Application of remote sensing data for drought monitoring
Introduction to EUMETSAT Land SAF products
Wednesday November 13, 2013
Session 1: LSA-SAF evapotranspiration
Nicolas Ghilain
About the Royal Meteorological Institute of Belgium
Main activity: Meteorology
“… services supported by research and long term standardised meteorological, climatological and geophysical observations …” (http://www.meteo.be)
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ResearchGeomagnetism
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Link between Meteorology and Water Studies: HydroMeteorology
1. What is evapotranspiration ?
2. Satellite remote sensing: its role for ET monitoring
3. The LSA-SAF evapotranspiration
From (http://www.usgcrp.gov)
Energy balance: (1-a).S + e.(L-s.Tsk4) + H + LE – G = 0
Catchment Water balance: P – Q – ET – Dw – OF = 0
P=Precipitation; Q=Discharge; Dw=stock variation;
OF= flow at the outlet
1km² corn field ??? liters of water each dayAmount (examples)
Evapotranspiration = Evaporation from water bodies (sea, rivers, lakes)
+ Transpiration of plants
+ Evaporation from soil
90% from water bodies, 10% from plants
Land Evapotranspiration = Evaporation from water bodies (sea, rivers, lakes)
+ Transpiration of plants
+ Evaporation from soil
A. 50 liters
B. 1,200 liters
C. 30,000 liters
a large oak tree can transpire 151000 liters per year.
Evapotranspiration means loss of water
On land, ET returns 58% of precipitation !
48%
36%
16%transpiration
Evaporation from soil
Evaporation of intercepted water
The transpiration process
Plant « Breathing » and the Transpiration Process
3. Exchange plant-atmosphere
1. Root-zone water
2. Root water uptake
For transpiration to occur, there must be water available !
-Water taken to leaves through roots
-Light is necessary
Water vapour is released into the air through leaves stomata
What does influence evapotranspiration ?
Surface net radiation
EvapoTranspiration
Stomata respond to light.
Closed at night, open during day.
Plant typeSurface net radiation
EvapoTranspiration
What does influence evapotranspiration ?
Soil water availability
Plant typeSurface net radiation
EvapoTranspiration
Stomata respond to water stress.
If soil water ET .
What does influence evapotranspiration ?
Relative humidity
Soil water availability
Plant typeSurface net radiation
EvapoTranspiration
If Gradient In-Out ET
Rel Hum ET
What does influence evapotranspiration ?
Relative humidity
Wind and air movement
Soil water availability
Plant typeSurface net radiation
EvapoTranspiration
Wind: move/dry air around leaf
Gradient In-Out ET
Wind Speed ET
What does influence evapotranspiration ?
Air temperature
Relative humidity
Wind and air movement
Soil water availability
Plant typeSurface net radiation
EvapoTranspiration
What does influence evapotranspiration ?
Can we measure evapotranspiration ?
Spatial scale
Eddy covariance
Scintillometry
Eddy covariance from aircraft
Geodetical spaceborne
measurements
~1 m ~102 m ~1 km ~10 km ~103 km
Altitude
~-1 m ~1-10 m ~1-10 m ~100 m ~500 kmAtmospheric surface
layerAirborne SpaceborneSoil
~1-10 m
~102 m
Profile method
Lysimeter
From wikipedia
Yes, but indirectly
Spatial scale
Principle At each height, observation of Q (humidity), and U (wind speed).
)(... 21 QfUfFw
Determined theoretically (Monin-Obukhov)
)()( 21 zxzxx
z1
z2
(Andy Delcloo)From wikipedia
swF a .
For water flux: s= air humidity
Profile method Eddy covariance
How can we observe at large scale ?
Observations are local
do not allow a monitoring of ET at every location!
We need a model M M(remote sensing data) = ET
Other observations are needed: remote sensing
BUT, no electromagnetic signal emitted by ET process…
Objective: Build a model, fed by remote sensing derived data
Result: ET: - for all weather (no gap)
- in near-real time
- with a fine temporal sampling
1. What is evapotranspiration ?
2. Satellite remote sensing: its role for ET monitoring• Satellite characteristics
• Review of ET derived from satellites
• LSA-SAF context
3. The LSA-SAF evapotranspiration
How do we choose a suitable evapotranspiration model ?
