SUGARCANE YIELD PREDICTION USING MACHINE LEARNING IN SENTINEL-2 AND GOOGLE EARTH ENGINE FOR ANDHRA PRADESH, INDIA
用Sentinel-2影像与GEE结合随机森林预测印度安得拉邦甘蔗产量。
关注点
该研究用1998–2018年县级产量数据训练随机森林模型，验证了Sentinel-2植被指数在甘蔗估产中的可行性；但模型仅适用于安得拉邦，跨区域迁移需重新标定。
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Feature-level fusion of Sentinel-1 SAR and Sentinel-2 optical imagery for crop-type mapping in humid tropical mountainous terrain
融合Sentinel-1 SAR与Sentinel-2光学特征，在印尼热带山地实现93%精度的作物类
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Improving early season crop type mapping using machine learning algorithms with combined SAR and optical time series
融合Sentinel-1 SAR与Sentinel-2光学时序，用集成机器学习实现印度早季作物类型高
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