Caracterizacion de la fenología de la vegetación a escala global mediante series temporales SPOT VEGETATION

Altres ajuts: Programes Copernicus, le Pôle Thématique Surfaces Continentales THEIA, GIOBIO (32-566) i LONGLOVE (32-594).La fenología de la vegetación a escala global se caracterizó a partir de series temporales del índice de área foliar (LAI) SPOT VEGETATION a 1-km de resolución espacial en el periodo 1999-2010. Los patrones espaciales de la fenología estimada a partir de datos de satélite muestran una gran consistencia con la distribución de biomas y factores climáticos. La comparación de la fenología SPOT VEGETATION con medidas in-situ para las fenofases del abedul común (Betula pendula) en Europa muestra un gran acuerdo en el gradiente latitudinal de temperatura con un descenso en la duración de la estación de crecimiento de 5 días por grado de latitudWe characterized the phenology of the vegetation at the global scale from the mean seasonal leaf area index (LAI) estimated from 1-km SPOT VEGETATION time series for 1999-2010. The satellite-derived phenology was spatially consistent with the global distributions of climatic drivers and biome land cover. The rate of change of phenological leaf development from VEGETATION data and in-situ observations for the date of phenophases of European birch forests agreed very well with latitudinal temperature with a decrease in the length of season of approximately five days per degree of latitude

Leaf Area Index (LAI) monitoring at global scale (improved definition, continuity and consistency of LAI estimates from kilometric satellite observations)

Le suivi des variables biophysiques à l échelle globale sur de longues périodes de temps est essentiellepour répondre aux nouveaux enjeux que constituent le changement climatique et la sécurité alimentaire. L indice foliaire (LAI) est une variable de structure définissant la surface d interception du rayonnement incident et d échanges gazeux avec l atmosphère. Le LAI est donc une variable importante des modèles d écosystèmes et a d ailleurs été reconnue comme variable climatique essentielle (ECV). Cette thèse a pour objectif de fournir des estimations globales et continues de LAI à partir d observations satellitaires en temps quasi-réel en réponse aux besoins des utilisateurs pour fournir des diagnostiques et pronostiques de l état et du fonctionnement de la végétation. Quelques produits LAI sont déjà disponibles mais montrent des désaccords et des limitations en termes de cohérence et de continuité. Cette thèse a pour objectif de lever ces limitations. Dans un premier temps, on essaiera de mieux définir la nature des estimations de LAI à partir d observations satellitaires. Puis, différentes méthodes de lissage te bouchage des séries temporelles ont été analysées pour réduire le bruit et les discontinuités principalement liées à la couverture nuageuse. Finalement quelques méthodes d estimation temps quasi réel ont été évaluées en considérant le niveau de bruit et les données manquantes.Les résultats obtenus dans la première partie de cette thèse montrent que la LAI effectif et bien mieux estimé que la valeur réelle de LAI du fait de l agrégation des feuilles observée au niveau du couvert. L utilisation d observations multidirectionnelles n améliore que marginalement les performances d estimation. L étude montre également que les performances d estimation optimales sont obtenues quand les solutions sont recherchées à l intérieur d une enveloppe définie par l incertitude associée aux mesures radiométriques. Dans la deuxième partie consacrée à l amélioration de la continuité et la cohérence des séries temporelles, les méthodes basées sur une fenêtre temporelle locale mais de largeur dépendant du nombre d observations présentes, et utilisant la climatologie comme information a priori s avèrent les plus intéressantes autorisant également l estimation en temps quasi réel.Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

Special Issue on Global Land Product Validation

Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.This work was supported in part by the National Aeronautics and Space Administration under Grants EOS/03-0408-0637 and NNG04GL85G

