USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Australia
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification.
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Purpose There are presently a small number of global agricultural monitoring systems in place and a large number of national monitoring capabilities at different stages of development. The practical expertise for monitoring is found in those institutions that have demonstrated an operational capacity for providing such services. In addition, a number of research organizations and institutes are involved in developing basic research and, in some cases, managing the transition from proven methods into the operational domain. However, the potential for further improvement of national and global agricultural monitoring capacity is high, and it raises a few challenges. First, answering key questions, both generic and region-specific, requires coordinated research efforts: how can we develop early estimates of crop type and planted area using satellite imagery observations? How can we improve crop yield and production estimates taking into account knowledge about agricultural systems, meteorological information and forecasts, and near real-time satellite observations? How can we optimize the linkage between field observation networks and satellite imagery? How can we quantify uncertainties in estimates and forecasts?