1. RELIABLE VALIDATION OF SOIL MOISTURE
ESTIMATES FROM ASCAT AND AMSR-E SENSORS
THROUGH FOUR DIFFERENT APPROACHES
Brocca, L. (1), Melone, F.(1), Moramarco, T.(1) Matgen, P. (2), Wagner, W. (3)
Research Institute for Geo-Hydrological Protection, CNR, Via Madonna Alta 126, Perugia, Italy
Public Research Center - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria
Hydrology

2. Satellite soil moisture validation
in-situ
measurements
~50 cm
satellite
pixels
~25 km
HOW IS IT POSSIBLE TO VALIDATE SATELLITE SOIL MOISTURE ESTIMATES WITH IN-SITU MEASUREMENTS? VS
A= ~10-1 m2
A = ~109 m2

3. SMALL CATCHMENT SCALE ~50 km2
Soil moisture temporal stability
SMALL CATCHMENT SCALE ~50 km2
PLOT SCALE m2
CATCHMENT SCALE ~250 km2
Brocca et al., 2009 (GEOD) Brocca et al., 2010 (WRR) Brocca et al., 2011 (JoH, submitted)

4. Journal of Hydrometeorology, 2010
Application requirements
Journal of Hydrometeorology, 2010
"...different applications may require different, application-specific metrics to define soil moisture measurement requirements"
FLOODS
transition between low and high soil moisture conditions
EVAPORATION
drier portion of soil moisture range

5. Purposes
Analysis of 4 different approaches for the validation and intercomparison of ASCAT and AMSR-E soil moisture products:
comparison with in-situ observations
comparison with modelled data
comparison with antecedent wetness condition estimates at catchment scale
improvement in runoff prediction through soil moisture data assimilation into rainfall-runoff modelling

6. 1,2) Comparison with in-situ and modelled data
Brocca et al., 2010 (RSE)
Co-location of in-situ (observed and modelled) and satellite Surface Soil Moisture (SSM) product
Application of the exponential filter to satellite data, thus obtaining the Soil Wetness Index (SWI), and optimization of the T parameter (between 1 and 40 days)
Linear regression (REG) and CDF-matching application to satellite data to remove the systematic differences with respect to in-situ (and modelled) data
Computation of performance in terms of Correlation Coefficient (R) and Root Mean Square Difference (RMSD) for SSM and SWI products
It is worth of noting that if satellite soil moisture data have to be employed within hydrological (or others) models through data assimilation the rescaling must be done.
"Data assimilation techniques are designed to correct random errors in the model and rely on the assumption of unbiased background and observations. However, the model simulations and data are typically different and need to be rescaled before data assimilation."
Barbu et al., 2011 (BG)

7. 3) Antecedent wetness conditions at catchment scale
1. From a rainfall-runoff time series extract a number of flood events
2. For each event compute the volume of rainfall and direct runoff and then the “observed” S which is the indicator of AWC at catchment scale
3. Compare the “observed” S with the different satellite soil moisture products

8. 3) Antecedent wetness conditions at catchment scale
ERS SCATTEROMETER SOIL MOISTURE DATA
Italy Tiber River
Tevere - PN
Assino
Niccone
11 catchments km2
Cerfone
Genna
Caina
Brocca et al., 2009 (JoH) (JHE)
Beck et al., 2010 (JSTARS) Tramblay et al., 2010 (JoH), 2011 (NHESS)
Australia France
Timia
Topino
Nestore

9. SOIL WATER BALANCE MODEL EVENT-BASED RAINFALL-RUNOFF MODEL (MISD)
4) Data assimilation in rainfall-runoff modelling
MISDc: "Modello Idrologico Semi-Distribuito in continuo"
Brocca et al., 2011 (HYP)
e(t): evapotranspiration
f(t): infiltration
g(t): percolation
Wmax
W(t)
s(t): saturation excess
SOIL WATER BALANCE MODEL
outlet discharge
upstream discharge
directly draining areas linear reservoir IUH
EVENT-BASED RAINFALL-RUNOFF MODEL (MISD)
subcatchments
geomorphological IUH
channel routing diffusive linear approach
rainfall excess
SCS-CN
r(t): rainfall
S: soil potential maximum retention W(t)/Wmax: saturation degree
W(t)
S(t)
FREELY AVAILABLE !!!
EMPLOYED FOR OPERATIONAL FLOOD FORECASTING IN CENTRAL ITALY

