Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models

This work aims to assess the effectiveness of machine learning (ML) algorithms and semiempirical models for surface soil moisture (SSM) retrieval by exploring the Sentinel-1 backscatter and interferometric coherence data. First, three commonly used categories of ML algorithms are evaluated using dat...

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Bibliographic Details
Main Authors: Jamal Ezzahar, Abdelghani Chehbouni, Nadia Ouaadi, Mohammed Madiafi, Khabba Said, Salah Er-Raki, Ahmed Laamrani, Adnane Chakir, Zohra Lili Chabaane, Mehrez Zribi
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Backscattering models
irrigated and rainfed winter wheat
machine learning (ML)
semiarid regions
Sentinel-1 backscatter and interferometric coherence
surface soil moisture (SSM)
Online Access:https://ieeexplore.ieee.org/document/10342715/

Internet

https://ieeexplore.ieee.org/document/10342715/

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