Prediction of Vegetation Parameters Using Reflectance Measurements and Soil Moisture in Oklahoma Grassland.

Grasslands and grazing systems are dominant landscape features in the United States Southern Great Plains (SGP) and in similar climatic regions around the world. Dynamic Grassland vegetation parameters such as biomass and Fuel Moisture Content (FMC) are important for management and for research on carbon cycling, fire regimes, and hydrology. The variable climate of the SGP creates large year-to-year variability in grassland vegetation productivity and FMC. Remote sensing using spectral reflectance has shown potential for large scale, non-destructive monitoring of these vegetation dynamics. Also, vegetation parameters in the grasslands of the SGP are strongly influenced by soil moisture, but the potential value of soil moisture data in forecasting vegetation dynamics has not widely explored in these systems, in part because such data were not readily available. However, that situation is changing with the emergence of numerous soil moisture monitoring networks and increasingly advanced soil moisture satellites. Currently, there is a clear need to improve our ability to use soil moisture and reflectance data to monitor and forecast grassland vegetation parameters for the benefit of ranchers, extension workers, farmers, scientists, fire marshals, and government personnel. The objectives of the study are 1)  to understand the relationships between vegetation biomass, FMC, spectral reflectance and soil moisture in grasslands surrounding the Marena, Oklahoma, In Situ Sensor Testbed and 2) to determine the accuracy with which a handheld spectral reflectance sensor and soil moisture can be used for in situ nondestructive vegetation parameter estimating and forecasting.