Remote Sensing | |
Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation | |
Remi Madelon1  Mehrez Zribi1  Nemesio Rodriguez-Fernandez1  Simon Nativel1  Emna Ayari1  Nicolas Baghdadi2  Clement Albergel3  | |
[1] CESBIO, CNES/CNRS/INRAE/IRD/UPS, Université de Toulouse, 18 Av. Edouard Belin, Bpi 2801, CEDEX 9, 31401 Toulouse, France;CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France;European Space Agency Climate Office, ECSAT, Harwell Campus, Oxforshire, Didcot OX11 0FD, UK; | |
关键词: soil moisture; Sentinel-1; Sentinel-2; change detection; artificial neural network; | |
DOI : 10.3390/rs14102434 | |
来源: DOAJ |
【 摘 要 】
Soil moisture is an essential parameter for a better understanding of water processes in the soil–vegetation–atmosphere continuum. Satellite synthetic aperture radar (SAR) is well suited for monitoring water content at fine spatial resolutions on the order of 1 km or higher. Several methodologies are often considered in the inversion of SAR signals: machine learning techniques, such as neural networks, empirical models and change detection methods. In this study, we propose two hybrid methodologies by improving a change detection approach with vegetation consideration or by combining a change detection approach together with a neural network algorithm. The methodology is based on Sentinel-1 and Sentinel-2 data with the use of numerous metrics, including vertical–vertical (VV) and vertical–horizontal (VH) polarization radar signals, the classical change detection surface soil moisture (SSM) index
【 授权许可】
Unknown