Modelling soil organic carbon using vegetation indices across large catchments in eastern Australia.

ABSTRACT

Soil organic carbon (SOC) is an important soil component. However, examining SOC at the large catchment scale is difficult due to the intensive labour requirements. This study examines SOC distribution at large (>500 km2) catchment scales using field-sampled SOC data and remote sensed vegetation indices located in eastern Australia (Krui River catchment - 562 km2; Merriwa River catchment - 808 km2) on grazing land-use basalt soil. The SOC data obtained was compared to digital elevation model (DEM) derived elevation and insolation data, as well as Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) values corresponding to each sample site. These indices were obtained from the MODIS sensor (Terra/Aqua) and Landsat series satellites. Vegetation Indices (VI) captured immediately prior to sampling demonstrated a poor correlation with SOC. The use of multiple, aggregated, prior VI data sets provided a good match with SOC. The strongest match occurred for Landsat 8 EVI, indicating that VIs with higher spatial and spectral resolution, which can account for atmospheric interference, have the potential to produce more accurate SOC mapping (Krui samples in 2006, R2 = 0.31, P < 0.01; Krui sampled in 2014, R2 = 0.41, P < 0.01; Merriwa samples in 2015, R2 = 0.37, P < 0.01). A sensitivity test for both remote sensing platforms demonstrated that the findings were robust. The results demonstrate that VIs are a reliable surrogate for historical vegetation growth in pasture dominated landscapes and therefore soil carbon inputs allowing for mapping of SOC across large catchment scales. Both Landsat and MODIS produced similar results and demonstrate that SOC can be reliably predicted at the large catchment scale and for different catchments in this environment with RMSE range of 0.79 to 1.06. The method and data can be applied globally and provides a new method for environmental assessment.