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Near Surface Soil Moisture Estimating using Satellite Data

Near Surface Soil Moisture Estimating using Satellite Data. Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez. Introduction.

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Near Surface Soil Moisture Estimating using Satellite Data

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  1. Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez

  2. Introduction • Near-surface soil moisture is defined as the water content in the top few centimetres of soil surface which is actually considered as a thin soil surface layer. • It is widely considered as a key variable in many disciplines, including hydrology, agriculture, meteorology and climate change (Walker, 1999). • It is considered as a good response of the land surface to atmospheric forcing through the partitioning of rainfall into runoff and infiltration (Lakshimi et al,1997). • Soil moisture is a highly variable parameter both spatially and temporally due to the heterogeneity of soil properties, topography, land cover, evapotranspiration and precipitation. • As a result, soil moisture is often somewhat difficult to measure accurately in both time and space, especially at large scales. (Owe et al, 2001; Engman, 1991).

  3. Advanced Microwave Scanning RadiometerAMSR-E • AMSR-E is a twelve-channel, six-frequency, passive-microwave radiometer system. • measures horizontally and vertically polarized brightness temperatures at (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) • Spatial resolution varies from 5.4 Km at 89 GHz frequency to 56 Km at 6.9 GHz frequency. • Orbit altitude is 705 km from the earth surface • Swath width is 1445 km

  4. Radiometers records naturally thermal emission from the ground surface at microwave wavelengths (0.75-100 cm) in vertical and horizontal polarization. The recorded emission is expressed as brightness temperature • Brightness measurements are sensitive to soil moisture through the effects of moisture on the dielectric constant and hence the soil emissivity. Tb=es.T

  5. Land Parameter Retrieval Model / LPRM • LPRM is used to convert the observed brightness temperatures into the volumetric near surface soil moisture (Owe et al, 2008) • LPRM links surface geophysical variables such as the soil moisture and vegetation water content to the observed brightness temperatures • A first-order of radiative transfer theory is the bases of the LPRM • Contributions from the soil, vegetation and atmosphere are included and is given as a radiative transfer equation.

  6. Radiative Transfer Equation • Radiative transfer equation is explain the relationship between the surface parameters and the microwave brightness temperaturesTb (Njoku et al, 2003) Tb = Г(er Ts) + (1 - ω) Tc (1- Γ) + (1- er)(1 - ω) Tc (1 - Г) Г • Γ: transmissivity; ω: vegetation single scattering albedo • er: soil emissivity; Ts: single surface temperature

  7. The study area Brue catchment is considered as one of the UK rural area • It is mainly pasture land with some woodland areas in the higher eastern section, It has a drainage area of 135 square Kilometres • It is characterized as a non-extremely complex topography, located in Somerset, South West of England

  8. Model Uncertainties

  9. An analytical approach is used for calculating vegetation optical depth from the Microwave Polarization Difference Index (MPDI) and the dielectric constant of the soil. • h and Q are calibrated empirically using Water Balance Equation as a new approach. • The difference in the water storage (Δs) for selected flow events across two years is calculated first from: P = Q + E + ΔS • Then the changing in the volumetric soil moisture (Δθ) which is estimated from Microwave Radiative Transfer Model (MRTM) for those selected flow events is calculated from: Δθ= VSM 2 – VSM 1

  10. Results • Two-years time series of estimated daily soil moisture is obtained • Measured flow data is used for comparison

  11. Validation • An integral hydrological data set provided by the UK NERC HYREX Project is used for the validation purpose. • change in the water storage for a significant flow events Synchronized with the satellite measurements is worked out, then compared with the changing in the vsm for those selected events Δθ Δs

  12. Conclusions • A first-order of radiative transfer model is developed for soil moisture estimating using the two-years night-time of AMSR-E brightness temperatures at 6.9 GHz data set taken for Brue catchment study area. • The vegetation optical depth parameter was calibrated a priori in order to separate the surface roughness and vegetation effects. • The surface roughness parameter in terms of the h and Q parameters were empirically calibrated using the water balance equation. • Two-years surface soil moisture values have been obtained and the results well compared with measured flow data and good correlation between Δs and Δθ is worked out.

  13. Thanks Dleen.Al-shrafany@bristol.ac.uk

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