Satellite Soil Moisture in Research Applications

Transcription

1 Satellite Soil Moisture in Research Applications Richard de Jeu Thomas Holmes, Hylke Beck, Guojie Wang, Han Dolman (VUA) Manfred Owe (NASA-GSFC) Albert Van Dijk, Yi Liu (CSIRO) Tim Jupp (U. Exeter) Albrecht Weerts (WL Delft) Microrad 2008: 11 March

2 OUTLINE BACKGROUND EVALUATION AND VALIDATION APPLICATIONS Runoff modeling Climate Studies FUTURE SUMMARY Microwave Radiometry has been used for the remote sensing of Soil Moisture Schmugge et al., JGR 1974 Scientific and operational experiments showed microwave radiometry to be highly effective in solving some practical problems in agriculture, land reclamation, hydrogeology, etc. Shutko., IEEE 1982

3 BACKGROUND Objective To build a reliable long term global surface soil moisture dataset from historical passive microwave satellite observations 29 year of satellite data available +/- daily coverage +/ deg. resolution

4 BACKGROUND Why Soil Moisture? Soil Moisture is a key parameter in land surface processes: e.g. It controls the partitioning of rainfall into runoff It has a role in the prediction of precipitation It influences the land surface energy balance It controls different biogeochemical processes Remote sensing can provide spatially averaged soil moisture

5 BACKGROUND The Land Parameter Retrieval Model (LPRM) 1,2 one layer radiative transfer model (ω-τ model) analytical model for the vegetation optical depth (~vegetation water content) 3 canopy/soil temperature model based on high frequency observations 4,5 1. Owe et al.,ieee 2001; 2. Owe et al., JGR Meesters et al., IEEE 2005; 4. De Jeu and Owe, IJRS Holmes et al., submitted tot RSE 2008

6 BACKGROUND LPRM does not use external calibration sources such as observed soil moisture LPRM is applicable to dual polarized L,C,X, and Ku band microwave observations LPRM provides volumetric soil moisture values in m 3 m -3

7 BACKGROUND: SOIL MOISTURE PRODUCTS SMMR, July 1980 (C-band) ( ) TRMM, July 2004 (X-band) (1997-Now) SM in m3m-3 SSM/I, July 2004 (Ku-band) (1987-Now) AMSR-E, July 2004 (C-band) (2002-Now)

8 BACKGROUND Limitations Surface Soil moisture (top 1-2 cm) Soil moisture quality is a function of vegetation density High frequency soil moisture retrievals are more sensitive to vegetation density No values when the vegetation cover is to dense Microwave observations are sensitive to Radio Frequency Interference (RFI, i.e. radar systems close to airports etc.) No values when the soil is frozen and/or snow is on the ground

9 BACKGROUND Now data available by request, in Summer 2008 available at VU website (ADAGUC project)

10 EVALUATION AND VALIDATION Validation studies with in situ soil moisture data Forested Regions The Netherlands, Germany 1 Agricultural regions USA, Russia, Luxembourg, Germany, France 1,2,3,4,7 Semi Arid Regions Spain, Turkmenistan, Mongolia, Australia 2,5,6 Evaluation with other RS and modeled soil moisture products 1,4,5,6,7,8 Soil Moisture can obtained in regions with a sparse to moderate vegetation cover (vegetation optical depth < 0.6) with an uncertainty of: m 3 m -3 1 Rebel et al., in prep; 2 Owe et al., JGR 2008; 3 De Jeu and Owe, IJRS 2003; 4 Weerts et al., CAHMDA 2008; 5 Draper et al., MODSIM 2007; 6 Wagner et al., HG 2007; 7 Rudiger et al., submitted to JHM; 8 De Jeu et al., submitted to S.Geophys.;

11 APPLICATIONS: Runoff Models Near real time soil moisture in an operational system (FEWS) for the River Rhine (from June till October 2006) Visualization of initial soil moisture conditions > Valuable information for the flood forecaster.

12 APPLICATIONS: Runoff Models Can we use RS data to improve flood Forecasting. Study done by Hylke Beck Use Runoff model for a catchment with A dense measuring network (~ 250 stations in Arkansas River watershed) Model Runoff (STREAM; ~ HBV96) Change field observations with satellite observations. Study Results

13 APPLICATIONS: Runoff Models AMSR-E soil moisture gives an extreme high correlation with STREAM soil moisture Both in the high and low frequencies. AMSR-E soil moisture does not improve the runoff prediction significantly for the Arkansas River

14 APPLICATIONS: Runoff Models The improvement of runoff prediction with additional satellite soil moisture for a series of test sites (Including the river Rhine and Meuse (both in Europe) Murrumbidgee (Australia), Arkansas and Red River (both in the US)) is still small. Data assimilation is a difficult task: Model issues (what is the best model to use) Layer issues (0-2 cm satellite data versus model bucket layer) Soil Moisture has an effect on other important parameters within the runoff model; i.e. ET and P. A better integration might improve the runoff prediction

15 APPLICATIONS: Climate Studies Detection of strong El Niño signals in Soil Moisture dataset 1 Empirical Orthogonal Functions on 9 Year of TRMM Satellite Soil Moisture > Reveals - Dominant time series - Location of where these time series occur 1 Liu et al., GRL 2007

16 APPLICATIONS: Climate Studies Strong El Niño connection (r 2 =0.90) in Spring Soil Moisture (SON) in Eastern Australia Normalized time series of first EOF The first EOF in spring (SON) (red = dominant)

17 APPLICATIONS: Climate Studies Hot spot detection of land atmosphere interaction using bivariate regression models 1 INPUT: Satellite Soil Moisture (SM) and Precipitation (P) MODEL: P(t) = a * P(t-1) + b * SM(t-1) + ε(t) SM(t) = c * SM(t-1) + d * P(t-1) + η(t) 1 T. Jupp et al., in prep

18 APPLICATIONS: Climate Studies Soil Moisture Hotspots from GCM studies (Koster et al., 2004) Soil Moisture Hotspots according to our analysis with satellite observations

19 FUTURE Data Assimilation in Runoff models (Delft Hydraulics, CSIRO) Time series analysis on 30 year soil moisture data Further improvement on soil moisture feedback studies (EU WATCH) Extension of dataset with Navy satellite Windsat (C-band from Now) and new satellite missions like SMOS (L-band, 2008), Aquarius (L-band, 2011), AMSR II (C-band, 2013), GPM (X-band, 2013), SMAP (Lband, 20??) Integration of ERS soil moisture with AMSR-E soil moisture (ITC PhD project L. Dente)

20 SUMMARY A Historical dataset of satellite retrieved soil moisture is presented Satellite soil moisture coincides with field observations and is comparable to modeled soil moisture at global scale The soil moisture product is used in an operational flood forecasting system Data Assimilation studies are conducted for the improvement of flood forecasting El Niño signals can be revealed from the sm dataset Soil Moisture Hotspots can be detected with satellite observations