water conference poster-2013
TRANSCRIPT
Preliminary Results and Discussion
RELATIONSHIP BETWEEN SOIL MOISTURE AND VEGETATION WATER
CONTENT IN AN OKLAHOMA GRASSLAND Sonisa Sharma* and Tyson E. Ochsner
[email protected], Oklahoma State University, Plant and Soil Sciences, Ag Hall 368, Stillwater, OK 74078
Future work
Acknowledgement
This research is supported by the USDA Agriculture and Food
Research Initiative competitive grants program.
Figure 1. Average vegetation water content for each site from year 2010-2013
• Vegetation water content (VWC) is one of the most
important parameters for the retrieval of soil moisture
from active and passive microwave remote sensing
(Jackson et al., 1982, 2004).
• The sensitivity of the microwave brightness
temperature to soil moisture decreases as vegetation
water content increases (Wen et al., 2005). Bindish and
Barros (2002) found that an error of 1 kg m-2 in VWC
estimation could result in a relatively large error of 0.1
m3 m-3 in soil moisture for dry soil. But there are few in
situ datasets suitable for clarifying the relationship
between VWC and soil moisture.
• Objective. Determine the relationships between VWC
and soil moisture in grassland near Marena, Oklahoma
from 2010 - 2013.
Introduction
Figure 2. Average soil moisture for each site from year 2010-2013
Materials and methods
The field site at Marena, Oklahoma was selected
due to the availability of in-situ observation of
surface soil moisture and vegetation water content
along with other soil property data.
Surface soil moisture and vegetation water content
were measured in grassland near Marena,
Oklahoma from 2010-2013.
Seven vegetation samples from each of three
monitoring sites on each sampling dates were
taken. Each sample represented a 30 cm x 30 cm
area.
Wet samples were weighed then dried at 50ᵒ C.
Vegetation water content was calculated based on
the difference between fresh weight and dry weight.
Soil moisture from 0-6 cm was measured using a
Theta probe (Figure 4).
Average vegetation water content (VWC) and soil
moisture for each site for each sampling date were
calculated and plotted in MATLAB.
Similarly, we created a scatterplot of VWC versus
soil moisture. A linear regression equation was
calculated and R2 and p-value were reported.
• Soil moisture and VWC were higher in 2010 than in 2011
or 2012. Both soil moisture and VWC were often higher at
site D than at sites A or C.
• The drought in 2011 and 2012 was likely the dominant
factors contribution to low VWC in those years.
• On several dates, the soil was too dry to insert the soil
moisture sensor.
• With increase in soil moisture, there was an increase in
VWC with r2 = 0.2 , the equation is y = 0.0336+0.0149*x
and this equation is significant (p < 0.05).
• The observed data suggests that there is a positive
correlation between VWC and soil moisture at Marena.
References
• Explore remote sensing approaches for vegetation water
content such as Normalized Difference Vegetation Index
(NDVI) and Normalized Difference Water Index (NDWI) from
Landsat-8.
• Separate live and dead vegetation and evaluate the
influence of soil moisture on the water content of live
vegetation.
• Bindish, R., & Barros,A.P.(2002). Sub-pixel variability of
remotely sensed soil moisture: An inter-comparision study of
SAR and ESTAR. IEEE transactions on Geosciences and
Remote Sensing,40(12),326-337
• Jackson, T.J., Schmugge, T.J., & Wang,J.R.(1982). Passive
microwave remote sensing of soil moisture under vegetation
canopies. Water Resources Research,18,1137-1142.
• Jackson,T.J., Chen,D., Cosh,M., Li,F., Andreson,M.,
Walthal,C., et al.(2004). Vegetation water content mapping
using Landsat data derived normalized difference water index
for corn and soybean. Remote Sensing of Environment,
92,475-482
• Wen, Jun, Thomas J. Jackson, Rajat Bindlish, Ann Y. Hsu, Z.
Bob Su, 2005: Retrieval of Soil Moisture and Vegetation Water
Content Using SSM/I Data over a Corn and Soybean Region.
J. Hydrometeor, 6, 854–863.
Figure 4: Theta Probe used to measure soil moisture.
Conclusion
• Grassland vegetation water content varied by 1 kg m-2
over three years , enough to contribute significant
uncertainty to soil moisture remote sensing.
• Grassland vegetation water content was positively
related to 0-6 cm soil moisture as expected.
• At this measurement scale, temporal variability of
vegetation water content and soil moisture appears to
be greater than spatial variability.
Figure 3. Relation between vegetation water content and 0-6cm soil moisture
at Marena from year 2010 - 2013