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Numerical Terradynamic Simulation Group
11-2013
MODIS Global Terrestrial Evapotranspiration
(ET) Product (NASA MOD16A2/A3) Collection
5. NASA Headquarters
Qiaozhen Mu
Maosheng Zhao
Steven W. Running
University of Montana - Missoula
Follow this and additional works at: http://scholarworks.umt.edu/ntsg_pubs
Recommended Citation
Mu, Qiaozhen; Zhao, Maosheng; and Running, Steven W., "MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA
MOD16A2/A3) Collection 5. NASA Headquarters" (2013). Numerical Terradynamic Simulation Group Publications. 268.
http://scholarworks.umt.edu/ntsg_pubs/268
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MODIS Global Terrestrial Evapotranspiration (ET) Produet
(NASA MOD16A2/A3)
Algorithm Theoretieal Basis Doeument
Colleetion 5
NASA Headquarters
N ovem ber 20, 2013
Qiaozhen Mu, Maosheng Zhao, Steven W. Running
Numerieal Terradynamie Simulation Group
College of Forestry and Conservation
The University of Montana
Missoula, MX 59812
National Aeronautics and Space Administration
Headquarters
Washington, DC 20546-0001
November 20,2013
Reply to Attn of;
SMD/Earth Science Division
Dr. Steven Running and
Dr. Qiaozhen Mii
Numerical Terradynamie Simulation Group
College of Forestry and Conservation
The University of Montana
Missoula, MT 59812
Dear Drs. Running and Mu:
We have completed the evaluation of your Algorithm Theoretical Basis Document (ATBD) entitled
“MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) Algorithm
Theoretical Basis Document, Collection 5” (08-ATBDl 1-0001), submitted to the Science Mission
Directorate’s Earth Science Division. A peer review panel evaluated your document in terms o f its
scientific and technical merit, feasibility, potential value of the data product, and readiness for
implementation.
I am pleased to inform you that the panel rated the ATBD as “A,” which means: the theoretical basis for
the algorithm is sound, and the algorithm is readj' for coding and production. The panel recommended
that you take the next steps toward securing an implementation of this standard product. You can cite this
successful ATBD review to provide justification regarding the particular next steps to be taken. If you
have an> questions regarding the details of the review process related to your ATBD, please contact me
at: (202) 358-0245 or [email protected].
A copy of the panel review summary for your ATBD is enclosed. Also enclosed are copies of all the
individual evaluations received from panelists and non-panelist, “mail-in” reviewers.
I would like to extend my personal congratulations to you and your team and wish you well as you move
toward the production of this exciting “new” MODIS product. Again, please accept my most abject
apologies for the long delay in completing this review.
Sincerely
U
Dr. Diane E. Wickland
Manager, Terrestrial Ecology Program and
Lead, Carbon Cycle and Ecosystems Focus Area
Earth Science Division
Science Mission Directorate
Enclosures
cc:
Science Mission Directorate/Dr. Bontempi
Dr. Maiden
University of Colorado/Dr. King
NASA Goddard Space Flight Center/Dr. Xiong
Table of Contents
H ISTO RY OF M OD 16 EV A PO TR A N SPIR A TIO N ...................................................................I
A B STR A C T.......................................................................................................................................... II
FIG U RE C A PTIO N S.........................................................................................................................Ill
TA BLE C A P T IO N S...........................................................................................................................V
1. IN T R O D U C T IO N ........................................................................................................................... 1
2. B A C K G R O U N D ............................................................................................................................. 1
2.1 Energy Partitioning L o g ic .................................................................................................. 2
2.2 Surface Energy B alance M o d e ls ....................................................................................... 2
2.3 M odels U sing Relationship between V egetation Index and L S T ............................. 3
2.4 Penm an-M onteith L o g ic ...................................................................................................... 4
3. M OD 16 ET A LG ORITHM D E S C R IP T IO N S ......................................................................... 7
3.1 V egetation Cover F ra c tio n ................................................................................................. 7
3.2 D aytim e and N ighttim e E T ................................................................................................ 8
3.3 Soil H eat F lu x .........................................................................................................................8
3.4 W et Surface F ractio n..........................................................................................................11
3.5 Evaporation from W et Canopy S u rface........................................................................ 11
3.6 Plant T ranspiration ............................................................................................................. 12
3.6.1 Surface Conductance to Transpiration........................................................ 12
3.6.2 A erodynam ic R esistance................................................................................ 15
3.6.3 Plant Transpiration...........................................................................................15
3.7 Evaporation from Soil S u rface.........................................................................................16
3.8 Total Daily E vapotranspiration....................................................................................... 17
4. INPUT D A T A S E T S ..................................................................................................................... 18
4.1. Daily M eteorological D a ta .............................................................................................. 19
4.2. Dependence on M ODIS Land Cover Classification (M 0 D 1 2 Q 1 )......................... 19
4.3. Time Variable M ODIS Input D a ta ................................................................................ 20
5. PA R A M ETERIZA TIO N OF M OD 16 ET A LG O R IT H M ..................................................22
5.1. Eddy Covariance Flux T o w ers....................................................................................... 22
5.2 Pre-processing Tower O bserved D a ta ........................................................................... 25
5.3 P aram eterization..................................................................................................................26
6 . RESULTS AND U N C E R T A IN T IE S ....................................................................................... 27
6.1 Algorithm Perform ance at the Eddy Flux Tow er S ite s ............................................. 27
6.2 Im plem enting ET Algorithm at the Global Scale........................................................32
6.3 A lgorithm Perform ance at Global W atersheds.............................................................36
6.4 U ncertainties........................................................................................................................ 37
7. M 0 D 1 6 P R O D U C T S ..................................................................................................................39
7.1 M 0 D 1 6 V ariab les.............................................................................................................. 39
7.2 M OD 16 H DFEOS 10-degree Tiles, M ap Projection and File N am e....................... 40
7.3 M OD 16 Product D ata S ize...............................................................................................42
8. SU M M A R Y .................................................................................................................................... 43
9. R E F E R E N C E S .............................................................................................................................. 43
Figure 1
3
Figure 2
7
Figure 3
22
Figure 4
23
Figure 5
28
Figure 6
31
Figure 7
32
Figure 8
33
Figure 9
34
Figure 10
35
Figure 11
36
Figure 12
37
Figure 13
41
Table 1
10
Table 2
15
Table 3
18
Table 4
20
Table 5
24
Table 6
27
Table 7
29
Table 8
43
History of MOD16 Evapotranspiration
In the original EOS proposal competition in 1989, Dr. Steve Running proposed
and was selected as M ODIS Science team m em ber responsible for L ea f area index,
evapotranspiration and photosynthesis/net prim ary production, then designated as M OD
15, 16 and 17. A t the ATBD review for at-launch products in 1995, N A SA decided to
give M OD 15 LA I/FPA R to Dr. Ranga M yneni to provide a m ore theoretically based
algorithm, and Dr. Running was directed to focus on M OD 17 PSN/NPP for the Terra atlaunch data product. M OD 16 ET was not dropped, but was deprioritized. A t the EOS
recom pete in 2003 N A SA selected another investigator to build a M OD 16 ET product
but this investigation was not renewed in 2007. In the interim Dr. Running and the
N T SG group had changed from an energy balance - surface resistance concept to a
Penm an-M onteith concept, and had greater success building a globally applicable
algorithm. Since much o f the processing paralleled our M OD 17 product, N T SG tested,
then generated initial global ET datasets. In the 2010 renewal com petition for the
M ODIS Science Team, Dr. Running reproposed M OD 16, based on the new algorithm
and global ET datasets now developed, and published in refereed journals. Now, with
selection o f our 2010 renewal proposal complete, we offer the ATBD. This docum ent
represents our formal ATBD for establishing this algorithm and dataset as the official
M OD 16 Evapotranspiration product.
Abstract
This A lgorithm Theoretical Basis D ocum ent (ATBD) describes a level 4 M ODIS
land data product, M 0 D 1 6 , the global 8-day (M OD16A2) and annual (M OD16A3)
terrestrial ecosystem Evapotranspiration (ET) dataset at 1-km spatial resolution over the
109.03 M illion km^ global vegetated land areas. The M 0 D 1 6 algorithm is based on the
logic o f the Penm an-M onteith equation w hich uses daily meteorological reanalysis data
and 8-day remotely sensed vegetation property dynam ics from M ODIS as inputs.
The M OD 16 ET algorithm runs at daily basis and temporally, daily ET is the sum
o f ET from daytime and night. Vertically, ET is the sum o f w ater vapor fluxes from soil
evaporation, w et canopy evaporation and plant transpiration at dry canopy surface.
M ODIS 8-day FPA R is used as vegetation cover faction to quantify how m uch surface
net radiation is allocated betw een soil and vegetation; M ODIS 8-day albedo and daily
surface downward solar radiation and air tem perature from daily meteorological
reanalysis data are used to calculate surface net radiation and soil heat flux; daily air
temperature, vapor pressure deficit (VPD) and relative humidity data, and 8-day M ODIS
LA I are used to estimate surface stomatal conductance, aerodynam ic resistance, w et
canopy, soil heat flux and ofher key environmental variables. M ODIS land cover is used
to specify the biom e type for each pixel, and the biom e-dependent constant param eters
for the algorithm are saved in a B iom e-Property-Lookup-Table (BPLUT). Except for
minim um daily air tem perature and VPD, w hich are directly adopted from the existing
algorithm o f the M ODIS global terrestrial gross and net prim ary production (M ODIS
GPP/NPP), the BPLU T is tuned largely based on a set o f targeted annual ET for each
biom e derived from M ODIS GPP and w ater use efficiency calculated from eddy flux
fowers.
The M OD 16 ET has been validafed wifh ET m easured af eddy flux tow ers and ET
estim ated from 232 watersheds. Averaged over 2000-2010, the total global annual ET
over the vegetated land surface is 63.4X 10^ km^, w ith an average o f 569 ± 358 mm y r '\
com parable to the recent global estimates. Similar to other M ODIS level 3 or level 4
M ODIS land data products, 8-day and monthly M OD 16A 2 and annual M OD 16A3
datasets are saved in 10-degree Sinusoidal H DFEOS tiles. Thanks to the powerful
internal com pression o f HDFEOS, for each year, the size o f the M OD16A2 and
M OD 16A3 together takes about 39GB. Since 2006, there have been 193 users from 30
countries requesting M ODIS ET data from us and now M OD 16 from 2000 to 2010 are
ready and have been released to the public for free download at our ftp site,
ftp://ftp.ntsg.um t.edu/pub/M O D IS/M irror/M O D 16/.
II
Figure Captions
Figure 1 Seven-year mean percentage o f M ODIS 8-day L eaf A rea Index (LAI) period
contam inated by unfavorable atm ospheric conditions, especially by cloud cover, during growing
season, defined as annual N PP quality (Zhao et ah, 2005). Similar situation can be applied to
M ODIS LST, making it im practical to use an energy balance model to calculate ET globally. W hite
colored area in land is barren or inland water.
Figure 2 Flow chart o f the im proved M OD 16 ET algorithm. LAI: leaf area index; FPAR:
Fraction o f Photosynthetically Active Radiation.
Figure 3 The 8-day com posite leaf area index (LAI) in A m azon region for the 8-day period 081
(M arch 21-2 8 ) in 2001 for (a) the original w ith no temporal interpolation o f the LA I and (b) the
tem porally interpolated LAI.
Figure 4 D istribution o f the 46 Am eriFlux eddy flux towers used for validation o f the im proved
ET algorithm. The background is the M 0D 12Q 1 land cover type 2, w ith the blue color for the
w ater body.
Figure 5 The ET m easurem ents (black dots, OBS), the ET estimates driven by flux tow er
m easured meteorological data (red lines) and GMAO m eteorological data (blue lines) over 20002006 at seven tow er sites, D onaldson (a), L E A Tapajos KM 67 M ature Forest (b). W illow Creek
(c). Little Prospect Hill (d), Tonzi Ranch (e). W alnut R iver (f) and B ondville (g).
Figure 6 Comparisons o f the average ET observations to the average daily ET estimates w ith the
GM AO param eterized algorithm (a,b) and M ER R A GM AO param eterized algorithm (c, d)
across all the available days at the 46 flux tow er sites. These data w ere created using (1) towerspecific m eteorology (a, c), (2) global GM AO m eteorology (b) and M ERR A GMAO
meteorology (d). The solid red lines represent that the ratio o f ET estimates to ET measurements
is 1.0 and the solid black lines are the regression o f the ET estimates to measurements.
Figure 7 Global annual M OD 16 evapotranspiration (top) over 2000-2006 driven by global
GM AO (v4.0.0) meteorological data and (bottom) over 2000-2010 driven by global M ERRA
GM AO meteorological data.
Figure 8 Comparison o f the histograms o f climatological average o f global annual
evapotranspiration driven by GM AO m eteorological data (red solid line) over 2000-2006 and by
M ERRA GM AO meteorological data (solid black line) over 2000-2010. The GM AO-driven
global average ET is 568.4 m m /yr and 568.7 m m /yr driven by M ER RA GMAO meteorology
(see text). These com parisons are only for vegetated land surfaces. The vegetated land area is
shown as the colored area in Fig. 7.
Figure 9 Climatological zonal mean o f global annual evapotranspiration by GMAO
meteorological data over 2000-2006.
Figure 10 Spatial pattern o f the global M OD 16 ET seasonality during 2000-2010.
Figure 11 Spatial pattern o f global M ODIS ET to PET ratio anom alies during 2000-2009. Largescale ET/PET negative anom alies w ere m ainly caused by droughts.
Ill
Figure 12 (Left) D istribution o f the 232 w atersheds used for validation o f global M OD 16 ET
data. Each watershed is depicted in yellow. (Right) Comparison o f annual pseudo ET
observations (ET OBS, precipitation minus stream flow) from the 232 w atersheds and the
M ODIS ET estimates averaged over each w atershed over at least five years during 2000-2006.
The runoff data for the w atersheds were provided by Ke Zhang.
Figure 13 M ODIS Sinusoidal “ 10-degree” tile system. For land data products, there are 317
tiles w ith land pixels, o f w hich 286 tiles w ith vegetated pixels located between 60°S to 80°N.
IV
Table Captions
Table 1 The Biom e Properties Look-Up Table (BPLUT) for M ODIS ET. ENF: evergreen
needleleaf forest; EBF: evergreen broadleaf forest; DNF: deciduous needleleaf forest; DBF:
deciduous broadleaf forest; MF: m ixed forest; WE: woody savannas; SV: savannas; CSH: closed
sbrubland; OSH: open sbrubland; Grass: grassland, urban and built-up, barren or sparsely
vegetated; Crop: cropland.
Table 1.1 BPLUT using Global M odelling and A ssim ilation Office (GM AO v. 4.0.0) global
reanalysis data as input daily meteorological data.
Table 1.2 BPLU T using M odern Era Retrospective-analysis for Research and A pplications o f
Global M odelling and A ssim ilation Office (M ERRA GM AO) as input daily m eteorological data.
Table 2 O ther param eter values as used in the im proved ET algorithm.
Table 3 Input non-satellite m eteorological data, satellite data, and output ET data.
Table 4 The U niversity o f M aryland (UM D) landcover classification from M ODIS land cover
dataset (M 0D 12Q 1) used in the M 0 D 1 6 Algorithm. The data field nam e is Land_Cover_Type_2
in the M 0D 12Q 1 data field.
Table 5 The tow er names, abbreviations, latitude (lat), longitude (Ion), biom e types in the
parentheses, num ber o f days w ith valid tow er m easurem ents (Days), average daily tow er
evapotranspiration m easurem ents over all the days with valid values (ET OBS: mm/day).
Table 6 The tow er m easured annual GPP, tow er m easured annual ET summed over all the
available days divided by the num ber o f years (<365 days/year), and W U E calculated from
equation (39) averaged over all the tow ers for each vegetation type; the annual M ODIS GPP
averaged over each vegetation type; the expected M ODIS ET as calculated from equation (40);
the actual average annual M ODIS ET over each vegetation type. E N F : evergreen needleleaf forest;
E B F : evergreen broadleaf forest; D N F : deciduous needleleaf forest; D B F : deciduous broadleaf
forest; MF: m ixed forest; WL: woody savannas; SV: savannas; CSH: closed sbrubland; OSH: open
sbrubland; Grass: grassland, urban and built-up, barren or sparsely vegetated; Crop: cropland. N /A
m eans that no data is available.
