Articles | Volume 13, issue 5
Earth Syst. Sci. Data, 13, 2437–2456, 2021
https://doi.org/10.5194/essd-13-2437-2021
Earth Syst. Sci. Data, 13, 2437–2456, 2021
https://doi.org/10.5194/essd-13-2437-2021
Data description paper
01 Jun 2021
Data description paper | 01 Jun 2021

A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

Bowen Cao et al.

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Cited articles

Atzberger, C. and Rembold, F.: Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets, Remote Sens., 5, 1335–1354, https://doi.org/10.3390/rs5031335, 2013. 
Bargiel, D.: A new method for crop classification combining time series of radar images and crop phenology information, Remote Sens. Environ., 198, 369–383, https://doi.org/10.1016/j.rse.2017.06.022, 2017. 
Biradar, C. M. and Xiao, X.: Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005, Int. J. Remote Sens., 32, 367–386, https://doi.org/10.1080/01431160903464179, 2011. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Zenodo [data set], https://doi.org/10.5281/zenodo.3895585, 2020. 
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Short summary
In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.