Assimilating seasonality information derived from satellite data time series in crop modelling for rice yield estimation


The agricultural sector is facing important global challenges due to the pressure of food demand, increased price-competition produced by market globalization and food price volatility (G20 Agriculture Action Plan), and the necessity of more environmentally and economically sustainable farming. Earth Observation (EO) systems can significantly contribute to these topics by providing reliable real time information on crop distribution, status and seasonal dynamics. ERMES FP7 project aims to create added-value information for the rice agro-sector by integrating EO-products in crop models. Time series of moderate resolution satellite data are analyzed exploiting the PhenoRice algorithm to retrieve seasonal occurrence of agro-practices and phenological stages. Eleven years (2003-2013) of rice seasonal metrics were derived and used in WARM crop model to set up a crop forecasting systems, with the aim to provide crop yield estimates for regional authorities. Preliminary test conducted in Italy on indica rice ecotype demonstrated that the system can provide rice yield estimates explaining up to 90% of interannual variability. © 2015 IEEE.

International Geoscience and Remote Sensing Symposium (IGARSS), (2015-November), pp. 157-160,