Different classes of ET models
1. Empirical Methods
2. Energy Balance Models
3. Land Surface Models
#Required Input #Parameters AccuracyApplicability other situations
1. Empirical Models
-Models calibrated on local observations
-Use statistical relations of input to create output
-The more input, the more accurate
2. Energy Balance Models
-Models based on physics
-Assumptions needed to approximate
Examples of models: SEBAL, SEBS
aasatradpa
a gzTTcr
H )( ,
EB: (1-a).S + e.(L-s.Trad,sat4) + H + LE – G = 0 (S, a, L, Trad,sat, G obtained from data)
LE is the residual of the equation
(http://lis.gsfc.nasa.gov/LIS_whatis.php)
Conceptual modeling:
-Parameterization of turbulent exchanges
-Dynamical equations for soil variables
-Forced by Radiation and Meteo.
Used in: - Numerical Weather Prediction
- Climate models
- Land water management
Output: - Soil water content
- evapotranspiration
- turbulent fluxes
3. Land Surface Models (e.g. ISBA, TESSEL, CLM, JULES, …)
Remote Sensing Opportunities
Orbits preferred for earth monitoring
Terra Orbit Tracks: 2 consecutive days
1. (Near-) Polar orbiter, sun-synchronous
-low orbit (~1000 km)-earth revolution in ~100 min
Need of several ground receivers for the data flow
Polar orbiters: Terra, Aqua, METOP, …
Space-based platforms & sensors
2. Geostationary (geo-synchronous)
-altitude (~36000 km)-over equator
Geostationary satellites: Meteosat, GOES, …
Sensors for earth monitoring
Depending on longitude, need to correct longitudinal drift
Very High resolution: FORMOSAT-2, SPOT-5,IKONOSHigh resolution : LandSAT-7, ASTER
Moderate resolution : MetOP, MERIS, MODISLow resolution : MSG
What scale does it involve ?
IKONOS ASTER MODIS MSG
Frequency of imaging over same location
Resolution of imaging
15 min
1 m 10 m 100 m 1 km 10 km
1 day1 month
Resolution of imaging: example
MSG MODIS
Different sensors (characteristics) useful for different applications
What useful variables derived from remote sensing ?
1. Describe surface properties
Land cover
Soil classification
Topography
Static properties:
use of high to medium resolution satellites
2. Surface variables
Land surface temperature
LAI Snow cover
Albedo Emissivity
Energy balance: (1-a).S + e.(L-s.Tsk4) + H + LE – G = 0
2.1. Radiation terms
2.2. Rapid changes of the surface
3. Atmosphere
Clouds: location and typeatmospheric water and aerosols
Solar Radiation at the surface
EUM
ETSA
T SA
TELL
ITE
APP
LIC
ATIO
N
FAC
ILIT
IES
(SA
F’S)
NWP –SAF Numerical WeatherPrediction
OSI -SAF Ocean and Sea Ice
GRAS -SAF Meteorology
O3M Ozone Monitoring
NWC-SAF - Nowcasting and very Short Range Forecasting
CM -SAF Climate Monitoring
Albedo (AL)
Down welling Surface Short-wave Flux ( DSSF)
• Down welling Surface Long-wave Flux (DSLF)
• Evapotranspiration (MET) => DMET
Land Surface Temperature (LST)
• Risk of Fire Mapping (RFM)
• Snow Cover ( SC)
• Surface Emissivity ( EM)
• Vegetation characteristics (FVC, LAI, etc)
LSA -SAF OBJECTIVESDevelop techniques to retrieve parameters related to
land, land-atmosphere interactions and biosphericapplications, by using data from MSG and EPS satellites
LSA -SAF Land Surface Analysis
EUMETSAT SAF on Land Surface Analysis
METEOSAT SECOND GENERATION (MSG/SEVIRI)
geostationary satellite altitude: 35 800 km 12 channels:-3 VIS (0.635, 0.75, 0.81 µm)-7 IR (1.64, 3.92, 8.70, 9.66, 10.8, 12.0, 13.4 µm)-2 WV (6.25, 7.35 µm)Spatial resolution:-3 km-1 km for High Resolution VIS (0.75 µm) Imaging frequency:-1 slot / 15 min
