integrating active and passive soil moisture
products!ACTIVE + PASSIVEPASSIVE-ONLY
Prototype SMAP-Based Data Assimilation System
![Page 20: Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.](https://reader030.fdocuments.in/reader030/viewer/2022032804/56649e545503460f94b4b6b9/html5/thumbnails/20.jpg)
• 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
Observation
ModelModel
Current SMOS Data Assimilation Product
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Global Rank Correlations for Various Models
Rank correlation between soil moisture for month i versus NDVI for month i+1
COMPLEX
COMPLEX
COMPLEX
SIMPLE