Table 7 The tow er abbreviations, average daily tow er evapotranspiration (ET) measurements over
all the days w ith valid values (ET OBS: mm/day); the biases (BIAS: mm/day), mean absolute
biases (MAE: mm/day), correlation coefficients (R) and Taylor skill scores (S) o f ET estimates
relative to tow er ET m easurem ents for the 46 A m eriFlux eddy flux towers. 1: tow er-driven results;
2: G M AO-driven results.
Table 8. 321 users from 38 countries requesting M ODIS ET/PET/LE data over 2006-2012. N:
num ber o f users.
V
1. Introduction
All organism s require w ater for their survival (Oki and Kanae, 2006). U nlike m ost other
natural resources, w ater circulates and forms closed hydrological cycles. The terrestrial w ater
cycle is o f critical im portance to a w ide array o f Earth system processes. It plays a central role in
climate and meteorology, plant com munity dynamics, and carbon and nutrient biogeochem istry
(Vorosm arty et ah, 1998). D em and for the w orld’s increasingly scarce w ater supply is rising
rapidly, challenging its availability for food production and putting global food security at risk.
Agriculture, upon w hich a burgeoning population depends for food, is com peting w ith industrial,
household, and environm ental uses for this scarce w ater supply (Vorosm arty et al 2010; Rosegrant
et al., 2003). The w ater w ithdraw als from the renew able freshw ater resources include blue w ater
from the surface and groundw ater as w ater resources, and green w ater from the beneficial
evapotranspiration (ET) as a loss from the precipitated w ater over non-irrigated croplands (Oki
and Kanae, 2006). Global climate change will affect precipitation and ET, and hence influence
the renew able freshw ater resources. ET is the second largest com ponent (after precipitation) o f
the terrestrial w ater cycle at the global scale, since ET returns more than 60% o f precipitation on
land back to the atmosphere (Korzoun et al., 1978; L'vovich and W hite, 1990) and thereby conveys
an im portant constraint on w ater availability at the land surface. In addition, ET is an im portant
energy flux since land ET uses up m ore than h alf o f the total solar energy absorbed by land surfaces
(Trenberth et al., 2009). A ccurate estimation o f ET not only meets the growing com petition for
the limited w ater supplies and the need to reduce the cost o f the irrigation projects, but also it is
essential to projecting potential changes in the global hydrological cycle under different climate
change scenarios (Teuling et al. 2009).
This is the A lgorithm Theoretical Basis D ocum ent (ATBD) o f a global M ODIS land data
product, M ODIS ET dataset, w hich is a N A SA -planned Earth Observing System (EOS) dataset,
nam ed M OD 16 in the M ODIS datasets. The global M OD 16 ET includes evaporation from w et
and m oist soil, evaporation from rain w ater intercepted by the canopy before it reaches the ground,
and the transpiration through stomata on plant leaves and stems. The M OD16A2/A3 ET products
are produced at the 8-day, monthly and annual intervals. The objectives o f this ATBD are: (1) to
give a review o f the current m ethods for rem otely sensed ET estimates, (2) to describe M ODIS ET
algorithm, w hose logic follows the Penm an-M onteith equation, (3) to introduce the required input
datasets, daily m eteorological reanalysis dataset and 8-day com posite M ODIS albedo and M ODIS
vegetation dynam ics datasets (FPAR/LA l), (4) to detail how param eters are calibrated based on
measurem ents from eddy flux fowers and a mafure M ODIS global GPP dafasef, (5) fo show fhe
validafion resulfs af eddy flux fowers and global wafersheds and global M ODIS 1-km ET from
2000 fo 2010, (6) fo defail M OD 16 variables, dafa file formal, map projecfion, file name, and size,
and finally (7) fo summarize fhe ATBD.
2. Background
Remofe sensing has long been recognized as fhe mosf feasible means fo provide spafially
disfribufed regional ET informafion on land surfaces. Remofely sensed dafa, especially fhose
from polar-orbifing safellifes, provide femporally and spafially confinuous informafion over
vegefafed surfaces useful for regional m easurem enf and moniforing o f surface biophysical
variables affecfing ET, including albedo, biom e fype and leaf area index (LAI) (Los ef al., 2000).
1
The M O D erate Resolution Imaging Spectroradiom eter (M ODIS) onboard N A S A ’s Terra and
A qua satellites, provide unprecedented global inform ation on vegetation dynamics and surface
energy variations (Justice et al., 2002), w bicb can be used for regional and global scale ET
estimation in near real-time. Three m ajor types o f methods have been developed to estimate ET
from rem ote sensing data: ( 1) em pirical/statistical methods w bicb upscale point m easured or
estimated ET to large scales w ith rem otely sensed vegetation indices (N agler et al. 2005; Glenn
et al. 2008a, 2008b; Jung et al., 2010); (2) physical models that calculate ET as the residual o f
surface energy balance (SEE) through remotely sensed therm al infrared data (Bastiaanssen et al.,
1998a, 1998b; Su et al., 2002; O vergaard et al., 2006; Bastiaanssen et al. 2005; Allen et al. 2007;
K ustas and Anderson 2009); (3) and other physical models such as using the Penm an-M onteith
logic (M onteith 1965) to calculate ET (Cleugh et al. 2007; M u et al. 2007, 2009, 2011).
2.1 Energy Partitioning Logic
Energy partitioning at the surface o f the earth is governed by the following three coupled
equations:
T —T
H =p C p ^ ^
ra
( 1)
AE =
(2)
Y
A' =
~ ^
ra + Ts
- A S - G = H + AE
(3)
w here H, AE and i4' are the fluxes o f sensible heat, latent heat and available energy for H and
AE; Rnet is net radiation, G is soil heat flux; AS is the heat storage flux. A is fhe lafent heaf o f
vaporizafion. p is air densify, and Cp is fhe specific heaf capacify o f air; T^,
are fhe
aerodynam ic surface and air temperafures;
is fhe aerodynam ic resisfance; egat, ^ are fhe wafer
vapour pressure af fhe evaporafing surface and in the air; is the surface resistance to
evapotranspiration, w hich is an effective resistance to evaporation from land surface and
transpiration from the plant canopy. The psychrom etric constant y is given by
7 = Cp X
X M a /( A X M ^ )
w here
and
pressure.
are the m olecular masses o f dry air and w et air and
(4)
is atm ospheric
2.2 Surface Energy Balance M odels
Because rem ote sensing can provide LST inform ation through thermal spectral bands,
SEE- based models w ere proposed and widely being used. In the early stage o f energy-balancebased models, m ost studies used the high resolution remote sensing data, some data sources are
even from airborne sensors or sensor m ounted above a site (e.g., N orm an et al., 1995;
Bastiaanssen et al., 1998a, 1998b). The energy balance models calculate the ET through the
residual o f the surface absorbed energy as AE =
— AS — G — H.
D espite surface aerodynam ic tem perature is different from rem otely sensed LST, the
surface energy balance m ethod (SEE) calculates the flux o f sensible heat from Equation
Error! Reference source not found, by substituting the rem otely-sensed radiative surface
tem perature (LST) forL^, using the m easured air tem perature {To) and calculating the
aerodynam ic resistance {R ^) from:
(z — d \
k^U
( z — d \]
y ~ r l\
(z — d \
(z — d \
(~ r)
(5)
In this equation, k is von K arm an’s constant (0.4); U is w ind speed at the reference height z; d is
the zero-plane displacem ent height; Zq, Zq^ are the roughness lengths for mom entum and
sensible heat, respectively; and ipH,
^re the stability correction functions for m om entum and
heat w hich depend on the M onin-O bukhov length L (Kaimal and Finnigan, 1994). AE is then
calculated as the residual o f the energy balance using Equation Error! Reference source not found..
2.3 M odels Using Relationship between Vegetation Index and LST
A nother family o f method using LST to estimate ET is based on the relationship between
vegetation index (VI) and LST. Nem ani and Running (1989) showed the utility o f a scatterplot o f
VI-LST on a group o f pixels inside a fixed square region in a satellite image. The air temperature,
soil and vegetation surface tem perature required for ET estimates are obtained through the VI-LST
triangle plot for an im age w indow (N ishida 2003a, 2003b). However, Hope et al. (2005) found
that the relationship betw een therm al-IR based LST and N D V I at high-latitudes is opposite to that
o f m id-latitude regions because arctic tundra ecosystems characterized by perm afrost provide a
large sink for energy below the ground surface. And the algorithm is too com plex and some key
biophysical param eters are hard to be param eterized at the global scale. M ore importantly, the
method requires LST and this constrains its application at global scale as detailed below.
0
16
55
50
66
B3
100
meon % filled MODIS 8 -d o y periods (2 0 0 0 -2 0 0 6 )
Figure 1 Seven-year mean percentage of MODIS 8-day Leaf Area Index (LAI) period contaminated by
unfavorable atmospheric conditions, especially by cloud cover, during growing season, defined as annual
NPP quality (Zhao et al., 2005). Similar situation can be applied to MODIS LST, making it impractical to
use an energy balance model to calculate ET globally. White colored area in land is barren or inland water.
Both energy-balance-based and VI-LST triangle m ethods require reliable remotely sensed
LST, w hich makes them im practical to be applied at the global scale. Though w e have so far most
advanced M ODIS sensor and standard 8-day M ODIS LST at I-km resolution, tw o m ajor reasons
restrain the application o f energy balance based models at the global scale. First, M ODIS LST is
the average o f cloud-free LST (W an et al., 2002), and thus an 8-day com posite daytime LST may
be overestim ated at the average overpass tim e due to exclusion o f cloudy days. In regions with
high frequency o f cloudiness, it is alm ost im possible to get tem porally continuous LST. Figure I
shows the percentage o f missed 8-day M ODIS LA I during the grow ing season due to cloudiness
(Zhao et al. 2005), w hich clearly shows that the frequency o f cloud cover at an 8-day interval is
considerably high, especially for areas w ith rain forests and maritim e climate. Globally, for
vegetated land, the mean percentage o f m issing 8-day M ODIS data due to unfavorable atmospheric
conditions is 44.6I(±23.65)% , with 38.43% vegetated areas having more than 50% missing 8-days
in a grow ing season (Fig. I).
U nlike surface contam inated albedo or LAI, w hich is generally a slow surface variable and
can be simply tem porally filled w ith data in adjacent clear sky periods, contam inated LST cannot
be simply filled because it is largely influenced by synoptic w eather conditions and has large
variations. A regional ET estimate using N O A A /A V H R R data over m ost parts o f the central U SA
has clearly dem onstrated that the energy balance model cannot w ork for areas w ith cloud cover
(Fig. 4 in M ecikalski et al., 1999). Secondly, these LST-required ET algorithm s have uncertainties
largely due to uncertainties in LST. Zhan et al. (1996) assessed four energy-balance-based ET
models and found only one w ith estimates close to the measured, and models are sensitive to AE
and other surface parameters. Similarly, Cleugh et al. (2007) com pared a surface energy balance
model w ith the Penm an-M onteith (hereafter P-M ) m ethod (M onteith, 1965), and found that the
energy balance model failed because o f its sensitivity to small errors in LST. Because o f these
problems, energy balance models are im practical for application at the global scale in an
operational manner. H ow ever they often w ork well w ithin a narrow range o f surface conditions
for w hich they w ere developed and calibrated (e g., W ood et al., 2003; French et al., 2005;
Bastiaanssen et al., 2005; Courault et al., 2005; Tasumi et al., 2005; M cCabe and W ood, 2006).
Courault et al. (2005), Su (2005), and Glenn et al. (2008a) have given excellent reviews o f these
LST based ET model.
2.4 Penm an-M onteith Logic
A nother fundam entally different approach to developing a satellite-based
evapotranspiration algorithm is the w ell-know n Penm an-M onteith (hereafter P-M ) equation.
M onteith (1965) elim inated surface tem perature from Equations (I) - (3) to give:
_ s x A ' + p x C p X (esat - e ) /r g _ s x A ' + p x
S + 7 X
(1 +
V slV a)
S + 7 X
(1 +
x V P D /r^
V slV a)
w here s = dKeg^^^/dT, the slope o f the curve relating saturated w ater vapor pressure
to
temperature; i4' is available energy partitioned betw een sensible heat and latent heat fluxes on land
surface. VPD =
— e is the air vapor pressure deficit. All inputs have been previously defined
except for surface resistance
w hich is an effective resistance accounting for evaporation from
the soil surface and transpiration from the plant canopy. The aerodynam ic resistance, r«, can be
estim ated from Equation (5) using z o v (the roughness length for w ater vapor) in place o f z o h
although in practice the tw o are usually assumed to be equal.
Over extensive, m oist surfaces when rs approaches zero, or w hen
reduces to the equilibrium evapotranspiration rate:
« r^. Equation (6)
s X i4'
^Eed = — —
^
s + 7
(7)
w hich is limited only by available energy. R aupach (2001) dem onstrates why (7) is the
theoretical upper lim it for regional evapotranspiration from land surfaces w here moisture
availability is not constrained. Conversely w hen
« r^, evapotranspiration is largely
controlled by the surface resistance and Equation (6) then reduces to:
p X C ^ x VPD
AErs = --------------------------------------------YXr s
The full P-M equation provides a more robust approach to estimating land surface ET because: 1)
it com bines the main drivers o f ET in a theoretically sound way; 2) it provides an energy constraint
on the ET rate; 3) m odeled ET fluxes are not overly sensitive to any o f the inputs, i.e.
differentiation o f A E shows that (independent) changes in any o f the input term s on the right-hand
side o f Equation 6 yield a conservative change in predicted A E (Thom, 1975 provides a more
extensive discussion about the sensitivity o f the P-M equation to its inputs); and 4) it has been
successfully used to both diagnose and predict land surface ET.
D espite its theoretical appeal, the routine im plem entation o f the P-M equation is often
hindered by requiring meteorological forcing data (A ', Ta and VPD) and the aerodynam ic and
surface resistances (r« and rs). Radiation and soil heat flux m easurem ents are needed to
determ ine A'; air tem perature and hum idity to calculate VPD; and w ind speed and surface
roughness param eters to determine r«. These problem s are not unique to the P-M equation, since
A ’, Ta and ra are also required by all o f the approaches using radiative surface tem perature and
the surface energy balance to calculate AE, including the resistance-surface energy balance
model.
M ulti-tem poral im plem entation o f the P-M model at regional scales requires routine
surface m eteorological observations o f air temperature, humidity, solar radiation and w ind speed.
D eterm ining the surface resistance, rs, is difficult. For a fully closed canopy, w here LA I > 3, the
surface resistance is the parallel sum o f the leaf stomatal resistances, i.e. Fg = r ^ / L A l , where
is the mean stomatal resistance (e.g. M onteith, 1980) w hich can be m easured directly using
porometry. M odels for estimating maxim um stomatal conductance exist (K elliher et al., 1995)
but including the effect o f limited soil w ater availability and stomatal physiology requires either
a fully coupled biophysical model such as that by Tuzet et al. (2003) or resorting to the empirical
( 8)
discount functions o f Jarvis (1976), w hich m ust be calibrated: neither o f these are appropriate for
land surface evapotranspiration model that is to be im plem ented routinely across the globe at
kilom etre spatial resolution. D eterm ining a surface resistance for partial canopy cover is even
more challenging w ith various dual source models proposed (e.g. Shuttleworth and W allace,
1985) to account for the presence o f plants and soil. Given the im pedim ent that presents to
using the P-M equation, Cleugh et al. (2007) developed a rem otely sensed ET model using a P-M
approach driven by M ODIS derived vegetation data and daily surface meteorological inputs
including incom ing solar radiation, surface air tem perature and VPD. Stability corrections to
(Equation 5) was neglected, although this is justifiable because the P-M equation is relatively
insensitive to aerodynam ic resistance - especially when « rs and at daily timescales. Surface
albedo and em issivities o f the surface and atm osphere needed to determine A \ and the
aerodynam ic roughness needed for r«, can be derived from rem otely sensed radiance data or
from models.
