Selection of PolSAR Observables for Crop Biophysical Variable Estimation With Global Sensitivity Analysis

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Título: Selection of PolSAR Observables for Crop Biophysical Variable Estimation With Global Sensitivity Analysis
Autor/es: Erten, Esra | Taşkın, Gülşen | Lopez-Sanchez, Juan M.
Grupo/s de investigación o GITE: Señales, Sistemas y Telecomunicación
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal
Palabras clave: Agriculture | Global sensitivity analysis (GSA) | Polarimetry | Radarsat-2 | Synthetic aperture radar
Área/s de conocimiento: Teoría de la Señal y Comunicaciones
Fecha de publicación: 1-feb-2019
Editor: IEEE
Cita bibliográfica: IEEE Geoscience and Remote Sensing Letters. 2019. doi:10.1109/LGRS.2019.2891953
Resumen: The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), is utilized to develop a regression method in conjunction with a GSA in the context of determining key input drivers in the estimation of crop biophysical variables from polarimetric synthetic aperture radar data. A multitemporal Radarsat-2 data set is used for the retrieval of three biophysical variables of barley: leaf area index, normalized difference vegetation index, and Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie stage. The HDMR technique is first adopted to estimate a regression model with all available polarimetric features for each biophysical parameter, and sensitivity indices of each feature are then derived to explain the original space with a smaller number of features in which a final regression model is established. To evaluate the applicability of this methodology, root-mean square and coefficient of determination were performed under different amounts of samples. Results highlight that HDMR can be used effectively in biophysical variable estimation for not only reducing computational cost but also for providing a robust regression.
Patrocinador/es: The authors would like to thank the support of the Scientific Research Projects Coordination of Istanbul Technical University under Project MGA-2018-41152. This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and the European Funds for Regional Development (FEDER) under Project TEC2017-85244-C2-1-P.
URI: http://hdl.handle.net/10045/88208
ISSN: 1545-598X (Print) | 1558-0571 (Online)
DOI: 10.1109/LGRS.2019.2891953
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 2019 IEEE
Revisión científica: si
Versión del editor: https://doi.org/10.1109/LGRS.2019.2891953
Aparece en las colecciones:INV - SST - Artículos de Revistas

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