Preprints
https://doi.org/10.5194/essd-2021-343
https://doi.org/10.5194/essd-2021-343
 
24 Jan 2022
24 Jan 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

A 30-m annual maize phenology dataset from 1985 to 2020 in China

Quandi Niu1, Xuecao Li1,2, Jianxi Huang1,2, Hai Huang1, Xianda Huang1, Wei Su1,2, and Wenping Yuan3 Quandi Niu et al.
  • 1College of Land Science and Technology, China Agricultural University, Beijing 100083, China
  • 2Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • 3School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 5120245, Guangdong, China

Abstract. Crop phenology information provides essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets agree with in-situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding their spatial patterns and temporal dynamics. We observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the change of temperature and precipitation, which further alter the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).

Quandi Niu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-343', Anonymous Referee #1, 17 Feb 2022
  • RC2: 'Comment on essd-2021-343', Anonymous Referee #2, 21 Feb 2022
  • AC1: 'Comment on essd-2021-343', Q. D. Niu, 27 Apr 2022

Quandi Niu et al.

Data sets

A 30-m annual maize phenology dataset from 1985 to 2020 in China Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan https://doi.org/10.6084/m9.figshare.16437054

Quandi Niu et al.

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Short summary
In this manuscript, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenology indicators agree well with in-situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.