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Exploring the potential of AMSU-based soil wetnessindices for the description of soil watercontent over the experimental b...
OUTLINEThe role of soil moisture;Field campaign and data;Methods and Techniques;Discussion on the ability of AMSU-based in...
Soil MoistureSoil moisture controls the partitioning of rainfall into runoff and infiltration.Moreover, it influences the ...
Geographical position              of the experimental           Description of the soil-              basin “Fiumarella o...
Monitoring campaign of soil moisture in situ              The in-situ SM has been measured using a portable Time          ...
Monitoring campaign of soil moisture in situ
Advanced Microwave Scanning Unit                                      AMSU is one of the sensors aboard                   ...
AMSU-based soil wetness indicesSurface Wetness Index                   SWI (x,y,t) = BT89 – BT23In order to reduce effects...
SM measured by TDR and the AMSU SWIComparison betweenin situ SM measuredby TDR and the AMSUSWI at the five sitesstudied he...
Description of the hydrological model DREAM  "   Continuous simulation is performed by means of an innovative      hydrolo...
Soil moisture maps by DREAM 1.1
Comparison between the simulated and measured SM                                     Manfreda et al. (HESSD 2011)
Soil moisture vs AMSU_SWI and SWI*The AMSU-based SWI and the SWVI index have been used as rough data, but alsofiltered in ...
Temporal dynamics of soil moisture and AMSU SWI*                                       Manfreda et al. (HESS 2011)
Soil moisture vs AMSU_SWVI and SWVI*
Soil moisture vs AMSU_SWVIwe adopted thresholdvalues of SWVI rangingfrom 0.5 up to 3.5 observingan increase of thecorrelat...
Conclusion •    The analysis over different land-soil units provided an interesting insight on      the temporal dynamics ...
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Exploring the Potential of AMSU-based Soil Wetness Indices

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Talk given at the 3rd Meeting on Meteorology and Climatology of the Mediterranean entitled "Exploring the potential of AMSU-based soil wetness indices for the Description of Soil Water content over the experimental basin of Corleto"

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Exploring the Potential of AMSU-based Soil Wetness Indices

