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Agrometeorological Earth Observations Indicators

AGMET EO Indicators
What We Do

The GEOGLAM-NASA Harvest Agrometeorological (AGMET) Earth Observation Indicators utilize a wealth of Earth observation data to provide valuable insights on in-season crop development and current crop conditions at the sub-national scale. Each AGMET Indicator consists of several EO data plots that quantify critical indicators of crop health for a specific region over the cropping season. The AGMET Indicators are being produced for all GEOGLAM Crop Monitor countries and are updated every 7-12 days to ensure that users are provided with the most up to date information.

Location

Global

How Satellites Make This Work

Satellites are a critical Earth observation tool as they facilitate the remote monitoring of Earth’s climate and environmental patterns, especially for areas that are physically inaccessible. With this information, scientists can closely monitor the indicators of crop health and identify possible early warnings of declining crop conditions.

 

Displaying climate, environmental, and vegetative variables that impact agricultural outcomes, the AGMET Indicators were born from an agricultural stakeholder-identified need for a simpler way to visualize and monitor crop health throughout the growing season, and with the ultimate goal of identifying potential cropping concerns early on in the season before a food shortage disaster actually materializes. One of the many benefits of agricultural monitoring during the growing season using satellite data is that we can efficiently keep track of how well food production is shaping up compared to previous years. This allows farmers, economists, policymakers, humanitarian agencies, and other agricultural players around the world to more sufficiently plan, ensuring communities do not go hungry and that food markets remain stable.

 

The AGMET Indicators use satellite data to help the agricultural community monitor crop production as it’s happening and enable mitigation measures to be taken early on as problematic conditions are developing. In order to simplify the complicated interconnected nature of agricultural monitoring data each AGMET Indicator is composed of quantitative plots for: NDVI, recent NDVI 5-year comparisons, evaporative stress index, cumulative precipitation (versus the 5 year mean), daily precipitation, surface soil moisture, maximum temperature, and minimum temperature that are processed over the agricultural baseline datasets from the GEOGLAM Crop Monitor including crop calendars and crop masks.In this way, the AGMET Indicators take the data processing burden off of crop analysts and provide a quick, effective, and easily-digestible means of interpreting massive amounts of global satellite data on croplands as the season progresses.

 

Additionally, the AGMET Indicators are being produced for each country covered in the GEOGLAM Crop Monitors, which together comprise over 90% of the world’s croplands. Within each country, the AGMET Indicator plots are available for two different regional breakdowns: Admin Level 1 and Crop Monitor regions. The Admin Level 1 regions include the administrative boundaries of the first sub-national level while the Crop Monitor regions include an aggregation admin level 1 or sub-admin level 1 units that share similar agro-climatic growing conditions for crops.

 

Already, the AGMET graphics are being used to support operational monitoring and are frequently implemented as key evidence in several international reports, including the GEOGLAM Crop Monitor monthly bulletins, Crop Monitor Special Reports featuring areas of developing concern, and in country updates from the FAO Global Information and Early Warning System on Food and Agriculture (GIEWS). To explore the online AGMET Indicators tool, visit https://cropmonitor.org/tools/agmet/.

Lead
Ritvik Sahajpal, University of Maryland
Inbal Becker-Reshef, University of Maryland
Christina Jade Justice, University of Maryland
Team Members
Antonio McCabe, University of Maryland
Brian Barker, University of Maryland
Estefania Puricelli, University of Maryland
Kara Mobley, University of Maryland
Greg Husak, University of California Santa Barbara