Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements

Moulin, Sophie; Kergoat, Laurent; Viovy, Nicolas; Dedieu, Gérard

doi:10.1175/1520-0442(1997)010<1154:gsaovp>2.0.co;2

Cited by 324 publications

(217 citation statements)

References 58 publications

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“…Over savannah, a decrease of LAI centered on August is striking for MODIS and, to a lesser extent for CYCLOPES. This could be attributed to the well known little dry season affecting these areas during August and September but it has been associated to the increase of the cloud cover at this period of the year by Moulin et al [1997]. The absence of a significant decrease of rainfall during this period tends to corroborate this assumption (Figure 6).…”

Section: Ctessel Against the Satellite-derived Lai And Bias Correctionmentioning

confidence: 57%

“…Another striking feature is also the artificial break in the growing phase of the LAI for the MODIS product during the spring months. It is certainly an artifact of the satellite products due to bad atmospheric corrections [Moulin et al, 1997] as previously discussed. Nevertheless, it has been minimized thanks to the smoothing filter with the upper envelope.…”

Section: Lai Time Seriesmentioning

confidence: 91%

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Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

et al. 2008

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[1] A new version of the land surface model of the European Centre for Medium-Range Weather Forecasts (Carbon-TESSEL, or CTESSEL) includes a vegetation growth model. This study describes a leaf area index (LAI) data assimilation system (LDAS) based on CTESSEL and satellite LAI for operational Net Ecosystem Exchange (NEE) predictions. The LDAS is evaluated over West Africa. A preliminary experiment shows a significant impact of the LAI on the CTESSEL NEE. The LAI is compared to two satellite products: the predicted annual cycle is delayed over the Sahel and savannah, and the LAI values differ from the satellite products. Preliminary to their use in the LDAS, the LAI products are rescaled to the CTESSEL predictions. The LDAS simulations are confronted to measurements of biomass and LAI for a site in Mali. The LAI analysis is shown to improve the predicted biomass and the annual cycles of the water (latent heat flux, or LE) and carbon (NEE) fluxes. Afterward, the LDAS is run over West Africa with the Moderate-Resolution Imaging Spectroradiometer products (2001)(2002)(2003)(2004)(2005). The analysis of LAI shows a limited impact on LE, but it impacts strongly on NEE. Finally, the CTESSEL NEE are compared to two other models' outputs (simple biosphere (SIB) and Carnegie-Ames-Stanford (CASA)). The order of magnitude of the three data sets agrees well, and the shift in annual cycle of CTESSEL is reduced by the LDAS. It is concluded that a LAI data assimilation system is essential for NEE prediction at seasonal and interannual timescales, while a LAI satellite-based climatology may be sufficient for accurate LE predictions.

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Section: Ctessel Against the Satellite-derived Lai And Bias Correctionmentioning

confidence: 57%

Section: Lai Time Seriesmentioning

confidence: 91%

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

et al. 2008

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“…The beginning of a new phenological cycle can then occur. This means that several phenological cycles can theoretically occur in 1 year (for example, spring and autumn greening of grasses in Mediterranean-type climates [Moulin et al, 1997]). …”

Section: Photosynthesis and Stomatal Conductancementioning

confidence: 99%

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Krinner

Viovy

Noblet‐Ducoudré

et al. 2005

Global Biogeochemical Cycles

2,049

2,063

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[1] This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphereocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrologyvegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

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“…Therefore, NDVI cannot clearly discriminate the growing period from other conditions with a simple site-independent single threshold. We must consider the presence or absence of snow/water cover and understory vegetation for each target when we use NDVI, as pointed out by several studies [24][25][26][27]. Furthermore, as Nagai et al [13] have reported, the canopy condition of autumn coloring corresponded to different NDVI values from year to year.…”

Section: Applicability Of Grvi As An Indicator Of Vegetation Phenologymentioning

confidence: 99%

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

et al. 2010

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Abstract:We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) "GRVI = 0" can be a site-independent single threshold for detection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.

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Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements

Cited by 324 publications

References 58 publications

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: Application to West Africa

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

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