While the current constellation of geostationary sensors provides near-global coverage (60N to 60S) – it requires merging data from 7 satellites [resolving time differences; view angles; atmospheric correction]. Polar orbiting sensors such as MODIS and VIIRS provide daily global coverage of LST at higher resolutions than GEO sensors but at only two times per day

Anderson, Martha C.

Hain, Christopher

Neale, Christopher M.U.

Schull, Mitch

English

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University of Nebraska - Lincoln 
DigitalCommons@University of Nebraska - Lincoln 
Water for Food Faculty Publications Water for Food 
2015 
Demonstration of a Daily High-Resolution (375-m) ALEXI 
Evapotranspiration Product for the NENA Region 
Christopher Hain 
NASA Marshall Space Flight Center, christopher.hain@nasa.gov 
Martha C. Anderson 
USDA-ARS Hydrology and Remote Sensing Lab, martha.anderson@ars.usda.gov 
Mitch Schull 
University of Maryland at College Park 
Christopher M.U. Neale 
University of Nebraska- Lincoln, cneale@nebraska.edu 
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Hain, Christopher; Anderson, Martha C.; Schull, Mitch; and Neale, Christopher M.U., "Demonstration of a 
Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region" (2015). Water for 
Food Faculty Publications. 28. 
https://digitalcommons.unl.edu/wffdocs/28 
This Article is brought to you for free and open access by the Water for Food at DigitalCommons@University of 
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Contact Information:
christopher.hain@nasa.gov 
Atmosphere-Land-Exchange-Inversion Model (ALEXI)
ALEXI is a two-source energy balance model which was 
initially developed to address issues dealing with the 
monitoring of surface fluxes, including actual 
evapotranspiration (ET), from a satellite-based platform 
(Anderson et al., 1997; Fig.1 ). Flux partitioning within 
ALEXI is driven by time changes in land surface 
temperature (LST): the amplitude of the diurnal surface 
temperature wave has been found to be a good 
indicator of surface flux partitioning, and using a time-
differential measurement significantly reduces model 
sensitivity to errors in LST retrieval.   Model evaluation 
through disaggregation over flux sites in the US and 
other regions indicate accuracy on the order of 10% at 
daily time steps(e.g., Cammalleri et al, 2013, 2014; Fig 2).
Figure 1. Multi-scale modeling framework: (left) ALEXI system, using 
morning surface temperature rise from geostationary (GEO) satellites; 
(right) flux dsaggregation using LST from Landsat, MODIS, aircraft, etc.
Figure 3. Multi-scale ET maps for 1 July 2002 produced with 
ALEXI/DisALEXI using LST data from aircraft (30-m resolution), 
Landsat (60-m), MODIS (1-km), GOES Imager (5-km) and 
GOES Sounder (10-km). 
Training a Regression Model to Estimate Mid-morning LST rise from Day/Night MODIS/VIIRS Observations
Prototype VIIRS ET Results – Spatial Resolution Improvements with VIIRS
G
lo
b
al
(2
5
km
)
D
is
A
LE
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(L
an
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sa
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D
is
A
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ir
cr
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t)
W
ate
rsh
e
d
(6
0
m
)
Fie
ld
 scale
(3
0
m
)
SURFACE TEMPERATURE EVAPOTRANSPIRATION
A
LE
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I
(G
O
ES
 Im
ag
er
) R
e
gio
n
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(5
km
)
A
LE
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 S
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(1
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Te
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at
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re
 (C
)
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at (W
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-2)
A
LE
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)
1 July 2002 – 10:30AM LST
corn
soy
While the current constellation of geostationary sensors 
provides near-global coverage (60N to 60S) – it 
requires merging data from 7 satellites [resolving time 
differences; view angles; atmospheric correction]. 
Polar orbiting sensors such as MODIS and VIIRS provide 
