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

Remote Sensing

Emilie Bériaux¹  François Waldner¹  François Collienne²  Patrick Bogaert²  Pierre Defourny²  Heiko Balzter²  Clement Atzberger² 

[1] Earth and Life Institute—Environment, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium

关键词: Leaf Area Index;    water cloud model;    SAR;    model stability;    Bayesian fusion;   

DOI  :  10.3390/rs71215818

来源: mdpi

【 摘 要 】

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, 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.