As the worlds water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring

Anderson, Martha C.

Hain, Christopher R.

Neale, Christopher

Schull, Mitchell

Zhan, Xiwu

NASA Technical Reports Server

DEVELOPMENT OF A MULTI-SCALE FRAMEWORK FOR MAPPING GLOBAL 
EVAPOTRANSPIRATION 
 
Christopher R. Hain1, Martha C. Anderson2, Mitchell Schull3, Christopher Neale4 and Xiwu Zhan5 
 
 1Marshall Space Flight Center, Earth Science Office, Huntsville, AL, 2USDA/ARS Hydrology and 
Remote Sensing Laboratory, Beltsville, MD, 3University of Maryland, Earth System Science 
Interdisciplinary Center, College Park, MD, 4University of Nebraska-Lincoln, Daugherty Water for Food 
Institute, Lincoln, NE, 5NOAA/NESDIS Center for Satellite Applications and Research, College Park, 
MD.  
 
ABSTRACT (100-150 WORDS) 
 
As the world’s water resources come under increasing 
tension due to dual stressors of climate change and 
population growth, accurate knowledge of water 
consumption through evapotranspiration (ET) over a range 
in spatial scales will be critical in developing adaptation 
strategies. Remote sensing methods for monitoring 
consumptive water use (e.g, ET) are becoming increasingly 
important, especially in areas of significant water and food 
insecurity. One method to estimate ET from satellite-based 
methods, the Atmosphere Land Exchange Inverse (ALEXI) 
model uses the change in mid-morning land surface 
temperature to estimate the partitioning of sensible and 
latent heat fluxes which are then used to estimate daily ET. 
This presentation will outline several recent enhancements to 
the ALEXI modeling system, with a focus on global ET and 
drought monitoring.  
 
Index Terms— Evapotranspiration, remote sensing, 
water resources, drought 
 
1. INTRODUCTION 
 
The monitoring of evapotranspiration can be assessed 
through various modeling or remote-sensing based methods 
or models. Prognostic land-surface models require accurate 
a priori information about global precipitation patterns, soil 
moisture storage capacity, groundwater tables, and artificial 
controls on water supply (e.g., irrigation, dams and 
diversions, inter-basin water transfers, etc.) to reliably link 
rainfall to evaporative fluxes. In contrast, diagnostic 
estimates of ET can be generated, with no prior knowledge 
of the surface moisture state, by energy balance models 
using thermal-infrared (TIR) remote sensing of land-surface 
temperature (LST) as a boundary condition.  The LST inputs 
carry valuable proxy information regarding soil moisture and 
its effect on soil surface evaporation and canopy stresses 
limiting transpiration.   
 
2. MODEL METHODOLOGY 
 
The ALEXI surface energy balance model [1,2] was 
specifically designed to minimize the need for ancillary 
meteorological data while maintaining a physically realistic 
representation of land-atmosphere exchange over a wide 
range in vegetation cover conditions (Fig. 1).  It is one of 
few land-surface models designed explicitly to exploit the 
high temporal resolution afforded by geostationary satellites. 
Surface energy balance models estimate ET by partitioning 
the energy available at the land surface (RN – G, where RN 
is net radiation and G is the soil heat conduction flux, in 
Wm-2) into turbulent fluxes of sensible and latent heating (H 
and λE, respectively, Wm-2): The land-surface 
representation in ALEXI model is based on the series 
version of the two-source energy balance (TSEB) model of 
[3], which partitions the composite surface radiometric 
temperature, TRAD, into characteristic soil and canopy 
temperatures, TS and TC, based on the local vegetation cover 
fraction apparent at the thermal sensor view angle. With 
information about TRAD, LAI, and radiative forcing, the 
TSEB evaluates the soil (subscript ‘s’) and the canopy (‘c’) 
energy budgets separately, computing system and 
component fluxes of net radiation (RN=RNC+RNS), sensible 
Figure 1. Schematic for the ALEXI modeling system (left) and 
DisALEXI high-resolution modeling system (right). 
https://ntrs.nasa.gov/search.jsp?R=20170007220 2019-08-31T06:55:02+00:00Z
and latent heat (H=HC+HS and λE=λEC+λES), and soil heat 
(G).  Importantly, because  
angular effects are incorporated into the decomposition of 
TRAD, the TSEB can accommodate TIR data acquired at 
off-nadir viewing angles by geostationary satellites. The 
TSEB has a built-in mechanism for detecting thermal 
signatures of stress in the soil and canopy.  An initial 
iteration assumes the canopy transpiration (λEC) is occurring 
a potential (non-moisture limited) rate, while the soil 
evaporation rate (λES) is computed as a residual to the 
system energy budget.  If the vegetation is stressed and 
transpiring at significantly less than the potential rate, λEC 
will be overestimated and the residual λES will become 
negative.  Condensation onto the soil is unlikely midday on 
clear days, and therefore λES<0 is considered a signature of 
system stress.  Under such circumstances, the λEC is 
iteratively down-regulated until λES~0 (expected under dry 
conditions).     
 
