遥感植被健康预测的机器学习方法
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health
摘要：
干旱威胁着世界各地的粮食和水安全，在气候变化的情况下，这一威胁可能会变得更加严重。高分辨率的预测信息可以帮助农民、水资源管理者和其他人管理干旱的影响。我们创建了一个开源工具，使用全球覆盖的数据，以高空间分辨率对植被健康进行短期预测。该工具自动下载和处理中分辨率成像光谱仪（MODIS）数据集，并在数亿次观测中训练梯度增强的机器模型，以预测增强型植被指数的未来值。我们比较了两个地区不同的变量（原始光谱MODIS数据和3级MODIS产品）的预测能力与不同的农业生态系统，气候和云层覆盖：斯里兰卡和加利福尼亚州。我们的工具在保留数据集上提供了比简单的基线模型更大的预测能力。
英文摘要：
Drought threatens food and water security around the world, and this threat is likely to become more severe under climate change. High resolution predictive information can help farmers, water managers, and others to manage the effects of drought. We have created an open source tool to produce short-term forecasts of vegetation health at high spatial resolution, using data that are global in coverage. The tool automates downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, and training gradient-boosted machine models on hundreds of millions of observations to predict future values of the Enhanced Vegetation Index. We compared the predictive power of different sets of variables (raw spectral MODIS data and Level-3 MODIS products) in two regions with distinct agro-ecological systems, climates, and cloud coverage: Sri Lanka and California. Our tool provides considerably greater predictive power on held-out datasets than simpler baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
