Estimation of soil moisture from remote sensing products using an ensemble machine learning model: a case study of Lake Urmia Basin, Iran-Reference-Cited by-同舟云学术

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Estimation of soil moisture from remote sensing products using an ensemble machine learning model: a case study of Lake Urmia Basin, Iran

Published:2023-12-02 Issue:1 Volume:17 Page:385-400
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Asadollah Seyed Babak Haji Seyed,Sharafati Ahmad,Saeedi Mohammad,Shahid Shamsuddin

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-023-01172-8.pdf

Reference92 articles.

1. Abbasian MS, Najafi MR, Abrishamchi A (2021) Increasing risk of meteorological drought in the Lake Urmia basin under climate change: introducing the precipitation–temperature deciles index. J Hydrol 592:125586

2. Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data: a machine learning approach. Adv Water Resour 33:69–80

3. Albergel C, De Rosnay P, Gruhier C, Muñoz-Sabater J, Hasenauer S, Isaksen L, Kerr Y, Wagner W (2012) Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sens Environ 118:215–226. https://doi.org/10.1016/j.rse.2011.11.017

4. Alcaras E, Amoroso PP, Parente C (2022) The influence of interpolated point location and density on 3D bathymetric models generated by kriging methods: an application on the Giglio Island Seabed (Italy). Geosciences 12:62

5. Ali I, Greifeneder F, Stamenkovic J, Neumann M, Notarnicola C (2015) Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens 7:16398–16421

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