ALEXI/DisALEXI products for ECOSTRESS

35 Pages·2017·38.29 MB·English

ALEXI/DisALEXI
products for
ECOSTRESS
Martha C. Anderson, Feng
Gao, Yun Yang, Liang Sun,
Yang Yang, Wayne Dulaney,
USDA-Agricultural Research Service
Hydrology and Remote Sensing Laboratory
Beltsville, MD
Chris Hain
NASA-Marshall Space Flight Center
Huntsville, AL
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ECOSTRESS Science Team – May 2017
SURFACE TEMPERATURE EVAPOTRANSPIRATION

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Landsat 5 Landsat 7
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ECOSTRESS (LST)
Data Mining Sharpener
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TEXAS (960 to 60 m) ALASKA (960 to 60 m) IOWA (120 to 30 m)
ECOSTRESS Science Team – May 2017
On ECOSTRESS DAYS
INPUTS PREPROCESSING DisALEXI

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ECOSTRESS (68 m) ECOSTRESS (68 m) •  Disaggregate ALEXI
u LST Resample onto Landsat grid ETd (4km) to 30 m
d Emissivity Sharpen LST to 30m using ECOSTRESS
LST
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r Landsat (30 m) • Generate MODIS-
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consistent LAI
SR (Bands 3-7)
•  Generate SW and NIR
4
albedo
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MODIS (1 km)
3 •  Subset gridded LAI ETd at 30 m L3
L LAI (MCD15A3) datasets

A •  Gap-fill LAI, remove
outliers, apply spline filter
D
L4
f = ET/RET at 30 m
S RET
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ALEXI (4 km) •  Extract ALEXI ETd over
r ET (GET-D) Landsat grids
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Landsat
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g MET INPUTS (20 km) MODIS
•  Extract met data over
n CFSR:
Landsat grids
Atmospheric pressure
i ALEXI
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Vapor pressure •  Compute hourly and
s
e Wind speed daily reference ET (RET)
Air Temperature Met data
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Insolation
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ECOSTRESS
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ECOSTRESS Science Team – May 2017
USDA-ARS LTAR NETWORK
Core sites
PotenZal Zer 2 sites
Cook
Rosemount
BARC Choptank
Mead Ames
Bondville
CPER MOFLUX
Central Valley
Ti2on
WG_west WG_east
HIGH RESOLUTION
ET DATACUBES
… flux tower comparison
… ET dynamics
… ET/RefET (“ESI”)
Retrospective Landsat-based analyses in progress