Vegetation baseline phenology from kilometric global LAI satellite products

Land surface phenology derived from remotely sensed satellite data can substantially improve our macroecological knowledge and the representation of phenology in earth system models. We characterized the baseline phenology of the vegetation at the global scale from the GEOCLIM climatology of leaf area index (LAI) estimated from 1-km SPOT-VEGETATION time series for 1999-2010. The phenological metrics were calibrated over an ensemble of ground observations of the timing of leaf unfolding and autumnal colouring of leaves. The start and end of season were best identified using respectively 30% and 40% threshold of LAI amplitude values. The accuracy of the derived phenological metrics, evaluated using available ground observations for birch forests over Europe (and lilac shrubs over North America), improved as compared to those derived from MODIS-EVI and produced an overall root mean square error of 7 days (19 days) for the timing of the start of season, 15 for the end of season, and 16 for the length of season. The spatial patterns of the derived LAI phenology agreed well with those from MODIS-EVI and -NDVI, although the timing of the start, end, and length of season differed by about one month at the global scale, with higher uncertainties in areas of limited seasonality of the satellite signal and systematic biases due to the differences in the methodologies and datasets. The baseline LAI phenology was spatially consistent with the global distributions of climatic drivers and biome land cover

Forest species mapping using airborne hyperspectral APEX data

Abstract The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer's accuracy ranging from 60% to 86% and user's accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way

Using Automatic Differentiation to study the sensitivity of a crop model

Automatic Differentiation methods are often applied to codes that solve partial differential equations, e.g. in the domains of geophysical sciences, such as meteorology or oceanography, or Computational Fluid Dynamics. In agronomy, the differentiation of crop model has never been performed since the models are not fully deterministic but much more empirical. This study shows the feasability of constructing the adjoint model of a crop model referent in the agronomic community (STICS) with the TAPENADE tool, and the use of this adjoint to perform some robust sensitivity analysis. This aims at giving a return of experience from users working in the environmental thematic, and presents a somewhat unusual field of application of Automatic Differentiation

GEOCLIM : a global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999-2010

Altres ajuts: Programes Copernicus, le Pôle Thématique Surfaces Continentales THEIA, GIOBIO (32-566) i LONGLOVE (32-594).Land-surface modelling would benefit significantly from improved characterisation of the seasonal variability of vegetation at a global scale. GEOCLIM, a global climatology of leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR)-both essential climate variables-and fraction of vegetation cover (FCOVER), is here derived from observations from the SPOT VEGETATION programme. Interannual average values from the GEOV1 Copernicus Global Land time series of biophysical products at 1-km resolution and 10-day frequency are computed for 1999 to 2010. GEOCLIM provides the baseline characteristics of the seasonal cycle of the annual vegetation phenology for each 1-km pixel on the globe. The associated standard deviation characterises the interannual variability. Temporal consistency and continuity is achieved by the accumulation of multi-year observations and the application of techniques for temporal smoothing and gap filling. Specific corrections are applied over cloudy tropical regions and high latitudes in the Northern Hemisphere where the low number of available observations compromises the reliability of estimates. Artefacts over evergreen broadleaf forests and areas of bare soil are corrected based on the expected limited seasonality. The GEOCLIM data set is demonstrated to be consistent, both spatially and temporally. GEOCLIM shows absolute differences lower than 0.5 compared with MODIS (GIMMS3g) climatology of LAI for more than 80% (90%) of land pixels, with higher discrepancies in tropical and boreal latitudes. ECOCLIMAP systematically produces higher LAI values. The phenological metric for the date of maximum foliar development derived from GEOCLIM is spatially consistent (correlation higher than 0.9) with those of MODIS, GIMMS3g, ECOCLIMAP and MCD12Q2 with average differences within 14 days at the global scale

Local vegetation trends in the Sahel of Mali and Senegal using long time series FAPAR satellite products and field measurement (1982-2010)

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 Remote Sens. 2014, 6 2409 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

The MODIS (collection V006) BRDF/albedo product MCD43D: temporal course evaluated over agricultural landscape

The assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo product MCD43D over a Mediterranean agricultural area. Here, we present the results from the accuracy assessment of the MODIS blue-sky albedo. The analysis is based on collocated comparisons with higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situ measurements. The inter-sensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type, we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and − 0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty < 3.8% and accuracy < 0.003). The study demonstrates that neglecting the MODIS PSF when comparing the Formosat-2 albedo against the MODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedo