Abstract
Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.
Highlights
The continuous development of remote-sensing techniques has increased their popularity in the fields of earth science, ecology, conservation, and nature and land management [1,2,3,4,5,6,7,8]
Our results indicate that Remotely sensed vegetation indices (RSVIs) are a suitable tool to track different features of gross primary production (GPP)
Grasslands were the ecosystems best predicted by RSVI, while evergreen broad-leaved forests were the worst
Summary
The continuous development of remote-sensing techniques has increased their popularity in the fields of earth science, ecology, conservation, and nature and land management [1,2,3,4,5,6,7,8]. Sensed vegetation indices (RSVIs) are frequently used to broadly and efficiently monitor spatial and temporal variations in terrestrial primary productivity (e.g., ecosystem photosynthesis). Terrestrial primary productivity (in a broad sense) has many facets and sources of variability, and RSVIs may not be good at monitoring all of them. Several studies have reported good relationships between the spatial variability of biomass stocks or the production of aboveground biomass and RSVIs such as the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI) for boreal forests [16], shrublands [17], grasslands [10,18,19], and croplands [20]. Interannual variability of tree growth and fruit production have been satisfactorily monitored by various RSVIs in Mediterranean evergreen broadleaved forests [2,12], but few studies have compared the performances of RSVIs across multiple ecosystems
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