Ariyasena (2026) Suranjith19921023/RZSM-Loess-Plateau: Initial release — manuscript submission

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-03-30
• Authors: Suranjith Ariyasena
• DOI: 10.5281/zenodo.19325353

Research Groups

• Northwest A&F University

Short Summary

This study presents a machine learning framework for estimating multi-layer root zone soil moisture on the Loess Plateau, utilizing bias-corrected ERA5-Land reanalysis data.

Objective

• To develop and apply a machine learning framework for estimating multi-layer root zone soil moisture on the Loess Plateau using bias-corrected ERA5-Land reanalysis data.

Study Configuration

• Spatial Scale: Loess Plateau
• Temporal Scale: Not explicitly stated in the provided text.

Methodology and Data

• Models used: Machine Learning Framework (specific models not detailed in this snippet).
• Data sources: Bias-Corrected ERA5-Land Reanalysis Data.

Main Results

• Not available in the provided software release description.

Contributions

• Provides the code and data for a novel machine learning framework designed for multi-layer root zone soil moisture estimation on the Loess Plateau, leveraging bias-corrected ERA5-Land reanalysis data.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Ariyasena2026Suranjith19921023RZSMLoessPlateau,
  author = {Ariyasena, Suranjith},
  title = {Suranjith19921023/RZSM-Loess-Plateau: Initial release — manuscript submission},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19325353},
  url = {https://doi.org/10.5281/zenodo.19325353}
}