Advances in Remote Sensing, 2016, 5, 93-102 Published Online June 2016 in SciRes. http://www.scirp.org/journal/ars http://dx.doi.org/10.4236/ars.2016.52008
Abstract Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (