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: 2103836
• 负责人: Rodrigo Vargas
• 金额: $ 24.98万
• 依托单位: University of Delaware
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103836&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.

收集土壤水分数据的工具(如现场土壤传感器和卫星)具有不同的能力。原位土壤水分数据具有细粒度的空间和较高的时间分辨率，但仅限于有限的区域；卫星数据可在全球范围内获得，但分辨率较低。现有用于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诸如野火管理和粮食和水安全等紧迫实际问题的能力。土壤水分数据的准确收集和有效建模对于应对紧迫的环境挑战至关重要。这个跨学科项目为土壤水分应用程序设计、构建和共享一个数据驱动的软件生态系统。该软件生态系统在适合支持林业、精准农业和地球表面水文学研究的尺度上对土壤水分进行建模和预测。该项目连接了科学界的多学科进展(如在规模上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、可视化和从不断增长的数据集合(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)中提取知识，并将这些知识发布到环境科学中的应用程序。具体地说，该项目(A)开发了可扩展的方法，以综合和分析多个空间和时间尺度上的土壤水分数据；(B)实施了一个数据驱动的软件生态系统，以获取复杂的信息，并提供基础和应用知识，向对土壤水分动态感兴趣的研究人员和利益攸关方(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在云平台上的发现，降低资源障碍以提高可访问性和互操作性。这一奖项由高级网络基础设施办公室颁发，由NSF地球科学局内的水文科学计划、地球科学部门和综合与合作教育和研究部门联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

The unexplored role of preferential flow in soil carbon dynamics

• DOI: 10.1016/j.soilbio.2021.108398
• 发表时间: 2021-10
• 期刊: Soil Biology & Biochemistry
• 影响因子: 9.7
• 作者: Shane Franklin;A. Kravchenko;R. Vargas;B. Vasilas;J. Fuhrmann;Yan Jin

GEOtiled: A Scalable Workflow for Generating Large Datasets of High-Resolution Terrain Parameters

GEOtiled：用于生成高分辨率地形参数大型数据集的可扩展工作流程

• DOI: 10.1145/3588195.3595941
• 发表时间: 2023
• 期刊: ACM
• 作者: Roa, Camila;Olaya, Paula;Llamas, Ricardo;Vargas, Rodrigo;Taufer, Michela
• 通讯作者: Taufer, Michela

Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths

• DOI: 10.1002/ecs2.4170
• 发表时间: 2022-07
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: E. M. Smith;R. Vargas;M. Guevara;T. Tarín;R. Pouyat

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
• 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 for landscape applications: A case study in Delaware, USA

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