Calibration of Sentinel Land Surface Phenology with Ground Phenological Observations for agricultural applications.

Climate extremes have led to serious damages in Austria’s agriculture during the last years. Combining surface and remotely sensed observations on the phenological state of the crops and fruit trees, the area of potential damages can more precisely identified. One prototype was designed to support the detection of drought, hail and storm damage in case of maize and the other prototype the detection of late frost damage in apple plantations. Project partners were Cloudflight, Joanneum Research DIGITAL and ZAMG.

Via pixelwise training of the statistical analysis it was possible to identify pixels, which indicate drought conditions for maize. The independent drought indicator index (SPEI) corroborated the results attained through remote sensing. In case of apple late frost damage it turned out that the Sentinel time series of three years was too short to allow an unambiguous correlation between land surface phenology from space and ground phenology. Nevertheless a late frost damage assessment model has been installed for Golden Delicious in Austria based on near real time surface observations from Naturkalender.