Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/19870
Title: A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region
Contributor(s): Rahman, Muhammad Moshiur  (author)orcid ; Robson, Andrew  (author)orcid 
Publication Date: 2016
Open Access: Yes
DOI: 10.4236/ars.2016.52008Open Access Link
Handle Link: https://hdl.handle.net/1959.11/19870
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.
Publication Type: Journal Article
Source of Publication: Advances in Remote Sensing, 5(2), p. 93-102
Publisher: Scientific Research Publishing, Inc
Place of Publication: United States of America
ISSN: 2169-2688
2169-267X
Fields of Research (FoR) 2008: 070104 Agricultural Spatial Analysis and Modelling
Fields of Research (FoR) 2020: 300206 Agricultural spatial analysis and modelling
Socio-Economic Objective (SEO) 2008: 820304 Sugar
Socio-Economic Objective (SEO) 2020: 260607 Sugar
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article

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