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Remote Sens. 2015, 7(12), 16204-16225; doi:10.3390/rs71215818

Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Earth and Life Institute—Environment, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
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Academic Editors: Heiko Balzter, Clement Atzberger and Prasad S. Thenkabail
Received: 14 September 2015 / Revised: 17 November 2015 / Accepted: 19 November 2015 / Published: 3 December 2015
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Abstract

In order to monitor crop growth along the season with synthetic aperture radar (SAR) images, radiative transfer models were developed to retrieve key biophysical parameters, such as the Leaf Area Index (LAI). The semi-empirical water cloud model (WCM) can be used to estimate LAI values from SAR data and surface soil moisture information. Nevertheless, instability problems can occur during the model calibration, which subsequently reduce its transferability in both time and space. To avoid these ill-posed cases, three calibration methodologies are benchmarked in the present study. The accuracy of the retrieved LAI values for each methodology was analyzed, as well as the sensitivity of the signal to LAI for different soil moisture values. The sensitivity of the cross-polarization was highlighted especially for high LAI. The VV polarization was found sensitive for LAI values inferior to 2 m 2 /m 2 . Given the differential sensitivity of the C-band backscatter to maize canopies in each polarization, a Bayesian fusion of the LAI estimates in linear polarizations was developed. This fusion gives lower weights to estimates with a high uncertainty. This method systematically reduces the error and its associated variance. When considering all polarizations, the RMSE on LAI estimation decreased by 0.32 m 2 /m 2 , i.e., one fourth of the error value, as compared to the best estimation from a single polarization, and the associated uncertainty was reduced by a factor of two. Focusing on the two most sensitive polarizations to maize canopies (VV-HV), the error diminished by a third. This fusion framework shows thus a great potential to improve the accuracy and reliability of LAI retrieval of C-band quad-polarized data, as well as dual-polarized data, such as Sentinel-1. View Full-Text
Keywords: Leaf Area Index; water cloud model; SAR; model stability; Bayesian fusion Leaf Area Index; water cloud model; SAR; model stability; Bayesian fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bériaux, E.; Waldner, F.; Collienne, F.; Bogaert, P.; Defourny, P. Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model. Remote Sens. 2015, 7, 16204-16225.

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