Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models
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|Silvestro et al. - 2017 - Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data int.pdf||3.63 MB||Adobe PDF||Download|
|Title:||Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models||Authors:||Silvestro, Paolo Cosmo
|Permanent link:||http://hdl.handle.net/10197/10202||Date:||22-May-2017||Online since:||2019-04-30T07:47:52Z||Abstract:||Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models’ specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context.||Funding Details:||European Space Agency||Type of material:||Journal Article||Publisher:||MDPI||Journal:||Remote Sensing||Volume:||9||Issue:||5||Start page:||1||End page:||24||Copyright (published version):||2017 the Authors||Keywords:||Leaf area index (LAI); Canopy cover (CC); Landsat 8; HJ1A/B; Artificial neural network (ANN); Ensemble Kalman filter (EnKF); Particle swarm optimization (PSO)||DOI:||10.3390/rs9050509||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Biosystems and Food Engineering Research Collection|
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