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https://hdl.handle.net/1959.11/29171
Title: | Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level | Contributor(s): | Rahman, Muhammad Moshiur (author) ; Robson, Andrew (author) | Publication Date: | 2020-04-21 | Open Access: | Yes | DOI: | 10.3390/rs12081313 | Handle Link: | https://hdl.handle.net/1959.11/29171 | Abstract: | Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons' harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named 'bins'. Cloud free (<20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each 'bin' was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha-1) achieved for the respective bin over the five growing years, producing strong correlations (R2 = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha-1) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R2 = 0.87 and RMSE = 11.33 (t·ha-1)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach. | Publication Type: | Journal Article | Source of Publication: | Remote Sensing, 12(8), p. 1-15 | Publisher: | MDPI AG | Place of Publication: | Switzerland | ISSN: | 2072-4292 | Fields of Research (FoR) 2008: | 070104 Agricultural Spatial Analysis and Modelling 080106 Image Processing 070301 Agro-ecosystem Function and Prediction |
Fields of Research (FoR) 2020: | 300206 Agricultural spatial analysis and modelling 460306 Image processing 300402 Agro-ecosystem function and prediction |
Socio-Economic Objective (SEO) 2008: | 820603 Sugar Cane (Cut for Crushing) 820304 Sugar 810106 Logistics |
Socio-Economic Objective (SEO) 2020: | 260403 Sugar cane (cut for crushing) 260607 Sugar 140107 Logistics |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article School of Science and Technology |
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openpublished/IntegratingRahmanRobson2020JournalArticle.pdf | Published version | 5.64 MB | Adobe PDF Download Adobe | View/Open |
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