daily global coverage of LST at higher resolutions than 
GEO sensors but at only two times per day. 
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 (DTRAD) rise needed by ALEXI (Fig. 5). The 
regression model can provide reasonable estimates of 
the mid-morning rise in LST (RMSE ~ 5 to 8%; Fig. 6) from 
the twice daily MODIS or VIIRS LST observations.
Time of Day
La
n
d
 S
u
rf
a
c
e
 T
e
m
p
e
ra
tu
re
Local 
Noon
Sunrise
Morning LST Rise: ALEXI 
Window
MODIS 
Nighttime 
LST
MODIS 
Daytime 
LST
Figure 5. Schematic showing the typical diurnal cycle of LST with the morning 
LST required by ALEXI and typical MODIS LST observation times (1:30 am/pm).
Figure 6. Validation of estimated DTRAD and TRAD2 (LST at ALEXI 
time 2) from MODIS for the training period (a. and c.; 2004-2006) 
and the validation period (b. and d.; 2007-2012).
Figure 7. 3-month DTRAD composites estimated from MODIS for (a) 
JFM 2012, (b) AMJ 2012, (c) JAS 2012 and (d) OND 2012.
TRAD (φ),  fc
Rsoil
Tc
Tac
Hs
Ts
Ra H = Hc + Hs 
Rx
Hc
Ta 
ABL
Ta 
ALEXI DisALEXI
5 km
30 m
T
w
o
-S
o
u
r
c
e
 M
o
d
e
l
TRAD,i(φi),  fc,i
i
Ra,i
Blending height
Regional scale
DTRAD - Ge stationary
Ta - ABL model
Landscape scale
TRAD - Landsat, MODIS
Ta - ALEXI
Surface temp:
Air temp:
Because ET in ALEXI is computed based on energy balance 
rather than water balance, precipitation is not a required input 
to the modeling system.   The LST inputs to ALEXI inherently 
capture 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 (Yilmaz 
et al, 2014; Hain et al, 2015).  Techniques are being developed 
to integrate LST retrieved using thermal and microwave data 
from multiple sensors to provide improved spatiotemporal 
sampling over a broad scale range (Fig. 3)
BEAREX08 MEAD
Unirrigated cotton
Irrigated cotton
Irrigated corn
Unirrigated corn The LST inputs to ALEXI are a valuable diagnostic of 
biospheric stress resulting from soil moisture deficiencies.  Soil 
surface temperature increases with decreasing water
Figure 2.  ET evaluation over rainfed and irrigated 
crops in semiard (BEAREX08; Bushland TX) and 
temporate (Mead, NE) conditions.
content, while moisture 
depletion in the plant root 
zone leads to stomatal
closure, reduced 
transpiration, and 
elevated canopy 
temperatures that can be 
effectively detected from 
space.  
Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region
Christopher R. Hain1, Martha C. Anderson2, Mitch Schull3, Christopher Neale4
Marshall Space Flight Center, Earth Science Office, Huntsville, AL1
USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, Maryland2
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland3
Daugherty Water for Food Institute, University of Nebraska, Lincoln, NE4
MODIS MODIS MODIS
VIIRS
VIIRS VIIRS
Prototype VIIRS ET Results – 2015 Annual Evapotranspiration (mm)
Project Overview
Food and water security over the MENA (Middle East / North Africa) region is of increased importance as 
diminishing water supplies and a growing population continues to put strain on countries to provide 
adequate agriculture production. Satellite remote sensing of consumptive water use provides a mechanism 
to observe how efficiently, or in many cases inefficiently, local farmers are using water. 
In this project, we aim to produce near-real-time (latency less than 5 days) daily 375-m evapotranspiration 
estimates from VIIRS towards improvement monitoring of agricultural water use and as the primary input into 
Landsat-based DisALEXI simulations (~30-m resolution) over several targeted agricultural regions in the study 
domain.
Figure 8. Study domain for the MENA 
375-m VIIRS ET product. Shading shows 
the percentage of each pixel which 
has been classified as cropland.  
Figure 8. 7-day clear-sky latent heat flux (W m-2) on day ending 
2015155.  Figures to the left show several regions of interest with 
examples of the current MODIS (~1 km) and VIIRS (~375 m) 
fields. 
Figure 9. Annual 375-m VIIRS evapotranspiration 
(mm) for 2015. 
https://ntrs.nasa.gov/search.jsp?R=20180000604 2018-11-27T14:40:56+00:00Z


Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

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Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

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