10. 4) Data assimilation in rainfall-runoff modelling
The SWI was rescaled to match the saturation degree, , simulated by the RR model,  mod
time
saturation degree
assimilation time
Brocca et al., 2010 (HESS)
observations
modeled saturation degree
updated saturation degree
K is a constant
K=0 "perfect" model
K=1 direct insertion

11. Advanced SCATterometer
scatterometer (active microwave)
C-band (5.7 GHz)
VV polarization
resolution 50/25 km
daily coverage
ongoing
ASCAT
Change detection algorithm takes account indirectly for surface roughness and land cover variability 11

12. SOIL MOISTURE PRODUCTS
Advanced Microwave Scanning Radiometer
radiometer (passive microwave)
GHz
HH and VV polarization
74x43 km (6.9 GHz), 14x8 (36.5 GHz), resampled at ~25 km
daily coverage
ongoing
AMSR-E
SOIL MOISTURE PRODUCTS
Polarization Ratio (PR)
It is based on the computation of the polarization ratio of the AMSR-E brightness temperatures at different frequencies.
VUA algorithm
It is based on the Land parameter retrieval model (LPRM) that is a three-parameter retrieval model (soil moisture, vegetation water content, and soil/canopy temperature) for passive microwave data based on a microwave radiative transfer model.
NASA algorithm
It uses normalized polarization ratios to take vegetation and roughness into account through empirical relationships. Soil moisture is computed using the deviation of PR at GHz from a baseline value established from the monthly minima at each grid cell.
Njoku et al. (2006) RSE
Owe et al. (2008) JGR
Pellarin et al. (2008) GRL 12

13. In-situ soil moisture data
“Consistent validation of H-SAF soil moisture satellite and model products against ground measurements for selected sites in Europe” 13

14. In-situ soil moisture data
In-situ soil moisture data at different depths (5, 10, 15, 30, ...) for a total of 17 sites across four different countries (Italy, France, Spain and Luxembourg).
Considering both observed and modelled data:
29 DATA SETS 14

15. Rainfall-runoff data CENTRAL ITALY LUXEMBOURG Tiber River
Data period:
# Catchments: 10
Drainage area: km2
LUXEMBOURG
Alzette River
Data period:
# Catchments: 10
Drainage area: km2

16. 1,2) Comparison with in-situ and modelled data
AMSRE-PRI
Italy
Vallaccia
Modelled data
5 cm depth
ASCAT
AMSRE-NASA
AMSRE-LPRM

17. RELATIVE SOIL MOISTURE
1,2) Comparison with in-situ and modelled data
Correlation coefficient between all satellite products and ground data sets
RELATIVE SOIL MOISTURE
Brocca et al., 2011 (RSE, submitted)

18. SOIL MOISTURE ANOMALIES
1,2) Comparison with in-situ and modelled data
Correlation coefficient between all satellite products and ground data sets
SOIL MOISTURE ANOMALIES
Brocca et al., 2011 (RSE, submitted)

19. RELATIVE SOIL MOISTURE
1,2) Comparison with in-situ and modelled data
Italy
CAPOFIUME
Modelled data
5 cm depth
RELATIVE SOIL MOISTURE
SOIL MOISTURE ANOMALIES

20. 1,2) Comparison with in-situ and modelled data
Spain
REM-K10
In-situ data
5 cm depth
ASCAT+AMSRE

21. 1,2) Comparison with in-situ and modelled data
Australia In-situ data 5 cm depth
OZNET - M4
ASCAT and AMSRE-LPRM soil moisture products are in perfect accordance
OZNET - Y3

22. 3) Antecedent wetness conditions at catchment scale
ALZETTE RIVER

23. 3) Antecedent wetness conditions at catchment scale
TIBER RIVER

24. 3) Antecedent wetness conditions at catchment scale
TIBER RIVER
ALZETTE RIVER
+0.06
+0.11
ASCAT outperforms AMSRE-LPRM for the estimation of the Antecedent Wetness Conditions at catchment scale, mainly for Italian catchments.

