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

• Published in: Remote sensing of environment Vol. 132; pp. 145 - 158
• Main Authors: Hmimina, G., Dufrêne, E., Pontailler, J.-Y., Delpierre, N., Aubinet, M., Caquet, B., de Grandcourt, A., Burban, B., Flechard, C., Granier, A., Gross, P., Heinesch, B., Longdoz, B., Moureaux, C., Ourcival, J.-M., Rambal, S., Saint André, L., Soudani, K.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.05.2013; Elsevier
• Subjects: Agricultural sciences; Animal, plant and microbial ecology; Applied geophysics; autumn; Biological and medical sciences; canopy; climate; Crops; Deciduous forests; Earth sciences; Earth, ocean, space; ecosystems; Evergreen forests; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Ground-based NDVI; Internal geophysics; leaves; Life Sciences; moderate resolution imaging spectroradiometer; MODIS; monitoring; Phenology; prediction; radiometry; remote sensing; residual effects; savannas; screening; spring; Teledetection and vegetation maps; temporal variation; time series analysis; tropical rain forests; vegetation cover; weather; Evergreen forests; Phenology; Ground-based NDVI; Crops; MODIS; Deciduous forests; time series analysis; Vegetation index; Noise reduction; vegetation; evaluation; Field; Biome; climate variations; Ground based measurement; Space remote sensing; Prediction; Adaptive response; satellite measurements; contamination; quality; techniques; Timing; seasonal variations; Comparative study
• DOI: 10.1016/j.rse.2013.01.010
• ISSN: 0034-4257

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, phenological metrics are derived from time 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 obtained with 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). Phenological metrics identical to those provided with the MODIS 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 is more likely due to a pattern of noise in the NDVI 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. ► We compare ground- and MODIS-based NDVI time-series in different terrestrial biomes. ► We identify the most relevant NDVI based vegetation phenological metrics. ► We develop a new method to quantify the uncertainty of the phenological metrics. ► In tropical rainforests, MODIS-derived phenology should be interpreted with caution.