This document describes the development of a high-resolution 375-meter evapotranspiration (ET) product using Visible Infrared Imaging Radiometer Suite (VIIRS) data. It outlines the steps taken, including: 1) using nighttime and daytime land surface temperature (LST) differences from VIIRS to estimate mid-morning LST rise for the Atmosphere-Land Exchange Inverse (ALEXI) model; 2) deriving leaf area index and fractional vegetation cover from VIIRS enhanced vegetation index composites; and 3) initial ET results over the North American Energy and Environment Network region at both 375-meter and 1-kilometer resolutions. The high-resolution VIIRS ET product will allow for improved monitoring of

1. Demonstration of the High-
Resolution (375-m) ALEXI ET
Product for the NENA Region
Martha C. Anderson
USDA-Agricultural Research Service,
Hydrology and Remote Sensing
Laboratory
Christopher Hain
Earth System Science Interdisciplinary
Center, University of Maryland, NOAA-
NESDIS
Christopher Neale
Daugherty Water for Food Institute,
University of Nebraska, Lincoln
Wim Bastiaanssen
UNESCO-IHE, Institute for Water Eduation

2. Supplementing ALEXI Capabilities with Polar Orbiting Sensors
Time of Day
LandSurfaceTemperature
Local NoonSunrise
Morning LST Rise: ALEXI Window
VIIRS
Nighttime
LST
VIIRS
Daytime
LST
A technique has been developed and evaluated using GOES data to train a regression model to
use day-night LST differences from MODIS to predict the morning LST rise needed by ALEXI. The
regression model can provide reasonable estimates of the mid-morning rise in LST (RMSE ~ 5 to 8%)
from the twice daily VIIRS LST observations.

3. Development of a High-Resolution (375-m) VIIRS ET Product
Initial NENA region processing nodes (9º x 9º)
Circles denote active processing nodes.
*Shading indicates 1-km percentage of cropland from global synthesis of several RS-based land use maps

4. Development of a High-Resolution (375-m) VIIRS ET Product
VIIRS I5
Granule
Granule
Geolocation
Map to Grid Aggregate to
0.003° Grid
Convert to Tb
VIIRS Tb
VIIRS IPS
Cloud Mask
Clear-sky
VIIRS Tb
LST Retrieval
CFSR T/Q
Profiles
Night VIIRS LST Day VIIRS LST
Apply Cloud
Mask
LST
Regression
Parameters
Convert
Day/Night
LST to ALEXI
T1/T2 LST
VIIRS LST
ALEXI T1
VIIRS LST
ALEXI T2
1. Mid-morning change in Land Surface Temperature

5. Development of a High-Resolution (375-m) VIIRS ET Product
1. Mid-morning change in Land Surface Temperature

6. Development of a High-Resolution (375-m) VIIRS ET Product
2. Leaf Area Index and Fraction of Green Vegetation Cover (fc)
VIIRS EVI
Granule
Granule
Geolocation
Map to Grid Aggregate to
0.003° Grid
VIIRS EVI
VIIRS IPS
Cloud Mask
Apply Cloud
Mask
Clear-sky
VIIRS Tb
Composite
Past 7-Day
EVI
7-day VIIRS
EVI Composite
VIIRS EVI-
GVF
ParametersCompute fc
VIIRS 7-day fc
Composite
fc -> LAI
Transformation
VIIRS 7-day LAI

7. Development of a High-Resolution (375-m) VIIRS ET Product
2. Leaf Area Index and Fraction of Green Vegetation Cover (fc)

8. Development of a High-Resolution (375-m) VIIRS ET Product
3. Land Surface Albedo
• Only available VIIRS product is at 750-m – mapped to 375-m grid.
4. Incoming Solar Radiation
• Only available from geostationary platforms – Meteosat (3-km)
5. Meteorological Surface Fields (e.g., air temperature; wind speed; surface
pressure; incoming LW)
• Climate Forecast System Reanalysis (hourly; 0.50º)
6. Morning Profile of Potential Temperature
• Climate Forecast System Reanalysis (hourly; 0.50º)
7. Landcover / Vegetation Type
• Only available VIIRS product is at 1-km – insufficient for 375-m product;
• MERIS ESA-CCI Landcover (300-m)
8. Cloud Mask
• Only available VIIRS product is at 750-m – mapped to 375-m grid.

9. Initial 375-m VIIRS ET Results

10. Development of a High-Resolution (375-m) VIIRS ET Product
Initial NENA region processing nodes (9º x 9º)
Circles denote active processing nodes.
*Shading indicates 1-km percentage of cropland from global synthesis of several RS-based land use maps

11. Current MODIS Latent Heat Flux (W m-2) Capability (1-km)

12. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

13. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

14. Development of a High-Resolution (375-m) VIIRS ET Product
Initial NENA region processing nodes (9º x 9º)
Circles denote active processing nodes.
*Shading indicates 1-km percentage of cropland from global synthesis of several RS-based land use maps

15. Current MODIS Latent Heat Flux (W m-2) Capability (1-km)

16. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

17. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

18. Development of a High-Resolution (375-m) VIIRS ET Product
Initial NENA region processing nodes (9º x 9º)
Circles denote active processing nodes.
*Shading indicates 1-km percentage of cropland from global synthesis of several RS-based land use maps

19. Current MODIS Latent Heat Flux (W m-2) Capability (1-km)

20. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

21. Current VIIRS Latent Heat Flux (W m-2) Capability (375-m)

22. We can’t manage
what we can’t measure …
Monitoring changes in water use with changing
climate, land-use and population
Improved hydrologic monitoring (flood, drought,
runoff) to better cope with extremes
Improved accounting of current water use and crop
water productivity (crop per drop)
Crop stress detection and yield estimation
Conclusions
Satellite Evapotranspiration
NENA Stakeholders Workshop – October 2015

23. Applications for Drought
Monitoring

24. ESI Methodology
ALEXI ESI represents temporal anomalies in the ratio of actual ET to potential ET.
• ESI does not require precipitation data, the current surface moisture state is
deduced directly from the remotely sensed LST , therefore it may be more robust
in regions with minimal in-situ precipitation monitoring.
• Signatures of vegetation stress are manifested in the LST signal before any
deterioration of vegetation cover occurs, for as example as indicated in NDVI, so
TIR-based indices such as ESI can provide an effective early warning signal of
impending agricultural drought.
• ALEXI ESI inherently includes non-precipitation related moisture signals (such as
irrigation; vegetation rooted to groundwater; lateral flows) that need to be modeled
a priori in prognostic LSM schemes.
• ALEXI ESI provides an independent assessment of current drought conditions,
supplementing precipitation and modeling-based indices – an invaluable resource
to decision-makers who usually depend on a convergence of information in the
decision making process.

25. ESI Methodology

26. ESI Methodology

27. Backup Slides

28. CoupledThermal/MWALEXISystem The synergy between TIR and MW observations is further being exploited
by the development of LST observations from MW observations(Ka-band).
The integration of MW LST into a coupled TIR/MW ALEXI system will allow
for retrieval of surface fluxes under cloud cover (where TIR-only retrievals
are not possible).
This capability fills in a significant gap in a TIR-only system over tropical
equatorial regions where clear-sky retrievals may only be possible 1 to 3
times per month, particularly during the wet season .