PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION
Keywords: Sugarcane, Time series, Image processing, Vegetation indices, Prediction, Landsat
Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.