Dhahi Al-Shammari1, Thomas F.A. Bishop1, Ignacio Fuentes1, Patrick Filippi1
1 Sydney Institute of Agriculture, School of Life & Environmental Science, The University of Sydney, Central Ave, Eveleigh, Sydney, New South Wales, 2015
A phenology-based crop type classification was carried out to map cotton in New South Wales and Queensland, Australia. The workflow was implemented in Google Earth Engine (GEE) platform as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time-series of images were generated from Landsat 8 Surface Reflectance (L8SR) data. The Normalised Difference Vegetation Index (NDVI)time series was calculated from satellite specifically Landsat imagery and a Harmonic Model (HM) was fitted to it to produce the Harmonised NDVI (H-NDVI). Phase and amplitude images were generated to visualise active cotton in the targeted fields. These images were used as predictor variables with H-NDVI and other raw bands in the Random Forest (RF) classification model. The results of RF proved that both phase and amplitude increased the accuracy of the classification. Moreover, cotton classification accuracy increased as the season progressed.