CRII: OAC: Real-time Computational Modeling of Crop Phenological Progress towards Scalable Satellite Precision Farming

CRII：OAC：作物物候进展的实时计算建模，实现可扩展的卫星精准农业

• 批准号: 1849821
• 负责人: Chunyuan Diao
• 金额: $ 17.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849821&HistoricalAwards=false
• 关键词: CRII; OAC; Real; time; Computational

Precision agriculture aims to leverage advanced data-intensive technology to maximize agricultural productivity and to reduce environmental footprints. With considerable technological advancements, it is a pivotal time to harness the intensive data collected in space and time to benchmark precision agriculture systems worldwide. The recent launch of groups of satellites designed to work together -- known as "satellite constellations" -- opens up unprecedented opportunities to revolutionize precision agriculture, through monitoring the crop phenological progress at fine spatial and temporal scales. However, the gigantic amount of data brings significant challenges to conventional remote sensing software and tools. The overarching goal of this project is to prototype advanced remote sensing cyberinfrastructure in support of both data- and compute-intensive satellite-based precision agriculture systems. This advanced cyberinfrastructure is applicable to a diverse range of agricultural systems, especially for resource poor and vulnerable smallholder farming systems. With its potential to improve global farming practices, the cyberinfrastructure helps optimize the trajectory of agricultural development to meet future crop demands as well as lower environmental impacts. The integrated educational and training activities of the project offer unique learning opportunities to students of various academic levels and backgrounds, and enhance the broader engagement of diverse scientific communities, especially minority and underrepresented groups. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.The advanced remote sensing cyberinfrastructure focuses on the development of an innovative real-time phenological computational (RTPC) model and a high-performance system to harness massive parallelism in modeling crop phenological progress towards scalable satellite-based precision farming. The RTPC model integrates dynamic complex networks with time series remote sensing, and is unique to predict the real-time crop phenological progress at both fine spatial and temporal scales. The high-performance system enhances the parallelism of the RTPC using a hybrid computation model, including a node-level computation model and a system-wide data distribution model. The node-level computation model takes advantage of multi-core architecture of computing nodes to parallelize the compute-intensive RTPC in predicting dynamic network characteristics of crop phenology. The system-wide data distribution model devises a novel Space-and-Time parallel decomposition strategy in distributing massive remote sensing time series data to reduce memory requirements and to achieve high scalability. An open-source toolkit is designed to facilitate the open development and adoption of the remote sensing cyberinfrastructure across a broad range of disciplines. Through leveraging the power of high performance computing and this hybrid computation model, the cyberinfrastructure can analyze PB-level remotely sensed data in a highly scalable manner to conduct real-time monitoring of earth system dynamics.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.

精准农业旨在利用先进的数据密集型技术，最大限度地提高农业生产力，减少环境足迹。随着技术的巨大进步，现在是利用在空间和时间上收集的密集数据来衡量全球精准农业系统的关键时刻。最近发射的旨在协同工作的卫星组-称为“卫星星座”-通过在精细的空间和时间尺度上监测作物物候进展，为精确农业的革命化提供了前所未有的机会。然而，巨大的数据量给传统的遥感软件和工具带来了巨大的挑战。该项目的总体目标是制作先进的遥感网络基础设施原型，以支持数据和计算密集型卫星精准农业系统。这种先进的网络基础设施适用于各种农业系统，特别是资源贫乏和脆弱的小农农业系统。网络基础设施具有改善全球农业实践的潜力，有助于优化农业发展的轨迹，以满足未来的作物需求，并降低对环境的影响。该项目的综合教育和培训活动为不同学术水平和背景的学生提供了独特的学习机会，并加强了不同科学界，特别是少数群体和代表性不足群体的更广泛参与。因此，本研究符合NSF的使命，以促进科学的进步，促进国家的健康，繁荣和福利。先进的遥感网络基础设施的重点是开发一个创新的实时物候计算（RTPC）模型和一个高性能的系统，利用大规模的并行模拟作物物候进展可扩展的基于卫星的精准农业。RTPC模型将动态复杂网络与时间序列遥感相结合，在精细时空尺度上实时预测作物物候进程方面具有独特性。该高性能系统使用混合计算模型增强了RTPC的并行性，该混合计算模型包括节点级计算模型和系统级数据分布模型。节点级计算模型利用计算节点的多核架构，将计算密集型RTPC并行化，实现作物物候动态网络特征预测。系统级数据分发模型设计了一种新的时空并行分解策略，用于大规模遥感时间序列数据的分发，降低了存储需求，实现了高扩展性。设计了一个开放源码工具包，以便利在广泛的学科中开放开发和采用遥感网络基础设施。通过利用高性能计算和这种混合计算模型的力量，网络基础设施可以以高度可扩展的方式分析PB级遥感数据，以进行地球系统动态的实时监测。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。

项目成果

Innovative pheno-network model in estimating crop phenological stages with satellite time series

• DOI: 10.1016/j.isprsjprs.2019.04.012
• 发表时间: 2019-07
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: C. Diao

Complex network-based time series remote sensing model in monitoring the fall foliage transition date for peak coloration

• DOI: 10.1016/j.rse.2019.05.003
• 发表时间: 2019-08
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: C. Diao

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

• DOI: 10.3390/rs13245005
• 发表时间: 2021-12
• 期刊: Remote. Sens.
• 作者: Zijun Yang;C. Diao;Bo Li

Hybrid phenology matching model for robust crop phenological retrieval

• DOI: 10.1016/j.isprsjprs.2021.09.011
• 发表时间: 2021-11
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: C. Diao;Zi-Yan Yang;F. Gao;Xiaoyang Zhang;Zhengwei Yang

Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages

• DOI: 10.1016/j.rse.2020.111960
• 发表时间: 2020-10-01
• 期刊: REMOTE SENSING OF ENVIRONMENT
• 影响因子: 13.5
• 作者: Diao, Chunyuan