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, 06128 Perugia, Italy Public Research Center - Gabriel Lippmann, Belvaux, Grand-Duchy of Luxemburg Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria luca.brocca@irpi.cnr.it Hydrology

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

SMALL CATCHMENT SCALE ~50 km2 Soil moisture temporal stability SMALL CATCHMENT SCALE ~50 km2 PLOT SCALE 400-9000 m2 CATCHMENT SCALE ~250 km2 Brocca et al., 2009 (GEOD) Brocca et al., 2010 (WRR) Brocca et al., 2011 (JoH, submitted)

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

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

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)

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

3) Antecedent wetness conditions at catchment scale ERS SCATTEROMETER SOIL MOISTURE DATA Italy Tiber River Tevere - PN Assino Niccone 11 catchments 130-4000 km2 Cerfone Genna Caina Brocca et al., 2009 (JoH) 2009 (JHE) Beck et al., 2010 (JSTARS) Tramblay et al., 2010 (JoH), 2011 (NHESS) Australia France Timia Topino Nestore

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

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

Advanced SCATterometer scatterometer (active microwave) C-band (5.7 GHz) VV polarization resolution 50/25 km daily coverage 2006 - ongoing ASCAT Change detection algorithm takes account indirectly for surface roughness and land cover variability http://www.ipf.tuwien.ac.at/radar/dv/ascat/ 11

SOIL MOISTURE PRODUCTS Advanced Microwave Scanning Radiometer radiometer (passive microwave) 6.9 - 10.7 - 18.7 - 36.5 GHz HH and VV polarization 74x43 km (6.9 GHz), 14x8 (36.5 GHz), resampled at ~25 km daily coverage 2002 - 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 10.65 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

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

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

Rainfall-runoff data CENTRAL ITALY LUXEMBOURG Tiber River Data period: 2007-2008 # Catchments: 10 Drainage area: 10-1000 km2 LUXEMBOURG Alzette River Data period: 2007-2010 # Catchments: 10 Drainage area: 90-900 km2

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

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)

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)

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

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

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

3) Antecedent wetness conditions at catchment scale ALZETTE RIVER

3) Antecedent wetness conditions at catchment scale TIBER RIVER

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.

4) Data assimilation in rainfall-runoff modelling Niccone Migianella 137 km2 Central Italy 2007-2010 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

4) Data assimilation in rainfall-runoff modelling Niccone Migianella 137 km2 Central Italy 2007-2010 NO ASSIMILATION

4) Data assimilation in rainfall-runoff modelling Niccone Migianella 137 km2 Central Italy 2007-2010 ASCAT ASSIMILATION

4) Data assimilation in rainfall-runoff modelling Niccone Migianella 137 km2 Central Italy 2007-2010 AMSRE-LPRM ASSIMILATION

4) Data assimilation in rainfall-runoff modelling Niccone Migianella 137 km2 Central Italy 2007-2010 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

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 

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, 1831-1877, doi:10.5194/bgd-8-1831-2011, 2011. Beck, H.E. et al. (2010). Improving Curve Number based storm runoff estimates using soil moisture proxies. IEEE JSTAR, 2(4), 1939-1404. Brocca, L., et al. (2009). Soil moisture temporal stability over experimental areas of central Italy. Geoderma, 148 (3-4), 364-374, doi:10.1016/j.geoderma.2008.11.004. 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, 153-165. Brocca, L., et al. (2010). Improving runoff prediction through the assimilation of the ASCAT soil moisture product. HESS, 14, 1881-1893. Brocca, L., et al. (2010). ASCAT Soil Wetness Index validation through in-situ and modeled soil moisture data in central Italy. RSE, 114 (11), 2745-2755 Brocca, L., et al. (2010). Spatial-temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46, W02516, doi:10.1029/2009WR008016 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:10.1002/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, 176-187. Tramblay, Y. et al. (2011). Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation. NHESS, 11, 157-170. FOR MORE INFORMATION URL:http://www.irpi.cnr.it/it/scheda.php?cognome=BROCCA&nome=Luca URL IRPI:http://www.irpi.cnr.it/it/idrologia_it.htm Thanks for your attention 31