Extraction of maize growth stages in the Sanjiang Plain of China from 2003 to 2022 and their spatio-temporal changes in response to meteorological variables

How to quickly monitor the growth process of maize on a large scale is crucial for regional maize growth assessment, yield estimation, and farmland management. This article takes the Sanjiang Plain in Northeast China as the research area, which is the main grain production area in China. Using MODIS NDVI time series data and Savitzky Golay and Whittaker filtering techniques, a remote sensing extraction method for key growth stages of maize (i.e., jointing stage, tasseling stage, and maturity stage) was established. The spatiotemporal characteristics of these growth stages from 2003 to 2022 were analyzed, alongside their meteorological influences. Results show the Whittaker filter achieves high accuracy, with errors under 8 days. Jointing stages typically fall between June 9<sup>th</sup> and June 25<sup>th</sup>, tasseling stages between July 20<sup>th</sup> and August 5<sup>th</sup>, and maturity stages between September 13<sup>th</sup> and September 29<sup>th</sup>. From 2003 to 2022, jointing and tasseling stages advanced by 0.43 and 0.19 days/year, respectively, while the maturity stage was delayed by 0.38 days/year, indicating an extended growing season correlated with rising surface temperatures and precipitation in the preceding month. These findings offer theoretical and technical guidance for crop growth monitoring, yield assessment, and farmland management.

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Publication Details

Journal
Frontiers in Plant Science
Published
2025-08-19
DOI
https://doi.org/10.3389/fpls.2025.1558990
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.72

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article

Extraction of maize growth stages in the Sanjiang Plain of China from 2003 to 2022 and their spatio-temporal changes in response to meteorological variables

Bingrui Zhang, Minghao Qin, Shanyu Huang, Wei Peng et al.
1 citations
Frontiers in Plant Science
Remote Sensing in Agriculture
1.72
article

Extraction of maize growth stages in the Sanjiang Plain of China from 2003 to 2022 and their spatio-temporal changes in response to meteorological variables

Bingrui Zhang, Minghao Qin, Shanyu Huang, Wei Peng, Huichun Ye, Huabing Wang, Chaojia Nie
article en
1 citations

Abstract

How to quickly monitor the growth process of maize on a large scale is crucial for regional maize growth assessment, yield estimation, and farmland management. This article takes the Sanjiang Plain in Northeast China as the research area, which is the main grain production area in China. Using MODIS NDVI time series data and Savitzky Golay and Whittaker filtering techniques, a remote sensing extraction method for key growth stages of maize (i.e., jointing stage, tasseling stage, and maturity stage) was established. The spatiotemporal characteristics of these growth stages from 2003 to 2022 were analyzed, alongside their meteorological influences. Results show the Whittaker filter achieves high accuracy, with errors under 8 days. Jointing stages typically fall between June 9<sup>th</sup> and June 25<sup>th</sup>, tasseling stages between July 20<sup>th</sup> and August 5<sup>th</sup>, and maturity stages between September 13<sup>th</sup> and September 29<sup>th</sup>. From 2003 to 2022, jointing and tasseling stages advanced by 0.43 and 0.19 days/year, respectively, while the maturity stage was delayed by 0.38 days/year, indicating an extended growing season correlated with rising surface temperatures and precipitation in the preceding month. These findings offer theoretical and technical guidance for crop growth monitoring, yield assessment, and farmland management.

Frontiers in Plant ScienceVol. 16
China University of Mining and Technology (CN), Beijing Institute of Big Data Research (CN), Aerospace Information Research Institute (CN), Sanya University (CN), Ministry of Agriculture and Rural Affairs (CN), Beijing Machine Tool Research Institute (CN), Taiyuan University of Science and Technology (CN), Shenyang Jianzhu University (CN), Taiyuan University of Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, National Key Research and Development Program of China, Youth Innovation Promotion Association
Openalex Percentile: Top 14%
Remote Sensing in Agriculture
1.72
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