The Open UniversitySkip to content
 

Global Sensitivity Analysis of Polarimetric Data to Retrieve Biophysical Parameters of Canola and Barley Crops

Erten, Esra; Taskin, Gulsen and Lopez-Sanchez, Juan M. (2018). Global Sensitivity Analysis of Polarimetric Data to Retrieve Biophysical Parameters of Canola and Barley Crops. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Piscataway, NJ, pp. 3844–3847.

Full text available as:
Full text not publicly available (Version of Record)
Due to publisher licensing restrictions, this file is not available for public download
Click here to request a copy from the OU Author.
DOI (Digital Object Identifier) Link: https://doi.org/10.1109/IGARSS.2018.8518169
Google Scholar: Look up in Google Scholar

Abstract

Tracking crop’s biophysical parameters using temporal Pol-SAR (Polarimetric Synthetic Aperture Radar Data) data is an active research topic in precision agriculture due to the sensitivity of PolSAR acquisition to canopy’s physical and geometrical structure. Reconstruction of polarimetric features from collection of SAR data is computationally expensive, and more important, the inter-features correlations cause decreased performance in regression based biophysical parameter estimation. With the scope of operational crop monitoring, this study provides key variables to drive Leaf Area Index (LAI) from polarimetric data based on global sensitivity analysis (GSA) addressing the ranking of the most influential features. We applied variance-based GSA for temporal fully polarimetric RadarSAT-2 images acquired through the cultivation period of two crops; canola and barley. Among 20 polarimetric features, anisotropy and correlation magnitude between co-polar channels were found to be the most influential polarimetric features for canola and barley, respectively.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Institute of Electrical and Electronics Engineers, Inc.
ISBN: 1-5386-7150-6, 978-1-5386-7150-4
Keywords: polarimetry; SAR; precision agriculture; LAI; metamodels
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Physical Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Space
Item ID: 57630
Depositing User: Esra Erten
Date Deposited: 26 Nov 2018 10:45
Last Modified: 02 May 2019 19:51
URI: http://oro.open.ac.uk/id/eprint/57630
Share this page:

Metrics

Altmetrics from Altmetric

Citations from Dimensions

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU