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|Title:||Experimental Assessment of the Sentinel-2 Band Setting For RTM-Based LAI Retrieval of Sugar Beet and Maize|
|Citation:||CANADIAN JOURNAL OF REMOTE SENSING vol. 35 no. 3 p. 230-247|
|Publisher:||CANADIAN AERONAUTICS SPACE INST|
|Type:||Articles in periodicals and books|
|Abstract:||The present work aimed at testing the potential of the upcoming E.O. satellite Sentinel-2 (European GMES/Kopernikus programme) for the operational estimation of the Leaf Area Index (LAI) of two contrasting agricultural crops (sugar beet and maize). Mapping of LAI was achieved by using a Look-up table (LUT) based inversion of a physically based radiative transfer model (SAILH+PROSPECT). Besides the Sentinel-2 spectral sampling, another band set described as ¿ideal¿ for vegetation studies, has been evaluated in a comparative way. Analyses were mainly carried out using hyperspectral data acquired by the optical airborne instrument CASI during the ESA AgriSAR 2006 campaign. Additionally, data from two other experiments were tested to extend the validation database. Alternative inversion methods, i.e. an iterative optimization technique (SQP) and a neural network (NN) have been evaluated for comparison purposes. The GMES/Kopernikus defined precision of 10 % for LAI estimation, evaluated with in situ LAI measurements, was met for sugar beet (8-9 %), but not for maize (16-22%). The inversion approach and band setting had only a minor influence on the retrieval accuracy, with the only exception of the iterative optimization technique which failed to give reliable results. The results demonstrate the importance of using an appropriate radiative transfer model for each crop. For row crops with strong leaf clumping and not covering completely the soil surface, such as maize at early stage, the standard SAILH+PROSPECT does not appear suitable.|
|JRC Directorate:||Space, Security and Migration|
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