Comparison of Google Earth Engine Machine Learning Algorithms for Mapping Smallholder Irrigated Areas in a Mountainous Watershed, Upper Blue Nile Basin, Ethiopia-Reference-Cited by-同舟云学术

Comparison of Google Earth Engine Machine Learning Algorithms for Mapping Smallholder Irrigated Areas in a Mountainous Watershed, Upper Blue Nile Basin, Ethiopia

Published:2024-03 Issue:3 Volume:52 Page:569-582
ISSN:0255-660X
Container-title:Journal of the Indian Society of Remote Sensing
language:en
Short-container-title:J Indian Soc Remote Sens

Author:

Mekonnen Yilkal Gebeyehu^ORCID,Alamirew Tena,Chukalla Abebe Demissie,Hunegnaw Amare Tesfaw,Malede Demelash Ademe

Funder

Africa Centre of Excellence for Water Management

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12524-024-01846-w.pdf

Reference59 articles.

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2. Ajaz, A., Karimi, P., Cai, X., De Fraiture, C., & Akhter, M. S. (2019). Statistical data collection methodologies of irrigated areas and their limitations: a review. Irrigation and Drainage, 68(4), 702–713. https://doi.org/10.1002/ird.2365

3. Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14(19), 4978. https://doi.org/10.3390/rs14194978

4. Basukala, A. K., Oldenburg, C., Schellberg, J., Sultanov, M., & Dubovyk, O. (2017). Towards improved land use mapping of irrigated croplands: Performance assessment of different image classification algorithms and approaches. European Journal of Remote Sensing, 50(1), 187–201. https://doi.org/10.1080/22797254.2017.1308235

5. Bazzi, H., Baghdadi, N., Amin, G., Fayad, I., Zribi, M., Demarez, V., & Belhouchette, H. (2021). An operational framework for mapping irrigated areas at plot scale using Sentinel-1 and Sentinel-2 data. Remote Sensing. https://doi.org/10.3390/rs13132584

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