Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis.The ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by +0.7, −0.7, and −5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by −0.5, +2.7, and −0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by −0.7, +3.8, and −1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change.
Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar.
We combined the PhenoRice algorithm with a growing season division method to extract rice pixels and cropping calendars in different growing seasons. Firstly, we identified possible crop heading periods based on a weighted and smoothed EVI time series curve in each image pixel. Then we input the possible heading periods into the PhenoRice algorithm to divide potential growing seasons and check whether the corresponding EVI time series belongs to rice. Lastly, we estimated rice planting, heading, and maturity dates and categorized them into early-, middle-, and late-season calendars according to the respective transplanting and maturity times.
The data quality is good.
| # | number | name | type |
| 1 | 31861143015 | National Natural Science Foundation of China |
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CSTR:11738.11.NCDC.DVN.DB6449.2024
10.7910/DVN/EUP8EY