Using high resolution landscape and soils data to understand spatiotemporal variability in net pasture productivity as derived from low spatial resolution remote sensing

Abstract

Spatial variability in pasture production, especially at within-field scales challenges land managers seeking to optimise management to increase the overall productivity of their grazing operations. In this study, a relatively coarse, remote, spatial-based measure of net primary production on a farming landscape of predominately tall fescue ('Festuca arundinacea') pasture was derived using accumulative NDVI from weekly MODIS satellite imagery. This data was evaluated against two, higher spatial resolution, on-ground descriptors often linked with productivity; namely soil texture, via a electromagnetic induction instrument (EM38) and elevation data. Net primary production was observed to be larger within the lower slopes and valley floors of paddocks; the same areas most likely associated with higher levels of long term soil moisture. The implications of using relatively low spatial resolution remote sensing products (100-200 m) to monitor and forecast pasture production, and avenues for increasing the spatial resolution of such products using third-party, on-ground datasets like EM38 are also discussed.