Identification and yield prediction of sugarcane in the south-central part of Guangxi Zhuang Autonomous Region, China based on multi-source satellite-based remote sensing images
LUO Wei1(), LI Xiuhua1,2(), QIN Huojuan1, ZHANG Muqing2, WANG Zeping3, JIANG Zhuhui4
1. School of Electrical Engineering, Guangxi University, Nanning 530004, China 2. Guangxi Key Laboratory of Sugarcane Biology, Guangxi University, Nanning 530004, China 3. Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China 4. Guangxi Sugar Industry Group, Nanning 530022, China
This study aims to solve the challenges faced in the prediction of sugarcane yield in Guangxi, such as varied crops, complex investigations in the sugarcane planting areas, and difficult acquisition of remote-sensing images caused by the changeable weather. To this end, an improved semantic segmentation algorithm based on Sentinel-2 images was proposed to automatically identify sugarcane planting areas, and an extraction method for representative spectral features was developed to build a sugarcane yield prediction model based on multi-temporal Sentinel-2 and Landsat8 images. First, an ECA-BiseNetV2 identification model for sugarcane planting areas was constructed by introducing an efficient channel attention (ECA) module into the BiseNetV2 lightweight unstructured network. As a result, the overall pixel classification accuracy reached up to 91.54%, and the precision for sugarcane pixel identification was up to 95.57%. Then, multiple vegetation indices of different growth periods of the identified sugarcane planting areas were extracted, and the Landsat8 image-derived vegetation indices were converted into Sentinel-2 image-based ones using a linear regression model to reduce the differences of the indices derived using images from the two satellites. Subsequently, after the fitting of time-series data of the extracted vegetation indices using a cubic curve, the maximum indices were obtained as the representative spectral features. Finally, a yield prediction model was built using multiple machine learning algorithms. The results indicate that the test set of the decision tree model built using the fitted maximum values of the vegetation indices yielded R? of up to 0.759, 4.3%, higher than that (0.792) of the model built using the available actual maximum values. Therefore, this method can effectively resolve the difficulty in developing an accurate sugarcane yield prediction model caused by changeable weather-induced lack of remote sensing images of sugarcane of the key growth periods.
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LUO Wei, LI Xiuhua, QIN Huojuan, ZHANG Muqing, WANG Zeping, JIANG Zhuhui. Identification and yield prediction of sugarcane in the south-central part of Guangxi Zhuang Autonomous Region, China based on multi-source satellite-based remote sensing images. Remote Sensing for Natural Resources, 2024, 36(3): 248-258.
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2024, Vol. 36 
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