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Using reanalysis in crop monitoring and forecasting systems
A Toreti1, A Maiorano1, G De Sanctis2
1European Commission, Joint Research Centre, Italy.
Agricultural Systems
|February 19, 2019
Summary
Reanalysis data, such as AgMERRA and ERA-Interim, shows strong correlation for wheat and maize yield forecasting in Europe. While biases exist, reanalysis integration offers promising opportunities for improved crop monitoring.
Area of Science:
- Agricultural Meteorology
- Climate Science
- Remote Sensing
Background:
- Weather observations are crucial for crop monitoring but often lack spatial representativeness.
- Reanalysis datasets offer a potential alternative for comprehensive meteorological data.
Purpose of the Study:
- To assess the feasibility of using reanalysis data (AgMERRA, ERA-Interim) for crop monitoring and forecasting in Europe.
- To compare reanalysis-driven crop models against observation-driven systems for wheat and maize yields (1980-2010).
Main Methods:
- Utilized the European Commission's Joint Research Centre system for crop monitoring.
- Employed gridded daily meteorological observations, AgMERRA, and ERA-Interim reanalysis data.
- Analyzed inter-annual yield correlations at the country scale under potential and water-limited conditions.
Main Results:
- Reanalysis-driven systems demonstrated high inter-annual yield correlations (>0.6) for wheat and maize across EU28 countries.
- Significant yield biases were observed in both crops across all reanalysis simulations.
- Simulations using reanalysis data showed correlations comparable to FAO reported yield time series.
Conclusions:
- Reanalysis data is a feasible and valuable tool for enhancing current crop monitoring and forecasting systems.
- The study highlights opportunities with upcoming higher-resolution, near real-time reanalysis data.
- Integration of reanalysis data can improve the spatial and temporal coverage of meteorological inputs for agriculture.
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