Anderson, Martha C.

Gao, Feng

Hain, Christopher

Holmes, Thomas R.

Otkin, Jason

Svoboda, Mark

Wardlow, Brian

Zhan, Xiwu

English

NASA Technical Reports Server

Development of  a Global 
Evaporative Stress Index Based 
on TIR and MW LST
Martha C. Anderson, Feng Gao
USDA-Agricultural Research Service
Hydrology and Remote Sensing Laboratory
Beltsville, MD
Christopher Hain
NASA, Marshall Space Flight Center 
Xiwu Zhan
NOAA-NESDIS-STAR
Jason Otkin
U. of Wisconsin
Thomas Holmes
NASA-GSFC
Mark Svoboda, 
Brian Wardlow
U. Of Nebraska, NDMC
https://ntrs.nasa.gov/search.jsp?R=20190034049 2020-03-11T16:14:52+00:00Z
Project Objective: North American Drought MonitoringExample of h  Evolution of Agricult ral Drought
A
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m
ap
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ng
 E
T
soil evaporation
Root uptake
infiltration
drainage
PRECIPITATION
transpiration &
evaporation 
Rootzone moisture
Sfc moisture
Soil hydraulic parms
Veg stress parms
Bare soil evap parms
Soil moisture
holding capacity
Root distribution parms
Given known radiative energy inputs, 
how much water loss is required to keep 
the soil and vegetation at the observed 
temperatures?
runoff
WATER BALANCE APPROACH
(prognostic modeling)
SURFACE TEMPERATURE
TSoil
Tsoil & Tveg
transpiration &
evaporation 
soil evaporation
Tveg
ENERGY BALANCE APPROACH
(diagnostic modeling)
ARSET Remote Sensing Training Program
Global Evaporative Stress Index Methodology
ALEXI ESI represents temporal anomalies in the ratio of actual ET to potential ET. 
• The current surface moisture state is deduced directly from the remotely sensed LST
• Signatures of vegetation stress are manifested in the LST signal before any 
deterioration of vegetation cover occurs
• Inherently includes non-precipitation related moisture signals (such as irrigation; 
vegetation rooted to groundwater; lateral flows)
1 August 
2012
Project Stakeholders
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
Thermal LST Observations
MODIS Terra
MODIS Aqua
NPP VIIRS
MW Ka-Band LST Observations
TRMM
SSMI
AMSR-E
Windsat
GCOM-W1
GPM Core
GCOM-W2
FY-3B
SSMIS
Development of a Multi-Scale Remote-Sensing Based Framework for Mapping 
Drought over North America 
Christopher Hain (U. of Maryland)
Flash drought are rapid onset events 
typically driven by:
1) precipitation deficits, 
2) high temperature anomalies;
3) strong winds;
4) Anomalous incoming solar 
radiation.  
ESI provides early warning of the onset 
of vegetation stress. 
Provides information physically related 
to “actual” stress. 
North Central US Flash Drought of 2017
Early Warning Metrics for Onset of Vegetation Stress
N
LD
A
S 
TC
4W
K
ES
I
4W
K
C
RO
P 
YI
EL
D
VE
G
DR
I
Winter
Wheat
Spring
Wheat SoybeansCorn •Strong relationship 
between wheat 
yield and the ESI 
and VegDRI during 
critical crop stages
• NLDAS has strong 
(weak) relationship 
to corn/soybeans 
(wheat) yield
• ESI had strongest 
correlation to the 
wheat, corn, and 
soybean yield 
departures
• ESI is strongly correlated with soil moisture, thus providing an independent assessment of 
current moisture conditions that can be compared to models that are driven by 
observations precipitation.
• ESI may better represent “dry fuel load” than remote sensing techniques such as NDVI 
which is focused on how “green” or “brown” vegetation is. 
• Stressed vegetation may susceptible to rapid fire growth before “brownness” is 
observed in a vegetation index such as NDVI. 
28-day change in ESI 
(October 16 –
November 12)
Rapid changes in ESI 
over California show 
significant increases in 
vegetation and 
moisture stress with 
the vegetation
Development of a Multi-Scale Remote-Sensing Based Framework for Mapping 
Drought over North America 
Christopher Hain (U. of Maryland)
Wildfire Applications
Development of a Multi-Scale Remote-Sensing Based Framework for Mapping 
Drought over North America 
Christopher Hain (U. of Maryland)
ESI in Operational Situational Awareness
12/20/2019
10
WetDry
• Near-real-time ESI products are important for 
tracking rapidly changing drought 
conditions. 
• For example, the evolution of the flash 
drought in the SE US (August – October) was 
accurately captured by ESI. 
13 August 2019
8 October 2019
SPoRT-LIS 10-Day Soil Moisture 
Percentile Forecast
C
ou
pl
ed
 T
he
rm
a
l /
 M
W
 A
LE
XI
 S
ys
te
m
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 will allow for retrieval of surface fluxes under 
cloud cover.
This capability fills in a significant gap in a TIR-only system over tropical 
equatorial regions.
A
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y 
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ith
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ow
 
June 7th, 2011
LST-Based Drought Indicators
• Diagnostically captures non-precipitation related 
moisture sources/sinks (irrigation, shallow groundwater, 
drainage)
• Provides early warning of on-set of actual vegetation 
stress
• Provides information about current soil moisture state 
without the need for knowledge of antecedent 
precipitation
Contact: 
Christopher Hain (christopher.hain@nasa.gov)
Martha Anderson (martha.anderson@ars.usda.gov)
NRT data feed: https://gis1.servirglobal.net/data/esi/C
on
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Development of a Global Evaporative Stress Index Based on TIR and MW LST

Development of a Global Evaporative Stress Index Based on TIR and MW LST

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