near surface soil moisture estimating using satellite data researcher: dleen al- shrafany...
TRANSCRIPT
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.
