Spatial pattern of soil organic carbon acquired from hyperspectral imagery at reynolds creek critical zone observatory (RC-CZO)

Soil Organic Carbon (SOC) is a key soil property and is important for understanding carbon storage and soil-vegetation dynamics. Hyperspectral imagery (imaging spectroscopy) providing detailed spectral signatures of vegetation and soil make it possible to continuously map SOC content over a watershed scale. In this paper, the Next Generation Airborne Visible / Infrared Imaging Spectrometer (AVTPJSng) was used with an unmixing algorithm, the Multiple Endmember Spectral Mixture Analysis, to differentiate fractional cover of healthy vegetation, stressed vegetation and soil at the Reynolds Creek Critical Zone Observatory (PC-CZO). The fractional cover information was used to remove noisy spectra and the resulting residual spectra were used to predict SOC by Partial Least Squares Regression (PLSP). The results showed that the root mean standard error and mean bias of the predicted SOC (%) are 0.75 and 2.4, respectively. We found the best relationship between SOC and spectra after filtering out the influence of green vegetation from mixed spectra. The resulting residual, spectra comprised of stressed vegetation and soil, contained enough information for mapping SOC distribution within the shrub dominated regions of the watershed. This may provide a method to better understand the interaction of soil and vegetation in semiarid ecosystems.