Open Access
E3S Web Conf.
Volume 350, 2022
International Conference on Environment, Renewable Energy and Green Chemical Engineering (EREGCE 2022)
Article Number 01001
Number of page(s) 6
Section Green Chemical Engineering
Published online 09 May 2022
  1. B. Wu, J. Meng and Q. Li, China crop watch system with remote sensing. Journal of Remote Sensing, 8 481-497. (2004) [Google Scholar]
  2. V.S. Konduri, J. Kumar, J.W.W. Hargrove, et al. Mapping crops within the growing season across the United States. Remote Sensing of Environment, 251 112048. (2020) [CrossRef] [Google Scholar]
  3. Z. Chen, J. Ren, H Tang, et al. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767 (2016) [Google Scholar]
  4. L. Zhong, P. Gong and G. Biging, Efficient corn and soybean mapping with temporal extend ability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment, 140 1-13 (2014) [CrossRef] [Google Scholar]
  5. H. Thi, T Nguyen, L.V. Nguyen, et al. Mapping Maize Cropping Patterns in Dak Lak, Vietnam Through MODIS EVI Time Series. Agronomy, 10: 1-16. (2020) [Google Scholar]
  6. Q. Hu, W. Wu, Y. Shao, et al. Advances in remote sensing extraction of crop planting structure. Scientia Agricultura Sinica., 48 1900-1914. (2015) [Google Scholar]
  7. Q. Xu, G. Yang, H. Long, et al. Crop information identification based on MODIS NDVI time-series data. Transactions of the Chinese Society of Agricultural Engineering, 30 134-144. (2014) [Google Scholar]
  8. C. Jun, Y. Ban and S. Li, China: Open access to Earth land-cover map. Nature, 514: 434-434. (2014) [CrossRef] [Google Scholar]
  9. National Bureau of Statistics. Statistics of main crop sown areas in each province of China. (2021). [Google Scholar]
  10. Q. Hu, Research on crop remote sensing recognition method based on time series MODIS images. Chinese Academy of Agricultural Sciences. (2018) [Google Scholar]
  11. N. You, J. Dong, J. Huang, G. Du, et al. The 10-m crop type maps in Northeast China during 2017– 2019. Scientific Data, 8 1-11. (2021) [CrossRef] [PubMed] [Google Scholar]
  12. L. Gu, Y. Shuai, C. Shao, et al. Angle Effect on Typical Optical Remote Sensing Indices in Vegetation Monitoring. Remote. Sensing, 13: 1699. (2021) [CrossRef] [Google Scholar]
  13. B. Gao, NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58: 257-266. (1996) [CrossRef] [Google Scholar]
  14. B. Somers, S Delalieux, W.W. Verstraeten, et al. An automated wave band selection technique for optimized hyperspectral mixture analysis. International Journal of Remote Sensing. 31 55495568. (2010) [CrossRef] [Google Scholar]
  15. Z. Peng and Y. Sun, Fuzzy Mathematics and its Applications. Wuhan University Press, Wuhan. (2007) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.