Abstract
Satellites have provided large-scale monitoring of vegetation for over three decades, and several satellite-based Normalized Difference Vegetation Index (NDVI) datasets have been produced. Here we intercompare four long-term NDVI datasets based largely on the AVHRR sensor (NDVIg, NDVI3g, STAR, VIP) and three datasets based on newer sensors (SPOT, Terra, Aqua) and evaluate the effectiveness of homogenizing the datasets using the green vegetation fraction (GVF) and the impact it has on phenology trends. Results show that all NDVI datasets are highly correlated with each other. However, there are significant differences in the regression slopes that vary spatially and temporally. There is a general trend towards higher maximum annual NDVI over much of the temperate forests of the US and a longer greening period due mostly to a delayed end of the season. These trends are less well-defined over rainfall dependent ecosystems in Mexico and the southwest US Compared with the NDVI datasets, the derived GVF datasets show more one-to-one relationships, have reduced interannual variation, preserve their relationships better over the entire time period and are characterized by weaker trends. Finally, weak agreement between the trends in the datasets stresses the importance of using multiple datasets to evaluate changes in vegetation and its phenology.
Highlights
Understanding ecosystem responses and feedbacks to a changing climate in a complex environment requires vegetation datasets that are consistent, accurate, and precise and directly represent real biophysical parameters of a vegetation ecosystem
As expected the Satellite Pour l’Observation de la Terre (SPOT) Normalized Difference Vegetation Index (NDVI) is correlated highest with Terra (0.97) as the sensors behind these two datasets have a higher spectral resolution and the NIR and red bands were designed for more appropriate vegetation monitoring than the Advanced Very High Resolution Radiometer (AVHRR) sensor
VIP, and NDVI3g tend to have higher values compared to SPOT, NDVIg, and Satellite Applications and Research (STAR)
Summary
Understanding ecosystem responses and feedbacks to a changing climate in a complex environment requires vegetation datasets that are consistent, accurate, and precise and directly represent real biophysical parameters of a vegetation ecosystem. Dynamic vegetation models that simulate long-term vegetation growth require accurate vegetation datasets for validation in order to provide confidence in prognostic forecasts of vegetation change due to the factors listed above. The most accurate way to detect vegetation changes is through direct ground observations. Detecting these changes across large areas would require a large number of research sites such as the. It is not feasible to scale data from isolated research sites up to the task of large-scale long-term continuous monitoring of vegetation activity. The most effective method to monitor vegetation across these scales is through satellite-based vegetation estimates
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