For regional-scale applications of the TSEB, the air 
temperature boundary condition, TA in Fig. 1 must be 
specified at the spatial resolution of the geostationary 
thermal data (typically 3-10 km).  Due to localized land-
atmosphere feedback, this cannot be accomplished with 
adequate accuracy using standard synoptic measurements, 
with typical spacing in the US of 100 km.  To overcome this 
limitation, the TSEB has been coupled with an atmospheric 
boundary layer (ABL) model, thereby simulating land-
atmosphere feedback internally.  In the ALEXI model, the 
TSEB is applied at two times during the morning ABL 
growth phase (~ t1=1.5 and t2=5.5 h after local sunrise), 
using TIR data obtained from a geostationary platform.  
Energy closure over this interval is provided by a simple 
slab model of ABL development [4], which relates the rise 
in air temperature in the mixed layer to the time-integrated 
influx of sensible heat from the land surface.  As a result of 
this configuration, ALEXI uses only time-differential 
temperature signals, thereby minimizing flux errors due to 
absolute sensor calibration and atmospheric and emissivity 
corrections [5].  The primary radiometric signal is the 
morning surface temperature rise, while the ABL model 
component uses only the general slope (lapse rate) of the 
atmospheric temperature profile [1], which is more reliably 
analyzed from synoptic radiosonde data than is the absolute 
temperature reference. 
 
3. EXPLOITING TWICE-DAILY POLAR 
OBSERVATIONS FOR GLOBAL ET MAPPTING 
 
Until recently, ALEXI ESI has been limited to areas with 
high resolution temporal sampling of geostationary sensors. 
The use of geostationary sensors makes global mapping a 
complicated process, especially for real-time applications, as 
data from as many as five different sensors are required to 
be ingested and harmonized to create a global mosaic. 
However, our research team has developed a new and novel 
method of using twice-daily observations from polar-
orbiting sensors such as MODIS and VIIRS to estimate the 
mid-morning rise in LST that is used to drive the energy 
balance estimations within ALEXI. This allows the method 
to be applied globally using a single sensor (in this case, 
initially MODIS with a planned transition to VIIRS) rather 
than a global compositing of all available geostationary data. 
Other advantages of this new method include the higher 
spatial resolution provided by MODIS and VIIRS and the 
increased sampling at high latitudes where oblique view 
angles limit the utility of geostationary sensors. Three 
different TIR data feeds (see Fig. 2) will be ingested to 
provide LST data over the ESI period of record (March 
2000 to present): MODIS Terra (March 2000 to present); 
MODIS Aqua (July 2002 to present) and NPP VIIRS 
(October 2012 to present). Figure 1 shows how the ALEXI 
sampling window relates to twice daily observations from 
MODIS or VIIRS (Anderson et al. 2015; Hain et al. 2016). 
The twice daily observations are related to the mid-morning 
rise needed by ALEXI using a rule-based regression model 
generated with the Cubist software package (RuleQuest). 
The following variables are used in the regression 
methodology: (1) MODIS or VIIRS day-night LST 
difference; (2) MODIS or VIIRS day LST; (3) MODIS or 
VIIRS night LST; (4) leaf area index; and (5) topographic 
variability. Global 0.05º time series for each TIR sensor will 
be generated over the time period they are available. 
However, differences will exist between the three LST time 
series. For example, Terra provides an LST retrieval around 
10:30 am/pm local time, while Aqua and VIIRS have 
overpass times at 1:30 am/pm local time. Additionally, 
algorithm differences exist between MODIS (Terra/Aqua) 
and VIIRS LST products. A bias correction scheme will be 
Figure 2. Schematic showing the relationship between twice-
daily observations of LST from polar orbiting sensors (e.g., 
MODIS, VIIRS) and the ALEXI/ESI sampling window used to 
calculate the mid-morning rise in LST. 
implemented to harmonize morning temperature rise signals 
obtained from each TIR data source (note that ALEXI and 
ESI are relatively insensitive to errors in absolute 
temperature, only depending on the morning rate of LST 
rise). The bias correction will exploit a significant data 
overlap period (2003-2005 for Terra and Aqua overlap; 
2013-2015 for Aqua and VIIRS overlap) between the three 
LST time series to minimize the differences and maintain 
data continuity over the period of record to be used to 
compute ESI. Improvements to the spatial resolution of the 
thermal infrared wavelengths on the VIIRS instrument, as 
compared to MODIS (375-m VIIRS vs. 1-km MODIS), 
allows for a much higher resolution ALEXI product than has 
been previously available.  Therefore, recent developments 
have been to generate 375-m ALEXI ET products over 
several pilot regions (e.g. western US and the MENA 
region). Figure 3 shows the annual evapotranspiration (mm) 
over a section of the Middle East for 2015. The monitoring 
of consumptive water use over regions where significant 
groundwater pumping for irrigation is employed is important 
to accurately quantify the efficiency of water use in the 
region.  
 
 
Figure 3. Example of the 375-m VIIRS ALEXI ET (mm) product 
for 2015. 
4. APPLICATIONS FOR GLOBAL MONITORING OF 
DROUGHT AND VEGETATION STRESS 
 
The Evaporative Stress Index (ESI) represents anomalies in 
the ratio of actual-to-potential ET (fPET), generated with the 
thermal remote sensing based Atmosphere-Land Exchange 
Inverse (ALEXI) surface energy balance model (Anderson 
et al. 1997; 2011). The LST inputs to ESI have been shown 
to provide early warning information about the development 
of stress (Anderson et al. 2013; Otkin et al. 2013; 2015), 
with stress-elevated canopy temperatures observed well 
before a decrease in “greenness” is detected in remotely 
sensed vegetation indices. Whereas many drought indicators 
based on precipitation or atmospheric conditions capture 
meteorological drought, the ESI is one of few indicators of 
agricultural drought that reveals actual vegetation stress 
conditions realized on the ground.  This distinction is 
particularly important for the agricultural sector where 
human activity can alter how the drought is actually 
affecting crop health (e.g., through irrigation, use of 
drought-resistant crop varietals, etc.). Not all meteorological 
droughts develop into an agricultural drought, and having 
indicators specifically related to crop vegetation stress is 
imperative for improved decision-making by agricultural 
stakeholders. 
 
