Cluster 1 - Data driven modeling
Uncertainties in model simulations can be related to input data, model parameters and/or model algorithms. Model uncertainties are still high for quantifying ecosystem services like reduction of greenhouse gas emissions or nitrate leaching based on complex modelling solutions at the sub-field scale. Observations from Unmanned Aerial Vehicules (UAV), tractor or satellite based sensors with high spatial and temporal resolution together with ensemble simulations have the potential to reduce model uncertainties at the sub-field scale, thus supporting spatially explicit field operations for farmers. In addition, the highly resolved crop information can assist in improving model calibration for specific site conditions. The objectives of Cluster 1 are (1) to improve analysis and interpretation of remote sensing information for the purpose of calibration of agro-ecosystem and grassland models and (2) exploring the potential of data assimilation to reduce uncertainties of sub-field scale model simulations for estimating ecosystem services and resource use efficiency.
Key references
Tewes, A., Hoffmann, H., Krauss, G., Schafer, F., Kerkhoff, C., Gaiser, T., 2020. New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations. Agronomy-Basel 10, 21.
Tewes, A., Hoffmann, H., Nolte, M., Krauss, G., Schäfer, F., Kerkhoff, C., Gaiser, T., 2020. How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level? Remote Sensing, 664178, 1-21.
Tewes, A., Montzka, C., Nolte, M., Krauss, G., Hoffmann, H., Gaiser, T., 2020. Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat. Agronomy 10, 1813.