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

Muhammad Moshiur Rahman,Andrew J Robson

doi: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 (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (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.

Highlights

Some infrequent variations in green normalized difference vegetation index (GNDVI) values are related to the phenology of the crop and climatic conditions which was reported by Bégué et al [23] as well
This study identified how time series Landsat imagery could be effectively used for plotting the historic temporal pattern of sugarcane crop production in the Bundaberg growing region
In terms of yield forecasting, the maximum crop vigouror GNDVI value was historically achieved at 145 days from planting i.e. early April

Summary

Introduction

Accurate and timely prediction of yield offers the global sugar industry improved efficiency and profitability by supporting decision making processes such as crop harvesting scheduling, marketing, milling and forward selling strategies. The in-season estimation of yield is undertaken using visual or destructive sampling techniques by either growers or mill funded productivity officers. This method is labour intensive with accuracies influenced by varied seasonal climatic conditions, crop age due to an extended harvest period and human error. Remote sensing technologies have been evaluated in recent years as a more accurate and cost effective method of sugarcane yield prediction [4]

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