Open Access
E3S Web Conf.
Volume 350, 2022
International Conference on Environment, Renewable Energy and Green Chemical Engineering (EREGCE 2022)
Article Number 01008
Number of page(s) 3
Section Green Chemical Engineering
Published online 09 May 2022
  1. A.D. Richardson, T.F. Keenan, M. Migliavacca, Y. Ryu, O. Sonnentag, M. Toomey, Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For Meteorol, 169 156-173 (2013) [CrossRef] [Google Scholar]
  2. E.K. Melaas, D. Sulla-Menashe, J.M. Gray, T.A. Black, T.H. Morin, A.D. Richardson and M.A. Friedl, Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat. Remote Sens. Environ., 186: 452-464. (2016) [CrossRef] [Google Scholar]
  3. Z. Zhu and C.E. Woodcock, Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ., 144: 152-171. (2014) [CrossRef] [Google Scholar]
  4. X. Li, Y. Zhou, L. Meng, G.R Asrar, C. Lu and Q. Wu, A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States. Earth Syst. Sci. Data, 11: 881-894. (2019) [CrossRef] [Google Scholar]
  5. G. Feng, J. Masek, M. Schwaller, F. Hall, On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote Sens., 44: 2207-2218. (2006) [CrossRef] [Google Scholar]
  6. X. Zhu, J. Chen, F. Gao, X. Chen and J.G Masek, An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ., 114: 2610-2623. (2010) [CrossRef] [Google Scholar]
  7. Q. Yang, L. Shi, J. Han, J. Yu and K. Huang, A near real-time deep learning approach for detecting rice phenology based on UAV images. Agric. For Meteorol., 287. (2020) [Google Scholar]
  8. M. Cao, Y. Sun, X. Jiang, Z. Li and Q. Xin, Identifying Leaf Phenology of Deciduous Broadleaf Forests from PhenoCam Images Using a Convolutional Neural Network Regression Method. Remote Sens. 13. (2021) [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.