the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 30-m annual maize phenology dataset from 1985 to 2020 in China
- 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
- 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).
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Quandi Niu et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2021-343', Anonymous Referee #1, 17 Feb 2022
Title: A 30-m annual maize phenology dataset from 1985 to 2020 in China
Authors: Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, Wenping Yuan
Journal: Earth System Science Data (ESSD)
Date: 17/02/2022
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Comment to Editor
Dear Editor,
This manuscript demonstrates the applied methods to produce a nationwide and long-term of dataset on maize phenology in China. The authors verified the accuracy of the Landsat derived product by comparing the estimated phenological parameters to those obtained from various types of data sources (i.e., ground truth, PhenoCam and MODIS). A dataset which covers large area and time span of phenological information is crucial for government officials and researchers to have a better understanding of the nationwide dynamic changes in crop phenology and their drivers. However, I don’t think the paper can be accepted by ESSD in its present form. The major issues are:
- Lack of novelty and interest. The authors follow the basic procedures/steps to generate the phenological dataset, which has already been widely available for different sites and countries around the world. I don’t see any challenges for the methods applied and new findings from the analysis of the generated dataset. To improve its originality and make the paper more interesting, the authors could further analyze the spatial variabilities in maize phenology and to what extent they relate to weather conditions spatially, rather than only a simple comparison from the trend plots of phenology and weather conditions as shown in Fig14.
- Unclear descriptions about the dataset (layers of polygons) used to identify maize farmland. The author should firstly clarify the basic attributes/forms of maize planting areas in the target research area China. This could be the common type of maize planting (smallholder farms or industrial agricultural system), average size of individual fields, or the management schemes, etc. This information is important to give readers a good overview of the maize planting system in the research area and the performance of the output product according to the given maize planting conditions. On top of this basic information, the authors should better explain the dataset (i.e., shapefile) used to identify maize farmland. The authors applied two data sources to delineate maize areas with different spatial resolution, which is not good for consistency and result in possible uncertainties subsequently. In addition, the dataset for defining the maize areas is not clearly stated, causing the following analysis less convincing. Alongside a better explanation of the dataset, perhaps the authors can also present the dataset in form of polygons on a map under a zoomed in view.
- The manuscript should be better polished. I find a lot of typos, and descriptions that are hard to understand. It’s easy to distract readers from the content itself. I would suggest the authors to get editing help from someone with full professional proficiency in English.
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RC2: 'Comment on essd-2021-343', Anonymous Referee #2, 21 Feb 2022
This article aims to provide a long-term national maize phenology dataset with a high spatial resolution. The adopted method and newly released dataset should have good application value for crop phenology monitoring and agricultural production management at different regional scales, the main concerns are as follows:Â
- The maize distribution map may be more consistent compared with land use change map, but it is better to try harder to reduce the impact of the assumption that the maize distribution was regarded as persistent over 30 years, for example, maybe using GEE to get maize classification maps.
- From Fig.8, it is not as described that the correlations of two phenology indicators of summer maize is significantly higher than that of spring maize, or the author may put the wrong figure here.
- Fig.13 does not show that summer maize is more sensitive to temperature and precipitation than spring maize, and this description needs to be supported by quantitative evidence or scientific findings.
- Considering that there are about 10 provinces where summer maize is grown, it may be required to expand the region of interest instead of only selecting Beijing-Tianjin-Hebei area to reveal the impact of climate materials for deeper analysis and better persuasion.
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AC1: 'Comment on essd-2021-343', Q. D. Niu, 27 Apr 2022
Dear Topical Editor and Reviewer,
We thank you very much for reviewing our paper and giving your comments. We have revised the contents according to your suggestions. The point-by-point response to the comments is listed in the supplement.
Looking forward to hearing from you.
Best regards,
Niu Quandi
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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