| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Advancing Sustainable Agriculture Through Multiscale Spatiotemporal Data Integration and High-Performance Computing |
| Author(s) |
Olatunde David Akanbi, Vibha Mandayam, Ethan Tobey, Adaezeogo Ezeogo-Enwo, HyangMok Baek, Atharva Gupta, Laura S Bruckman, Yinghui Wu, Erika I Barcelos, Jeffrey M Yarus, Roger H French |
| On-Site Speaker (Planned) |
Olatunde David Akanbi |
| Abstract Scope |
This research presents a transformative approach to sustainable agriculture by leveraging multiscale spatiotemporal data integration and high-performance computing. Utilizing a comprehensive framework called CRADLE (Common Research Analytics and Data Lifecycle Environment), we efficiently integrate and process diverse geospatial datasets, including satellite imagery, soil surveys, and environmental variables. Our work encompasses analyses from daily crop growth monitoring using Normalized Difference Vegetation Index time series from the MODIS Aqua satellite to hydrological assessments, precipitation monitoring using NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, and soil nutrient modeling. Advanced spatiotemporal analytical techniques uncover complex spatiotemporal patterns and relationships within agricultural systems. Novel datasets, including Sentinel-5P methane observations and locations of Concentrated Animal Feeding Operations and Wastewater Treatment Plants, enable the identification of emission hotspots and development of targeted mitigation strategies. Our holistic, data-driven approach empowers stakeholders to make informed decisions, optimize resource management, and promote sustainable agricultural practices. |