用卫星数据和集成学习精准预测突尼斯橄榄产量
Leveraging Novel Ensemble Learning Techniques and Landsat Multispectral Data for Estimating Olive Yields in Tunisia
- 融合遥感影像与实地数据，自动构建集成模型预测产量
- Landsat-8表现更优，决定系数达0.8635，误差仅1.17吨/公顷
- 方法可推广至全球类似农业区，适合农情监测与政策制定者
橄榄是地中海气候区的重要木本作物，但受气候变化影响产量波动大。本研究针对突尼斯卡伊鲁安和苏塞两省，构建了高效的橄榄产量估算流程。利用Landsat-8 OLI与Landsat-9 OLI-2多光谱影像提取反射率波段及植被指数，结合数字高程模型数据，并与田间调查数据融合形成结构化表格数据集。采用AutoGluon实现自动化集成学习框架，通过堆叠策略训练并评估多种机器学习模型，使用五折交叉验证生成稳健预测结果。结果显示，两颗卫星均表现良好：Landsat-8 OLI的R²为0.8635，RMSE为1.17吨/公顷；Landsat-9 OLI-2的R²为0.8378，RMSE为1.32吨/公顷。该方法具有可扩展性、低成本与高精度优势，具备在全球多样化农业区推广应用的潜力。
原文摘要 · Abstract (English)
Olive production is an important tree crop in Mediterranean climates. However, olive yield varies significantly due to climate change. Accurately estimating yield using remote sensing and machine learning remains a complex challenge. In this study, we developed a streamlined pipeline for olive yield estimation in the Kairouan and Sousse governorates of Tunisia. We extracted features from multispectral reflectance bands, vegetation indices derived from Landsat-8 OLI and Landsat-9 OLI-2 satellite imagery, along with digital elevation model data. These spatial features were combined with ground-based field survey data to form a structured tabular dataset. We then developed an automated ensemble learning framework, implemented using AutoGluon to train and evaluate multiple machine learning models, select optimal combinations through stacking, and generate robust yield predictions using five-fold cross-validation. The results demonstrate strong predictive performance from both sensors, with Landsat-8 OLI achieving R2 = 0.8635 and RMSE = 1.17 tons per ha, and Landsat-9 OLI-2 achieving R2 = 0.8378 and RMSE = 1.32 tons per ha. This study highlights a scalable, cost-effective, and accurate method for olive yield estimation, with potential applicability across diverse agricultural regions globally.
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