Meteosat Second Generation
1. What is evapotranspiration ?
2. Satellite remote sensing: its role for ET monitoring
3. The LSA-SAF evapotranspiration
• The model
• The quality
• The products
• The validations
• New developments
Bare soil
Grass
High vegetation
The tile approach: The energy exchanges between the surface and theatmosphere is modelled using a resistance scheme. Each pixel in theimage is divided into ‘tiles’ of homogeneous vegetation types
The LSA-SAF evapotranspiration model
Energy balance: (1-a).S + e.(L-s.Tsk,i4) + Hi + LEi – Gi = 0
ET ~ S LEi (Land Cover, LAI, Wind, Tair, Qair, Soil Moist)
ECMWF weather forecasts
ECOCLIMAP-I
MSG/SEVIRI pixel
Meteosat Second Generation
(Ghilain N., Arboleda A., Gellens-Meulenberghs F., 2011, Hydrol. Earth Syst. Sci.)
The LSA-SAF evapotranspiration model
Sensible heat flux (Hi)
Latent heat flux (LEi)
iiiisk GLEHTLS )()1( 4,
Energy balance at the skin layer
)()()( , aaisksat
ca
avi TqTq
rrLLE
ii
aaiskpa
ai gzTTc
rH
i
)( ,
netii RLAIfG )(
Ground heat flux (Gi)
The LSA-SAF evapotranspiration model
Canopy resistance rci
ai
sc DffSf
LAIr
r i
i 321min,
Lz
Ldz
zdz
kur h
ha
hh
aa 0
0
*
ln
1Aerodynamic resistance
Lz
Ldz
zdz
kUum
ma
mm
a
a
0
0
*
ln
.
LLE608.0
TcHkg
uL
aap
3*a
iiHH
iiLELE
v
pixelpixel L
LEET
.
Heat fluxes at pixel level
rc
dr
d h
z0
Turbulence generation by wind
Maximum transpiration rate(plant species dependent)
Total surface leaves(scaling leaf properties to landscape)
Empirical functions(plant response to ambiant factors)
The LSA-SAF evapotranspiration model
from
sat
ellit
es
MSG FVC -Fractional Vegetation Cover-
LST -Land Surface Temperature-
LAI -Leaf Area Index-
AL -Surface ALbedo-DSSF-Shortwave flux at surfaceDSLF-Longwave flux at surface
Ti -Tiles in pixel-FVi -Fraction of tiles-Rsi -Tile minimum stomatal resistanceLAIi -Tile Leaf Area Index -FVCi -Tile fractional vegetation coverALM -Monthly pixel AlbedoFr
om d
atab
ase
(EC
OC
LIM
AP)
From
NW
P (E
CM
WF)
Ta -Air temperature-U -Wind speed-
Pa -Air pressure -Td -Dew-point temperature-ST -Soil Temperature-SM -Soil Moisture-
MODELFORMULATION
MET
QF
*
* -To be included in future versions
The LSA-SAF evapotranspiration model
DSSF and corresponding Quality flag image
Albedo and corresponding Error image
DSLF and corresponding Quality flag image
The LSA-SAF evapotranspiration model
Air temperature Dewpoint temperature Surface layer temp
Wind speed Surface layer humidity Air pressure
The LSA-SAF evapotranspiration model
Main tile in every pixel Main tile percentage
Main tile Rsmin Main tile LAI
The LSA-SAF evapotranspiration model
This image cannot currently be displayed.
Euro
NAfr
SAme
SAfr
Euro
NAfr
SAme
SAfr
The LSA-SAF products are generated over 4 geographical areas within MSG disk
Temporal resolution: 30 min.
