A Global Agricultural Monitoring (GLAM) Use Case to Evaluate Amazon Web Services for Near-Real Time Decision Support
Abstract
The University of Maryland Global Agricultural Monitoring (GLAM) system is a web-based vegetation monitoring tool used operationally by several national and regional organizations around the world to track agricultural productivity in near-real-time throughout the growing season. Developed in the mid-2000s with USDA and NASA, the UMD GLAM system empowered for the first time non-remote sensing experts to transform Earth observations (EO) into meaningful agricultural information with minimal technical lift.
In the nearly 15 years since the GLAM system was launched, the EO and information technology worlds have experienced colossal shifts. Volumes of data within the MODIS/VIIRS archive are increasing alongside growing possibilities for continuous Landsat/Sentinel-class monitoring, all with the advent of cloud computing and the advantages it offers such as cost, speed, and scalability. In response to these shifts, UMD through NASA Harvest has completely rebuilt the GLAM system in Amazon Web Services, as traditional on-premises workflows and funding mechanisms do not have a 1-to-1 translation into cloud architectures. Herein, we present cost, longevity, and performance benefits and drawbacks of different system architectures, emphasizing key lessons learned from a "next user" (developer) and "end user" (analyst) perspectives, with relevance across EO applications development. We provide insight into useful actions that can be taken on the data production and distribution side to empower other applications to best leverage these revolutions in cloud process and EO data availability.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN22A..04W
- Keywords:
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- 1694 Instruments and techniques;
- GLOBAL CHANGE;
- 1910 Data assimilation;
- integration and fusion;
- INFORMATICS;
- 1994 Visualization and portrayal;
- INFORMATICS;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS