Application of remote sensing data for drought …Soil Airborne Spaceborne ~1-10 m ~102 m Profile...

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)

“Reliable public service realised by empowered staff and based on research, innovation and continuity.”

Weather Forecasting and Climatological

Information

Meteorological Climatological

ResearchGeomagnetism

Observations

Support

Structure

http://www.meteo.be

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


Soil water availability


EvapoTranspiration

Stomata respond to water stress.

If soil water ET .


Relative humidity



EvapoTranspiration

If Gradient In-Out ET

Rel Hum ET


Relative humidity

Wind and air movement



EvapoTranspiration

Wind: move/dry air around leaf

Gradient In-Out ET

Wind Speed ET


Air temperature

Relative humidity

Wind and air movement



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


2. Satellite remote sensing: its role for ET monitoring• Satellite characteristics

• Review of ET derived from satellites

• LSA-SAF context


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


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




• 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.)


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)


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)


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


DSSF and corresponding Quality flag image

Albedo and corresponding Error image

DSLF and corresponding Quality flag image


Air temperature Dewpoint temperature Surface layer temp

Wind speed Surface layer humidity Air pressure


Main tile in every pixel Main tile percentage

Main tile Rsmin Main tile LAI


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 (-)


Example of hourly ET (mm/h) over MSG disk for 28/04/2010


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.


a) DMET (mm/day) b) Percent of missing values for every pixel

0 5 0 100

(mm/d) (%)


DMET images from 06/09 to 15/09 2009

ET [mm/day]


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


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 ?


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



Off-line



Consistency checks


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





Stand-alone



Off-line



Consistency checks


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)


Analysis over year 2007 (March to November)






Stand-alone



Off-line



Consistency checks


Consistency with LSA-SAF LST (Land Surface Temperature)

LSA-SAF LST is compared with the ‘skin’ temperature from LSA-SAF ET model


Consistency with other LSA-SAF products




• 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).

Application of remote sensing data for drought …Soil Airborne Spaceborne ~1-10 m ~102 m Profile...

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