论文标题
智能广播：在深度学习框架中使用地球观察数据预测土壤水分插值到未来
SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework
论文作者
论文摘要
土壤水分是农作物健康的关键组成部分，监测它可以实现进一步的行动，以提高产量或防止灾难性消失。随着气候变化的增加，极端天气事件的可能性并降低了天气的可预测性，并且非最佳土壤水分可能会变得更有可能。在这项工作中，我们进行了一系列LSTM体系结构，以分析从卫星图像中得出的土壤水分和植被的测量。该系统学会预测这些测量值的未来值。这些在空间上稀疏的值和指数用作插值方法的输入特征，以推断空间密集的水分图以确保未来的时间点。这有可能对土壤水分进行预警，这可能是对监测能力有限的地区的农作物可能无害的。
Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases the likelihood of extreme weather events and reduces the predictability of weather, and non-optimal soil moistures for crops may become more likely. In this work, we a series of LSTM architectures to analyze measurements of soil moisture and vegetation indiced derived from satellite imagery. The system learns to predict the future values of these measurements. These spatially sparse values and indices are used as input features to an interpolation method that infer spatially dense moisture map for a future time point. This has the potential to provide advance warning for soil moistures that may be inhospitable to crops across an area with limited monitoring capacity.
