A machine learning-based framework for spatio-temporal extension and filling of SMOS surface soil moisture observations over Canada
Author:
Affiliation:
1. Department of Civil Engineering and Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, Canada
Funder
Canadian Space Agency
Trottier Institute for Sustainability in Engineering and Design
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2371083
Reference32 articles.
1. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape
2. Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe
3. Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)
4. Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS
5. Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau
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