remote sensing of evapotranspiration with modis daniel siegel
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Remote Sensing of Evapotranspiration
with MODIS
Remote Sensing of Evapotranspiration
with MODIS
Daniel Siegel
What is MODIS?What is MODIS?Moderate-Resolution Imaging Spectroradiometer
Launched in 1999 aboard the EOS AM (Terra); EOS PM (Aqua) followed in 2002
Monitors 36 spectral bands between 0.4 m and 14.4 m
Images entire Earth every 1-2 days at 1 km resolution
Moderate-Resolution Imaging Spectroradiometer
Launched in 1999 aboard the EOS AM (Terra); EOS PM (Aqua) followed in 2002
Monitors 36 spectral bands between 0.4 m and 14.4 m
Images entire Earth every 1-2 days at 1 km resolution
Why use MODIS?Why use MODIS?
ASTER and Landsat have 60 m resolution but available once a month at best
Geostationary satellites capture data with 15 min frequency but 5 km resolution
ASTER and Landsat have 60 m resolution but available once a month at best
Geostationary satellites capture data with 15 min frequency but 5 km resolution
Relevent MODIS Products
Relevent MODIS Products
MOD11 - Surface temperature and emissivity
MOD43 - Albedo
MOD15 - Leaf Area Index (LAI)
MOD13 - NDVI
Mod07 - Atmospheric stability; temperature and vapor pressure at 20 vertical levels
MOD03 - Lattitude, longitude, ground elevation, solar zenith angle, satellite zenith angle and azimuth angle
MOD11 - Surface temperature and emissivity
MOD43 - Albedo
MOD15 - Leaf Area Index (LAI)
MOD13 - NDVI
Mod07 - Atmospheric stability; temperature and vapor pressure at 20 vertical levels
MOD03 - Lattitude, longitude, ground elevation, solar zenith angle, satellite zenith angle and azimuth angle
NDVINDVI
NDVI RIR RredRIR Rred
First measured by the original Landsat in 1972
Measurement of a pixel’s “greenness”
Accessing MODIS DataAccessing MODIS Data
Level 1 and Atmosphere Archive and Distribution System (LAADS)
Warehouse Inventory Search Tool (WIST) submits orders via EOS ClearingHouse (ECHO)
HDF can interface with C, Fortran, Perl, MATLAB, IDL or Mathmatica
Level 1 and Atmosphere Archive and Distribution System (LAADS)
Warehouse Inventory Search Tool (WIST) submits orders via EOS ClearingHouse (ECHO)
HDF can interface with C, Fortran, Perl, MATLAB, IDL or Mathmatica
WISTWIST
Go Rd + Ld - s
sc
Go = Rn[c + (1-fc)(s - c)]
fc = percentage of ground covered by vegetation
= Measured by MODIS
= Variables
RnRd + Ld - sRnGo E
Surface Energy Balance System (Su 2002)
Surface Energy Balance System (Su 2002)
Calculating HCalculating H
= cannot be measured remotely
z0m and z0h z0m and z0h
Can vary by several orders of magnitude
Using LAI and wind speed, z0m can be calculated as afunction of canopy height following Massman (1997)
Zoh = zom/exp(kB-1) Wind speed
Limiting CasesLimiting Cases
Hdry = Rn - Go
Constraining the result between these values decreases the uncertainty considerably
Summary: Local Variables
Summary: Local Variables
Rd - Measured with a radiation sensor
Ld - Stephen-Boltzman equation using air temp
Wind speed and canopy height must be measured on site
Rd - Measured with a radiation sensor
Ld - Stephen-Boltzman equation using air temp
Wind speed and canopy height must be measured on site
ResultsResults
Triangle Method
(Jiang and Islam 2001)
Triangle Method
(Jiang and Islam 2001)
E (Rn G)
max
desdT TTa
f (Ta )
min 0
f (NDVI, soil moisture)
ResultsResults
Triangle Method Original Priestly-Taylor Eq
Complementary Model
(Venturini & Islam 2007)
Complementary Model
(Venturini & Islam 2007)
ET + ETpot = 2Etwet (Bouchet 1963)ET + ETpot = 2Etwet (Bouchet 1963)
EF = ET / (Rn-G)
From Priestly-Taylor
From Penman
Uses temp profile as surrogatefor humidity deficit
Benefits of Isolating EF
Benefits of Isolating EF
Rn is a large source of error because of atmospheric interference and cloud cover
Generally constant during daytime
Useful for mapping drought conditions
Rn is a large source of error because of atmospheric interference and cloud cover
Generally constant during daytime
Useful for mapping drought conditions
ResultsResults
Future ResearchFuture Research
Removing cloud-contaminaed pixels biases results, ignores diffuse radiation
Nocturnal transpiration
3°K error in in Ts causes 75% error in H
Removing cloud-contaminaed pixels biases results, ignores diffuse radiation
Nocturnal transpiration
3°K error in in Ts causes 75% error in H