Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data

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.

Effects of Temperature and Water Availability on Northern European Boreal Forests

“…NDVI is a radiometric measure of the amount of photosynthetically active radiation (∼400-700 nm) absorbed by vegetation and provides an indirect measure of vegetation photosynthetic activity, among other ecosystems, also in boreal forests (Park et al, 2016). Even though some aspects of productivity are not well-captured by this index (Fernández-Martínez et al, 2019;Tei et al, 2019), NDVI is often used as proxy of forest productivity (Olofsson et al, 2008;Sulla-Menashe et al, 2018) because it is well-correlated with tree growth, according to several independent validations against tree rings (Beck and Goetz, 2011;Berner et al, 2011). Further, NDVI has some advantages with respect to other remotelysensed products.…”

Weather and trade-offs between growth and reproduction regulate fruit production in European forests

“…These studies tested this inference using remotely sensed vegetation indices such as the normalized difference vegetation index [NDVI, (Camarero et al 2010)] and the enhanced vegetation index [EVI, (Fernández-Martínez et al 2015)]. These indices are useful in comparative studies of spatial and temporal variations in the crown cover and, by extension, of variations in the photosynthetic capacity of forests (Huete et al, 2002;Garbulsky et al 2013;Fernández-Martínez et al 2019).…”

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

“…The NIRv has been found to be strongly correlated with the gross primary productivity (GPP) in a large number of studies [51][52][53][54], and we found that the correlation between the NIRv and the sparse woody AGB is also better than those of the other vegetation indices, which may be due to the strong ability of the NIRv to characterize canopy structures and reduce the impact of the background on the spectral signal of the woody canopy [55]. However, because the spectral signal of the woody canopy can be easily affected by the background (e.g., green herbaceous vegetation) at the 30 m scale in a sparse forest, the heterogeneity of the background of the woody vegetation can seriously affect the estimation accuracy of the VI-AGB model.…”