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

协作研究：要素：SENSORY：知识发现的软件生态系统 - 土壤湿度应用的数据驱动框架

• 批准号: 2103845
• 负责人: Michela Taufer
• 金额: $ 35万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103845&HistoricalAwards=false
• 关键词: Collaborative; Research; Elements; SENSORY; Software

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.

收集土壤湿度数据的工具（例如原位土壤传感器和卫星）具有不同的功能，原位土壤湿度数据具有细粒度的空间分辨率和高时间分辨率，但仅在有限的区域可用；卫星数据在全球范围内可用。现有的用于研究土壤湿度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湿度的能力受到限制，这些限制阻碍了科学家解决诸如野火管理等紧急实际问题的能力。以及粮食和水安全。土壤湿度数据的准确收集和有效建模对于解决紧迫的环境挑战至关重要，该跨学科项目设计、构建和共享用于土壤湿度应用的数据驱动的软件生态系统。该软件生态系统以适合支持的规模对土壤湿度进行建模和预测。该项目将整个科学界的多学科进步（例如大规模生成数据集和支持基于云的网络基础设施）结合起来，开发一个数据驱动的软件生态系统，用于分析、可视化、并提取从不断增长的数据收集（从细粒度的原位土壤传感器信息到粗粒度的全球卫星测量）中获取知识，并将这些知识应用于环境科学具体而言，该项目（a）开发可扩展的方法来集成和分析。多个空间和时间尺度的土壤湿度数据；(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，为对土壤湿度动态感兴趣的研究人员和利益相关者（科学家、教育工作者、政府机构、政策制定者）​​提供信息） ）; 和(c) 建设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。该奖项由先进网络基础设施办公室颁发，得到水文科学计划、地球科学部和综合与科学部的共同支持NSF 地球科学理事会内的合作教育和研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Building Trust in Earth Science Findings through Data Traceability and Results Explainability

通过数据可追溯性和结果可解释性建立对地球科学发现的信任

• DOI: 10.1109/tpds.2022.3220539
• 发表时间: 2022-01
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Olaya, Paula;Kennedy, Dominic;Llamas, Ricardo;Valera, Leobardo;Vargas, Rodrigo;Lofstead, Jay;Taufer, Michela
• 通讯作者: Taufer, Michela

Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework

使用模块化空间推理框架缩小卫星土壤湿度

• DOI: 10.3390/rs14133137
• 发表时间: 2022-07
• 期刊: Remote Sensing
• 影响因子: 5
• 作者: Llamas, Ricardo M.;Valera, Leobardo;Olaya, Paula;Taufer, Michela;Vargas, Rodrigo
• 通讯作者: Vargas, Rodrigo

Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA

缩小卫星土壤湿度的景观应用：美国特拉华州的案例研究

• DOI: 10.1016/j.ejrh.2021.100946
• 发表时间: 2021-12-01
• 期刊: Journal of Hydrology: Regional Studies
• 作者: D. Warner;M. Guevara;J. Callahan;R. Vargas
• 通讯作者: R. Vargas