摘要翻译：
干旱威胁着世界各地的粮食和水安全，在气候变化下，这种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。我们创建了一个开放源码工具，利用覆盖全球的数据，在高空间分辨率下制作植被健康的短期预测。该工具自动下载和处理中分辨率成像光谱仪(MODIS)数据集，并根据数亿次观测数据训练梯度增强机器模型，以预测增强植被指数的未来值。我们比较了斯里兰卡和加利福尼亚两个农业生态系统、气候和云层覆盖不同的地区的不同变量集（原始光谱MODIS数据和三级MODIS产品）的预测能力。与简单的基线模型相比，我们的工具对持久数据集提供了相当大的预测能力。
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英文标题：
《A Machine Learning Approach to Forecasting Remotely Sensed Vegetation
  Health》
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作者：
John J. Nay, Emily Burchfield, Jonathan Gilligan
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最新提交年份：
2017
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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.
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PDF链接：
https://arxiv.org/pdf/1602.06335