Chances and Challenges of Data Assimilation for Seasonal Yield Predictions Using Sentinel-3 Satellite Data and the Agro-Ecosystem Model LPJmL

Accurate and timely seasonal yield predictions before harvest are becoming ever more relevant due to increasing pressure on the agricultural sector under climate change. Especially for agricultural planning, logistics, and food markets, seasonal predictions are of significant importance in the context of food security and price stability.

A novel approach to enhance early-season yield forecasts at the regional scale will be presented. Earth observation (EO) data from the Copernicus Sentinel-3 satellite are able to trace spatio-temporal vegetation dynamics (e.g., crop phenological status, crop growth, photosynthesis via FAPAR, or chlorophyll indices) in near real-time. By deriving daily satellite composites and combining these data with physical modelling using the Lund-Potsdam-Jena managed Land (LPJmL) dynamic global vegetation model (DGVM) in a newly developed assimilation process, enhanced yield forecasts can be achieved. There are currently no interfaces for continuous assimilation of EO data for the LPJmL model, thus, approaches such as parameter forcing and ensemble methods allowing for continuous parameter optimization during the course of the growing season are presented and compared conceptually to improve the LPJmL model for seasonal yield predictions. Existing methods for model parameter calibration and optimization with EO data using machine learning are applied to agricultural areas in the study area.

While these results focus on the study area of Bavaria, southern Germany, the approach is scalable also on national or European scale. For demonstration purposes, the year 2018 – a comparably dry year – was chosen due to the availability of detailed land use data. LPJmL was designed for global simulations, hence, a regional downscaling is necessary for its application at the regional scale.

Integrating different remote sensing data sources enables a more detailed picture of plant growth, which will allow a regional early warning system for food security and farmer’s turnover in the future. The combination of process- and data-based approaches is likely to improve accuracy and lag time.