Unsupervised self-training method based on deep learning for soil moisture estimation using synergy of sentinel-1 and sentinel-2 images
Author:
Affiliation:
1. FRB-CESAB, Montpellier, France
2. RIADI Laboratory, National School of Computer Science, Manouba, Tunisia
3. Laboratoire d’Eremologie et de LCD (LR16IRA01), Institut des Regions Arides (IRA), University of Gabes, Gabes, Tunisia
Funder
The authors have no funding to report
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences,Computer Science Applications
Link
https://www.tandfonline.com/doi/pdf/10.1080/19479832.2022.2106317
Reference31 articles.
1. Soil Moisture Estimation From Smap Observations Using Long Short- Term Memory (LSTM)
2. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data
3. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale
4. Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation
5. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning
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