eprintid: 10066675
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/06/66/75
datestamp: 2019-02-08 10:52:04
lastmod: 2021-09-30 22:46:03
status_changed: 2019-02-08 10:52:04
type: article
metadata_visibility: show
creators_name: Huang, J
creators_name: Ma, H
creators_name: Sedano, F
creators_name: Lewis, P
creators_name: Liang, S
creators_name: Wu, Q
creators_name: Su, W
creators_name: Zhang, X
creators_name: Zhu, D
title: Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST-PROSAIL model
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F26
keywords: WOFOST, PROSAIL, Canopy reflectance, Data assimilation, Winter wheat yield estimation
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: To estimate regional-scale winter wheat (Triticum aestivum) yield, we developed a data-assimilation scheme that assimilates remotely sensed reflectance into a coupled crop growth–radiative transfer model. We generated a time series of 8-day, 30-m-resolution synthetic Kalman Smoothed reflectance by combining MODIS surface reflectance products with Landsat surface reflectance using a KS algorithm. We evaluated the assimilation performance using datasets with different spatial and temporal scales (e.g., three dates for the 30-m Landsat reflectance, 8-day and 1-km MODIS surface reflectance, and 8-day and 30-m synthetic KS reflectance) into the coupled WOFOST–PROSAIL model. Then we constructed a four-dimensional variational data assimilation (4DVar) cost function to account for differences between the observed and simulated reflectance. We used the shuffled complex evolution–University of Arizona (SCE-UA) algorithm to minimize the 4DVar cost function and optimize important input parameters of the coupled model. The optimized parameters were used to drive WOFOST and estimate county-level winter wheat yield in a region of China. By assimilating the synthetic KS reflectance data, we achieved the most accurate yield estimates (R2 = 0.44, 0.39, and 0.30; RMSE = 598, 1288, and 595 kg/ha for 2009, 2013, and 2014, respectively), followed by Landsat reflectance (R2 = 0.21, 0.22, and 0.33; RMSE = 915, 1422, and 637 kg/ha for 2009, 2013, and 2014, respectively) and MODIS reflectance (R2 = 0.49, 0.05, and 0.22; RMSE = 1136, 1468, and 700 kg/ha for 2009, 2013, and 2014, respectively) at the county level. Thus, our method improves the reliability of regional-scale crop yield estimates.
date: 2019-01
date_type: published
publisher: ELSEVIER SCIENCE BV
official_url: https://doi.org/10.1016/j.eja.2018.10.008
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1616565
doi: 10.1016/j.eja.2018.10.008
language_elements: English
lyricists_name: Lewis, Philip
lyricists_name: Wu, Qingling
lyricists_id: PELEW26
lyricists_id: QQWHW55
actors_name: Wu, Qingling
actors_id: QQWHW55
actors_role: owner
full_text_status: public
publication: European Journal of Agronomy
volume: 102
pagerange: 1-13
pages: 13
issn: 1873-7331
citation:        Huang, J;    Ma, H;    Sedano, F;    Lewis, P;    Liang, S;    Wu, Q;    Su, W;         ... Zhu, D; + view all <#>        Huang, J;  Ma, H;  Sedano, F;  Lewis, P;  Liang, S;  Wu, Q;  Su, W;  Zhang, X;  Zhu, D;   - view fewer <#>    (2019)    Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST-PROSAIL model.                   European Journal of Agronomy , 102    pp. 1-13.    10.1016/j.eja.2018.10.008 <https://doi.org/10.1016/j.eja.2018.10.008>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10066675/1/PDFsam_EURAGR7797R2.pdf