Cargando…

A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data

Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China’s seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimati...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Junyi, Hou, Xianpeng, Chen, Shuaiming, Mu, Yanhua, Huang, Hai, Wang, Hengbin, Liu, Zhe, Li, Shaoming, Zhang, Xiaodong, Zhao, Yuanyuan, Huang, Jianxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513754/
https://www.ncbi.nlm.nih.gov/pubmed/37746025
http://dx.doi.org/10.3389/fpls.2023.1201179
_version_ 1785108587401445376
author Liu, Junyi
Hou, Xianpeng
Chen, Shuaiming
Mu, Yanhua
Huang, Hai
Wang, Hengbin
Liu, Zhe
Li, Shaoming
Zhang, Xiaodong
Zhao, Yuanyuan
Huang, Jianxi
author_facet Liu, Junyi
Hou, Xianpeng
Chen, Shuaiming
Mu, Yanhua
Huang, Hai
Wang, Hengbin
Liu, Zhe
Li, Shaoming
Zhang, Xiaodong
Zhao, Yuanyuan
Huang, Jianxi
author_sort Liu, Junyi
collection PubMed
description Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China’s seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R(2): 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.
format Online
Article
Text
id pubmed-10513754
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-105137542023-09-22 A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data Liu, Junyi Hou, Xianpeng Chen, Shuaiming Mu, Yanhua Huang, Hai Wang, Hengbin Liu, Zhe Li, Shaoming Zhang, Xiaodong Zhao, Yuanyuan Huang, Jianxi Front Plant Sci Plant Science Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China’s seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R(2): 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10513754/ /pubmed/37746025 http://dx.doi.org/10.3389/fpls.2023.1201179 Text en Copyright © 2023 Liu, Hou, Chen, Mu, Huang, Wang, Liu, Li, Zhang, Zhao and Huang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Liu, Junyi
Hou, Xianpeng
Chen, Shuaiming
Mu, Yanhua
Huang, Hai
Wang, Hengbin
Liu, Zhe
Li, Shaoming
Zhang, Xiaodong
Zhao, Yuanyuan
Huang, Jianxi
A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title_full A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title_fullStr A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title_full_unstemmed A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title_short A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data
title_sort method for estimating yield of maize inbred lines by assimilating wofost model with sentinel-2 satellite data
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513754/
https://www.ncbi.nlm.nih.gov/pubmed/37746025
http://dx.doi.org/10.3389/fpls.2023.1201179
work_keys_str_mv AT liujunyi amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT houxianpeng amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT chenshuaiming amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT muyanhua amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT huanghai amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT wanghengbin amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT liuzhe amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT lishaoming amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT zhangxiaodong amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT zhaoyuanyuan amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT huangjianxi amethodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT liujunyi methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT houxianpeng methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT chenshuaiming methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT muyanhua methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT huanghai methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT wanghengbin methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT liuzhe methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT lishaoming methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT zhangxiaodong methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT zhaoyuanyuan methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata
AT huangjianxi methodforestimatingyieldofmaizeinbredlinesbyassimilatingwofostmodelwithsentinel2satellitedata