A 30 m annual maize phenology dataset from 1985 to 2020 in China
<p>Crop phenology indicators provide 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 resolu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/2851/2022/essd-14-2851-2022.pdf |
| _version_ | 1811335177035579392 |
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| author | Q. Niu X. Li X. Li J. Huang J. Huang H. Huang X. Huang W. Su W. Su W. Yuan |
| author_facet | Q. Niu X. Li X. Li J. Huang J. Huang H. Huang X. Huang W. Su W. Su W. Yuan |
| author_sort | Q. Niu |
| collection | DOAJ |
| description | <p>Crop phenology indicators provide 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 in dates when the
vegetation index in a specific year reaches the same magnitude as its
long-term mean. The derived maize phenology datasets are consistent 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 the spatial
patterns and temporal dynamics. Furthermore, we observed a noticeable
difference in maize phenology temporal trends before and after 2000, which
is likely attributable to the changes in temperature and precipitation,
which further altered the farming activities. The extracted maize phenology
dataset can support precise yield estimation and deepen our understanding of
the future agroecosystem response to global warming. The data are available
at <a href="https://doi.org/10.6084/m9.figshare.16437054">https://doi.org/10.6084/m9.figshare.16437054</a>
(Niu et al., 2021).</p> |
| first_indexed | 2024-04-13T17:20:17Z |
| format | Article |
| id | doaj.art-3e415566cd314d70a14148c7c382be10 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-13T17:20:17Z |
| publishDate | 2022-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-3e415566cd314d70a14148c7c382be102022-12-22T02:38:00ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-06-01142851286410.5194/essd-14-2851-2022A 30 m annual maize phenology dataset from 1985 to 2020 in ChinaQ. Niu0X. Li1X. Li2J. Huang3J. Huang4H. Huang5X. Huang6W. Su7W. Su8W. Yuan9College of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaKey Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaKey Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaKey Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510245, Guangdong, China<p>Crop phenology indicators provide 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 in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent 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 the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming. The data are available at <a href="https://doi.org/10.6084/m9.figshare.16437054">https://doi.org/10.6084/m9.figshare.16437054</a> (Niu et al., 2021).</p>https://essd.copernicus.org/articles/14/2851/2022/essd-14-2851-2022.pdf |
| spellingShingle | Q. Niu X. Li X. Li J. Huang J. Huang H. Huang X. Huang W. Su W. Su W. Yuan A 30 m annual maize phenology dataset from 1985 to 2020 in China Earth System Science Data |
| title | A 30 m annual maize phenology dataset from 1985 to 2020 in China |
| title_full | A 30 m annual maize phenology dataset from 1985 to 2020 in China |
| title_fullStr | A 30 m annual maize phenology dataset from 1985 to 2020 in China |
| title_full_unstemmed | A 30 m annual maize phenology dataset from 1985 to 2020 in China |
| title_short | A 30 m annual maize phenology dataset from 1985 to 2020 in China |
| title_sort | 30 thinsp m annual maize phenology dataset from 1985 to 2020 in china |
| url | https://essd.copernicus.org/articles/14/2851/2022/essd-14-2851-2022.pdf |
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