Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements

Hmimina Gabriel, Dufrêne Eric, Pontailler J.Y., Delpierre Nicolas, Aubinet Marc, Caquet B., De Grandcourt Agnès, Burban Benoit, Flechard Christophe, Granier André, Gross P., Heinesch Bernard, Longdoz Bernard, Moureaux Christine, Ourcival Jean-Marc, Rambal Serge, Saint André Laurent, Soudani Kamel. 2013. Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sensing of Environment, 132 : 145-158.

Quartile : Q1, Sujet : REMOTE SENSING / Quartile : Q1, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q1, Sujet : ENVIRONMENTAL SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : Vegetation phenology is the study of the timing of seasonal events that are considered to be the result of adaptive responses to climate variations on short and long time scales. In the field of remote sensing of vegetation phenology, phenologicalmetrics are derived fromtime series of optical data. For that purpose, considerable effort has been specifically focused on developing noise reduction and cloud-contaminated data removal techniques to improve the quality of remotely-sensed time series. Comparative studies between time series composed of satellite data acquired under clear and cloudy conditions and from radiometric data obtainedwith high accuracy from ground-based measurements constitute a direct and effective way to assess the operational use and limitations of remote sensing for predicting the main plant phenological events. In the present paper, we sought to explicitly evaluate the potential use of MODerate resolution Imaging Spectroradiometer (MODIS) remote sensing data for monitoring the seasonal dynamics of different types of vegetation cover that are representative of the major terrestrial biomes, including temperate deciduous forests, evergreen forests, African savannah, and crops. After cloud screening and filtering, we compared the temporal patterns and phenological metrics derived from in situ NDVI time series and from MODIS daily and 16-composite products. We also evaluated the effects of residual noise and the influence of data gaps in MODIS NDVI time series on the identification of the most relevant metrics for vegetation phenology monitoring. The results show that the inflexion points of a model fitted to a MODIS NDVI time series allow accurate estimates of the onset of greenness in the spring and the onset of yellowing in the autumn in deciduous forests (RMSE?one week). Phenologicalmetrics identical to those providedwith theMODIS Global Vegetation Phenology product (MDC12Q2) are less robust to data gaps, and they can be subject to large biases of approximately two weeks or more during the autumn phenological transitions. In the evergreen forests, in situ NDVI time series describe the phenology with high fidelity despite small temporal changes in the canopy foliage. However, MODIS is unable to provide consistent phenological patterns. In crops and savannah, MODIS NDVI time series reproduce the general temporal patterns of phenology, but significant discrepancies appear between MODIS and ground-based NDVI time series during very localized periods of time depending on the weather conditions and spatial heterogeneity within the MODIS pixel. In the rainforest, the temporal pattern exhibited by a MODIS 16-day composite NDVI time series ismore likely due to a pattern of noise in theNDVI data structure according to both rainy and dry seasons rather than to phenological changes. More investigations are needed, but in all cases, this result leads us to conclude that MODIS time series in tropical rainforests should be interpreted with great caution.

Mots-clés Agrovoc : végétation, forêt, phénologie, télédétection, changement climatique, variation saisonnière, modèle de simulation, modèle mathématique, écosystème, productivité primaire

Classification Agris : F40 - Écologie végétale
U30 - Méthodes de recherche
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Axe 6 (2005-2013) - Agriculture, environnement, nature et sociétés

Auteurs et affiliations

• Hmimina Gabriel, CNRS (FRA)
• Dufrêne Eric, CNRS (FRA)
• Pontailler J.Y., CNRS (FRA)
• Delpierre Nicolas, CNRS (FRA)
• Aubinet Marc, Université de Liège (BEL)
• Caquet B., CRDPI (COG)
• De Grandcourt Agnès, CRDPI (COG)
• Burban Benoit, CIRAD-FORET-UMR ECOFOG (GUF)
• Flechard Christophe, INRA (FRA)
• Granier André, INRA (FRA)
• Gross P., INRA (FRA)
• Heinesch Bernard, Université de Liège (BEL)
• Longdoz Bernard, INRA (FRA)
• Moureaux Christine, Université de Liège (BEL)
• Ourcival Jean-Marc, CEFE (FRA)
• Rambal Serge, CEFE (FRA)
• Saint André Laurent, CIRAD-PERSYST-UMR Eco&Sols (FRA)
• Soudani Kamel, CNRS (FRA)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/567856/)

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