Qian (2026) Dataset for developing and validating surface soil moisture estimation and spatiotemporal transfer models in agricultural regions

Identification

• Journal: Mendeley Data
• Year: 2026
• Date: 2026-09-14
• Authors: Jiaxin Qian
• DOI: 10.17632/dvrcjn882h

Research Groups

• Department of Earth Sciences, University of Manitoba
• Department of Soil Science, University of Manitoba

Short Summary

This study presents a dataset for developing and validating surface soil moisture estimation models in agricultural regions, with a focus on Manitoba, Canada.

Objective

• Investigate the feasibility of using remote sensing data to estimate surface soil moisture in agricultural areas.

Study Configuration

• Spatial Scale: Regional scale (Manitoba, Canada)
• Temporal Scale: Long-term monitoring (multiple years)

Methodology and Data

• Models used: Surface soil moisture estimation models based on satellite data (e.g., Sentinel-1, Sentinel-2)
• Data sources: Satellite remote sensing data, in-situ measurements from weather stations and soil moisture sensors

Main Results

• The dataset provides a comprehensive collection of surface soil moisture estimates at various spatial and temporal scales.
• The results show that the developed models can accurately estimate surface soil moisture with high precision.

Contributions

• This study contributes to the development of reliable surface soil moisture estimation models for agricultural regions, which is essential for optimizing water management practices and crop yields.
• The dataset presented in this study can be used as a benchmark for evaluating the performance of various surface soil moisture estimation models.

Funding

• This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grants program.

Citation

@article{Qian2026Dataset,
  author = {Qian, Jiaxin},
  title = {Dataset for developing and validating surface soil moisture estimation and spatiotemporal transfer models in agricultural regions},
  journal = {Mendeley Data},
  year = {2026},
  doi = {10.17632/dvrcjn882h},
  url = {https://doi.org/10.17632/dvrcjn882h}
}