Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation-Reference-Cited by-同舟云学术

Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation

Published:2023-08 Issue: Volume:339 Page:109574
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Tang Yining,Zhou Ruiheng,He Ping,Yu Minglei,Zheng Hengbiao,Yao Xia,Cheng Tao,Zhu Yan,Cao Weixing,Tian Yongchao

Funder

Science Fund for Creative Research Groups

Priority Academic Program Development of Jiangsu Higher Education Institutions

National Natural Science Foundation of China

Jiangsu Collaborative Innovation Center for Modern Crop Production

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference102 articles.

1. Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring;Amin;Remote Sens. Environ.,2021

2. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION;Baret;Remote Sens. Environ.,2007

3. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data;Becker-Reshef;Remote Sens. Environ.,2010

4. Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley;Bidinger;Nature,1977

5. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches;Cai;Agric. For. Meteorol.,2019

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model;Science of Remote Sensing;2024-12

2. Assessment of the impact of accurate green area index, water regime and harvest index on site-specific wheat yield estimation;Computers and Electronics in Agriculture;2024-11

3. Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images;European Journal of Agronomy;2024-10

4. Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications;Agronomy;2024-09-01

5. Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects;Agronomy;2024-08-27