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Development of an Assimilation Scheme for the Estimation of Drought-Induced Yield Losses Based on Multi-Source Remote Sensing and the AcquaCrop Model
2014-05-29, Silvestro, Paolo Cosmo, Casa, Raffaele, Pignatti, Stefano, et al.
In the context of the Dragon-3 Farmland Drought project, our research deals with the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing, into the AquaCrop model, in order to estimate the yield losses due to drought both at the farm and at the regional scale. The first part of this project was employed to refine a methodology to obtain maps of leaf area index (LAI), canopy cover (CC), fraction of adsorbed photosynthetically active radiation (FAPAR) and chlorophyll (Cab) from satellite optical data, using algorithms based on the training of artificial neural networks (ANN) on PROSAIL model simulations. In the second part, retrieved values of CC were assimilated into the AquaCrop model using the assimilation method of the Ensemble Kalman Filter to estimate grain wheat yield at the field scale.
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Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models
2016-07-08, Silvestro, Paolo Cosmo, Casa, Raffaele, Pignatti, Stefano, et al.
The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL. For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter. These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015. Results were validated by utilizing yield data both from ground measurements and statistical survey.