1. Lisbon, Portugal 8-10 March 2006
2nd LSA SAF WORKSHOP
Lisbon, Portugal March 2006
Mapping surface components of the hydrologic cycle through sequential assimilation of Land Surface Temperature
Lorenzo Campo a, Francesca Caparrini b, Fabio Castelli a, Dara Entekhabi c
a Dipartimento di Ingegneria Civile, Università di Firenze (Italy)
b Eumechanos, Firenze (Italy)
c Department of Civil and Environmental Engineering
Massachusetts Institute of Technology, Cambridge MA (USA)

2. Motivation
Land Surface Temperature dynamics contains the “signature” of evapotranspiration and sensible heat fluxes, and can be detected by a variety of remote sensing instruments.
The key-issue is to develop a physically-based data assimilation framework for the retrieval of land-atmosphere moisture and heat fluxes and the parameters that regulate such fluxes from sequences of remotely sensed Land surface temperatures.
The methodology :
is not bound to any specific RS set-up
can use multi-scale, multi-sensor data
minimizes the need of ancillary information

3. ? ROLE OF VEGETATION Signatures on land Surface Temperature dynamics
(Science questions) G
NET
RADIATION LE
LATENT
HEAT
FLUX
SENSIBLE
GROUND H Ts
Land
Atmosphere Rn G
NET
RADIATION LE
LATENT
HEAT
FLUX
SENSIBLE
GROUND H Ts
Land
Atmosphere Rn
Efficiency of turbulent exchanges CH
LST
Hour of day
Hour of day
LST
ROLE OF VEGETATION
?????? ?
Partitioning due to moisture availability G
NET
RADIATION LE
LATENT
HEAT
FLUX
SENSIBLE
GROUND H Ts
Land
Atmosphere Rn Rn
NET
RADIATION LE H
LATENT
HEAT
FLUX
SENSIBLE
HEAT
FLUX
Atmosphere EF G
WET
DRY
Land Ts
GROUND
FLUX

4. Fast transition from exceptionally dry conditions (Summer 2003 drougth) to flood season

5. MOBIDIC
MOdello Bilancio Idrologico
DIstribuito e Continuo
ACHAB
Assimilation Code for HeAt and moisture Balance

6. How do I optimally extract the relevant infomation from data?
“Technical” questions
1. Dual source issues
Radiometric Land Surface temperature observed by satellite comes from soil and vegetation emission: TR Tv
How to treat the land surface in the formulation of the assimilation algorithm with regard to soil and vegetation contribution? Ts
Land Surface Temperature can be detected by a variety of sensors with different spatial and temporal resolution
How do I optimally extract the relevant infomation from data?
i.e. geostationary mid. res/high revistit freq.
Orbiting High res. Thermal imagery…
MW images when cloudy…
2. Scale Issues

7. per unit vegetated area
Two-source model: formulation
CHS=heat transfer coefficient from soil to the air within the canopy
CHV=heat transfer coefficient from leaves to the air within the canopy
CH=heat transfer coefficient from the air within the canopy to BL air ( ) a U q T , v w s H E Rn
soil evaporative fraction
vegetation evaporative fraction
Fluxes:
per unit bare soil area
per unit vegetated area
per unit total area
… for the time being
f=fractional vegetation cover

8. constant during daytime hours
Model closures
Energy balance
Soil:
Ground heat flux ?
Static approach
Force-restore approximation
Dynamic approach
Vegetation:
Canopy has lower thermal inertia
Gv<<Rn
Temporal constraints
soil evaporative fraction
constant during daytime hours
vegetation evaporative fraction
Heat transfer coeffcients (CH, CHS, CHV) vary on a monthly time scale.
Atmospheric stability correction with f(RicB)

9. Multi-scale Remote Sensing 1D(time)-VAR assimilation scheme
e.g. SEVIRI
e.g. MODIS, ASTER, LANDSAT

10. Sequences of Land Surface Temperature maps
Input Data
Sequences of Land Surface Temperature maps
Downwelling Shortwave and Longwave Radiation
Air Temperature
Wind speed
Fractional Vegetation Cover/LAI

11. A ‘Regional’ case study: Tuscany, central Italy
AgroMet Radiation. Network
Elev. [m]

12. Land cover: not only vineyards and oliveyards
Land cover: not only vineyards and oliveyards. Broadleaf and pine forests also.

13. Simulation period: August-September 2005

14. Results
Bulk heat transfer coefficient

15. Results
Evaporative Fraction

16. Results
Latent Heat Flux (soil)

17. Results
Latent Heat Flux (vegetation)

18. EF soil
EF veg

19. Results
Diurnal cycles
Note: Variability of Ground Heat Flux

20. Comparison with ground based flux measurements
(previous app. on SGP97 with LST, Rs from AVHRR, GOES, SSM/I)
Average diurnal cycle
Caparrini et al., 2005, WRR
Note: Phase shift between latent and sensible heat fluxes not correctly represented

21. Sensitivity to diffrent Solar Radiation input data
SAF Downwelling SW radiation (DSSF)
SW Radiation from AgroMet stations
In both cases, LW radiation computed from air temperature/humidity in this experiments
(RadSAF-RADmet)/RadSAF

24. Concluding Remarks
The variational assimilation scheme (ACHAB!) allows optimal use of remotely sensed Land Surface Temperature to map surface hydrologic components over large areas.
The multi scale formulation allows to use all available LST information and may be crucial to retrieve multiple parameters.
The method allows to obtain maps of bulk heat transfer coefficients that can improve the modelling of evapotranspiration in distributed hydrological models
Some open problems:
Even 2S cannot correctly resolve the usually observed phase shift between latent and sensible heat fluxes over vegetated areas.
Need to introduce plant physiology models?
How is the performance of the variational assimilation scheme in energy-limited conditions (e.g winter)?

25. Thank you