ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
-
Published:2024-04-04
Issue:4
Volume:16
Page:1689-1701
-
ISSN:1866-3516
-
Container-title:Earth System Science Data
-
language:en
-
Short-container-title:Earth Syst. Sci. Data
Author:
Li Hui, Wang XiaoboORCID, Wang Shaoqiang, Liu Jinyuan, Liu Yuanyuan, Liu ZhenhaiORCID, Chen Shiliang, Wang QinyiORCID, Zhu Tongtong, Wang Lunche, Wang Lizhe
Abstract
Abstract. 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. 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 (https://doi.org/10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, 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.
Funder
National Natural Science Foundation of China
Publisher
Copernicus GmbH
Reference62 articles.
1. Atzberger, C. and Eilers, P. H.: Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements, Int. J. Remote Sens., 32, 3689–3709, 2011. 2. Aybar, C., Montero, D., Barja, A., Herrera, F., Gonzales, A., and Espinoza, W.: Combining R and Earth Engine, in: Cloud-Based Remote Sensing with Google Earth Engine: Fundamentals and Applications, Cham, Springer International Publishing, 629–651, https://doi.org/10.1007/978-3-031-26588-4_31, 2023. 3. Bai, H. and Xiao, D.: Spatiotemporal changes of rice phenology in China during 1981–2010, Theor. Appl. Clim., 140, 1483–1494, 2020. 4. Boschetti, M., Stroppiana, D., Brivio, P., and Bocchi, S.: Multi-year monitoring of rice crop phenology through time series analysis of MODIS images, Int. J. Remote Sens., 30, 4643–4662, 2009. 5. Boschetti, M., Busetto, L., Manfron, G., Laborte, A., Asilo, S., Pazhanivelan, S., and Nelson, A.: PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series, Remote Sens. Environ., 194, 347–365, 2017.
|
|