Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-2437-2021
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, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong

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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.
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