Spatial resolution: MSG
The LSA-SAF evapotranspiration products
Two images are generated: the first one contains instantaneous ET estimates in mm/h whilethe second one is the quality flag image, provides information on the quality of estimatespixel by pixel
ET (mm/h) for 2010/10/29 at 12:00 UTC Associated quality flag (-)
The LSA-SAF evapotranspiration products
Example of hourly ET (mm/h) over MSG disk for 28/04/2010
The LSA-SAF evapotranspiration products
482
11
t
tiMETDMET
-Daily evapotranspiration product (DMET): temporal integration of instantaneous(METi) product.
- METi : instantaneous evapotranspiration for i time-step between 00:30 UTC and 24:00UTC.
- In optimal conditions (no missing slots) 48 images are integrated for a given day.
The LSA-SAF evapotranspiration products
a) DMET (mm/day) b) Percent of missing values for every pixel
0 5 0 100
(mm/d) (%)
The LSA-SAF evapotranspiration products
DMET images from 06/09 to 15/09 2009
ET [mm/day]
The LSA-SAF evapotranspiration products
1.Acquisition of MSG/SEVIRI corrected radiances andClouds mapping (from EUMETSAT, Darmstadt,Germany)
2.Computation of radiation components (Land SAFcomputing facility)
3. Acquisition of the global weather forecasts (ECMWF),reproject and interpolate
4. Computation of ET over 4 areas (Europe, North/SouthAfrica and South America)
5. Compute a quality index
6. Creation HDF5 files
7. Distribution through web or satellite to users
8. Distribution of regular update of User Manual andValidation Reports
Near-real time production: how it works
Validation is essential for
1. Prototypes design
2. Definition of product accuracy
4. Benchmark for successive product improvements
Stand-alone
Models inter-comparison
6. Check consistency with the other LSA-SAF products
Off-line
5. Check competitivity of the product
3. Check product accuracy
Consistency checks
Evaluation of the accuracy
- Model forced by locally measured radiation
- Comparison of model output with local observations
Long time series
Can be in the past (before MSG launch)
Large panel of bioclimatic conditions
Allows to check: - long variability of modelled ET
- response of the model to short term perturbations
- quantitative accuracy of the prototype
Point-scale model Selected locations (worldwide)
Stand-alone validation
Cabauw, The Netherland (grass): 1995.01.01 to 1996.12.31Some examples
Comparison of modelled andobserved half-hourly ET rates
Comparison of modelled and observed 10-dayscumulated ET check on long-term variability
Stand-alone validation
Statistical assessment with the large series.
Comparison of modelled and observed half-hourlyET check on short-term variability
Example: response to clouds passages
2. How well does it correlate with the observations ?
3. What is the mean point-by-point error (dispersion) ?
4. A global skill score (Nash-Sutcliff) ?
1. Is the modelled ET globally biased ?
Stand-alone validation
Puéchabon, France (Mediterranean forest): 2002.01.01 to 2003.08.31
Hesse, France (Deciduous broad-leaf forest): 1997.01.01 to 1999.12.31
Santarem (Tropical forest):2002. 10.01 to 2003.08.31
Stand-alone validation
Validation is essential for
1. Prototypes design
2. Definition of product accuracy
4. Benchmark for successive product improvements
Stand-alone
Models inter-comparison
6. Check consistency with the other LSA-SAF products
Off-line
5. Check competitivity of the product
3. Check product accuracy
Consistency checks
Evaluation of the accuracy
Products generation at the LSA-SAF operational production system :
-LSA-SAF MET (30 minutes) and DMET (daily) products-Auxilliary files (internal use) for validation (30 minutes)
Auxilliary file contains ET rates for selected pixels, as well as the ‘tile’ ET rates
-Location ground measurement stations- Representative landscapes
- Files regularly checked- ET rates compared to ground observations (when available)
What we do with those files:
Off-line validation
veg1
veg2
veg3
veg4
ET=15%.ET+
50%.ET+
35%.ET
ET=100%.ET
Auxilliary file contains ET rates for selected pixels, as well as the ‘tile’ ET rates
MSG pixel
Observation device
footprint
Model MSG pixel
Model MSG pixel
Model tile
Mean ET diurnal cycle for March to May 2007 at Vielsalm (BE)
+ ground observations
model MSG pixel ET
model ‘tile’ ET
Model ‘tile’ closer to observation !
