UNIV PADOVA (IT)
The project will develop an EO-service for Hyper-resolution crop yield estimates and extreme events crops shocks monitoring by integrating multiple SATellite data and modeling, able to support farmers precision agriculture.This will be based on a dynamic calibration/validation system which takes advantage of the high spatial resolution (5-8m) of combined harvesters’ crop yield information. The system procedures will be substantially all based on available data from EO information and web-based available ground database, plus some specific ground data for models validation.
EO data in the different bands from different sensors will be used in order to retrieve leaf area index (LAI) and land surface temperature (LST) information from Sentinel 2 at 10 m of spatial resolution and also from the Third-party missions LANDSAT data at 30 to 60-100 m. The procedure will use the high-resolution remote sensing data for driving different water-energy-crop models based on two hypotheses: parameters-saving vs complex physical representation both for crop dynamic and evapotranspiration modeling. Data assimilation procedures of EO data will be routinely implemented along the crop season with the objective of detecting and monitoring crop exposure to shocks due extreme events non-reproducible by the model alone which alter the canopy morphology and physiology, such as weather disasters (e.g. extreme heat or cold, water logging etc.), saline stress, plant diseases and insect pests. Leaf area index (LAI) and land surface temperature (LST) from multiple remote sensing data will be assimilated to improve both the crop evapotranspiration and growth dynamics. At the end of the crop season the modeled crop yield estimates will be calibrated / validated against ground yields maps from georeferenced combine harvesters, allowing producing hyper-resolution crop yield estimates and soil – vegetation parameters (5-8 m). The procedure will be integrated in a loop allowing to have hyper-resolution yields estimates between the different seasons.
The proposed methodology will be applied in irrigated and not irrigated fields in the North of Italy over maize and winter wheat fields where combine harvester fleets have been operating for several years. Yield and quality data will be used for a dynamic calibrating procedure. Expected innovative products will respond to the call activity line “EO for a Resilient Society”, impacting directly on agricultural farmers and their associations in the case studies, but it will be immediately exportable to any area of the world where combine harvesters equipped with yield sensors and eventually quality sensors are used. Of course, the scientific community will gain new knowledge on the retrieval of hyper-resolution crop yields. In this way, the project will deliver: protocol for retrieving and using data collected by fleets of combine harvesters;
Identified end-users:
User representative can be contacted via the TO or project manager.
