Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

L. Ornella, G. Kruseman, J. Crossa
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引用次数: 5

Abstract

Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers.
卫星数据和监督学习预防干旱对作物生产的影响:气象干旱
反复出现的极端天气事件给农业部门带来了挑战。遥感和监督学习(SL)的融合可以为气候变化带来的问题提供解决方案。SL方法从训练集构建一个函数,该函数将一组变量映射到输出。这个函数可以用来预测新的例子。由于它们是非参数的,这些方法可以挖掘大量的卫星数据来捕捉气候变量和作物之间的关系,或者成功地取代自回归综合移动平均(ARIMA)模型来预测天气。反映影响作物生长条件的土壤水分状况的农业指数(AIs)在时间和资源方面的监测成本很高。因此,在某些情况下,气象指数可以代替人工智能。本文讨论了基于历史卫星数据预测干旱的气象指标,综述了适合于干旱预测的SL方法。我们还包括一些说明性案例研究。最后,我们将调查网络上现有的降雨产品和一些处理数据的替代方案:从能够处理tb规模数据集的高性能计算系统到允许使用个人电脑的开源软件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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