SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions
SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions
复制标题
SOMOSPIE：基于数据驱动决策的模块化土壤湿度空间推理引擎
DOI：
10.1109/escience.2019.00008
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Taufer
中科院分区：
文献类型：
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作者：
Danny Rorabaugh;M. Guevara;R. Llamas;J. Kitson;R. Vargas;M. Taufer
目前大面积土壤湿度数据来自卫星遥感技术（即基于雷达的系统），但这些数据分辨率较低，并且往往表现出较大的空间信息差距。如果数据对于给定需求（例如精准农业）而言过于粗糙或稀疏，则可以利用机器学习技术与其他环境信息源（例如地形）相结合，以更精细的空间分辨率（即增加的粒度）生成无间隙信息。为此，我们开发了一个由模块化阶段组成的空间推理引擎，用于处理空间环境数据、使用机器学习技术生成预测并分析这些预测。我们通过土壤湿度高度多样化的美国生态区域（即大西洋中部沿海平原）的多个预测地图展示了这种方法的功能以及数据处理选择的效果。我们工作的相关性源于改善土壤湿度空间表征的迫切需要，以应用于环境科学（例如，生态位建模、碳监测系统和其他地球系统模型）和精准农业（例如，优化灌溉实践和其他土地管理决策）。
The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision farming), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision farming (e.g., optimizing irrigation practices and other land management decisions).