Exploring the potential of AMSU-based soil wetness indices for the description of soil water content 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

The role of soil moisture; Field campaign and data; Methods and Techniques; Discussion on the ability of AMSU-based indices; Conclusion.

OUTLINE

Soil moisture controls the partitioning of rainfall into runoff and infiltration. Moreover, it influences the partitioning of the incoming energy into latent and sensible heat components. Soil moisture, thus provides a key link between the water and energy balances.

•  Numerical Weather Forecasting

•  Climate Prediction

•  Shallow Landslide Forecasting

•  Agriculture and Plant Production

•  Flood Prediction and Forecasting

Soil Moisture

Figure 1. Geographical position of Agri basinFigure 1. Geographical position of Agri basin

Geographical position of the experimental basin “Fiumarella of Corleto”

Uso del suolo.shparbustiboscobosco alterbaseminativsuolo spo

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Description of the soil-landscape units of the basin

rain-gauge

meteo-hydrological station hydrometer and rain-gauge

soil moisture measurements

Shrub Forest Tall Forest Herbaceous Crop Bare soil

Uso del suolo.shparbustiboscobosco alterbaseminativsuolo spo

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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.

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Monitoring campaign of soil moisture in situ

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Landuse

Shrub Forest Tall Forest Herbaceous Crop Bare soil

Monitoring campaign of soil moisture in situ

Advanced Microwave Scanning Unit

Channel (GHz)

AMSU-A (48 km) Advanced Microwave Sounding

Unit-A

Instrument Component

1 23.8 A2 2 31.4 A2 3 50.3 A1-2 4 52.8 A1-2 5 53.6 A1-2 6 54.4 A1-1 7 54.9 A1-1 8 55.5 A1-2 9 57.2 A1-1 10 57.29±217 A1-1 11 57.29±322±0.48 A1-1 12 57.29±322±0.22 A1-1 13 57.29±322±0.10 A1-1 14 57.29±322±0.045 A1-1 15 89.0 A1-1

Sat Launch date

LTAN

EOS-Aqua 04/05/2002 13:30:00 NOAA 18 05/20/2005 14:00:00 MetopA 19/10/2006 21:31:00 NOAA 19 06/02/2009 13:38:00

AMSU-A is now operational on:

AMSU is one of the sensors aboard NOAA satellites since 1998 (NOAA 15). It consists of two modules: AMSU-A and AMSU-B

At least 5-6 sensor passes every day at mid-latitudes!!!!

0.5-2 cm

30 cm

AMSU-based soil wetness indices

Surface Wetness Index

Soil Wetness Variation Index Lacava et al. (2005, 2010 - RSE)

SWI (x,y,t) = BT89 – BT23

),(),(),,(),,(

yxyxtyxSWItyxSWVI

SWI

SWI

σµ−

=

In order to reduce effects related to the presence of vegetation and permanent water within the pixel, the Robust Satellite techniques (RST–Tramutoli, 1998) has been implemented on SWI.

Exponential filter Wagner et al. (1999 - RSE) Brocca et al. (2009) [ ]*

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* )( −− −+= nnnnn XtXKXX

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X(tn): surface satellite soil moisture data: SWI or SWVI

X*n: profile satellite soil moisture data: SWI* or SWVI* t: time tn: acquisition time of X(tn) Kn: gain T: characteristic time length

SM measured by TDR and the AMSU SWI

Comparison between in situ SM measured by TDR and the AMSU SWI at the five sites studied herein and also with the mean value of SM obtained excluding the site at the outlet.

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

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 also filtered in the form of SWI* and SWVI* in order to account for the discrepancy existing between the skin satellite measurement, that obviously produces a time series with higher temporal variability due to the control volume, and the measurements that are averaged over 30 cm of depth.

Temporal dynamics of soil moisture and AMSU SWI*

Manfreda et al. (HESS 2011)

Soil moisture vs AMSU_SWVI and SWVI*

Manfreda et al. (HESS 2011)

Soil moisture vs AMSU_SWVI

we adopted threshold values of SWVI ranging from 0.5 up to 3.5 observing an increase of the correlation with the threshold, as shown by the results reported in Table 4. In particular, correlation coefficient systematically increases as far as threshold increases up to a value of 0.81.

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.

Thanks...