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Remote Sens. 2013, 5(10), 5265-5284; doi:10.3390/rs5105265
Article

Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data

1
,
2,3
 and
1,*
1 EOLAB, Parc Científic Universitat de València, C/ Catedràtic José Beltrán, 2, E-46980 Paterna, Spain 2 Global Ecology Unit, CREAF-CEAB-CSIC-UAB, E-08193 Cerdanyola del Vallés, Spain 3 EMMAH-UMR 1114-INRA UAPV, Domain Saint Paul, Site Agroparc, F-84914 Avignon, France
* Author to whom correspondence should be addressed.
Received: 31 July 2013 / Revised: 11 October 2013 / Accepted: 14 October 2013 / Published: 21 October 2013
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Abstract

Efficient monitoring of Canopy Water Content (CWC) is a central feature in vegetation studies. The potential of hyperspectral high spatial resolution CHRIS/PROBA satellite data for the retrieval of CWC was here investigated using empirical and physical based approaches. Special attention was paid to the spectral band selection, inversion technique and training process. Performances were evaluated with ground measurements from the SEN3EXP field campaign over a range of crops. Results showed that the optimal band selection includes four spectral bands: one centered about 970 nm absorption feature which is sensible to Cw, and three bands in green, red and near infrared to estimate LAI and compensate from leaf- and canopy-level effects. A simple neural network with a single hidden layer of five tangent sigmoid transfer functions trained over PROSAIL radiative transfer simulations showed benefits in the retrieval performances compared with a look up table inversion approach (root mean square error of 0.16 kg/m2 vs. 0.22 kg/m2). The neural network inversion approach showed a good agreement and performances similar to an empirical up-scaling approach based on a multivariate iteratively re-weighted least squares algorithm, demonstrating the applicability of radiative transfer model inversion methods to CHRIS/PROBA for high spatial resolution monitoring of CWC.
Keywords: canopy water content; model inversion; neural networks; look up tables; empirical up-scaling; CHRIS/PROBA canopy water content; model inversion; neural networks; look up tables; empirical up-scaling; CHRIS/PROBA
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.

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Cernicharo, J.; Verger, A.; Camacho, F. Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data. Remote Sens. 2013, 5, 5265-5284.

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