Initial Experiments with a Scalable Machine Learning Based Approach for Downscaling the MOD16A2 Evapotranspiration Product
Author:
Affiliation:
1. Computer Science and Engineering, Indian Institute of Technology, India
2. School of Information Technology, Indian Institute of Technology, India
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3674829.3675068
Reference39 articles.
1. 2021. India State of Forest Report (2021). Technical Report. Forest Survey of India, Dehradun.
2. Richard Allan L. Pereira and Martin Smith. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Vol. 56.
3. H. Beaudoing and M. Rodell. 2020. NASA/GSFC/HSL GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1. Greenbelt Maryland USA Goddard Earth Sciences Data and Information Services Center (GES DISC) (2020). https://doi.org/10.5067/E7TYRXPJKWOQ
4. Ling Bian and Rachel Butler. 1999. Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data. Photogrammetric engineering and remote sensing 65 (1999), 73–84.
5. Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian’s Northeast
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