The coordination of EO data is one of the cross-cutting components of GEOGLAM. It is being developed with the support of the Committee on Earth Observations Satellites (CEOS) for satellite data and WMO for the weather data. A CEOS Ad Hoc Team for GEOGLAM was created to take responsibility for bridging the gap between the two communities. The Ad Hoc Team is composed of representatives from the following agencies: CONAE, CSA, ESA, INPE, ISRO, JAXA, NASA, USGS, and UMD. It includes participation from CEOS Agency staff with expertise in satellite mission planning, coordination, and management, and the use of space-based EO data to generate actionable information for agricultural decision-makers.
In addition to providing the mechanisms to translate scientific data requirements into real data acquisitions, processing, and delivery to users, the Ad Hoc team has also developed a detailed and explicit set of EO data requirements to carry out a variety of agricultural monitoring activities, including the mapping of cropped and crop type area, the monitoring of crop condition, biophysical, and environmental variables, the estimation of crop yield and production, and the early warning of crop failure (Figure 1). These EO requirements are being continually refined, paired with candidate missions (Figure 2), and translated into an acquisition strategy, which CEOS then acts upon via coordination with various space agencies and commercial data providers. In November 2013, the CEOS Acquisition Strategy for GEOGLAM Phase 1 was endorsed at the CEOS Plenary.
Figure 1: First CEOS Ad Hoc Team for GEOGLAM Workshop on tabulating the EO data requirements for monitoring. Hosted by the Canadian Space Agency, Montreal, CA, July 2012.
Figure 2: An example of the overpass analysis run by Brian Killough et al. (NASA-Langley) using the COVE tool (www.ceos-cove.org
) in support of GEOGLAM. This overpass simulation shows the combined revisit frequency of NASA/USGS Landsat 8 and ESA Sentinel missions (Sentinel-2A/2B), providing the frequent temporal sampling at moderate spatial resolution that is ideal for agricultural monitoring.