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A comparative analysis of five global cropland datasets in China

  • Research Paper
  • Special Topic: GlobeLand30 remote sensing mapping innovation and large data analysis
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Abstract

Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including GlobalLand30, FROM-GLC, GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of GlobeLand30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using GlobeLand30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and GlobCover having the poorer accuracies. Regarding the spatial location of cropland, GlobeLand30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with GlobCover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of GlobCover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 41501483) and Research Foundation for Mapping Geographic Information Public Welfare of China (Grant No. 201512028).

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Authors and Affiliations

  1. Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Miao Lu, WenBin Wu, Li Zhang & HuaJun Tang

  2. National Geomatics Centre of China, Beijing, 100830, China

    AnPing Liao & Shu Peng

Authors
  1. Miao Lu
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  2. WenBin Wu
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  3. Li Zhang
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  4. AnPing Liao
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  5. Shu Peng
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  6. HuaJun Tang
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Correspondence to WenBin Wu or HuaJun Tang.

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Lu, M., Wu, W., Zhang, L. et al. A comparative analysis of five global cropland datasets in China. Sci. China Earth Sci. 59, 2307–2317 (2016). https://doi.org/10.1007/s11430-016-5327-3

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  • DOI: https://doi.org/10.1007/s11430-016-5327-3

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