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

Authors

Marcos Fernandez-Martinez, University of AntwerpFollow
Rong Yu, University of Nebraska - LincolnFollow
John Gamon, University of Nebraska-LincolnFollow
Gabriel Hmimina, University of Nebraska - LincolnFollow
Iolanda Filella, CSIC, Global Ecology Unit, CREAF-CSIC-UABFollow
Manuela Balzarolo, CSIC, Global Ecology Unit, CREAF-CSIC-UABFollow
Benjamin Stocker, CSIC, Global Ecology Unit, CREAF-CSIC-UABFollow
Josep Penuelas, CSIC, Global Ecology Unit, CREAF-CSIC-UABFollow

Date of this Version

2019

Citation

Remote Sens. 2019, 11, 874

Comments

© 2019 by the authors.

Open access

doi:10.3390/rs11070874

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 serieswas 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.