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

Title: Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model
Authors: Yang, Guijun
Yang, Hao
Xiuliang, Jin
Silvestro, Paolo Cosmo
et al.
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Date: 8-Jul-2016
Online since: 2019-04-30T07:43:02Z
Abstract: Drought 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.
Type of material: Conference Publication
Publisher: ESA
Keywords: DroughtChinaCrop yield lossesSynthetic Aperture Radar (SAR)Crop growth modelFAO AquaCropParticle Swarm Optimization (PSO)Extended Fourier Amplitude Sensitivity Test (EFAST)Water consumption
Language: en
Status of Item: Not peer reviewed
Is part of: Proceedings of Dragon 3 Final Results and Dragon 4 Kick-Off Symposium
Conference Details: Dragon 3 Final Results and Dragon 4 Kick-Off Symposium, 4-8 July 2016, Wuhan, China
ISBN: 978-92-9221-304-6
Appears in Collections:Biosystems and Food Engineering Research Collection

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