自然资源遥感2024，Vol.36Issue(3)：248-258,11.DOI:10.6046/zrzyyg.2023093
基于多源卫星遥感影像的广西中南部地区甘蔗识别及产量预测
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
摘要
Abstract
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(EC A)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.关键词
语义分割/植被指数/甘蔗产量预测/卫星遥感/时间序列Key words
semantic segmentation/vegetation index/sugarcane yield prediction/satellite remote sensing/time-series分类
农业科技引用本文复制引用
罗维,李修华,覃火娟,张木清,王泽平,蒋柱辉..基于多源卫星遥感影像的广西中南部地区甘蔗识别及产量预测[J].自然资源遥感,2024,36(3):248-258,11.基金项目
广西重大科技专项项目"广西数字蔗田技术平台的构建与应用示范"(编号:桂科AA22117004)、广西重大科技创新基地建设项目"广西甘蔗生物学重点实验室"(编号:桂科2018-266-Z01)和国家自然科学基金项目"低空航拍图像融合田间环境及气象信息立体构建甘蔗长势、品质及产量预测模型"(编号:31760342)共同资助. （编号:桂科AA22117004）
