DADrought: Data Assimilation of Satellite Observations with Dynamic Land Use for Enhanced Agricultural Drought Risk Assessment

• Applicant: Dr. Roland Baatz
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 564094719

Agricultural production is crucial for food security but consumes vast natural resources, including about 70% of the world's freshwater. With the increasing frequency and intensity of drought events in Europe, there is a critical need to enhance agricultural drought risk assessment and crop yield prediction to minimize economic and yield losses. Despite advances in agroecosystem modelling and forecasting, significant challenges remain, particularly in the effective use of data assimilation to integrate satellite observations and dynamic land use information with agricultural models. The main challenge addressed by this proposal is the underutilization of available remote sensing data. Existing forecasting tools and agroecosystem models often fail to incorporate dynamic, crop-specific land cover and appropriate thermal information such as land surface temperature (LST), thus failing to fully exploit the potential of these data for drought risk assessment. The project "DADrought: Data Assimilation of Satellite Observations with Dynamic Land Use for Enhanced Agricultural Drought Risk Assessment" aims to tackle these issues by developing a framework that enhances data assimilation techniques. We will quantify the value of GSAA data for drought risk assessment in regional crop yield predictions, determining its impact on enhancing the accuracy of drought assessments. We will also explore the feasibility and develop methods for assimilating remote sensing data at a regional scale, aiming to improve the integration of satellite observations into agroecosystem models. This will require and anticipate the development of innovative canopy reflectance models for vegetation indices and new crop-specific biophysical LST models. Both models are designed to be versatile for use from plot to regional scales in agroecosystem models. By bridging these gaps, the project will contribute significant knowledge to the research field, particularly enhancing the precision of drought risk assessments and yield forecasts through improved data assimilation. This effort will include collaboration with Wageningen University and Research, leveraging their expertise in soil-hydrological simulation, statistical modelling, and data assimilation in agroecosystem models. The anticipated results are expected to advance the state of the art in simulation systems and enable a more accurate quantification of agroecosystem states concerning drought risk, ultimately supporting more robust agricultural management and planning.

• DFG Programme: Research Grants