water conference poster-2013

Preliminary Results and Discussion

RELATIONSHIP BETWEEN SOIL MOISTURE AND VEGETATION WATER

CONTENT IN AN OKLAHOMA GRASSLAND Sonisa Sharma* and Tyson E. Ochsner

sonisa@okstate.edu, 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