Cleugh et al. (2007) used the more theoretically based P-M equation 6 (1965) to estimate
ET over A ustralia w ith M ODIS data. Based on Cleugh et al.’s model (2007), M u et al. (2007)
developed a rem otely sensed ET model (RS-ET) to get the first rem otely sensed global terrestrial
ET map, suggesting it is applicable to operationally estimate global ET in near real tim e at satellite
sensor resolution. Based on M u et al.’s 2007 R S-ET model, Zhang et al. (2009) developed a model
to estimate ET using remotely sensed N D V I data; Yuan et al. (2010) m odified M u et al.’s 2007
RS-ET model by adding the constraint o f air tem perature to stomatal conductance and calculating
the vegetation cover fraction using LA I instead o f EVI.
There are also other methods using remote sensing data to estimate global ET. For
example, Fisher et al. (2008) used Priestley-Taylor (1972) m ethod to estimate global ET using
A V H RR data; Jung et al. (2010) used a m achine-learning m ethod to upscale the point
FLU XN ET tow er data to calculate the global ET w ith rem otely sensed data.
In M u et al.’s R S-ET algorithm (2007), ET w as calculated as the sum o f the evaporation
from m oist soil and the transpiration from the vegetation during daytime. N ighttim e ET was
assumed to be small and negligible. Soil heat flux (G) was assumed to be zero. For daily
calculations, G m ight be ignored (G avilana et al., 2007). G is a relatively small com ponent o f
the surface energy budget relative to sensible and latent energy fluxes for m ost forest and
grassland biom es (Ogee et al., 2001; da R ocha et al., 2004; Tanaka et al., 2008) and is generally
less than 20% o f net incom ing radiation for the forest and grassland sites from this investigation
(e.g. W eber et al. 2002; Granger, http://w w w .taiga.net/w olfcreek/Proceedings_04.pdf).
However, the assum ption o f negligible G in RS-ET algorithm is a significant concern for tundra.
In the A rctic-Boreal regions, G can be a substantial am ount o f net radiation, especially early in
the growing season. The assumption o f a negligible G may be valid in m id-latitude regions on a
daily basis, how ever in these areas a substantial portion o f net radiation m elts ice in the active
layer, especially early in the grow ing season (Harazono et al., 1995; Engstrom et al., 2006). The
RS-ET algorithm neglected the evaporation from the intercepted precipitation from plant canopy.
A fter the event o f precipitation, part o f the vegetation and soil surface is covered by water. The
evaporation from the saturated soil surface is much higher than the evaporation from the
unsaturated soil surface, and the evaporation from the intercepted w ater by canopy is different
from canopy transpiration. M u et al. (2011) have im proved the 2007 algorithm by 1) simplifying
the calculation o f vegetation cover fraction; 2) calculating ET as the sum o f daytime and
nighttim e components; 3) calculating soil heat flux; 4) im proving the methods to estimate
stomatal conductance, aerodynam ic resistance and boundary layer resistance; 5) separating dry
canopy surface from the wet, and hence canopy w ater loss includes evaporation from the w et
canopy surface and transpiration from the dry surface; and 6) dividing soil surface into saturated
w et surface and moisture surface, and thus soil evaporation includes potential evaporation from
the saturated w et surface and actual evaporation from the moisture surface. This im proved ET
algorithm is the official M OD 16 ET algorithm used to produce the official global terrestrial
M OD 16 ET p ro d u ct
3. MOD 16 ET Algorithm Descriptions
M OD 16 ET algorithm is based on the Penm an-M onteith equation (M onteith, 1965) as in
equation 6 . Figure 2 shows Ihe logic behind Ihe im proved M OD 16 ET Algorithm for calculating
daily M OD 16 ET algorithm.
"J:. I.'jf
'W--. ' . | >n
H-iVe
-.a i- p if tln r
''l . ; " t
r n . 1 :)r i p y
Air p re s s u re ,
air tem p eratu re.
humidity
ii.-tij':"
71#■ -il.i li i l i 'll
-fiy
lij. -.ii- jE vapoiranspiraliori
(ET)
5ealar;
Kadlalipn:
A ir t e m p e r a t u r e
s:i1
■it- rr ;iol r n
Le II .■ !.eil
Furr i--i..'
‘ -.■■rpui. 1til-
■A ■rji.r
A , t lal >..il
-.■3p.oi.'trir
Tv
.
R adiation,
air te m p eratu re
ivuei«pi[
..
■I -Ivli n
K'jlarfju iu. lt
.■ j - .i [ - i r .'t 'i r
L eg e n d for th e ev ap o tran sp iratio n (E T ) flow chart
(^
R em o te S en sin g inputs
Osiiy
^
Metsorologic:?! inputs
I;;: In term ed iate algorithm oalculations
8-<lay, m onthiy, annual
Final algorithm ou tp u t
Figure 2 Flowchart of the improved M0D16 ET algorithm. LAI: leaf area index; FPAR: Fraction of
Photosynthetically Active Radiation.
3.1 V egetation Cover Fraction
N et radiation is partitioned betw een the canopy and soil surface based on vegetation cover
fraction (Fc). In the 2007 M 0 D I6 ET algorithm, Fc w as calculated as in equation 9 (M u et al.,
2007),
^
^
E V I-E V U i,
w here EVImm and EVImux w ere the minimum and m aximum EV I during the study period, set as
constants o f 0.95 and 0.05 (M u et al., 2007), respectively. In the im proved algorithm (M u et al.,
2011), to reduce num bers o f inputs from M ODIS datasets and to simplify the algorithm, we use 8day 1-km^ M OD 15A 2 FPA R (the Fraction o f Absorbed Photosynthetically Active R adiation) as a
surrogate o f vegetation cover fraction (Los et al., 2000),
Ec = EPAR
(1 0 )
3.2 D aytim e and Nighttim e ET
Daily ET should be the sum o f daytime and nighttim e ET. To get nighttim e average air
tem perature (T^j^^t), we assume that daily average air tem perature (Javg) is the average o f
daytime air tem perature (T^ay) and
Tnight = 2.0 X Ta^g ~ Tg^y
(1 1 )
The net incom ing solar radiation at night is assumed to be zero. B ased on the optim ization theory,
stomata will close at night to prevent w ater loss when there is no opportunity for carbon gain
(Dawson et al., 2007). In the im proved ET algorithm, at night, the stomata are assumed to close
com pletely and the plant transpiration through stomata is zero, except for the transpiration through
leaf boundary-layer and leaf cuticles (more details in section 3.6). B oth nighttim e and daytime
use the same ET algorithm except that different values at daytime and nighttim e are used for the
same variable.
3.3 Soil H eat Flux
In M 0 D 1 6 ET algorithm, the net incom ing radiation to the land surface (Rnet) is calculated
as the equations 12 and 13 (Cleugh et al., 2007).
Rnet = (1 - a ) X Rsi + (Sa ~ Eg) X (T X (2 7 3 .1 5 +
( 12)
Ea = l £5 = 0.97
w here a is M ODIS albedo, Rgi is the downward shortwave radiation,
is surface emissivity, £a
is atmospheric emissivity, and T is air tem perature in °C. A t daytime, if R m t is less than zero,
Rnet is set to be zero; at nighttime, if R^et is less than -0.5 tim es o f daytime Rnet-. nighttim e R ^ t
is set as -0.5 m ultiplying daytime RnetIn the im proved algorithm, there will be no soil heat flux (G) interaction between the soil
and atmosphere if the ground is 100% covered w ith vegetation. Energy received by soil is the
difference betw een the radiation partitioned on the soil surface and soil heat flux (G).
^net
Ac = F c X A
(1 3 )
Asoil = (1 ~ ^c) X A — G
w here A is available energy partitioned betw een sensible heat, latent beat and soil heat fluxes on
land surface;
is the net incom ing radiation received by land surface; Ac is the part o f A
allocated to the canopy and A s o il is the part o f A partitioned on the soil surface. In 1986, Clothier
et al. (1986) proposed a m ethod to estimate soil beat flux using remote sensing data as
Gsoil = (0-295 - 0 .0 1 3 3 5 2 /5 1 ) X A^
(1 4 )
w here 5 7 and 5 2 are the bandpasses o f SPOT filters 610-680 nm, and 790-890 nm, Ai is daytime
or nighttim e available energy partitioned betw een latent beat and sensible beat fluxes. K ustas and
D aughtry (1990) further im proved the m ethod using B2/B1 and NDVI'.
Gsoil = (0 .2 9 4 - 0 .0 1 6 4 5 2 /5 1 ) X A^
NDVI —
Gs o il
(1 5 )
~ ^ 1 )/
/(5 1 + 52)
= (0-325 - 0.2 0 8 X N D V I ^
X
A^
(1 6 )
D aughtry et al. (1990) com pared the soil beat flux using different methods w ith observed
data and found that the estimates using N D V I'in equation 16 bad the low est absolute error (13%)
w ith a small positive bias. Jacobsen and H ansen (1999) proposed some other methods to estimate
Gsoil as,
Gsoil = 4.73 x T ^ - 20.87
Gs o il
(1 7 )
= ( - 0 .2 7 X ND VI +0.39) X A^
(1 8 )
Gsoil = ( —0.025 X R n i r / R r e d + 0.35) x
(1 9 )
w here 5 m eans daytime or nighttim e average tem perature in °C.
W e adopted equations (17) and (18) globally with some constraints. A t the extremely hot
or cold places or w hen the difference between daytime and nighttim e tem perature is low (<5°C),
there is no soil beat flux. The soil beat flux is set to be zero in the 2007 version, now it is estimated
as
f 4.73 X Tj — 20.87
Gsoil = < 0.0
T’min.ciose ^ Tanriavg < 2S°C, T d a y — T n i g h t > 5°C
TanUavg > 25°C o r TanUavg < T’min.ciose or T d a y — T n i g h t < 5°C
( 0.39 * Ai
G = Gsoil X (1 - Fc)
abs(G soir) > 0.39 * a b s (A i)
(20)
in the im proved algorithm, w here Gsoil stands for the soil heat flux w hen
= 0; Tanriavg is
annual average daily temperature, and T^m_ciose is the threshold value below w hich the stomata
will close com pletely and halt plant transpiration (Table 1; Running et al., 2004; M u et al., 2007b;
M u et al., 2011).
A t daytime,
= 0.0 if
< 0.0 ; at nighttime.
Gsoil^igflf — A^lghf + 0.5 X A(l(iy i f
> 0.0 and A^lgh^
G -night
^
t).5 * Agg^y.
Table 1 The Biom e Properties Look-Up Table (BPLUT) for M ODIS ET. ENF: evergreen
needleleaf forest; EBF: evergreen broadleaf forest; DNF: deciduous needleleaf forest; DBF:
deciduous broadleaf forest; MF: m ixed forest; WL: woody savannas; SV: savannas; CSH: closed
sbrubland; OSH: open sbrubland; Grass: grassland, urban and built-up, barren or sparsely
vegetated; Crop: cropland.
Table 1.1 BPLUT using Global M odelling and A ssim ilation Office (GM AO v. 4.0.0) global
reanalysis data as input daily meteorological data.
PARAMETER
T m in open (°C)
T m in close (°C)
gl_sb (m s'^)
gl_e_w v (m s'^)
Cl (m/s)
RBL M IN (sm -i)
RBL M A X (s m 'i)
ENE
8.31
-8.00
3000
650
0.04
0.04
0.0032
65.0
95.0
EBE
9.09
-8.00
4000
1000
0.01
0.01
0.0025
70.0
100.0
DNE
10.44
-8.00
3500
650
0.04
0.04
0.0032
65.0
95.0
DEE
9.94
-6.00
2900
650
0.01
0.01
0.0028
65.0
100.0
ME
9.50
-7.00
2900
650
0.04
0.04
0.0025
65.0
95.0
PARAM ETER
T m in open (°C)
T m in close (°C)
V P D c i o s e (Pa)
V P D o p e n (Pa)
gl_sb (m s'^)
gl_e_w v (m s'^)
Cl (m/s)
RBL M IN (sm -i)
RBL M A X (s m 'i)
OSH
8.80
-8.00
4400
650
0.04
0.04
0.0065
20.0
55.0
WL
11.39
-8.00
3500
650
0.08
0.08
0.0065
25.0
45.0
SV
11.39
-8.00
3600
650
0.08
0.08
0.0065
25.0
45.0
Grass
12.02
-8.00
4200
650
0.02
0.02
0.0070
20.0
50.0
C ro p
12.02
-8.00
4500
650
0.02
0.02
0.0070
20.0
50.0
V P D c io s e ( P a )
V PD open (P a )
CSH
8.61
-8.00
4300
650
0.04
0.04
0.0065
20.0
55.0
Table 1.2 BPLU T using M odern Era Retrospective-analysis for Research and A pplications o f
Global M odelling and A ssim ilation Office (M ERRA GM AO) as input daily m eteorological data.
PARAMETER
T m in open (°C)
T m in close (°C)
V P D c io s e ( P a )
V PD open (P a )
ENE
8.31
-8.00
3000
650
EBE
9.09
-8.00
4000
1000
DNE
10.44
-8.00
3500
650
DBE
9.94
-6.00
2900
650
10
ME
9.50
-7.00
2900
650
CSH
8.61
-8.00
4300
650
gl_sh (m s'^)
gl_e_w v (m s'^)
Cl (m/s)
RBL M IN (s m 'i)
RBL M A X (sm -i)
0.04
0.04
0.0032
65.0
95.0
0.01
0.01
0.0032
65.0
95.0
0.04
0.04
0.0032
65.0
95.0
0.01
0.01
0.0032
65.0
95.0
0.04
0.04
0.0024
65.0
95.0
PARAM ETER
OSH
8.80
-8.00
4400
650
0.04
0.04
0.0065
20.0
45.0
WL
11.39
-8.00
3500
650
0.08
0.08
0.0070
15.0
45.0
SV
11.39
-8.00
3600
650
0.08
0.08
0.0070
15.0
45.0
Grass
12.02
-8.00
4200
650
0.02
0.02
0.0075
15.0
45.0
Crop
12.02
-8.00
4500
650
0.02
0.02
0.0075
15.0
45.0
T m in open ( ° C )
T m in close ( ° C )
V P D c io s e ( P ^ )
V PD open (P a )
gl_sh (m s'^)
gl_e_w v (m s'^)
Cl (m/s)
RBL M IN (s m 'i)
RBL M A X (s m 'i)
0.04
0.04
0.0065
20.0
45.0
3.4 W et Surface Fraction
In the 2007 M 0 D 1 6 ET algorithm, there w as no difference betw een the ET on the saturated
and moist bare soil surface, and there was no evaporation but transpiration on the canopy surface
(Figure 1 in M u et al., 2007). In the im proved ET algorithm (M u et al., 2011), ET is the sum o f
w ater lost to the atmosphere from the soil surface through evaporation, canopy evaporation from
the w ater intercepted by the canopy, and transpiration from plant tissues (Fig. 2). The land surface
is covered by the plant and the bare soil surface, and percentage o f the tw o com ponents is
determ ined by F^. Both the canopy and the soil surface are partly covered by w ater under certain
conditions. The w ater cover fraction ( F w e t ) is taken from the Fisher et al. (2008) ET model,
m odified to be constrained to zero w hen relative hum idity (RH) is less than 70%:
R H < 70%
70% < R H < 100%
( 21 )
w here R H is relative humidity (Fisher et al, 2008). W hen R H is less than 70%, 0% o f the surface
is covered by water. For the w et canopy and w et soil surface, the w ater evaporation is calculated
as the potential evaporation as described in sections 3.5 and 3.7.
3.5 Evaporation from W et Canopy Surface
Evaporation o f precipitation intercepted by the canopy accounts for a substantial amount
o f upw ard w ater flux in ecosystem s w ith high LAI. For the im proved algorithm, w hen the
vegetation is covered w ith w ater (i.e., F w e t is not zero), w ater evaporation from the w et canopy
surface will occur. ET from the vegetation consists o f the evaporation from the w et canopy surface
and transpiration from plant tissue, w hose rates are regulated by aerodynam ics resistance and
surface resistance.
