Sentinel Data and Machine Learning Algorithms for Soil Moisture Land Classification
Author:
Affiliation:
1. GREEN-TEAM Laboratory (LR17AGR01-INAT), Gabes National School of Engineers (ENIG), Gabes 6029, Tunisia
2. Olive Tree Institute Tunis Office, the University of Sfax, Ariana 3029, Tunisia
Publisher
MDPI
Link
https://www.mdpi.com/2673-9976/27/1/55/pdf
Reference5 articles.
1. Cereal crops soil parameters retrieval using L-Band ALOS-2 and C-Band Sentinel-1 Sensors;Ayari;Remote Sens.,2021
2. Bousbih, S., Zribi, M., Lili-Chabaane, Z., Mougenot, B., Pelletier, C., El Hajj, M., and Baghdadi, N. (2020, January 9–11). Sentinel-1 and Sentinel-2 data for characterizing the states of continental surface over a semi-arid region in Tunisia. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.
3. Mekki, I., Ferchichi, I., Taouajouti, N., Faysse, N., and Zaïri, A.A. (2022). Oasis extension trajectories in Kebili territory, Southern Tunisia: Drivers of development and actors’ discourse. New Medit Mediterr. J. Econ. Agric. Environ., 85–101.
4. Investigating the correlation between radar backscatter and in situ soil property measurements;Han;Int. J. Appl. Earth Obs. Geoinf.,2017
5. Ettalbi, M., Baghdadi, N., Garambois, P.-A., Bazzi, H., Ferreira, E., and Zribi, M. (2023). Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images. Remote Se1S S²Qwns, 15.
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