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Robson, A., Rahman, M.M., Falzon, G., Verma, N.K., Ohansen, K.J., Robinson, N., Lakshmanan, P., Salter, B. and Skocaj, D. (2016) Evaluating Remote Sensing Technologies for Improved Yield Forecasting and for the Measurement of Foliar Nitrogen Concentration in Sugarcane. 38th Australian Society of Sugar Cane Technologists, Mackay, in press.

has been cited by the following article:

• TITLE: A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region

  AUTHORS: Muhammad Moshiur Rahman, Andrew J. Robson

  KEYWORDS: Sugarcane, Yield Forecasting, Landsat, GNDVI

  JOURNAL NAME: Advances in Remote Sensing, Vol.5 No.2, June 6, 2016

  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 (st (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R2 = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.