11
The aerodynam ic resistance (rhrc, s m'^) and w et canopy resistance (rv c, s m'^) to
evaporated w ater on the w et canopy surface are calculated as
rhc
1.0
(jl S'/) X LAI X F w e t
= 4.0
+ 273.15)^
rhc X rrc
rhrc = —----------rhc + rrc
r vc
1.0
gl_e_w v X LAI x F w e t
w here rhc (s m'^) is the w et canopy resistance to sensible heat, rrc (s m'^) is the resistance to
radiative heat transfer through air; g l sh (s m'^) is le af conductance to sensible heat per unit LAI,
g l e j v v (m s'^) is le af conductance to evaporated w ater vapor per unit LAI, a (W m'^ K'"^) is
Stefan-Boltzm ann constant. Follow ing Biom e-BGC model (Thornton, 1998) w ith revision to
account for w et canopy, the evaporation on w et canopy surface is calculated as
_ (s X
^i^wet_c -
+
P
X Cp X {e^at - e) X F d r h r c ) x F w e t
P n X C ^ x rvc
s H— ---------------A x a X rhrc
^ ^
w here the resistance to latent heat transfer (rvc) is the sum o f aerodynam ic resistance (rhrc) and
surface resistance (rA) in equation 6 .
3.6
Plant Transpiration
3.6.1 Snrface Condnctance to Transpiration
Plant transpiration occurs not only during daytime but also at nighttime. Since m ost o f the
transpiration occurs at daytime, the nighttim e transpiration was neglected in the 2007 algorithm.
In the im proved algorithm, both the daytime and night tim e transpiration are included for the
calculation o f transpiration.
For many plant species, stomatal conductance (Cs) decreases as vapor pressure deficit
(VPD) increases, and stomatal conductance is further limited by both low and high tem peratures
(Jarvis, 1976; Sandford et al., 1986; K aw am itsu et al., 1993; Schulze et al., 1994; Leuning, 1995;
M arsden et al., 1996; D ang et al., 1997; Oren et al., 1999, 2001; Xu et al., 2003; M isson et al.,
2004). VPD is calculated as the difference betw een saturated air vapor pressure, as determined
from air tem perature (Murray, 1967), and actual air vapor pressure. Because high tem peratures
are often accom panied by high VPDs, w e have only added constraints on stomatal conductance
for VPD and minimum air temperature, ignoring constraints resulting from high temperature. For
the daytime plant transpiration, the stomatal conductance estimation has been improved. In the
12
2007 algorithm, surface conductance (Cc) was estim ated by using LA I to scale stomatal
conductance (Cs) from le af level up to canopy level (Landsberg and Gower, 1997),
Cs = C iX m ( T m in ) x m (V P D )
Cr = Cc X LAI
(2 4 )
w here C l is the mean potential stomatal conductance per unit leaf area, m(Tmin) is a m ultiplier that
limits potential stomatal conductance by m inim um air tem peratures (Tmin), and m (V P D ) is a
m ultiplier used to reduce the potential stomatal conductance when V P D (difference between
and e) is high enough to reduce canopy conductance (M u et al., 2007; Zhao et al., 2005). In the
case o f plant transpiration, surface conductance is equivalent to the canopy conductance (Cc), and
hence surface resistance (rs) is the inverse o f canopy conductance (C c). W e calculate the
constraints from m inim um air tem perature (Tmin) and V P D as:
^CCmin)
T
■ —
> T
■
^ m in
^m in_open
1.0
T
■ —Tmin
■ close
^min
^T ■
^m m _ o p en
min_ciose
0.0
'Cmin_close
'Cmin
'^min_open
T
^min <
—T
^mm close
(25)
4 .0
m (V P D ) =
VPD < VPD.o p e n
VPDciose - VPD
VPD,o p e n
VPDciose
VPDopen < V P D < VPDciose
0.0
VPD > VPDciose
w here close indicates nearly com plete inhibition (full stomatal closure) due to low Tmin and high
V P D , and open indicates no inhibition to transpiration (Table 1). W hen Tmin is low er than the
threshold value Tmin_dose, or V P D is higher than the threshold VPDciose, the strong stresses from
tem perature or w ater availability will cause stomata to close completely, halting plant transpiration.
On the other hand, when Tmin is higher than Tmin_open, and V P D is low er than VPDopen, there will be
no tem perature or w ater stress on transpiration. For Tmin and V P D falling into the range o f the
upper and low limits, the corresponding m ultiplier will be w ithin 0.0 to 1.0, im plying a partial
stomatal closure. The m ultipliers range linearly from 0 (total inhibition, lim iting r ; ) to 1 (no
inhibition) for the range o f biom es are listed in a Biom e Properties Look-U p Table (BPLUT)
(Table 1) (M u et al., 2007; 2011). Complete details on the derivation o f the algorithm and the
values used in the BPLU T can be found in section 5. The effect o f soil w ater availability is not
included in the ET algorithm. Some studies have suggested that atm ospheric conditions reflect
surface param eters (Bouchet, 1963; M orton, 1983), and VPD can be used as an indicator o f
environm ent w ater stress (Running et al., 1988; G ranger et al., 1989). In addition. M u et al. (2007b)
found that V P D alone can capture interannual variability o f the full w ater stress from both the
atmosphere and soil for alm ost all o f China and the conterm inous U.S., though it may fail to capture
the full seasonal w ater stress in dry regions experiencing strong sum m er monsoons.
13
In the 2007 algorithm, Cl was a constant for all biom e types. In the im proved algorithm,
Cl is set differently for different biom es as shown in Table 1 (K elliher et al., 1995; Schulze et al.,
1994; W hite et al., 2000). In the im proved algorithm, the way to calculate Cc has been revised.
Canopy conductance to transpired w ater vapor per unit LA I is derived from stomatal and cuticular
conductances in parallel w ith each other, and both in series w ith leaf boundary layer conductance
(Thornton, 1998; Running & Kimball, 2005).
1.0
101300
/ T, + 273.15V
I, 293.15 )
Pa
Gs day ^
Gcu
= G i X m ( T m i n ) x m ( V P D ) x Vcorr
3 —^ ^ ^
^ corr
Gs2 = g l_ sh
(2 6 )
( G$ 2 X ( G q
r
;1
+ Grij^
- ] n
Oc_t — ) 05j l + Gs2 + Gcu ^
I 0.0
^5
i =
^ (1-0 -
> 0-0' (1-0 -
> 0-0)
(LAI = 0.0, (1.0 - F w e t) = 0.0)
V cr ■
w here the subscript i means the variable value at daytime and nighttime; Gs day^ ^nd Gs nigh A
are daytime and nighttim e stomatal conductance, respectively;
leaf cuticular conductance;
Gs 2 is leaf boundary-layer conductance; g cu is cuticular conductance per unit LAI, set as a
constant value o f 0.00001 (m s'^) for all biomes; g l sh is leaf conductance to sensible heat per
unit LAI, w hich is a constant value for each given biom e (Table 1). The reason to use the
correction function Ff-oj-ris that, the conductance through air varies w ith the air tem perature and
pressure. The prescribed values are assumed to be given for standard conditions o f 20°C and
lOlSOOPa. B ased on the prescribed daily air tem perature (converted to K elvins) and an air
pressure estim ated from a prescribed elevation, the prescribed standard conductances are
converted to actual conductances for the day according to Jones (1992) and Thornton (1998). Fg
is the dry canopy surface resistance to transpiration from the plant. Instead o f setting the
atmospheric pressure ( P a ) as a constant value as in the 2007 algorithm,
is calculated as a
function o f the elevation (Elev) (Thornton, 1998).
t i = 1 .0 -
LR std ^ E le v
'STD
=
"°RR
LR std ^
(2 7 )
14
Pa — PsTD ^
w here L R std , T std , G std , R R , M A and P std are constant values as listed in Table 2. L R std (K m'^)
is standard tem perature lapse rate; T std (K) is standard tem perature at 0.0 m elevation; G std (m sis standard gravitational acceleration; R R (m^ Pa mol'^ K'^) is gas law constant; M A (kg mol'^)
is m olecular w eight o f air and P std (Pa) is standard pressure at 0.0 m elevation.
Table 2 Other param eter values as used in the im proved ET algorithm
L R std
(K m~^)
0.0065
T std
(K)
288.15
Gstd
(m s~^)
9.80665
RR
(m^ P a mol~^ K~^)
8.3143
MA
(kg mol~‘)
28.9644C-3
P std
(Pa)
101325.0
Based on the optim ization theory, stomata will close at night to prevent w ater loss when
there is no opportunity for carbon gain (Dawson et al., 2007). In the im proved ET algorithm, the
stomata are assumed to close com pletely at night, resulting in
= 0 .0 .
3.6.2 A erodynam ic R esistance
The transfer o f heat and w ater vapor from the dry canopy surface into the air above the
canopy is determ ined by the aerodynam ic resistance(ra), w hich was a constant o f 20 s m'^ in the
2007 algorithm. In the im proved algorithm,
is calculated as a parallel resistance to convective
(r/i) and radiative ( r r ) heat transfer following Biom e-BG C model (Thornton, 1998),
rh X r r
J' — ----------a
yy,
1.0
p xC-p
4.0 X 0- X (Tj + 273.15)3
w here g l bl (m s'^) is leaf-scale boundary layer conductance, w hose value is equal to leaf
conductance to sensible heat per unit LA I (gl sh (m s'^) as in section 3.5), and a (W m'^ K'"^) is
Stefan-Boltzm ann constant.
3.6.3 Plant Transpiration
Finally, the plant transpiration (((Efpans) is calculated as
_ ( s x A c + p x C p X (esat - e) X F c /r^ ) x (1 - F w e t)
s + Y X ( l + rs/ra)
w here r^is the aerodynam ic resistance calculated from equation 6 .
15
^
^
In addition, to m onitor environm ental w ater stresses and droughts, we also calculate
potential surface ET (see section 3.8). The potential plant transpiration (^E p o rjra n s) is calculated
following the Priestley-Taylor m ethod (1972).
cr X s X i4^ X ( 1
a
=
— F w e t)
7
^ E p o T tra n s ~
(30)
s + 7
1 .2 6
3.7 Evaporation from Soil Surface
The soil surface is divided into the saturated surface covered w ith w ater and the moist
surface by Fwet. The soil evaporation includes the potential evaporation from the saturated soil
surface and evaporation from the m oist soil surface. The total aerodynam ic resistance to vapor
transport (rtot) is the sum o f surface resistance (rs) and the aerodynam ic resistance for vapor
transport (rv) such that
(van de Griend, 1994; M u et al., 2007). In the 2007 algorithm,
a constant rtotc (107 s m'^) for rtot w as assumed globally based on observations o f the soil surface
in tiger-bush in southwest N iger (W allace and Holwill, 1997), but it was corrected (Tcorr) for
atmospheric tem perature (T.) and pressure (P„) (Jones, 1992) w ith standard conditions assumed to
be Ti = 20°C and Pa = lO lSO O Pa.
1.0
^corr
101300
m + 2273.
7 3 .1 5 \'
^
(
2
9
3
.1 5
Pa
j
rtot ~
F o tc
^
(31)
F orr
rtotc = 107.0
W e assume that rv (s m'^) is equal to the aerodynam ic resistance (r«: s m'^) in Equation 6
since the values o f rv and r« are usually very close (van de Griend, 1994). The aerodynamic
resistance at the soil surface (ras) is parallel to both the resistance to convective heat transfer (rhs:
s m'^) and the resistance to radiative heat transfer (rrs: s m'^) (Choudhury and DiGirolam o, 1998),
such that
' hs
^
'rs
^hs F 'f'rs
p X Cp
(3 2 )
4.0 X 0- X (Ti + 273.15)3
^hs ~ Fot
In the 2007 algorithm, only the soil evaporation from the m oist surface was calculated. To
exam ine the sensitivity o f actual soil evaporation to rtot in the 2007 M 0 D 1 6 ET algorithm, we used
different values for rtotc. The observed latent heat flux (EE) average over the 19 A m eriFlux towers
used to validate the 2007 M OD 16 algorithm was 66.9 W/m^, w hile the average EE estimate was
16
61.0 W/m^ driven by tow er meteorological data and 65.6 W/m^ driven by N A S A ’s Global
M odeling and A ssim ilation Office (GM AO, v. 4.0.0) data. W hen rtotc is 10 s m '\ much low er than
107 s m '\ soil evaporation is much higher, and hence LE is much higher, w ith the average towerdriven LE o f 86.0 W/m^ and G M A O -driven LE o f 98.7 W/m^. However, when rtotc ranges between
50 s m'^ and 1000 s m '\ there is little difference in the soil evaporation results, and there is,
therefore, little change in LE (tow er-driven LE average o f 54.4-64.6 W/m^ and G M AO-driven LE
average o f 58.9-70.0 W/m^). The value o f 50 s m'^ w as chosen in the 2007 algorithm as the low er
bound because it is very close to the mean boundary layer resistance for vegetation under semiarid
conditions, and there is little variation around this mean (van de Griend, 1994). In the im proved
M OD 16 ET algorithm, the rhs is assumed to be equal to boundary layer resistance, w hich is
calculated in the same way as total aerodynam ic resistance {rtot) in Equation 31 (Thornton, 1998)
only that, in the im proved algorithm, rtotc is not a constant. For a given biom e type, there is a
maxim um (rblmax) and a m inim um value (rblmin) for rtotc, and rtotc is a function o f VPD.
^totc
'
rb lm a x
= •{
V P D < V P D open
-
{rblmax ~ rblmin) ^ O^PDrlose ~ VPD)
------------v p n - v p n
^ rr'eiose
^ r L>open
VPD„„,n<VPD < VPD,^„„
rb lm in
^ ^
(33)
y P D > V P D oiose
The values o f rblmax and rblmm, VPDopen (when there is no w ater stress on transpiration) and
VPDciose (w hen w ater stress causes stomata to close alm ost completely, halting plant transpiration)
are param eterized differently for different biom es and are listed in Table 1.
The actual soil evaporation (AE^^^^) is calculated in equation 34
evaporation (lEso/z.
p ot
)
using potential soil
m oisture constraint function in the Fisher et al. (2008) ET
model. This function is based on the com plementary hypothesis (Bouchet, 1963), w hich defines
land-atm osphere interactions from air V P D and relative humidity {R H , % ).
_ (s
X A s o il + P X Cp X
(1.0 - Fc)
x V PD /V as) x
Fwet
_{sxAsoiL+pxCpX{l.Q-Fc)xVPD/Tas)x{lD-Fwet)
^ P sO IL — ^P\vet_SOIL
w here P was set as 100 in the 2007 algorithm, and is revised as 200 in the im proved algorithm.
3.8 Total Daily Evapotranspiration
In the im proved algorithm, the total daily ET is the sum o f evaporation from the w et canopy
surface, the transpiration from the dry canopy surface and the evaporation from the soil surface.
The total daily ET and potential ET{AE pot ) are calculated as in equation 35.
17
AE = AE,w e t
C
+ AE,t r a n s + AE:SOIL
AEpQj = AE,w e t
C
POT t r a n s
+ AE,w e t
(3 5 )
SOIL
+ AE:SOILpoT
Com bination o f ET w ith the potential ET can determine environm ental w ater stress and detect the
intensity o f drought.
4. Input Datasets
The M OD 16 uses daily m eteorological data and 8-day M ODIS datasets as input for daily
ET calculations. The input global daily m eteorological dataset is from M ER RA GM AO at about
0.5° X 0.6° resolution (http://disc.sci.gsfc.nasa.gov/daac-bin/FTPSubset.pn. For the M 0 D 1 6 ET
data product, the input M ODIS datasets include 1) global 1-km^ Collection 4 M ODIS land cover
type 2 (M 0D 12Q 1) (Friedl et al., 2002), 2) global l-km^ M ODIS Collection 5 FPAR/LA I
(M OD15A2) (M yneni et al., 2002), and 3) Collection 5 global 1-km^ albedo quality control and
albedo data (the 10th band o f the W hite-Sky-A lbedo from M CD 43B 2/M CD 43B3) (Schaaf et al.,
2002; Jin et al., 2003; Salomon et al., 2006). D ifferent from u sers’ expectation, the Collection 5
M ODIS FPA R/LA I is being generated w ith a frozen version o f the Colleciton 4 instead o f the
Colleciton 5 M 0D 12Q 1 land cover as an input by M ODIS A daptive Processing System
(M ODAPS) at NASA.
Table 3 Input non-satellite meteorological data, satellite data, and output ET data.
