Articles | Volume 14, issue 6
Earth Syst. Sci. Data, 14, 2851–2864, 2022
Earth Syst. Sci. Data, 14, 2851–2864, 2022
Data description paper
23 Jun 2022
Data description paper | 23 Jun 2022

A 30 m annual maize phenology dataset from 1985 to 2020 in China

Quandi Niu et al.

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Cited articles

Abbas, G., Ahmad, S., Ahmad, A., Nasim, W., Fatima, Z., Hussain, S., ur Rehman, M. H.​​​​​​​, Khan, M. A., Hasanuzzaman, M., Fahad, S., Boote, K. J., and Hoogenboom, G.: Quantification the impacts of climate change and crop management on phenology of maize-based cropping system in Punjab, Pakistan, Agric. For. Meteorol., 247, 42–55,, 2017. 
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Bolton, D. K. and Friedl, M. A.: Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics, Agric. For. Meteorol., 173, 74–84,, 2013. 
Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., and Friedl, M. A.: Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery, Remote Sens. Environ., 240, 111685,, 2020. 
Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456,, 2021. 
Short summary
In this paper 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 Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. 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.