Collaborative Research: Elements: SENSORY: Software Ecosystem for kNowledge diScOveRY - a data-driven framework for soil moisture applications

收集土壤湿度数据的工具（如现场土壤传感器和卫星）具有不同的功能。 现场土壤湿度数据具有精细的空间分辨率和高的时间分辨率，但只能在有限的地区获得;卫星数据可在全球获得，但分辨率较粗糙。现有的研究土壤水分数据动态特征的软件工具在多个空间和时间尺度上模拟土壤水分的能力有限，这些限制阻碍了科学家解决野火管理和粮食和水安全等紧迫实际问题的能力。准确收集和有效建模土壤水分数据对于应对紧迫的环境挑战至关重要。这个跨学科项目设计、构建和共享用于土壤水分应用的数据驱动软件生态系统。该软件生态系统模拟和预测土壤湿度的规模适合于支持林业，精准农业和地球表面水文学的研究。该项目连接了科学界的多学科进展（例如大规模生成数据集和支持基于云的网络基础设施），以开发数据驱动的软件生态系统，用于分析，可视化，从不断增加的数据收集中提取知识（从细粒度的现场土壤传感器信息到粗粒度的全球卫星测量数据），并将这些知识用于环境科学的应用。 具体而言，该项目（a）开发可扩展的方法，以在多个空间和时间尺度上整合和分析土壤水分数据;（B）实施数据驱动的软件生态系统，以获取复杂信息，并提供基础和应用知识，为对土壤水分动态感兴趣的研究人员和利益相关者提供信息（科学家、教育工作者、政府机构、决策者）;以及（c）构建网络基础设施以支持云平台上的发现，降低资源障碍，提高可访问性和互操作性。高级网络基础设施办公室的这一奖项由水文科学计划，地球科学部，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Tools for gathering soil moisture data (such as in situ soil sensors and satellites) have differing capabilities. In situ soil moisture data has fine-grained spatial and high temporal resolution, but is only available in limited areas; satellite data is available globally, but is more coarse in resolution. Existing software tools for studying the dynamic characteristics of soil moisture data are limited in their ability to model soil moisture at multiple spatial and temporal scales, and these limitations hamper scientists’ ability to address urgent practical problems such as wildfire management and food and water security. Accurate gathering and effective modeling of soil moisture data are essential to address pressing environmental challenges. This interdisciplinary project designs, builds, and shares a data-driven software ecosystem for soil moisture applications. This software ecosystem models and predicts soil moisture at scales suitable to support studies in forestry, precision agriculture, and earth surface hydrology.This project connects multi-disciplinary advances across the scientific community (such as generating datasets at scale and supporting cloud-based cyberinfrastructures) to develop a data-driven software ecosystem for analyzing, visualizing, and extracting knowledge from the growing data collections (from fine-grained, in situ soil sensor information to coarse-grained, global satellite measurements) and releasing this knowledge to applications in environmental sciences. Specifically, this project (a) develops scalable methodologies to integrate and analyze soil moisture data at multiple spatial and temporal scales; (b) implements a data-driven software ecosystem to access complex information and provide basic and applied knowledge to inform researchers and stakeholders interested in soil moisture dynamics (scientists, educators, government agencies, policy makers); and (c) builds cyberinfrastructures to support discovery on cloud platforms, lowering resource barriers to improve accessibility and interoperability.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Hydrologic Sciences Program, the Division of Earth Sciences, and the Division of Integrative and Collaborative Education and Research within the NSF Directorate for Geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.