Brandt M., Verger A., Diouf A.A., Baret F., Samimi C. (2014) Local vegetation trends in the sahel of mali and senegal using long time series FAPAR satellite products and field measurement (1982-2010). Remote Sensing. 6: 2408-2434.LinkDoi: 10.3390/rs6032408
Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits. The spatial variability in the observed vegetation trends in the Sahel area are mainly caused by varying tree-and land-cover, which are controlled by human impact, soil and drought resilience. A large proportion of the positive trends are caused by the increment in leaf biomass of woody species that has almost doubled since the 1980s due to a tree cover regeneration after a dry-period. This confirms the re-greening of the Sahel, however, degradation is also present and sometimes obscured by greening. GEOV1 as compared to GIMMS3g made it possible to better characterize the spatial pattern of trends and identify the degraded areas in the study region. © 2014 by the authors; licensee MDPI, Basel, Switzerland.
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.
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