Resolution
Spatial
Temporal
Input Non-Satellite Data (daily meteorologieal data)
2000-2006
I.00°xl.25° daily
GMAO
MERRA
0.5°x0.6°
2000-present
daily
GMAO
Input Remote Sensing Data
0.05°
MODIS (MOD43C) 2000-2006
16-day
Albedo
1km
MODIS (MCD43B) 2000-present
8-day
20I2->
VIIRS
1km
Land
MODIS
Cover
VIIRS
1km
LAI
MODIS
2000-present
20I2->
VIIRS
1km
FPAR
MODIS
2000-present
20I2->
VIIRS
Output MOD16 ET prodnets
1km
ET, LE,
MODIS
2000-present
8-day,
monthly,
PET,
20I2->
aimnal
PEE, QC VIIRS
Variable
Names
Sensors
Time Spans
Coverage
Output
Format
global
global
global
HDFEOS/
GEOTIFF
global
HDFEOS/
GEOTIFF
HDFEOS/
GEOTIFF
HDFEOS/
GEOTIFF
global
global
global
HDFEOS
Table 3 lists the input and output datasets o f the M 0 D 1 6 ET algorithm. In M u et al.’s 2011
M 0 D 1 6 ET algorithm im provem ent paper, w e used the GM AO (v4.0.0) global meteorological
data at 1.00° x 1.25° resolution and Collection 4 0.05-degree CM G M ODIS albedo (the 10th band
o f the W hite-Sky-A lbedo from M O D 43C1) to do the param eter calibrations and algorithm
18
validations over Jan. 2000-Dec. 2006. In this ATBD, we have tw o sets o f Biom e Properties LookUp Table (BPLUT) as shown in Table 1.
4.1. Daily M eteorological Data
The M OD 16 algorithm com putes ET at a daily tim e step. This is made possible by the
daily m eteorological data, including average and m inim um air temperature, incident PA R and
specific humidity, provided by N A S A ’s Global M odeling and A ssim ilation Office (GM AO or
M ERRA GM AO), a branch o f N A SA (Schubert et al. 1993). These data, produced every six
hours, are derived using a global circulation model (GCM), w hich incorporates both ground and
satellite-based observations. These data are distributed at a resolution o f 0.5° x 0.6° (M ERRA
GM AO) or 1.00° x 1.25° (GMAO, v4.0.0) in contrast to the 1-km gridded M 0 D 1 6 outputs. It is
assumed that the coarse resolution m eteorological data provide an accurate depiction o f ground
conditions and are hom ogeneous w ithin the spatial extent o f each cell.
Spatially interpolating GM AO reanalysis data
The resolution for GM AO (1.00° x 1.25° ) or M ERR A GM AO (0.5° x 0.6° )
meteorological data is too coarse for a 1-km^ M ODIS pixel. Zhao et al. (2005) found that, in the
Collection 4 M ODIS GPP/NPP algorithm (M 0D 17), each 1-km^ pixel falling into the same 1.00°
X 1.25° GMAO grid cell inherited the same m eteorological data, creating a noticeable GMAO
footprint (Fig. la ,c in Zhao et al., 2005). Such treatm ent may be acceptable on a global or regional
scale, but it can lead to large inaccuracies at the local scale, especially for terrain with
topographical variation or located in regions w ith steep climatic gradients. To enhance the
meteorological inputs, Zhao et al. (2005) have non-linearly interpolated the coarse resolution
GM AO data to the 1-km^ M ODIS pixel level based on the four GMAO cells surrounding a given
pixel. Theoretically, this GM AO spatial interpolation im proves the accuracy o f m eteorological
data for each 1-km^ pixel because it removes the abrupt changes from one side o f a GMAO
boundary to the other. In addition, for m ost W orld M eteorological O rganization (W M O) stations,
spatial interpolation reduced the root mean square error (RM SE) and increased the correlation
betw een the GMAO data and the observed W M O daily w eather data for 2000-2003, suggesting
that the non-linear spatial interpolation considerably im proves GM AO inputs. These interpolated
GM AO data are, therefore, used in our calculations o f ET.
4.2. Dependence on M O DIS Land Cover Classification (M OD12Q1)
One o f the first M ODIS products used in the M OD 16 algorithm is the Land Cover
Product, M 0D 12Q 1. The im portance o f this product cannot be overstated as the M 0 D 1 6
algorithm relies heavily on land cover type through use o f the BPLU T (Table 1). W hile, the
prim ary product created by M 0 D 1 2 is a 17-class IGBP (International Geosphere-Biosphere
Program m e) landcover classification map (Belward et al. 1999; Scepan 1999), the M 0 D 1 6
algorithm em ploys U niversity o f M aryland (UM D) landcover classification scheme (Table 4).
M ore details on these and other schemes and their quality control considerations can be found at
the Land Cover Product Team w ebsite
(http:// 2eo 2raphv. bu.edu/landcover/usersuidelc/index.htm l). Given the global nature and daily
19
tim e-step o f the M ODIS project, a broad classification scheme, w hich retains the essence o f land
cover, is necessary. Since all M ODIS products are designed at a 1-km^ grid scale, it can be
difficult to obtain accurate land cover in areas w ith com plex vegetation, and misclassification
can occur. However, studies have suggested that the M ODIS vegetation maps are accurate to
w ithin 65-80%, with higher accuracies for pixels that are largely homogeneous, and allow for
consistent m onitoring o f the global land cover (Hansen et al. 2000).
Table 4 The U niversity o f M aryland (UM D) landcover classification from M ODIS land cover
dataset (M 0D 12Q 1) used in the M 0 D 1 6 Algorithm. The data field nam e is
Land_Cover_Type_2 in the M 0D 12Q 1 data field.
UMD Land Cover Types
Class Value Class Description
0
1
2
3
4
5
6
7
8
9
10
12
13
16
254
255
W ater
Evergreen N eedleleaf Forest
Evergreen B roadleaf Forest
Deciduous N eedleleaf Forest
Deciduous B roadleaf Forest
M ixed Forest
Closed Shrubland
Open Shrubland
W oody Savanna
Savanna
Grassland
Cropland
U rban or Built-Up
Barren or Sparsely Vegetated
Unclassified
M issing D ata
4.3. Time V ariable M O DIS Input Data
As illustrated in Figure 2, the ET calculation also requires vegetation dynamic datasets,
8-day M ODIS FPA R/LA I (M OD 15), and surface albedo from 8-day M CD43B2/M CD43B3.
Fraction o f absorbed photosynthetically active radiation (FPAR) and L eaf area index
(LAI)
The FPA R/LA I product is an 8-day com posite product. The M OD 15 com positing
algorithm uses a simple selection rule w hereby the m aximum FPA R (across the eight days) is
chosen for the inclusion as the output pixel. The same day chosen to represent the FPA R
m easure also contributes the current pixel’s LA I value. This means that although ET is
calculated daily, the M OD 16 algorithm necessarily assumes that leaf area and FPA R do not vary
during a given 8-day period. Com positing o f LA I and FPA R is required to provide an accurate
depiction o f global le af area dynam ics with consideration o f spectral cloud contamination,
particularly in the tropics.
20
MODIS Albedo
The M CD43B2/B3 albedo products are 8-day com posite products. Both Terra and Aqua
data are used in the generation o f this product, providing the highest probability for quality input
data and designating it as an "MCD," m eaning "Combined," product. Version-5
M O D IS/Terra+A qua B RD F/A lbedo products are Validated Stage 1, indicating that accuracy has
been estimated using a small num ber o f independent m easurem ents obtained from selected
locations and tim e periods and ground-truth/field program efforts. A lthough there may be later
im proved versions, these data are ready for use in scientific publications.
BRD F/A lbedo Quality product (M CD43B2) describes the overall condition o f the other
BRDF and A lbedo products. The M CD43B2 product contains 16 days o f data at 1-km spatial
resolution provided as a level-3 gridded data set in Sinusoidal projection, and includes albedo
quality, snow conditions, ancillary inform ation, and inversion information.
M CD43B3 product provides 1-km data describing both directional hemispherical
reflectance (black-sky albedo) at local solar noon and bihem ispherical reflectance (white-sky
albedo). These M CD43B3 albedo quantities are produced from th e l6-day anisotropy models
provided in M CD43B1 and represent averages o f the underlying 500m values. If black-sky
albedos at different solar zenith angles are required then the M CD43B1 values should be used
directly to generate them. The M CD43B3 albedo quantities are provided as a level-3 gridded
product in the Sinusoidal projection.
Tem porally interpolating M O DIS data with bad QC or m issing data
The 8-day M OD 15A 2 LA I/FPA R (M yneni et al., 2002) and M CD43B3 (Schaaf et al.,
2002; Jin et al., 2003; Salomon et al., 2006) contain some cloud-contam inated or missing data.
W e tem porally filled the m issing or unreliable LA I/FPA R and M CD43B3 albedo at each 1-km^
M ODIS pixel based on their corresponding quality assessm ent data fields as proposed by Zhao et
al. (2005). The process entails tw o steps (see Fig. 5 in Zhao et al., 2005). If the first (or last) 8day LA I/FPA R or M CD43B3 albedo is unreliable or missing, it will be replaced by the closest
reliable 8-day value. This step ensures that the second step can be perform ed in w hich other
unreliable LA I/FPA R or M CD43B3 albedo will be replaced by linear interpolation o f the nearest
reliable values prior to and after the m issing data point.
Tropical rainforests, such as Am azon basin in South America, are the area w here the
cloud contam ination is the m ost serious and the LA I seasonality is very small. To explore how
the Q C-controlled interpolations alter and enhance the input M ODIS data quality, we compare
the 8-day com posited LA I in the A m azon for the original data integrated from M OD15A2
w ithout the tem poral interpolation and the enhanced LA I values w ith the interpolation for the
period o f M arch 21-28, 2001 during the w et season w ith the w orst cloud contam ination (Fig. 3).
The original LA I values are too small (<2.0 mVm^) for a large area surrounding the Amazon
River, the result o f severe cloud contamination. The M ODIS land cover indicates m ost forests in
the northern South A m erica in Figure 3 are evergreen broadleaf forests (EBF). Field LAI
observations revealed a mean LA I o f 4.8±1.7 for 61 observations in tropical EBF (Asner et al.,
21
2003; M alhi et al., 2004, 2006). There are a few pixels for w hich the enhanced LA I values are
smaller than the original data because o f the bad QCs. Overall, however, after tem poral filling,
LA I values in Am azon are much higher and the spatial pattern is more realistic.
(b) en h o n ced LAI
-7 5
-7 0
-6 5
-6 0
-5 5
-5 0
-4 5
-4 0
-3 5
-7 5
-7 0
-6 5
-6 0
-5 5
-5 0
-4 5
-4 0
-3 5
Figure 3 The 8-day composite leaf area index (LAI) in Amazon region for the 8-day period 081 (March
21-28) in 2001 for (a) the original with no temporal interpolation o f the LAI and (b) the temporally
interpolated LAI.
5. Parameterization of MOD16 ET Algorithm
Our method to calibrating param eters o f BPLUT is largely based on the concept o f w ater
use efficiency (WUE), defined as the ratio o f GPP to ET. W U Es derived from eddy flux towers
are used together w ith the mature M ODIS GPP dataset to estimate the expected mean annual ET
for each biome. B elow w e first describe how we process m easurem ents from flux towers, then we
detail how w e calibrate the BPLUT.
5.1. Eddy Covariance Flux Towers
The eddy covariance technique is a widely used and accepted m ethod to m easure
ecosystem -scale mass and energy fluxes. The A m eriFlux nefwork was esfablished in 1996,
providing continuous m easurem ents o f ecosystem level exchanges o f CO2, water, energy and
m om entum spanning diurnal, synoptic, seasonal, and interannual tim e scales and is currently
com posed o f sites from N orth America, Central America, and South Am erica
(http://public.ornl.gov/am eriflux/). A m eriFlux is part o f a "network o f regional networks"
(FLUXNET) including more than 500 tow er sites from about 30 regional networks across five
continents, providing half-hourly to hourly m easurem ents o f carbon dioxide, w ater vapor, and
energy exchanges betw een terrestrial ecosystem s and the atmosphere across a diverse range o f
ecosystem s
and
climates
on
a
long-term
basis
(Baldocchi,
2008;
http://ww w .daac.ornl.gov/FLUX NET/fluxnet.htm l). The insights and constraints provided by the
simultaneous m easurem ent o f these fluxes and fheir corresponding scalar fields ensure fhaf Fluxnef
provides an excellent data set for land surface model developm ent and testing.
22
W e obtained the level 4 m easured m eteorological data and latent heat flux (LE) data at 72
A m eriFlux eddy covariance tow ers to calibrate param eters and test the perform ance o f M OD 16
ET algorithm. To ensure a reliable m easured data from these towers, first, 51 tow ers w ere left
after w e excluded those tow ers w ith actual vegetation type different from M OD 12 land cover type
2 at any o f the surrounding 3 x 3 1-km^ pixels. Then w e further excluded those tow ers w ith fewer
than h alf a year o f m easurem ents during 2000-2006. As a result, there are 46 A m eriFlux eddy
covariance tow er sites involved in W U E calaulation and evaluation o f the algorithm. The tow er
m easured ET in w ater depth was calculated from tow er m easured LE data using the following
equation.
LE
ET = —
(3 6 )
A
w here k is the latent heat o f vaporization (J kg'^). M OD 16 ET algorithm was tested at these 46
Am eriFlux eddy covariance tow er sites (Table 5, Fig. 4) w ith available level 4 ET m easurem ents
over 2000-2006. These 46 flux tow ers cover nine typical land cover types and a w ide range o f
climates. The nine land cover types am ong the tow ers include evergreen needleleaf forest (ENF),
evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), m ixed forest (MF), open
shrublands (OSH), close shrublands (CSH), woody savanas (WE), grasslands (Grass), and
croplands (Crop).
ENF EBF DNF DBF MF CSH OSH WSV
SV
GRS
GRP
urbanbarren
Figure 4 Distribution of t h e 46 AmeriFlux eddy flux to w ers used for validation of th e improved ET
algorithm. The background is th e M0D12Q1 land cover type 2 , with th e blue color for th e w a te r body.
23
Table 5 The tow er names, abbreviations, latitude (lat), longitude (Ion), biom e types in the
parentheses, num ber o f days w ith valid tow er m easurem ents (Days), average daily tow er
evapotranspiration m easurem ents over all the days with valid values (ET OBS: mm/day).
Site
ARM SGP Main
Bondville
Mead Irrigated
Mead Irrigated Rotation
Mead Rainfed
Rosemount_G 19_Altemative_Manage
ment Com Soybean Rotation
Rosemount G2I Conventional Mana
gement Com Soybean Rotation
Sky Oaks Old
Bartlett Experimental Forest
Missouri Ozark
Morgan Monroe State Forest
Ohio Oak Openings
UMBS
Willow Creek
FBA Tapajos KM67 Mature Forest
FBA Tapajos KM83 Fogged Forest
Blodgett Forest
Donaldson
Flagstaff Unmanaged Forest
Metolius First Young Pine
Metolius Intermediate Pine
Metolius New Young Pine
Niwot Ridge
UCI 1850
UCI 1930
UCI 1964
UCI I964wet
UCI 1981
UCI 1989
UCI 1998
Wind River Crane Site
Wisconsin Mature Red Pine
ARM SGP Bum
ARM SGP Control
Atqasuk
U SA R M (Crop)
U S B o l (Crop)
U S N el (Crop)
USNe2 (Crop)
USNeS (Crop)
lat
36.6
40.0
41.2
41.2
41.2
Ion
-97.5
-88.3
-96.5
-96.5
-96.4
Days
1129
I6I6
1080
1022
1027
ET O
BS
1.43
1.82
1.62
1.56
1.46
U SRo3 (Crop)
44.7
-93.1
573
1.35
U S R o l (Crop)
U S S 0 2 (CSH)
U SBar (DBF)
USM Oz (DBF)
44.7
33.4
44.1
38.7
39.3
41.6
45.6
45.8
-2.9
-3.0
38.9
29.8
35.1
44.4
44.5
44.3
40.0
55.9
55.9
55.9
55.9
55.9
55.9
56.6
45.8
46.7
35.5
35.5
70.5
-93.1
- I I 6.6
-71.3
-92.2
-86.4
-83.8
-84.7
-90.1
-55.0
-55.0
- 120.6
-82.2
- I I I .8
-I2 I.6
-I2 I.6
-I2 I.6
-105.5
-98.5
-98.5
-98.4
-98.4
-98.5
-99.0
-99.9
- 122.0
-91.2
-98.0
-98.0
-157.4
574
333
614
606
1483
371
1205
1246
1008
I2 8 I
1586
1585
308
545
707
361
1535
429
431
488
236
503
494
411
974
308
553
554
244
1.39
1.04
0.84
2.20
1.16
1.94
1.22
0.97
3.08
3.63
1.99
2.68
1.24
0.99
1.18
0.93
1.54
0.56
0.57
0.54
0.38
0.58
0.53
0.59
1.54
2.09
2.15
2.36
O.II
Abbrev.
USM M S (DBF)
U SO ho (DBF)
U SUM B (DBF)
U SW C r (DBF)
B R S a l (EBF)
BRSaS (EBF)
USBIo (ENF)
USSP3 (ENF)
U SF u f(E N F )
USM eS (ENF)
USM e2 (ENF)
USM e3 (ENF)
U S N R l (ENF)
C A N S l (ENF)
CANS2 (ENF)
CANS3 (ENF)
CANS4 (ENF)
CANS5 (ENF)
CANS6 (ENF)
CANS? (ENF)
U SW rc (ENF)
USW14 (ENF)
U SARb (Grass)
USARc (Grass)
USAtq (Grass)
24
Audubon Grasslands
Kendall Grassland
Walnut River
Fort Peck
Fort Dix
Little Prospect Hill
Sylvania Wildemess
Ivotuk
Flagstaff Wildfire
Freeman Ranch Mesquite Juniper
Tonzi Ranch
Average
U SAud (Grass)
U SW kg (Grass)
USWIr (Grass)
U SFPe (Grass)
U SD ix (M F)
U SL PH (M F)
U SSyv (M F)
USIvo (OSH)
U SF w f (W L)
U SFR2 (W L)
USTon (W L)
31.6
31.7
37.5
48.3
40.0
42.5
46.2
68.5
35.4
29.9
38.4
-110.5
-109.9
-96.9
-105.1
-74.4
-72.2
-89.3
-155.8
- 111.8
-98.0
- 121.0
1431
929
885
1095
412
667
825
210
338
649
1342
0.78
0.63
1.86
0.77
1.56
1.35
0.89
0.19
0.94
2.08
1.13
1.34
5.2 Pre-processing Tower Observed Data
The A m eriFlux tow er data are given every 30 minutes.
W hen the num ber (N) o f the
reliable 30-minute m easurem ents is no less than 40 a day, the daily average values o f the incoming
solar radiation (SWrad), air tem perature (Tavg), VPD, and L E are the averages o f these
measurements. For each 30-minute tim e period, ET (mm /30minutes) is calculated as
X = (2.5 0 1 - 0 .0 0 2 3 6 1 X T J x 10^
F TIn =
Li
LEn X 60.0 X 30.0
I
(3 7 )
w here n is the nth 30-minute observation o f each day, X is calculated using the equation in
M aidm ent’s book (M aidment, 1993). W hen the num ber o f the reliable 30-minute measurements
{N) o f both L E and T are no less than 40, the daily total ET is calculated as
ET =
N
(3 8 )
I f A is less than 40, the daily measurem ents are set as fill value. The daily maxim um and minimum
air tem peratures are obtained through the process when calculating the daily average air
temperature.
The daytime and nighttim e are distinguished by SWrad. I f SW rad >10.0 (W m'^), it’s
daytime, otherwise, nighttime. The m easured daytime VPD (VPDday) and air tem perature {Tday),
and nighttim e VPD (VPDnight) and air tem perature (Tnight) are the averages over daytime and
nighttime. W hen there are few er than 20 reliable m easurem ents during daytime or nighttime, both
daytime and nighttim e values are set as fill value.
25
5.3 Param eterization
For param eterization o f the im proved ET algorithm, we largely follow the method for
calibrating param eters o f M ODIS GPP/NPP algorithm (Zhao et al., 2005). Both M ODIS
GPP/NPP and M ODIS ET algorithm s use the same controlling factors from VPD and minimum
tem perature {Tmin) on stomatal conductance. W e first adopt the param eters o f VPD and Tmin
setting from those for M ODIS GPP/NPP algorithm (Table 1), then calibrate other param eters for
each biome. B elow we detail the procedure to param eterize M ODIS ET.
The tow er derived annual GPP and tow er m easured annual ET w ere summed over all the
available days divided by the num ber o f years (<365 days/yr). Then annual average W U E for each
tow er site was calculated as
GPP
W U E = ——
ET
(3 9 )
^ ^
For a given biom e type in Table 1, the tow er GPP, ET and W U E are averaged over all the towers
w ith the same biom e (Table 6). Finally, the expected annual total ET for a given biom e is
calculated by using the m ultiyear mean annual total M ODIS GPP (Zhao et al., 2005) and towerbased W U E (listed in Table 6) as
^T'exp =
M O D IS GPP
T77T7T
WUE
(4 0 )
W e use ETexp as one target (Table 6) to calibrate other param eters in Biom e-Property-LookU p-Table (BPLUT) except Tmin and VPD, w hich are directly adopted from M ODIS GPP
param eters as m entioned above. Each time, the im proved ET algorithm is run globally using a set
o f param eter values at the 0.5° resolution over 2000-2006. The annual M ODIS GPP and estimated
annual M ODIS ET averaged globally for each biom e type (ETmod) may greatly differ from the
tow er GPP and ETexp because, 1) only 46 A m eriFlux tow er sites are used to get tow er GPP, ET
and W UE, and thus they may not represent average conditions for a biom e type at the global scale;
2) W U E is the w ater use efficiency, w hich should be the ratio o f GPP to ET via transpiration.
Considering the evaporation included in ET, there is some bias in the calculated W U E and hence
ETexp (Law et al., 2002). Therefore, w hen we calibrate param eters in BPLUT at global scale, not
only ETmod is com pared to ETexp, but also the spatial pattern o f average annual ET over 2000-2006
is com pared w ith Chen et al.’s 0.5° global precipitation data (Chen et al, 2002). A t the arid and
semi-arid areas, up to 50% or even higher than 100% o f the annual precipitation is returned to the
atmosphere as ET (M ellouli et al.; 2000). A t the local scale, the im proved ET algorithm is run at
the 46 tow er sites and the RM SE between the daily ET estimates and ET m easurem ents is
calculated. W e modify BPLU T and repeat the cycle o f com parison till w e choose one set o f
param eter values that perform the best both globally and locally for BPLU T (Table 1). There are
no tow ers with deciduous needle-leaf forest (DNF) or savannas (SV) in the 46 A m eriFlux towers.
26
W e made an assumption that the ET for the DNF should be close to the one for ENF, and the ET
for SV should be a little low er than the one for woody savannas.
Table 6 The tow er m easured annual GPP, tow er m easured annual ET summed over all the
available days divided by the num ber o f years (<365 days/year), and WETE calculated from
equation (39) averaged over all the tow ers for each vegetation type; the annual M ODIS GPP
averaged over each vegetation type; the expected M ODIS ET as calculated from equation (40);
the actual average annual M ODIS ET over each vegetation type. E N F : evergreen needleleaf
forest; EBF: evergreen broadleaf forest; DNF: deciduous needleleaf forest; DBF: deciduous
broadleaf forest; MF: mixed forest; WL: woody savannas; SV: savannas; CSH: closed shrubland;
OSH: open shrubland; Grass: grassland, urban and built-up, barren or sparsely vegetated; Crop:
cropland. N /A means that no data is available.
EC
ENF
EBF
DNF
DBF
MF
CSH
OSH
WE
SV
Grass
Crop
Tower
Tower
annual
GPP (g
C/mVyr)
annual
ET
(mm/yr)
978.98
2781.55
N /A
1303.88
911.17
909.51
193.60
625.81
N /A
645.68
1089.70
423.64
1123.03
N /A
449.44
332.88
484.82
160.2
353.39
N /A
417.06
536.79
Annual
W U E (g
C/mm/m^)
Annual
M ODIS
2.42
2.51
N /A
3.01
2.84
1.80
1.35
1.70
N /A
1.46
1.97
876.78
2698.53
727.00
1340.12
1133.64
811.91
308.79
1368.58
1209.21
393.09
883.91
GPP (g
C/yr)
Expected
annual
M ODIS
ET
(mm/yr)
362.89
1073.96
N /A
444.94
398.60
451.88
229.04
805.20
N /A
269.71
447.82
Actual
M ODIS
E T l*
(mm/yr)
Actual
M ODIS
ET2*
(mm/yr)
301.01
1180.16
334.57
533.47
488.12
333.31
272.34
925.62
749.52
352.65
472.84
304.63
1182.63
349.54
474.53
499.44
334.66
270.19
944.41
792.09
350.39
470.49
1* means the M ODIS ET driven by v4.0.0 GMAO; 2* means M ODIS ET driven by M ERRA
GMAO
6. Results and Uncertainties
In this section, w e show the validation results o f M OD 16 ET at eddy flux towers, global
232 watersheds, as well as global results over the past 11 years (2000 to 2010). W e also discuss
the sources o f uncertainties to the global M OD 16 ET product.
6.1 Algorithm Perform ance at the Eddy Flux Tower Sites
W e cut out the input M ODIS data for the 3 x 3 1-km^ pixels surrounding each tower. W e
drove the M OD 16 ET algorithm w ith both tow er observed meteorological data and global GMAO
27
meteorological data. W e got the average ET estimates over those o f the 3 x 3 1-km^ pixels w here
the tow er actual vegetation type is the same as M 0 D 1 2 land cover type 2. Then w e com pared the
ET estimates w ith the tow er ET observations. For each o f the seven biom e types among the 46
flux tow ers except for CSH and OSH since there is only one tow er w ith few er than 365
m easurem ents for each o f them, we chose one tow er to show the perform ance o f M OD 16 ET
algorithm (Fig. 5). W e use the Taylor skill score (Taylor, 2001) to evaluate the skill o f the
perform ances (Table 7).
5 =
4 x (1 + /?)
( 41 )
( a + l / & y X (1 + Ro)
w here R is the correlation coefficient, Rq is theoretical maxim um correlation, and 6 is the standard
deviation o f ET estimates norm alized by the standard deviation o f ET measurements.
ttf O B S )
E T [ to w e r_ m e t
E T fG M A O -m e
E tfO B S )
E T f to w e r_ m e t]
E T fG M A O _ m e0
1000
1500
Doys since Jon
7.0
6.5
2000
2000
500
'
1000
1500
2000
Doys since Jon 1, 2 0 0 0
ttfO B S
'
E T [ to w e r _ m e t]
E T [G M A O _ m e0 i
DBF
6.0
2500
f ‘
5.5
^ 5,0
I 4,5
3.5
I 3.0
2.5
I
i 1.5
1,0
0.5
0.0
500
1000
1500
Doys since Jon
2000
2000
2500
500
28
100 0
1500
2000
Doys since Jon 1, 2 0 0 0
2500
ttfO BS)
E T f to w e r_ m e t]
E T fG M A O -m et)
500
6 .5 6 ,0
-
EtfOBS)
W o o d v -S o v arin a
1 000
1500
2000
Dovs since Jon 1, 2 0 0 0
E tfO B S )
E T {tow er_m etl
E J(SM A O _m et)
G r o s s lo n d s
E T f to w e r_ m e t)
E T fG M A O -riren
2500
500
1000
1500
Doys since Jon
2000
2000
2500
C ro p la n d s
1 000
1500
Doys since Jon
2000
2000
2500
Figure 5 The ET measurements (black dots, OBS), the ET estimates driven by flux tower measured
meteorological data (red lines) and GMAO meteorological data (blue lines) over 2000-2006 at seven
tower sites, Donaldson (a), EBA Tapajos KM67 Mature Forest (b). Willow Creek (c), Eittle Prospect Hill
(d), Tonzi Ranch (e). W alnut River (f) and Bondville (g).
The average daily ET biases betw een ET observations and ET estimates across the 46
tow ers are -0.11 mm /day driven by tow er meteorological data and -0.02 mm/day driven by GMAO
m eteorological data (Table 7). The average mean absolute errors (M AE) are 0.33 mm day'^
(tower-specific m eteorology) and 0.31 mm day'^ (GM AO meteorology). The M A E values are
24.6% and 24.1% o f the ET measurements, w ithin the 10-30% range o f the accuracy o f ET
observations (Courault et al. 2005; Jiang et al. 2004; K alm a et al. 2008). The scores are 0.55
(tow er-specific) and 0.53 (GM AO) across the 46 towers.
Table 7 The tow er abbreviations, average daily tow er evapotranspiration (ET) measurements
over all the days w ith valid values (ET OBS: mm/day); the biases (BIAS: mm/day), mean
absolute biases (MAE: mm/day), correlation coefficients (R) and Taylor skill scores (S) o f ET
estimates relative to tow er ET m easurem ents for the 46 A m eriFlux eddy flux towers. 1: towerdriven results; 2: G M AO-driven results.
29
Site
ET OBS
BIASl
BIAS2
M AEl
MAE2
R1
R2
SI
S2
USARM (Crop)
1.43
-0.62
-0.3
0.62
0.3
0.42
0.41
0.76
0.71
USBol (Crop)
1.82
-0.3
-0.16
0.3
0.16
0.78
0.73
0.54
0.61
USNel (Crop)
1.62
-0.6
-0.48
0.6
0.48
0.87
0.81
0.45
0.70
USNe2 (Crop)
1.56
-0.62
-0.48
0.62
0.48
0.85
0.80
0.12
0.05
USNe3 (Crop)
1.46
-0.47
-0.35
0.47
0.35
0.85
0.79
0.31
0.45
USRo3 (Crop)
1.35
-0.22
-0.21
0.22
0.21
0.72
0.75
0.38
0.50
USRol (Crop)
1.39
-0.27
-0.26
0.27
0.26
0.71
0.72
0.32
0.23
U SS02 (CSH)
1.04
-0.71
-0.51
0.71
0.51
0.02
0.06
0.86
0.82
USBar (DBF)
0.84
0.48
0.66
0.48
0.66
0.90
0.83
0.68
0.58
USMOz (DBF)
2.2
-0.03
-0.08
0.03
0.08
0.84
0.76
0.10
0.16
USMMS (DBF)
1.16
0.27
0.27
0.27
0.27
0.88
0.82
0.43
0.53
USOho (DBF)
1.94
-0.14
-0.17
0.14
0.17
0.86
0.83
0.44
0.29
USUMB (DBF)
1.22
-0.02
0.05
0.02
0.05
0.93
0.89
0.25
0.36
USWCr (DBF)
0.97
0.18
0.35
0.18
0.35
0.91
0.85
0.77
0.89
BRSal (EBF)
3.08
-0.44
-0.11
0.44
0.11
0.76
0.33
0.64
0.17
BRSa3 (EBF)
3.63
-0.29
-0.45
0.29
0.45
0.62
0.35
0.65
0.73
USBlo (ENF)
1.99
-0.57
-0.58
0.57
0.58
0.65
0.24
0.87
0.35
USSP3 (ENF)
2.68
0.28
0.51
0.28
0.51
0.52
0.48
0.80
0.50
USFuf(ENF)
1.24
-0.59
-0.61
0.59
0.61
0.62
0.42
0.66
0.72
USMeS (ENF)
0.99
-0.11
0.01
0.11
0.01
0.25
0.26
0.28
0.26
USMe2 (ENF)
1.18
-0.08
-0.1
0.08
0.1
0.32
0.29
0.25
0.24
USMe3 (ENF)
0.93
-0.37
-0.12
0.37
0.12
0.39
0.38
0.41
0.40
USNRl (ENF)
1.54
-0.66
-0.69
0.66
0.69
0.68
0.64
0.60
0.59
CANSl (ENF)
0.56
0.1
0.04
0.1
0.04
0.74
0.70
0.56
0.52
CANS2 (ENF)
0.57
0.08
0.03
0.08
0.03
0.78
0.75
0.14
0.26
CANS3 (ENF)
0.54
0.12
0.11
0.12
0.11
0.75
0.73
0.77
0.85
CANS4 (ENF)
0.38
0.24
0.27
0.24
0.27
0.71
0.76
0.85
0.87
CANS5 (ENF)
0.58
0.18
0.19
0.18
0.19
0.77
0.71
0.46
0.48
CANS6 (ENF)
0.53
0.14
0.09
0.14
0.09
0.76
0.72
0.92
0.92
CANS7 (ENF)
0.59
-0.11
-0.17
0.11
0.17
0.74
0.69
0.67
0.72
USWrc (ENF)
1.54
0.94
0.67
0.94
0.67
0.48
0.41
0.67
0.70
USW14 (ENF)
2.09
0.34
0.41
0.34
0.41
0.29
0.25
0.30
0.15
USARb (Grass)
2.15
-0.51
-0.43
0.51
0.43
0.90
0.86
0.46
0.43
USARc (Grass)
2.36
-0.77
-0.63
0.77
0.63
0.90
0.86
0.89
0.78
USAtq (Grass)
0.11
0.02
0.16
0.02
0.16
0.11
0.03
0.63
0.70
USAud (Grass)
0.78
-0.37
-0.07
0.37
0.07
0.47
0.40
0.73
0.74
USWkg (Grass)
0.77
-0.19
0
0.19
0
0.51
0.46
0.62
0.62
30
Citations
Jenkins et al., 2007
Cook et al., 2004
Hutyra et al., 2007;
Rocha et al., 2009;
Fisher et al., 2009
Gholz & Clark, 2002;
Clark et a l, 2004
Anthoni et al., 2002
Thomas et al., 2009
Vickers et al., 2010
USWlr (Grass)
0.63
-0.3
0.11
0.3
0.11
0.85
0.80
0.22
0.13
USFPe (Grass)
1.86
-0.15
-0.2
0.15
0.2
0.27
0.26
0.62
0.56
USDix (MF)
1.56
0.43
0.87
0.43
0.87
0.69
0.68
0.51
0.54
USLPH (MF)
1.35
0.81
0.83
0.81
0.83
0.86
0.76
0.66
0.54
USSyv (MF)
0.89
0.47
0.62
0.47
0.62
0.81
0.78
0.93
0.92
USIvo (OSH)
0.19
0.02
0.02
0.02
0.02
0.35
0.01
0.92
0.86
USFwf (WL)
0.94
-0.43
-0.31
0.43
0.31
0.24
0.35
0.86
0.87
USFR2 (WL)
2.08
-0.1
0.29
0.1
0.29
0.69
0.79
0.10
0.31
USTon (WL)
Average
1.13
0.01
0.02
0.01
0.02
0.78
0.75
0.37
0.32
1.34
-0.11
-0.02
0.33
0.31
0.65
0.58
0.55
0.53
4
4
3.5
3.5
3
3
2.5
2.5
2
2
1.5
1.5
Hadley et al., 2008
Desai etal., 2005
Baldocchi et al., 2004;
Xu & Baldocchi 2003
3
1
1
0.5
0.5
0
G
0
0.5
1
1.5
2
2.5
3
3.5
4
0
G.5
1
T ovf er E T (mm.' d av)
1.5
2
2.5
3
3.5
4
3
3.5
4
TowerET (mmday)
R=0.S4
3.5
J 3.5
E
E
O
S
E
>
6
1.5
o
o
1.5
V
••
0
0.5
1
1.5
2
25
3
3.5
4
0
0.5
1
1.5
2
25
Tower ET (mm day)
Tower ET (mm day)
Figure 6 Comparisons o f the average ET observations to the average daily ET estimates with the GMAO
parameterized algorithm (a,b) and MERRA GMAO parameterized algorithm (c, d) across all the available
31
days at the 46 flux tower sites. These data were created using (1) tower-specific meteorology (a, c), (2)
global GMAO meteorology (b) and MERRA GMAO meteorology (d). The solid red lines represent that
the ratio o f ET estimates to ET measurements is 1.0 and the solid black lines are the regression o f the ET
estimates to measurements.
Figure 6 shows the com parisons o f the average ET observations to the average daily ET
estimates across all the available days at the 46 flux tow er sites. Both the GM AO param eterized
algorithm and M ER RA GM AO param eterized algorithm w ere driven by tow er-specific
meteorology (Fig. 6a, 6c) and the global m eteorology (Fig. 6b, 6d). The correlation coefficients
betw een M 0 D 1 6 ET estimates and the ET observations are 0.86 (tower-specific. Fig. 6a, 6c), 0.86
(GM AO-driven, Fig. 6b) and 0.84 (M ERRA GM AO-driven, Fig. 6d).
6.2 Im plem enting ET Algorithm at the Global Scale
0
250
500
750
1000
1250
1 50 0
Globol Annual W0D16 ET ( 2 0 0 0 —2 0 0 6 ) m m / y r
0
250
500
750
1000
1250
1 50 0
Globol Annuo! MODIS ET ( 2 0 0 0 —2 0 1 0 ) m m / y r
32
Figure 7 Global annual MOD 16 evapotranspiration (top) over 2000-2006 driven by global GMAO
(v4.0.0) meteorological data and (bottom) over 2000-2010 driven by global MERRA GMAO
meteorological data.
M OD 16 ET algorithm w ere im plem ented globally at 1-km^ resolution using the preprocessed
M ODIS remote sensing data and 1) GMAO m eteorological data over 2000-2006, 2) M ERRA
GM AO meteorological data over 2000-2010 as detailed in section 4. Figure 7 shows that the
highest ET happens over the tropical forests, whereas dry areas and areas w ith short growing
seasons have the low est estimates o f ET. The ET for tem perate and boreal forests lies between
the tw o extremes (Fig. 7). A veraged over 2000-2006 (G M AO-driven) and 2000-2010 (M ERRAGM AO driven), the total global annual ET over the vegetated land surface is 62.8X 10^ km^, and
63.4X 10^ km^, respectively, a little less than 65.5X 10^ km^ reported by Oki and K anae (2006),
because M OD 16 ET doesn’t include urban and barren areas since there is no M ODIS derived
FPA R/LA I for these land cover types. Figure 8 shows the histograms o f the global annual ET by
both GM AO and M ERR A GMAO m eteorological datasets. The G M A O -driven global ET has a
global average o f 568 ± 378 mm y f \ and the M ERRA G M AO-driven global ET has a global
average o f 569 ± 358 mm y r 'f
o
o
o
Global Annual M 0D 16 ET Histogram
mean ET (GMAO) = 568.4 mm/yr
CO
o
O d
mean ET (MERRA GMAO) = 568.7 mm/yr
o
O d
o
o
o
400
800
1200
E T (m m /yr)
1600
2000
Figure 8 Comparison o f the histograms o f climatological average o f global annual evapotranspiration
driven by GMAO meteorological data (red solid line) over 2000-2006 and by MERRA GMAO
meteorological data (solid black line) over 2000-2010. The GMAO-driven global average ET is 568.4
mm/yr and 568.7 mm/yr driven by MERRA GMAO meteorology (see text). These comparisons are only
for vegetated land surfaces. The vegetated land area is shown as the colored area in Fig. 7.
33
Based on M 0D 12Q 1 land cover types 2, barren/deserts take up 24% o f the Earth's land
surface. If w e assume that the ET from the barren/deserts is zero, the average M ODIS ET estimate
w ith the im proved algorithm over the entire land surface is 568*(100-24)/100=432 mm yr'^
(G M AO-driven) or 569*(100-24)/100=432 mm yr'^ (M ERRA GM AO-driven). In reality, ET at
the barren/deserts is not zero, so the ET estimates should be in the range o f a little higher than 432
(432) mm y r 'f O ver the entire land surface o f the globe, precipitation averages around 750 mm
yr'^ (Fisher et al., 2005). Some studies concluded that ET returns more than 60% o f precipitation
on land back to the atmosphere (Korzoun et al., 1978; L'vovich and W hite, 1990). Based on these
published data, the actual ET over the global land surface should be around 750*60% =450m m y f
k Our average M ODIS ET estimate by the im proved algorithm over the com plete land surface is
very close to the actual ET calculated from precipitation.
Figure 9 shows zonal mean o f global annual ET driven by GM AO over 2000-2006. The
peak happens at the southern tropical area, w ith the second peak at the northern tropical area w here
rainforests exist. Liski et al. (2003) reported that the ET in boreal and tem perate forests across
Europe (34 sites) ranged from 328 to 654 mm y r'k w hile the average ET w as 466 mm yr'^ for
Canada (18 sites) and 642 mm yr'^ for the US and Central A m erica (26 sites) for biom es ranging
from arctic tundra to tropical rainforest. M OD 16 ET estimates in boreal and tem perate forests are
w ithin the range o f these reported ET from field data.
1400
—
1000
800
400
200
-56
48
40
-32
-24
-16
8
0
8
16
24
32
40
48
56
64
72
Figure 9 Climatological zonal mean of global annual evapotranspiration by GMAO meteorological data
over 2000-2006.
Seasonality
Over 2000-2010, the ability o f M OD 16 ET algorithm to capture seasonality has been examined.
Figure 10 shows the seasonality o f global M OD 16 ET. In the N orthern Hemisphere, spring
(M AM , Fig. 10) is the onset o f the grow ing season. ET increases, reaching a peak in summer
(JJA). In autumn (SON), ET begins to drop, w ith the low est values in w inter (DIE). Regionally,
JJA and SON are relatively dry seasons in the Amazon, and H uete et al. (2006) found that
vegetation grows better in dry seasons than in w et seasons (M AM and DJF). Transpiration, the
34
m ajor com ponent o f ET in dense vegetation, dominates. Therefore, plants grow better during JJA
and SON, and ET is higher (Fig. 10). H utyra et al. (2007) also found that ET and GPP are higher
at a rainforest flux tow er in A m azon in dry seasons than in w et seasons.
0
15 30 45 60 75 90 105 120
Global MODIS ET (m m /m o n th )
Figure 10 Spatial pattem of the global M0D16 ET seasonality during 2000-2010.
Interannual variability
The M OD 16 ET algorithm also has the ability to capture the responses o f terrestrial
ecosystem s to extreme climatic variability at the regional scale. W e drove the M OD 16 ET
algorithm w ith global M ERRA GM AO meteorological data and Collection 5 1-km^
M CD43B2/M CD43B3 to produce the 1-km^ global terrestrial M OD 16 ET product over 20002010. The ratio o f ET to PET is com monly used as an indicator o f w etness or droughts. Figure
11 shows the anom alies o f global ET to PET ratio at growing season from 2000-2009 as
estimated from the M O D IS-based ET product, dem onstrating the sensitivity o f terrestrial
ecosystem to w idespread drought in N orth A m erica and China in 2000 (Cook et al., 2007; Fan et
al., 2003; Pandey et al., 2007); extensive drought over N orth A m erica and A ustralia in 2002 (Cook
et al., 2007; Lawrimore et al., 2002; Horridge et al., 2005); heat w ave in Europe (Ciais, et al., 2005)
and drought in A ustralia in 2003 (Nicholls, 2004); severe droughts in Amazon, A frica and
A ustralia in 2005 (Phillips et al., 2009; Hopkin, 2005; Watkins, 2005). However, a negative
anomaly o f ET/PET ratio in southern China in 2008 w as not caused by drought but by damaged
trees during severe snow storm in January 2008 (Zhou et al, 2010). The dam aged trees lowered
the plant transpiration in summer, and hence low ered ET and ET/PET. PET in our CD R can be
used to rule out these false droughts. Though radiation is the dom inant lim iting factor for
vegetation grow th in the A m azon (Nemani et al., 2003), the Am azon experienced the w orst
drought in 40 years during 2005 (Hopkin, 2005), and w ater becam e the dom inant lim iting factor
(Phillips et al., 2009; Zhao and Running, 2010). Com bining global M 0 D 1 6 ET/PET and
M OD 13A 2 N D V I products. M u et al. (2013) developed a M ODIS global terrestrial drought
severity index to m onitor and detect droughts and to help the decision makers to mitigate the
adverse effects from droughts.
35
2000
2002
2003
2004
2005
2006
2007
O '':
2008
-9
-6
-3
0
3
6
9
Growing S e a s o n ET/PET A n o m o l y (%)
Figure 11 Spatial pattem of global MODIS ET to PET ratio anomalies during 2000-2009. Earge-scale
ET/PET negative anomalies were mainly caused by droughts.
6.3 Algorithm Perform ance at Global W atersheds
As a different more spatially integrated evaluation, w e obtained the stream flow data at
global w atersheds (Dai et al., 2009). Theoretically, over a relatively long tim e period, gauged
catchm ent ET can be roughly estim ated as the difference betw een precipitation and stream flow
by assum ing fhaf fhere is no change in soil wafer sforage (Budyko, 1974; Donohue ef al., 2007).
36
The average annual gridded precipitation data o f Chen et al (2002) and the Global Precipitation
Climatology Centre (R udolf and Schneider, 2005) was subtracted by stream flow to get pseudo
ET observations (ET OBS) for the watersheds. 232 w atersheds having at least five years o f
w ater discharge data w ere used to do the com parison (Fig. 12). Figure 12 shows the com parison
o f annual pseudo ET OBS from these 232 w atersheds w ith the M ODIS ET (driven by M ERRA
GM AO m eteorology) averaged over each w atershed over at least five years during 2000-2006.
The M OD 16 ET estimates can explain 85% o f the variations o f the pseudo ET observations for
these 232 watersheds.
1600
400
200
500
BOO
400
200
200
400
600
800
1000
1200
1400
1600
A n n u a l ET OBS ( m m / y r )
Figure 12 (Left) D istribution o f the 232 w atersheds used for validation o f global M OD 16 ET
data. Each watershed is depicted in yellow. (Right) Comparison o f annual pseudo ET
observations (ET OBS, precipitation minus stream flow) from the 232 w atersheds and the
M ODIS ET estimates averaged over each w atershed over at least five years during 2000-2006.
The runoff data for the w atersheds were provided by Ke Zhang.
6.4 Uncertainties
The existing biases between the ET estimates and the ET m easurem ents arises from below
m ajor causes,
1)
Algorithm input data. M ODIS LA I and FPA R (M OD 15) are o f the m ost im portant
biophysical variables that control the exchange o f energy, mass (e.g., w ater and CO2) and
mom entum betw een the earth surface and atmosphere (Dickinson et al., 1993; Sellers et al., 1996;
Tian et al., 2004; Dem arty et al., 2007). However, there are uncertainties in M ODIS LA I/FPA R
retrievals, for example, M ODIS LA I tends to be higher and the grow ing season is too long over
boreal forests (Demarty et al., 2007). M ODIS LA I validation suggests three key factors that
influence the accuracy o f LA I retrievals: 1) uncertainties in input land cover data, 2) uncertainties
in input surface reflectance, and 3) uncertainties from the model used to build the look-up tables
accom panying the algorithm (Yang et al., 2006). M u et al. (2012) analyzed the variance and
uncertainty in M OD 16 ET driven by three different m eteorological datasets, GMAO, ECM W F
and N C E P l. M OD 16 ET driven by GM AO has m ore detailed spatial ET variations than the other
two, largely because first, GM AO has the finest resolution (1.00°xl.25°) am ong the three
meteorological datasets, and second, overall, GM AO has the best quality at the global scale, except
for its low radiation in equatorial regions. H einsch et al. (2006) com pared tow er meteorological
37
data w ith GM AO data, and the 1-km^ Collection 4 M ODIS LA I (M OD 15) and M ODIS land cover
(M 0D 12) w ith ground-based measurements, finding existing biases in both the GMAO data and
the M ODIS data w hen com pared to observations. W hile approxim ately 62% o f M ODIS LAI
estimates were w ithin the estimates based on field optical measurements, rem aining values
overestim ated site values (Heinsch et al., 2006). Comparison o f LA I at the patch level can
significantly im prove the agreements, but the Collection 3 M ODIS LA I still tends to be higher
(W ang et al., 2004). Overestim ates o f LA I may result in overestimates o f ET even if other input
data such as the meteorological data and M ODIS albedo data are relatively accurate. Although
the temporal filling o f unreliable M ODIS data, including LAI, FPA R and albedo, greatly im proves
the accuracy o f inputs, the filled values are artificial and contain uncertainties. There is a
hypothesis that all the uncertainties associated w ith the M ODIS data are contained in the quality
flags M ODIS QA, an assumption w hich proved efficient for reducing the w eight o f unreliable
satellite products, especially over tropical forests (Demarty et al., 2007). However, the M ODIS
QA remains a qualitative m easure o f uncertainty, and does not quantitatively accounts for each
source o f error in the M ODIS data retrieval procedure (sensor calibration, atm ospheric corrections,
land cover m apping radiative transfer forward and inverse m odelling) (Demarty et al., 2007).
