Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model

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dc.contributor.authorYang, Guijun-
dc.contributor.authorYang, Hao-
dc.contributor.authorXiuliang, Jin-
dc.contributor.authorSilvestro, Paolo Cosmo-
dc.contributor.authoret al.-
dc.descriptionDragon 3 Final Results and Dragon 4 Kick-Off Symposium, 4-8 July 2016, Wuhan, Chinaen_US
dc.description.abstractDrought is the most costly natural disasters in China and all over the world. It is very important to evaluate the drought-induced crop yield losses and further improve water use efficiency at regional scale. Firstly, crop biomass was estimated by the combined use of Synthetic Aperture Radar (SAR) and optical remote sensing data. Then the estimated biophysical variable was assimilated into crop growth model (FAO AquaCrop) by the Particle Swarm Optimization (PSO) method from farmland scale to regional scale. At farmland scale, the most important crop parameters of AquaCrop model were determined to reduce the used parameters in assimilation procedure. The Extended Fourier Amplitude Sensitivity Test (EFAST) method was used for assessing the contribution of different crop parameters to model output. Moreover, the AquaCrop model was calibrated using the experiment data in Xiaotangshan, Beijing.At regional scale, spatial application of our methods were carried out and validated in the rural area of Yangling, Shaanxi Province, in 2014. This study will provide guideline to make irrigation decision of balancing of water consumption and yield loss.en_US
dc.relation.ispartofProceedings of Dragon 3 Final Results and Dragon 4 Kick-Off Symposiumen_US
dc.subjectCrop yield lossesen_US
dc.subjectSynthetic Aperture Radar (SAR)en_US
dc.subjectCrop growth modelen_US
dc.subjectFAO AquaCropen_US
dc.subjectParticle Swarm Optimization (PSO)en_US
dc.subjectExtended Fourier Amplitude Sensitivity Test (EFAST)en_US
dc.subjectWater consumptionen_US
dc.titleFarmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Modelen_US
dc.typeConference Publicationen_US
dc.statusNot peer revieweden_US
dc.neeo.contributorSilvestro|Paolo Cosmo|aut|-
dc.neeo.contributoret al.||aut|-
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Appears in Collections:Biosystems and Food Engineering Research Collection
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