Evaluating capabilities of the spline and cubic spline interpolation functions in reference evapotranspiration estimation implementing satellite image data
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01127-z.pdf
Reference41 articles.
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2. Amani S, Shafizadeh-Moghadam H (2023) A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agric Water Manag. 284:108324. https://doi.org/10.1016/j.agwat.2023.108324
3. Araghi A, Martinez CJ, Adamowski JF (2023) Evaluation of TerraClimate gridded data across diverse climates in Iran. Earth Sci Inf 16:1347–1358. https://doi.org/10.1007/s12145-023-00967-z
4. Badescu V (2008) Modeling Solar Radiation at the Earth's Surface. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-77455-6
5. Bayram S, Çıtakoğlu H (2023) Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environ Monit Assess 195(1):67. https://doi.org/10.1007/s10661-022-10662-z
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