Garbulsky M.F., Filella I., Verger A., Penuelas J. (2014) Photosynthetic light use efficiency from satellite sensors: From global to Mediterranean vegetation. Environmental and Experimental Botany. 103: 3-11.LinkDoi: 10.1016/j.envexpbot.2013.10.009
Recent advances in remote-sensing techniques for light use efficiency (LUE) are providing new possibilities for monitoring carbon uptake by terrestrial vegetation (gross primary production, GPP), in particular for Mediterranean vegetation types. This article reviews the state of the art of two of the most promising approaches for remotely estimating LUE: the use of the photochemical reflectance index (PRI) and the exploitation of the passive chlorophyll fluorescence signal. The theoretical and technical issues that remain before these methods can be implemented for the operational global production of LUE from forthcoming hyperspectral satellite data are identified for future research. © 2013 Elsevier B.V.
Verger A., Baret F., Weiss M. (2014) Near real-time vegetation monitoring at global scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7: 3473-3481.LinkDoi: 10.1109/JSTARS.2014.2328632
The NRT algorithm for near-real time estimation of global LAI, FAPAR, and FCOVER variables from SPOT/VEGETATION (VGT) satellite data is described here. It consists of three steps: 1) neural networks (NNT) (one for each variable) to provide instantaneous estimates from daily VGT-P reflectances; 2) a multistep filtering approach to eliminate data mainly affected by atmospheric effects and snow cover; and 3) Savitzky-Golay and climatology temporal smoothing and gap filling techniques to ensure consistency and continuity as well as short-term projection of the product dynamics. Performances of NRT estimates were evaluated by comparing with other products over the 2005-2008 period: 1) the offline estimates from the application of the algorithm over historical time series (HIST); 2) the geoland2 version 1 products also issued from VGT (GEOV1/VGT); and 3) ground data. NRT rapidly converges closely to the HIST processing after six dekads (10-day period) with major improvement after two dekads. Successive reprocessing will, therefore, correct for some instabilities observed in the presence of noisy and missing data. The root-mean-square error (RMSE) between NRTand HIST LAI is lower than 0.4 in all cases. It shows a rapid exponential decay with the number of observations in the composition window with convergence when 30 observations are available. NRT products are in good agreement with ground data (RMSE of 0.69 for LAI, 0.09 for FAPAR, and 0.14 for FCOVER) and consistent with GEOV1/VGT products with a significant improvement in terms of continuity (only 1% of missing data) and smoothness, especially at high latitudes, and Equatorial areas.
Verger A., Vigneau N., Cheron C., Gilliot J.-M., Comar A., Baret F. (2014) Green area index from an unmanned aerial system over wheat and rapeseed crops. Remote Sensing of Environment. : 0-0.LinkDoi: 10.1016/j.rse.2014.06.006
Unmanned airborne systems (UAS) technology opens new horizons in precision agriculture for effective characterization of the variability in crop state at high spatial resolution and high revisit frequency. Green area index (GAI) is a key agronomic variable involved in many processes and used for decision making. This paper describes a physically based algorithm for estimating GAI from UAS acquisitions. The UAS plane platform used here was equipped with four cameras in green (550 nm), red (660 nm), red-edge (735 nm) and near infrared (790 nm). It provided multiple views by overlapping images along and between the tracks. A lookup table was generated to invert the PROSAIL radiative transfer model using the reflectances in the four bands and the specific view-sun angles for each individual image. The average of the ensemble of solutions corresponding to the individual images allows regularizing the solutions of the ill posed inverse problem. Around six images were required to get stable GAI estimates and the corresponding root mean square error (RMSE) value was used as a proxy for the associated uncertainties. Comparison with ground based measurements showed that the accuracy of UAS GAI estimates over wheat and rapeseed crops was around 0.2 in terms of RMSE. The use of normalized reflectances compared to absolute reflectances improved the performances of GAI estimates (0.17 compared to 0.26 GAI in terms of RMSE) particularly under unstable illumination conditions. High repeatability in the estimates from UAS flights at different acquisition times was observed. The use of the red-edge band normalized (absolute) reflectances brought 30% (10%) improvement of accuracy for the low to medium GAI values. © 2014 Elsevier Inc. All rights reserved.
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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