Laurent Kergoat

Grassland modeling and monitoring with SPOT-4 VEGETATION instrument during the 1997-1999 SALSA experiment.

A coupled vegetation growth and soil‐vegetation‐atmosphere transfer (SVAT) model is used in conjunction with data collected in the course of the SALSA program during the 1997‐1999 growing seasons in Mexico. The objective is to provide insights on the interactions between grassland dynamics and water and energy budgets. These three years exhibit drastically different precipitation regimes and thus different vegetation growth. The result of the coupled model showed that for the 3 years, the observed seasonal variation of plant biomass, leaf area index (LAI) are well reproduced by the model. It is also shown that the model simulations of soil moisture, radiative surface temperature and surface fluxes compared fairly well with the observations. Reflectance data in the red, near infrared, and short wave infrared (SWIR, 1600 nm) bands measured by the VEGETATION sensor onboard SPOT-4 were corrected from atmospheric and directional effects and compared to the observed biomass and LAI during the 1998‐1999 seasons. The results of this ‘ground to satellite’ approach established that the biomass and LAI are linearly related to the satellite reflectances (RED and SWIR), and to vegetation indices (NDVI and SWVI, which is a SWIR-based NDVI). The SWIR and SWVI sensitivity to the amount of plant tissues were similar to the classical RED and NDVI sensitivity, for LAI ranging from 0 and 0.8 m 2 m 2 and biomass ranging from 0 to 120 g DM m 2 . Finally, LAI values simulated by the vegetation model were fed into a canopy radiative transfer scheme (a ‘model to satellite’ approach). Using two leaf optical properties datasets, the computed RED, NIR and SWIR reflectances and vegetation indices (NDVI and SWVI) compared reasonably well with the VEGETATION observations in 1998 and 1999, except for the NIR band and during the senescence period, when the leaf optical properties present a larger uncertainty. We conclude that a physically-sound linkage between the vegetation model and the satellite can be used for red to short wave infrared domain over these grasslands. These different results represent an important step toward using new generation satellite data to control and validate model’s simulations at regional scale.

Desertification, Adaptation and Resilience in the Sahel: Lessons from Long Term Monitoring of Agro-ecosystems

The desertification paradigm has a long history in the Sahel, from colonial to modern times. Despite scientific challenge, it continued to be influential after independence, revived by the dramatic droughts of the 1970s and 1980s, and was institutionalized at local, national and international levels. Collaborative efforts were made to improve scientific knowledge on the functioning, environmental impact and monitoring of selected agricultural systems over the long term, and to assess trends in the ecosystems, beyond their short term variability. Two case studies are developed here: the pastoral system of the arid to semi-arid Gourma in Mali, and the mixed farming system of the semi-arid Fakara in Niger. The pastoral landscapes are resilient to droughts, except on shallow soils, and to grazing, following a non-equilibrium model. The impact of cropping on the landscape is larger and longer lasting. It also induces locally high grazing pressure that pushes rangeland resilience to its limits. By spatial transfer of organic matter and mineral, farmers’ livestock create patches of higher fertility that locally enhance the system’s resilience. The agro-pastoral ecosystem remains non-equilibrial provided that inputs do not increase stocking rates disproportionately. Remote sensing confirms the overall re-greening of the Sahel after the drought of the 1980s, contrary to the paradigm of desertification. Ways forward are proposed to adapt the pastoral and mixed farming economies and their regional integration to the context of human and livestock population growth and expanding croplands.

Diagnostic and prognostic modeling of the terrestrial biosphere with remotely sensed measurements. / Modélisation paramétrique et prédictive de la biosphère terrestre par des observations satellitaires

Modelisation parametrique et predictive de la biosphere terrestre par observations satellitaires ; Cet article presente un recapitulatif des divers modeles que nous avons developpes afin destimer les flux de carbone dans la biosphere terrestre, et des diverses utilisations dobservations satellitaires pour piloter, tester et ameliorer ces modeles. ; Le modele parametrique, dont le but est destimer la Productivite Primaire Nette a lechelle globale, beneficie dindices de vegetation satellitaires lui permettant de diagnostiquer a haute resolution spatiale et temporelle la fraction de rayonnement photosynthetiquement actif (f par ) absorbe par la vegetation. A lechelle pluriannuelle, cette methode a demontre sa capacite a restituer la reponse du couvert vegetal aux perturbations climatiques telles que les evenements El Nino. Les estimations de f PAR satellitaire sont aussi utilisees pour evaluer les resultats dun modele predictif de PPN base sur des processus. Ce modele est base sur une hypothese dajustement du LAI (Leaf Area Index) aux ressources hydriques. ; Enfin, nous montrons que certains parametres cles de ces modeles de vegetation peuvent etre ajustes a laide des mesures satellitaires par des approches dassimilation de donnees. ; Letude du cycle du carbone sinteresse au bilan net carbone des ecosystemes qui resulte de la difference entre la respiration heterotrophe (R h ) et la PPN. Nous decrivons une approche dajustement des parametres de R h , a partir des estimations satellitaires de PPN, des mesures in situ de concentrations et de composition isotopique du CO 2 atmospherique. Cette methode a permis dobtenir une distribution latitudinale de Q 10 dont la tendance est en accord avec les mesures rencontrees dans letat de lart. Outre lavantage evident de la teledetection dans le diagnostic des evolutions saisonnieres de la vegetation, ce resultat suggere la capacite du satellite a temoigner des caracteristiques latitudinales de la PPN saisonniere et annuelle.