Measuring spatial variability of crops and soils at sub-paddock scale using remote sensing technologies

Certain ground-based, airborne and space-borne sensors provide useful information to capture paddock scale variability. The combination of these sensors forms a suite of rapid mapping tools of crop and soil variability. The study exemplifies a dryland agricultural system of southern Australia, which is important for crops and animal production. This paper addresses the two key issues of precision farming: (1) Seasonal crop variability, and (2) Soil variability as contributor to crop variability. Crop yield has been derived from multi-temporal and multi-spectral Landsat data. The soil properties have been mapped using ground-based gamma ray spectrometers and the electrical conductivity measures. The results of image-derived yield were tested against the available yield data sets from the crop harvester. An existing algorithm of paddock zoning has been tested and modified to adequately map the seasonal crop variability and identify zones. This paper sets the scenario to explore how much seasonal yield variability can be attributed to the known soil properties so that the influence of other factors including, weather and management practices, can be studied further