Also, the inaccuracy in M ODIS FPA R will lead to m iscalculation o f Fc, and hence ET. All o f
these uncertainties from inputs can introduce biases in ET estimates that are difficult to detect.
2) Inaccuracy in the m easured data. Currently, the ground data from the eddy covariance
flux tow ers provide the best ET estimates. H owever, they have an error or uncertainties o f about
10-30% based on com parison o f multiple tow ers at the same site, or by com parison with
independent measurem ents o f ET by other methods such as lysim eters or sap flux sensors (Glenn
ef al., 2008b). Also, fhe eddy covariance flux tow ers have an energy balance closure problem that,
the sum o f the net radiation and the ground heat flux, was found in mosf cases fo be larger than the
sum o f turbulent fluxes o f lafent heaf and sensible heaf (Aubinef ef al., 2000; W ilson ef al., 2002).
Correcfing error and reducing uncertainty in the ET m easurem ents are still uncertain due to the
closure error (Shuttleworth, 2007). Scott (2010) used the w atershed w ater balance to evaluate the
accuracy o f eddy covariance ET m easurem ents at three semiarid ecosystems, and found that eddy
covariance tow ers usually underestim ated the ET at high values and overestim ated the ET at the
low values.
3) Scaling from tow er to landscape. The m easuring height and the horizontal scale o f
m easurem ent o f the turbulent fluxes like lafent heat fluxes and sensible heaf fluxes, usually 2-5m,
have significant influences on the footprint (Schmid, 1997) and the size o f underlying surface
(Token, 2008). Also, the com plex terrain (A ubinet et al., 2005; Feigenw inter et al., 2008) and
com plicated canopy structure, the stochastic nature o f turbulence (H ollinger and Richardson, 2005;
M oncrieff et al., 1996) can affect the eddy covariance m easurem ents (Yi et al., 2010). The
com parison o f m easured ET w ith the estim ated from the 3 x 3 1-km^ M ODIS across all 46 sites
may introduce uncertainties due to the differences in tow er footprints for different tow ers and
under varying environmental conditions for a given tower. For example, among the 46 towers
used to exam ine the perform ance o f the ET algorithms, there are seven eddy covariance tow ers at
M B, Canada (C A N S l... 7), w hich are very close and are all ENF (Table 5). The ET m easurem ents
at the seven CANS towers are quite different, w ith the average daily ET ranging from 0.38 to 0.59
mm day'^ (Table 7). The m agnitudes and interannual variability substantially differ among the
seven CANS towers. And in heterogeneous areas, the differing scales o f the tow er and M ODIS
ET estimates should be perform ed via an upscaling process, such as that used during the B igfoot
38
M ODIS validation project (Cohen et al., 2003; Turner et al., 2003a, 2003b). The expense and
intensity o f such studies, however, limits our ability to perform such comparisons.
4)
Algorithm limitations. A large num ber o f physical factors are involved in soil surface
evaporation and plant transpiration processes, including microclimate, plant biophysics for site
specific species and landscape heterogeneity, m aking accurate assessm ent o f ET a challenge
(Friedl, 1996; Vorosm arty, et al., 1998; M cV icar et al, 2007). Some issues rem aining in the ET
algorithm may contribute to the differences between the tow er ET m easurem ents and the ET
estimates by the algorithm. The algorithm doesn’t account for the stand age, disturbance history
or species composition. Biophysical param eters such as g l sh , rblmax and rblmin, VPDopen and
VPDciose used in the algorithm have uncertainties since the same values are used for a given biome
type globally. W e have little know ledge regarding some param eters (e.g., the soil heat fluxes, the
boundary layer resistance for soil evaporation) and the m echanism s involved. A lthough it is
generally assumed that stomata close at night, several studies have docum ented nighttim e stomatal
opening in many species over a range o f habitats (M usselm an and M innick 2000). Incom plete
stomatal closure during the night is observed in a diverse range o f vegetation types (Daley and
Phillips, 2006; Caird et al., 2007; Zeppel et al., 2010). A ssum ption o f the stomata closure at night
can induce biases to the nighttim e plant transpiration, and hence induce underestim ated daily total
ET. Increasing CO2 content tends to reduce plant transpiration due to a high-C02 induced partial
stomatal closure (Idso and Brazel, 1984). W ithin one or tw o decades, this effect on ET may be
negligible; however, as data record lengthening, this effect is needed to account for. As a result,
theoretically, w e may overestim ate ET. W e will add antitranspiration effect from enriched CO2 to
the transpiration m odule in our algorithm w hen w e study the long-term rem otely sensed ET
changes.
7. MOD16 Products
This section details M OD 16 variables, data file format, map projection, file name, and
size.
7.1 M O D16 Variables
The 8-day ET (0. lm m /8days for the 8-day before the last 8-day o f a year or 0. lm m /5days
for the last 8-day) is the sum o f ET during these 8-day time periods (5 days for 361 composite
data in regular years and 6 days for a leap year). The monthly ET (O.lm m /m onth) is the sum o f
monthly ET. For February, there are 29 days in a leap year and 28 days in regular years. The
annual ET (0. Im m /yr) is the sum o f the ET during each year. There are 366 days in 2000, 2004,
2008, and 365 days in 2001, 2002, 2003, 2005, 2006, 2007, 2009, and 2010. The 8-day, monthly
and annual latent heat flux (LE)/potential LE (PEE) (lO'"^ J/m^/day) is the average daily LE/PLE
over the corresponding tim e period.
The users should multiply 0.1 to get the real ET/PET values in m m /8day or mm/month,
or mm/yr, and 10"^ to get LE/PLE in J/mVday.
For the 8-day and monthly ET/LE/PET/PLE, annual LE/PLE, the valid value range is
-32767-32700.
39
Fill value, out o f the earth 32767
W ater body 32766
Barren or sparsely vegetated 32765
Perm anent snow and ice 32764
Perm anent w etland 32763
U rban or Built-up 32762
U nclassified 32761
For the annual ET/PET, the valid value range is 0- 65500.
Fill value, out o f the earth 65535
W aterb o d y 65534
Barren or sparsely vegetated 65533
Perm anent snow and ice 65532
Perm anent w etland 65531
U rban or Built-up 65530
U nclassified 65529
The M OD 16 global evapotranspiration (ET)/latent heat flux (LE)/potential ET
(PET)/potential EE (PEE) datasets are regular 1-km^ land surface ET datasets for the 109.03
M illion km^ global vegetated land areas at 8-day, monthly and annual intervals. The dataset
covers the time period 2000 - 2010. Future years will be produced and posted periodically, but
not in near-real time.
The output variables include, 8-day, monthly and annual ET, EE, PET, PEE and 8-day,
annual quality control (ET QC). The 8-day M OD16A2 QC field is inherited from M OD 15A 2 in
the same period. However, the cloud-contam inated FPA R/LA I has been tem porally filled with
those having good QC. For annual QC o f M OD 16A3 products, we used the m ethod proposed by
Zhao et al. (2005) to define a more meaningful annual ET QC as
QC = 100.0 X NUg/Totalg
w here N U g is the num ber o f days during grow ing season w ith unreliable or missing M ODIS LAI
inputs, and Totalg is total num ber o f days in the grow ing season. The grow ing season is defined
as all days w ith Tmin above the value w here stomata close as in the BPLUT. The M OD 16 ET
algorithm has a good perform ance in generating global ET data products, providing critical
inform ation on global terrestrial w ater and energy cycles and environm ental changes (M u et al.,
2007, 2009, 2011).
7.2 M O D16 HDFEO S 10-degree Tiles, M ap Projection and File Nam e
As a level 4 M ODIS data product, the M OD 16 global ET dataset follows the high level
o f global M ODIS data structure and file format. The data are saved in HDFEOS (Hierarchical
D ata Form at - Earth Observing System) file form at w ith Sinusoidal map projection, an equalarea global map projection. As shown in Figure 13, the globe is divided into 36 (horizontal
direction) by 18 (vertical direction) tiles w ith each tile called a M ODIS “ 10-degree” tile. For
M ODIS land products, there are 317 tiles w ith land pixels. For M OD 16 ET, similar to the level
40
3 FPA R/LA I and level 4 M ODIS GPP/NPP data products, we further exclude 31 tiles w ith no
vegetated pixels. These excluded 31 tiles are located in high latitudes beyond 80°N and 60°S.
h
0 1 2 3 ^ 5 6 7 3 0 TO11
12
13 H 15
16
17 IS 13 20 21 2223 24 25 23 27 23 20 30 31 32 3334 35
□n n
□
m
F mPI “
p
I'M
m
0_ _
5 n
"■
T
l-
r' '
IT
a ■ E
AI a
a B
s
m
A ,
*
lifeU
i
1^
_
_
l u r '^ s r
L
lE Ijnf=S Br=ar=
aBr=B2TB K B Sr=Br=aB
r= r=B B P iiiB iiiB iB B ^ H lL l
lUu
~ Tfx
■
■-
i
—
T*
-
t,” '
■
r.-.
~T
a w
1
pw
15
17
Figure 13 M ODIS Sinusoidal “ 10-degree” tile system. For land data products, there are 317
tiles w ith land pixels, o f w hich 286 tiles w ith vegetated pixels located between 60°S to 80°N.
The M OD 16 file nam e has the same nam ing style as other high level o f M ODIS data
products. For example, the filenam e M OD 16A 2.A 2002081.h02v06.105.2010355155223.hdf
indicates:
M OD 16A 2 - Product Short Name
.A2002081 - Julian D ate o f D ata A cquisition (A-YYYYDDD)
. h02v06 - Tile Identifier (horizontalX X verticalY Y )
.105 - Collection Version
. 2010355155223 - Julian D ate and tim e o f being generated (Y YY YDDDHHM M SS)
.hdf - D ata Form at (HDF-EOS)
For a nominal "1-km" MODIS ET, it has 1200 by 1200 pixels in a tile and th e real spatial resolution for a
pixel is [(10 / 1200) / 180] * Pi * 6371007.181 = 926.6254331 m eters, and h ere 6371007.181 m eters is
th e earth radius used by MODIS Sinusoidal m ap projection.
M OD 16 products have only one sources o f metadata: the em bedded HDF metadata. The
HDF m etadata contains valuable inform ation including global attributes and data set-specific
attributes pertaining to the granule. Some key features o f certain M ODIS m etadata attributes
include the following:
• The XDim and Y Dim represent the rows and columns o f the data, respectively
• The Projection and ProjParam s identify the projection and its corresponding projection
parameters. The value o f projection is GCTP SNSOID. The 1-dimentioanl array o f
41
ProjParam s contains other param eters for map projection. The first value
6371007.181000 is the earth radius used by M ODIS high level data products.
• The U pperLeftPointM trs is in projection coordinates, and identifies the very upper left
corner o f the upper left pixel o f the im age data
• The Low erRightM trs identifies the very low er right corner o f the low er right pixel o f
the image data. These projection coordinates are the only m etadata that accurately reflect
the extreme corners o f the gridded image
• There are additional BO U N D IN G RECTA N G LE and G RINGPOINT fields w ithin the
metadata, w hich represent the latitude and longitude coordinates o f the geographic tile
corresponding to the data
The D ata Set attributes contain specific SDS inform ation such as the data range and
applicable scaling factors for the data. An HDF-EO S file also contains EOS core m etadata
essential for EOS search services.
Currently, there is some free or com mercial software able to deal w ith M ODIS HDFEOS
files, such as M ODIS Reprojection Tool (M RT), H DF-EOS to GeoTIFF converter (HEG),
IDL/ENVI, ERDAS etc.
7.3 M O D16 Product Data Size
8-davM O D 16A 2
It contains five variables, including ET lkm , LE lkm , PET lkm , PLE lkm ,
ET QC lkm . The first four variables are in 2-byte short integer and the last one in 1-byte
unsigned integer. Therefore, the five variables have nine bytes. Then in theory, for a one year
(46 8-day) 286 tiles 1-km global data, they will have a size equal to 9 * 1200 * 1200 * 286 * 46
= 0.17 TB. Thanks to the powerful capability o f internal com pression o f HDF, only about 17%
o f size is required for the internal-zipped HDFEOS. As a result, global 8-day M O D16A2
requires about 30 GB for each year.
M onthlv M OD16A2
It only contains ET lkm , LE lkm , PET lkm , PLE lk m w hich have the same data types
as the corresponding four variables as in the 8-day M OD 16A 2 product. In theory, for one year,
total data size should be 8 * 1200 * 1200 * 286 * 12 = 40 GB. A fter internal compression, about
20% o f size is required, and global m onthly M O D16A 2 requires about 8.1 GB for each year.
Annual M OD 16 A3
It contains five variables, including ET lkm , LE lkm , PET lkm , PLE lkm ,
ET QC lkm . The first four variables are in either singed or unsigned 2-byte short integer, and
the last one is in unsigned one-byte integer. For one year, the data size should be 9 * 1200 *
1200 * 286 = 4 GB, and the internal com pression results in 0.9 GB o f global M O D16A 3 for
each year.
lu all, for each year, M O D16 ET product requires 30 + 8.1 + 0.9 = 39 GB. For 11
years from 2000 through 2010, there are totally about 429 GB data for the global M OD16
42
A2/A3 product. N ote that w e haven’t m entioned the space o f all the input datasets to the global
M OD 16 algorithm, w hich are detailed in section 4 above.
8. Summary
Table 8. 321 users from 38 countries requesting M ODIS ET/PET/LE data over 2006-2012. N:
num ber o f users.
N
20
Country
Australia
N
2
1
1
4
2
6
8
22
Azerbaijan
Argentina
Austria
Belgium
Brazil
Canada
China
1
5
3
8
1
10
13
Country
Czech
Republic
Columbia
Denmark
Ethiopia
France
Bolivia
Germany
India
N
2
Country
Iran
N
1
Country
Poland
N
5
Country
Switzerland
3
2
1
1
1
11
1
Italy
Japan
Mexico
New Zealand
Nepal
Netherlands
Pern
1
1
7
8
3
2
5
Portugal
North Africa
S. Africa
S. Korea
Spain
Sri Lanka
Sweden
1
1
12
144
1
Thailand
Tunisia
UK
USA
W. Africa
ET from the land surface is a key w ater cycle variable w hich is directly related to energy
budgets, w ater cycle, daily meteorology and climate, and ecosystem carbon fluxes. Terrestrial
ecosystem s are an inherent participant in the surface w ater cycle and energy exchange, and thus
ET is also a m etric o f ecosystem services, functions and status o f ecosystem health. W ith
climate change, the frequency, intensity and duration o f droughts have increased (Zhao and
Running, 2010; Dai, 2011; M u et al., 2013). There is a strong demand for regular regional and
global ET products at satellite sensor’s spatial resolution from the scientific community, land
m anagers and policy makers for the purposes o f w ater m anagem ent and environmental
monitoring. Extensive activities have been carried out to validate the M OD 16 product at
different ecosystem s globally w ith results having been and being published. Evaluating the
perform ance o f M OD 16 ET algorithm at tow er site level, w atershed level and globally verifies
the reliability o f the M OD 16 ET product (Ruhoff et al., 2012; Xia et a l, 2012). High-resolution
land surface w ater and energy balances are greatly desired by vast users. M OD 16 ET dataset has
been widely used by different com m unities and got some very interesting scientific findings,
w ith one published in N ature and another on Nature Climate Change (M ontenegro et al., 2009;
Jung et al., 2010; Loarie et al., 2011; Lathuilliere et al., 2012; M u et al., 201 lb; Sun et al., 2011).
There had been 268 scientists from 32 countries contacting us for using M 0 D 1 6 ET data over
2000-2012, even though we released the data to the public in Jan. 2011 (Table 8). N ow M 0 D 1 6
ET data is ready for users to download and answ er relevant environm ental questions.
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