As a diagnostic indicator of actual ET, the ESI requires no 
information regarding antecedent precipitation or soil 
moisture storage capacity - the current available moisture to 
vegetation is deduced directly from the remotely sensed LST 
signal. This signal also inherently accounts for both 
precipitation and non-precipitation related inputs/sinks to the 
plant-available soil moisture pool (e.g., irrigation, tile 
drainage; Hain et al. 2015), which can modify crop response 
to rainfall anomalies.   Independence from precipitation data 
is a benefit for global agricultural monitoring applications 
due to sparseness in existing ground-based precipitation 
networks, and time delays in public reporting. Even as 
satellite precipitation monitoring has closed some of the 
observational gaps, these data are usually provided at coarse 
resolution with accuracy dependent extensive calibration 
with ground-based precipitation estimates. Additionally, the 
ESI also provides an important distinction from drought 
indicators that focus on atmospheric demand which signal 
the potential for stress but provide no direct link to the onset 
of actual vegetation stress. In each of these respects, the ESI 
and ESI-derived Rapid Change Indices will give 
stakeholders a unique perspective for assessing impacts of 
agricultural drought on crop and rangeland productivity at 
the global scale. A global ESI data product is currently 
Figure 4. Thermal-only global MODIS/VIIRS 4-week ESI 
composite product for 1 August 2012. Grey shading denotes 
regions where significant cloud cover leads to inadequate clear-
sky retrievals. Brown shading denotes desert regions where ET is 
negligible. 
being processed in a “near-real-time” prototype towards 
development of an operational system which will provide 
weekly global 5-km ESI data products (Figure 4).  
 
5. REFERENCES 
 
[1] Anderson, M. C., J. Norman, G. Diak, W. Kustas, and J. R. 
Mecikalski, 1997: A two-source time-integrated model for 
estimating surface energy fluxes using thermal infrared remote 
sensing, Remote Sens. Environ., 60, 195-216. 
 
[2] Anderson, M. C., W. Kustas, J. M. Norman, C. R. Hain, J. R. 
Mecikalski, L. Schultz, M. P. González- Dugo, C. Cammalleri, G. 
d'Urso, A. Pimstein, and F. Gao, 2011: Mapping daily 
evapotranspiration at field to continental scales using geostationary 
and polar orbiting satellite imagery, Hydrol. Earth Syst. Sci., 15, 
223-239, doi:10.5194/hess-15-223-2011. 
 
[3] Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-
source approach for estimating soil and vegetation energy fluxes 
from observations of directional radiometric surface temperature, 
Agric. For. Meteorol. 77, 263-293. 
 
[4] McNaughton, K. G. and T. W. Spriggs, 1986: A mixed-layer 
model for regional evaporation, Boundary-Layer Meteorol., 74, 
262-288. 
 
[5] Kustas, W. P., T. J. Jackson, A. N. French, and J. I. 
MacPherson, Verification of patch- and regional-scale energy 
balance estimates derived from microwave and optical remote 
sensing during SGP97, J. Hydrometeor., 2 ,254–273,2001. 
 
[6] Anderson, M. C., C. Zolin, C. R. Hain, K. Semmens, M. T. 
Yilmaz and F. Gao, 2015: Comparison of satellite-derived LAI and 
precipitation anomalies over Brazil with a thermal infrared-based 
Evaporative Stress Index for 2003-2013, J. of Hydrology, 526, 
287-302. 
 
[7] Hain, C. R. and M. C. Anderson, 2016: Estimating Mid-
morning Change in Land Surface Temperature from MODIS 
Day/Night Land Surface Temperature, Geophys. Res. Letts., In 
Review. 
 
[8] Anderson, M. C., C. Hain, B. Wardlow, A. Pimstein, J. R. 
Mecikalski, W. P. Kustas, 2011a: Evaluation of Drought Indices 
Based on Thermal Remote Sensing of Evapotranspiration over the 
Continental United States. J. Climate, 24, 2025–2044. doi: 
10.1175/2010JCLI3812.1. 
 
[9] Hain, C. R., W. T. Crow, M. C. Anderson and M. T. Yilmaz, 
2015: Diagnosing Neglected Soil Moisture Source/Sink Processes 
via a Thermal Infrared-based Two-Source Energy Balance Model, 
J. of Hydrometeor., 16, 1070-1086. 
 


Development OF A Multi-Scale Framework for Mapping Global Evapotranspiration

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