Paddy Rice mapping in fragmented lands by improved phenology curve and correlation measurements on Sentinel-2 imagery in Google earth engine
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10661-023-11808-3.pdf
Reference63 articles.
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2. Cai, L., Chen, X., Huang, R., & Smettem, K. (2022). Runoff change induced by vegetation recovery and climate change over carbonate and non-carbonate areas in the karst region of south-West China. Journal of Hydrology, 604, 127231.
3. de Castro, C., Filho, H., de Carvalho, A., Júnior, O., Ferreira de Carvalho, O. L., Pozzobon de Bem, P., dos Santos de Moura, R., Olino de Albuquerque, A., et al. (2020). Rice crop detection using LSTM, bi-LSTM, and machine learning models from Sentinel-1 time series. Remote Sensing, 12(16), 2655.
4. Demirkan, D., Koz, A., & Duzgun, S. (2022). Hierarchical classification of sentinel 2-a images for land use and land cover mapping and its use for the Corine system. Journal of Applied Remote Sensing, 14, 1–21.
5. Ding, M., Guan, Q., Li, L., Zhang, H., Liu, C., & Zhang, L. (2020). Phenology-based rice paddy mapping using multi-source satellite imagery and a fusion algorithm applied to the Poyang Lake plain, Southern China. Remote Sensing, 12, 1022.
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