Near Surface Soil Moisture Estimating using Satellite Data

Researcher: Dleen Al- Shrafany

Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez

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).

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

• 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

Tb=es.T

• Brightness measurements are sensitive to soil moisture through the effects of moisture on the dielectric constant and hence the soil emissivity.

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.

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

• 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

Model Uncertainties

Uncertainties

Surface roughness

h parameter Q parameter

Vegetation canopy

Vegetation optical depth ( )

• 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

0 0.1 0.2 0.3 0.4 0.5 0.60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Q=0

Q=0.1

Q=0.125

Q=0.15

Q=0.16

Q=0.174

Q=0.18

Q=0.2

corr

elati

on b

etw

een

(Δ S

&

Δθ )

h parameter range (0-0.5)

Results

1 35 69 1031371712052392733073413754094434775115455796130

5

10

15

20

25

30

35

40

45

VS

M_

%

1 35 69 1031371712052392733073413754094434775115455796130

5

10

15

20

25

30

35

40

45

0

5

10

15

20

25

VSM_%

flow

• Two-years time series of estimated daily soil moisture is obtained

• Measured flow data is used for comparison

• 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

Validation

0

5

10

15

20

25

30

0

1

2

3

4

5

6

7

8

Δ S

Δθ

0 5 10 15 20 25 300

1

2

3

4

5

6

7

8

f(x) = 0.197690829725161 x + 1.31419267478106R² = 0.740108467285817

Δs

Δθ

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.

Thanks

Dleen.Al-shrafany@bristol.ac.uk