Cernicharo J., Verger A., Camacho F. (2013) Empirical and physical estimation of Canopy Water Content: From CHRIS/PROBA Data. Remote Sensing. 5: 5265-5284.LinkDoi: 10.3390/rs5105265
Efficient monitoring of Canopy Water Content (CWC) is a central feature invegetation studies. The potential of hyperspectral high spatial resolution CHRIS/PROBAsatellite 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. © 2013 by the authors.
Verger A., Baret F., Weiss M. (2013) GEOV2/VGT: Near real time estimation of global biophysical variables from VEGETATION-P data. MultiTemp 2013 - 7th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: "Our Dynamic Environment", Proceedings. : 0-0.LinkDoi: 10.1109/Multi-Temp.2013.6866023
The GEOV2 algorithm for continuous, consistent and near real time estimation of Leaf Area Index (LAI), fraction of absorbed photosynthetic active radiation (FAPAR) and vegetation cover fraction (FCOVER) from daily VEGETATION-P satellite data is here described. It consists of a series of procedures including (1) neural networks for providing instantaneous estimates from VGT-P reflectances, (2) a multi-step filtering approach to eliminate contaminated data mainly affected by atmospheric effects and snow cover, and (3) temporal techniques for ensuring consistency and continuity as well as short term projection of the product dynamics. First validation results show that GEOV2/VGT products have high consistency with previous GEOV1/VGT products and show similar accuracy levels as compared to ground measurements. GEOV2 significantly improves GEOV1 in terms of continuity (less than 1% of missing data in GEOV2 as compared to the 20% of gaps in GEOV1) and consistency (smoother products less affected by noise in the data), specially at high latitudes and Equatorial areas. Global GEOV2/VGT products at 1/112° spatial resolution for the period 1999-present with near real time estimates every 10 days will be freely available at Copernicus portal (http://land.copernicus.eu). © 2013 IEEE.
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