Journal of Agricultural Big Data >
The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022
Received date: 2024-04-12
Accepted date: 2024-06-03
Online published: 2024-12-02
The farmland ecosystem of the Huanghuai Plain primarily cultivates winter wheat and summer maize, which have played a crucial role in ensuring national food security. To grasp the key phenological period of primary crops precisely is of great significance for estimating crop yield, improving the level of agricultural production management, and preventing agricultural meteorological disasters. The dataset integrates ecological observation data on the phenology of different crops in a two-cropping winter wheat-summer maize continuous cropping system in the alluvial plain of the Old Yellow River over the past 15 years (2008-2022). It mainly includes information on observation plots, winter wheat phenological period data, and summer maize phenological period data. It will serve as a valuable resource for regional agricultural quantitative remote sensing, crop growth model simulation, agricultural climate change research, and decision-making in agricultural production and management.
Data summary：
| Items | Description |
|---|---|
| Dataset name | The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022 |
| Specific subject area | Agricultural Science |
| Research topic | Crop phenology period of winter wheat and summer maize |
| Time range | From 2008 to 2022 |
| Data types and technical formats | .xlsx |
| Dataset structure | The dataset includes the variety and crop phenology period of winter wheat and summer maize in six long-term observation plots at the Alluvium Plain of the Old Yellow River from 2008 to 2022. |
| Volume of dataset | 21.7 kB |
| Key index in dataset | The crop phenological period of winter wheat and summer maize |
| Data accessibility | CSTR：https://cstr.cn/17058.11.sciencedb.agriculture.00034 DOI：https://doi.org/10.57760/sciencedb.agriculture.00034 NASDC Access link: |
| Financial support | Central Public-Interest Scientific Institution Basal Research Fund (Y2024JC31, Y2024JC08, IFI2024-24); The Scientific and Technological Project of Henan Province(242102110222); National Agricultural Experimental Station for Agricultural Environment, Shangqiu (NAES038AE05). |
Key words： winter wheat; summer maize; crop phenology period; dataset
DING DaWei, YONG BeiBei, REN Wen, XIE Kun, ZHAO YongJian, WANG GuangShuai, CHEN JinPing, WANG MingHui . The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022[J]. Journal of Agricultural Big Data, 2024 , 6(4) : 552 -557 . DOI: 10.19788/j.issn.2096-6369.100029
| [1] | PIAO S L, LIU Q, CHEN A P, et al. Plant phenology and global climate change: Current progresses and challenges[J]. Global Change Biology, 2019, 25: 1922 - 1940. DOI: 10.1111/gcb.14619. |
| [2] | SCHR?TER D, CRAMER W, LEEMANS R, et al. Ecosystem service supply and vulnerability to global change in Europe[J]. Science, 2005, 310(5752): 1333-1337. DOI: 10.1126/science.1115233. |
| [3] | RICHARDSON A D, KEENAN T F, MIGLIAVACCA M, et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system[J]. Agricultural and Forest Meteorology, 2013, 169: 156-173. DOI: 10.1016/j.agrformet.2012.09.012. |
| [4] | DIAO C Y. Innovative pheno-network model in estimating crop phenological stages with satellite time series[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2019, 153: 96-109. DOI: 10.1016/j.isprsjprs.2019.04.012. |
| [5] | HOU Y Q, WU Y F, WU L S, et al. Identifying crop growth stages from solar-induced chlorophyll fluorescence data in maize and winter wheat from ground and satellite measurements[J]. Remote Sensing, 2023, 15(24): 5689. DOI: 10.3390/rs15245689. |
| [6] | JUNTTILA S, ARD? J, CAI Z Z, et al. Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function[J]. Science of Remote Sensing, 2023, 7: 100075. DOI: 10.1016/j.srs.2022.100075. |
| [7] | LIU Y, WU C Y, TIAN F, et al. Modeling plant phenology by MODIS derived photochemical reflectance index (PRI)[J]. Agricultural and Forest Meteorology, 2022, 324: 109095. DOI: 10.1016/j.agrformet.2022.109095. |
| [8] | JIN H X, EKLUNDH L. A physically based vegetation index for improved monitoring of plant phenology[J]. Remote Sensing of Environment, 2014, 152: 512-525. DOI: 10.1016/j.rse.2014.07.010. |
| [9] | 吴冬秀, 张琳, 宋创业, 等. 陆地生态系统生物观测指标与规范[M]. 北京: 中国环境出版集团, 2019. |
| [10] | 丁大伟, 陈金平, 申孝军, 等. 商丘地区不同降水年型冬小麦-夏玉米需水量和缺水量分析[J]. 灌溉排水学报, 2023, 42(9):9-18. |
| [11] | 王菲, 陈报章, 陈婧, 等. 华北平原冬小麦—夏玉米轮作区DLM与CLM5 模型模拟对比研究[J]. 地理科学进展, 2022, 41(2): 289-303. |
| [12] | 蒋博武, 孟丹, 郭晓彤, 等. 基于Landsat-8遥感数据的冬小麦种植区地表蒸散量时空分布研究[J]. 灌溉排水学报, 2022, 41(7): 140-146. |
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