  1. 1. Exploring the potential of AMSU-based soil wetnessindices for the description of soil watercontent over the experimental basin of Corleto M. Di Leo1, V. Iacobellis3 , T. Lacava2, S. Manfreda1, M.R. Margiotta1, B. Onorati1, N. Pergola1,2, V. Tramutoli1,2(1) Dipartimento di Ingegneria e Fisica dell’Ambiente, Università degli Studi della Basilicata, Italy(2) Institute of Methodologies for Environmental Analysis (IMAA), CNR, 85050 Tito Scalo(Pz), Italy(3) Dipartimento di Ingegneria delle Acque e di Chimica, Politecnico di Bari, Italy
  2. 2. OUTLINEThe role of soil moisture;Field campaign and data;Methods and Techniques;Discussion on the ability of AMSU-based indices;Conclusion.
  3. 3. Soil MoistureSoil moisture controls the partitioning of rainfall into runoff and infiltration.Moreover, it influences the partitioning of the incoming energy into latentand sensible heat components. Soil moisture, thus provides a key linkbetween the water and energy balances.•  Numerical Weather Forecasting•  Climate Prediction•  Shallow Landslide Forecasting•  Agriculture and Plant Production•  Flood Prediction and Forecasting
  4. 4. Geographical position of the experimental Description of the soil- basin “Fiumarella of landscapedel suolo.shp basin Uso units of the Corleto” arbusti Shrub bosco Forest bosco alt Tall Forest erba Herbaceous seminativ Crop suolo soil Bare spo NFigure 1. Geographical W Eposition of Agri basin S rain-gauge meteo-hydrological station hydrometer and rain-gauge soil moisture measurements
  5. 5. Monitoring campaign of soil moisture in situ The in-situ SM has been measured using a portable Time Domain Reflectometer (TDR) during a three months campaign taking 48 point measurements. The TDR campaign started from the February 2010 up to the May 2010. During this period, both wet and dry conditions were observed. Landuse Uso del suolo.shp arbusti Shrub bosco Forest bosco alt Tall Forest erba Herbaceous seminativ Crop 1 8 7 suolo spo Bare soil 1 2 3 4 5 11 6 6 9 10 7 2 9 8 8 7 9 3 6 10 5 1 2 8 7 11 4 N 12 2 9 6 5 3 13 W E 4 14 2 15 5 S 3 4 1 3 16 1
  6. 6. Monitoring campaign of soil moisture in situ
  7. 7. Advanced Microwave Scanning Unit AMSU is one of the sensors aboard NOAA satellites since 1998 (NOAA 15). It consists of two modules: AMSU-A and AMSU-B Channel AMSU-A (48 km) Instrument (GHz) Component Advanced Microwave Sounding Unit-A 1 23.8 A2 2 31.4 A2 3 50.3 A1-2 4 52.8 A1-2 AMSU-A is now operational on: 5 53.6 A1-2 6 54.4 A1-1Sat Launch LTAN At least 5-6 sensor passes every 7 54.9 A1-1 date 8 55.5 day at mid-latitudes!!!! A1-2 9 57.2 A1-1EOS-Aqua 04/05/2002 13:30:00 10 57.29±217 A1-1 11 57.29±322±0.48 A1-1NOAA 18 05/20/2005 14:00:00 12 57.29±322±0.22 A1-1 13 57.29±322±0.10 A1-1MetopA 19/10/2006 21:31:00 14 57.29±322±0.045 A1-1NOAA 19 06/02/2009 13:38:00 15 89.0 A1-1
  8. 8. AMSU-based soil wetness indicesSurface Wetness Index SWI (x,y,t) = BT89 – BT23In order to reduce effects related to the presence ofvegetation and permanent water within the pixel, theRobust Satellite techniques (RST–Tramutoli, 1998) hasbeen implemented on SWI.Soil Wetness Variation IndexLacava et al. (2005, 2010 - RSE) SWI ( x, y, t ) − µ SWI ( x, y ) SWVI ( x, y, t ) = 0.5-2 cm σ SWI ( x, y ) 30 cmExponential filterWagner et al. (1999 - RSE)Brocca et al. (2009) * * [ * X n = X n −1 + K n X (t n ) − X n −1 ] X(tn): surface satellite soil moisturewhere: K n −1 data: SWI or SWVI Kn = ⎛ t −t ⎞ X*n: profile satellite soil moisture data: SWI* or SWVI* −⎜ n n−1 ⎟ ⎝ T ⎠ t: time K n −1 + e tn: acquisition time of X(tn) Kn: gain T: characteristic time length
  9. 9. SM measured by TDR and the AMSU SWIComparison betweenin situ SM measuredby TDR and the AMSUSWI at the five sitesstudied herein andalso with the meanvalue of SM obtainedexcluding thesite at the outlet.
  10. 10. Description of the hydrological model DREAM "   Continuous simulation is performed by means of an innovative hydrological model, which was introduced by Manfreda et al. (2005). "   The model operates at two time scales one daily and the second hourly. The first module, now called D-DREAM, runs as long as a daily rainfall greater than s [mm/day] does not occur. In such a case the simulation is switched to hourly to reproduce flood events, and a different module, called H-DREAM and working at the hourly scale, is used. "  The combination of the two modules constitutes the proposed model, which has been finally called DREAM (acronym of Distributed model for Runoff Et Antecedent soil Moisture simulation). Manfreda et al., Adv. in Geosciences, 2005. Fiorentino et al., Adv. Water Resour., 2007
  11. 11. Soil moisture maps by DREAM 1.1
  12. 12. Comparison between the simulated and measured SM Manfreda et al. (HESSD 2011)
  13. 13. Soil moisture vs AMSU_SWI and SWI*The AMSU-based SWI and the SWVI index have been used as rough data, but alsofiltered in the form of SWI* and SWVI* in order to account for the discrepancyexisting between the skin satellite measurement, that obviously produces a timeseries with higher temporal variability due to the control volume, and themeasurements that are averaged over 30 cm of depth.
  14. 14. Temporal dynamics of soil moisture and AMSU SWI* Manfreda et al. (HESS 2011)
  15. 15. Soil moisture vs AMSU_SWVI and SWVI*
  16. 16. Soil moisture vs AMSU_SWVIwe adopted thresholdvalues of SWVI rangingfrom 0.5 up to 3.5 observingan increase of thecorrelation with thethreshold, as shown by theresults reported in Table 4.In particular, correlationcoefficient systematicallyincreases as far asthreshold increases up to avalue of 0.81. Manfreda et al. (HESS 2011)
  17. 17. Conclusion •  The analysis over different land-soil units provided an interesting insight on the temporal dynamics of soil moisture that is significantly influenced by land cover. In particular, we observed a good agreement between measured or modeled SM with remotely sensed data in presence of shallow rooted vegetation meaning that the comparison between these data becomes more reliable when they refer to similar control volume as well as to a less vegetated areas. •  Results of the field campaign have provided a preliminary description regarding the ability of SWI to describe SM fluctuations. In spite of the short period of observation, a certain degree of correlation between SWI and the in-situ SM measurements was observed. •  Over the larger temporal window where the simulated SM have been compared with the remotely sensed data, it is particularly clear how well SWI may describes the SM seasonal fluctuations, especially after the application of a low pass filter. •  It was found that SWVI can capture the SM variations with a precision that increases at the higher values of SWVI and may represent a good strategy to monitor the SM state for flood forecasting purposes.
  18. 18. Thanks...

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