25. 4) Data assimilation in rainfall-runoff modelling
Niccone
Migianella
137 km2
Central Italy
R ASCAT = 0.94 R AMSRE-LPRM = 0.88
High correlation coefficient between both ASCAT and AMSRE-LPRM satellite products with simulate soil moisture data by MISDc model

26. 4) Data assimilation in rainfall-runoff modelling
Niccone
Migianella
137 km2
Central Italy
NO ASSIMILATION

27. 4) Data assimilation in rainfall-runoff modelling
Niccone
Migianella
137 km2
Central Italy
ASCAT ASSIMILATION

28. 4) Data assimilation in rainfall-runoff modelling
Niccone
Migianella
137 km2
Central Italy
AMSRE-LPRM ASSIMILATION

29. 4) Data assimilation in rainfall-runoff modelling
Niccone
Migianella
137 km2
Central Italy
Performance in runoff prediction as a function of K, Kalman Gain
In terms of error on peak discharge and runoff volume the assimilation of ASCAT soil moisture product provides much better results than AMSRE-LPRM one

30. Conclusions
The application of different procedures for satellite soil moisture products validation provide a better assessment of their reliability
ASCAT and AMSRE-LPRM soil moisture products are found reliable for soil moisture estimation across Europe
ASCAT seems to provide better results, mainly in terms of runoff prediction
Soil moisture data obtained from remote sensing, even though with low spatial resolution, can provide useful information for hydrological applications
The proposed approaches (even improved) are going on to be applied for a larger number of catchments and regions. Who would like to contribute by sharing rainfall-runoff and soil moisture data is welcome 

31. Thanks for your attention
References cited
Questions?
Barbu, A.L. et al. (2011) Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study, Biogeosciences Discuss., 8, , doi: /bgd , 2011.
Beck, H.E. et al. (2010). Improving Curve Number based storm runoff estimates using soil moisture proxies. IEEE JSTAR, 2(4),
Brocca, L., et al. (2009). Soil moisture temporal stability over experimental areas of central Italy. Geoderma, 148 (3-4), , doi: /j.geoderma
Brocca, L., et al. (2009). Antecedent wetness conditions based on ERS scatterometer data. JoH, 364 (1-2), 73-87
Brocca, L., et al. (2009). Assimilation of observed soil moisture data in storm rainfall-runoff modelling. JHE 14,
Brocca, L., et al. (2010). Improving runoff prediction through the assimilation of the ASCAT soil moisture product. HESS, 14,
Brocca, L., et al. (2010). ASCAT Soil Wetness Index validation through in-situ and modeled soil moisture data in central Italy. RSE, 114 (11),
Brocca, L., et al. (2010). Spatial-temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46, W02516, doi: /2009WR008016
Brocca, L., et al. (2011). Coupling a soil water balance and an event-based rainfall-runoff model for flood frequency estimation and real time flood forecasting. HYP, in press, doi: /hyp.8042.
Brocca, L., et al. (2011). Soil moisture spatial-temporal variability at catchment scale. JoH, submitted.
Brocca, L., et al. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. RSE, submitted.
Entekhabi, D. et al. (2010) Performance Metrics for Soil Moisture Retrievals and Application Requirements. JHM, 11, 832–840
Njoku E.G. and Chan S.K. (2006) Vegetation and surface roughness effects on AMSR-E land observations RSE, 100(2), 190–199.
Owe M., et al. (2008) Multi-sensor historical climatology of satellite-derived global land surface moisture. JGR, 113.
Pellarin T., et al. (2008) Using spaceborne surface soil moisture to constrain satellite precipitation estimates over West Africa, GRL, 35, L02813.
Tramblay, Y. et al. (2010). Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling. JoH, 387,
Tramblay, Y. et al. (2011). Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation. NHESS, 11,
FOR MORE INFORMATION URL: URL IRPI:
Thanks for your attention 31