Development of a Sugarcane Plantation Classification Model Using Sentinel-1 and Sentinel-2 Satellite Imagery
Keywords:
Sugarcane, Sentinel-1/2 Imagery, Red-Edge Band, Google Earth Engine, Random Forest AlgorithmAbstract
Phu Khiao District, Chaiyaphum Province, is a significant sugarcane-growing area in northeastern Thailand, both in terms of cultivation area and production volume serving as the primary raw material for the sugar industry. However, sugarcane area mapping still relies largely on field surveys, leading to limitations in spatial detail and temporal consistency. This study develops a sugarcane plantation classification model using a Random Forest algorithm with 21 features comprising 13 Sentinel-2 spectral bands, 6 vegetation indices, and VV and VH polarizations from Sentinel-1, processed on Google Earth Engine. A total of 400 training polygons (54,304 pixels) and 400 independent field validation points were used. Four feature datasets were compared: (1) RGB optical data, (2) vegetation indices (VIs), (3) vegetation indices combined with SAR data (VIs + SAR), and (4) SAR data alone. The results indicate that integrating vegetation indices with SAR data (VIs + SAR) yields the best performance, achieving an Overall Accuracy of 88.50%, a Kappa Coefficient of 81.90%, and a Producer's Accuracy for sugarcane of 94.0%. The Red-edge indices, particularly CI red edge and SAREI, effectively reduced spectral overlap between sugarcane and other vegetation types, as sugarcane prior to harvest exhibits high and uniform chlorophyll content, resulting in a narrow interquartile range that is clearly distinguishable from other crops. These findings suggest that multi-sensor integration with Red-edge indices is effective for sugarcane plantation classification and can serve as a prototype for large-scale sugarcane monitoring.
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