A comparison between the use of machine learning techniques and the water cloud model for the retrieval of soil moisture from Sentinel-1A and Sentinel-2A products-Reference-Cited by-同舟云学术

A comparison between the use of machine learning techniques and the water cloud model for the retrieval of soil moisture from Sentinel-1A and Sentinel-2A products

Published:2022-08-24 Issue:10 Volume:13 Page:980-990
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Inoubli R.¹,Bennaceur L.²,Jarray N.¹³,Ben Abbes A.¹⁴^ORCID,Farah I. R.¹

Affiliation:

1. RIADI Laboratory, National School of Computer Science, Manouba, Tunisia

2. LTSIRS (Laboratoire de Télédétection et Systèmes Information à Référence Spatial) ENIT, Manar University, Tunis, Tunisia

3. Laboratoire d’Erémologie et deLCD (LR16IRA01), Institut des Régions Arides, University of Gabès, Médenine, Tunisia

4. FRB-CESAB: Centre de Syntheseet d’Analyse sur la Biodiversite, Montpellier, France

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2022.2115862

Reference14 articles.

1. Soil Moisture Estimation From Smap Observations Using Long Short- Term Memory (LSTM)

2. Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

3. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

4. Vegetation modeled as a water cloud

5. Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

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