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  5. 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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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

Author(s)
Silvestro, Paolo Cosmo  
Pignatti, Stefano  
Pascucci, Simone  
et al.  
Uri
http://hdl.handle.net/10197/10202
Date Issued
2017-05-22
Date Available
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.
Sponsorship
European Space Agency
Other Sponsorship
Ministry of Science and Technology (MOST) of the People's Republic of China
University of Tuscia PhD scholarship
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
Subjects

Leaf area index (LAI)...

Canopy cover (CC)

Landsat 8

HJ1A/B

Artificial neural net...

Ensemble Kalman filte...

Particle swarm optimi...

DOI
10.3390/rs9050509
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

Silvestro et al. - 2017 - Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data int.pdf

Size

3.54 MB

Format

Adobe PDF

Checksum (MD5)

2d4fc1ffc3b42d3f76c60442d0398cab

Owning collection
Biosystems and Food Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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