Off-line validation
Examples validation LSA-SAF MET product Dataset: 2007
Off-line validation
Off-line validation
Product Required Accuracy (LSA-SAF MET)
if ET > 0.4 mm h-1
if ET < 0.4 mm h-1
Error < 25%
Error < 0.1 mm h-1
Off-line validation
Examples validation LSA-SAF DMET product
Detecting anomalies improvement in further releases
Validation is essential for
1. Prototypes design
2. Definition of product accuracy
4. Benchmark for successive product improvements
Stand-alone
Models inter-comparison
6. Check consistency with the other LSA-SAF products
Off-line
5. Check competitivity of the product
3. Check product accuracy
Consistency checks
Evaluation of the accuracy
LSA-SAF MET product has unique features:
Continuous series (clear/cloudy sky)
Input from MSG derived data
MSG resolution
Time step 30 minutes
Near-real time
MSG coverage
ET products from other sources are available (with different caracteristics).
Examples: products from atmospheric models forecasts or data assimilation systems
ECMWF GLDAS
0.25°x0.25° spatial resolution
3-hours cumulates
0.25°x0.25° spatial resolution
3-hours cumulates0.03°x0.03° spatial res (Equator)
30-minutes
versus
GLDASECMWFLSA-SAF MET
Intercomparison with other operational systems
Comparison at the same temporal and spatial resolution (1°x1°, 3 hours)Example: July 6, 2007: 9-12 UTC
Global patterns are the same agreement between models
Differences induced by solar radiation
Differences induced by soil water availability (+ related model parameters)
Intercomparison with other operational systems
Analysis over year 2007 (March to November)
Intercomparison with other operational systems
Validation is essential for
1. Prototypes design
2. Definition of product accuracy
4. Benchmark for successive product improvements
Stand-alone
Models inter-comparison
6. Check consistency with the other LSA-SAF products
Off-line
5. Check competitivity of the product
3. Check product accuracy
Consistency checks
Evaluation of the accuracy
Consistency with LSA-SAF LST (Land Surface Temperature)
LSA-SAF LST is compared with the ‘skin’ temperature from LSA-SAF ET model
Energy balance: (1-a).S + e.(L-s.Tsk4) + H + LE – G = 0
Consistency with other LSA-SAF products
1. What is evapotranspiration ?
2. Satellite remote sensing: its role for ET monitoring
3. The LSA-SAF evapotranspiration
• The model
• The quality
• The products
• The validation
• New developments
New developments
We can take advantage from MSG Leaf Area Index.
Leaf Area Index quantifies
the green material
above the surface.
LST & Emssivity LongWave Flux ShortWave Flux Albedo
State Water Stress Wild fires
Vegetation
Surface Radiation
Day-to-day Interannual Continuous
Before
Now
Fraction of vegetation cover
partitions bare and vegetation %.
Vegetation is the main contributor
to evapotranspiration over land
New developments
Vegetation status corrects some model deficiencies
in water-limited regions of Europe
Available product In the future
LST = Land Surface Temperature
The retrieval is based on brightness temperature.It removes the atmopshere effect (total column of water vapour).
The model needsupdated soil moisture.
Soil moisture:Model Remote sensing
LST & Emssivity LongWave Flux ShortWave Flux Albedo
State Water Stress Wild fires
Vegetation
Surface Radiation
Hypothesis:Land surface temperature informs on soil moisture status (thermal inertia).
New developments
ET [mm/h] for 2010/10/29 at 12:00 UTC
(Provided by A. Arboleda)
Operational production in near-real time:
every 30 min & daily
3 km sub-satellite(http://landsaf.meteo.pt)
Summary
• Evapotranspiration estimationover wide and remote areasin near-real time,thanks to remote sensing.
• LSA-SAF evapotranspiration productsmonitor quantitatively the water lossfrom the surface into the atmosphere.
• Available products have been checkedand show good comparison with in-situobservations (at least) over Europe.
• New developments to be implementedsoon will increase the reliability mostlyover semi-arid regions by:
• Adapting the parameterization• Ingesting more LSA-SAF products
(LAI, FVC, SC, LST).
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