Modelling daily gross primary productivity with sentinel-2 data in the nordic region–comparison with data from modis

Research output: Contribution to journalArticle

Abstract

The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R2 = 0.84 for Sentinel-2; R2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data.

Details

Authors
Organisations
External organisations
  • Technical University of Denmark
  • Swedish University of Agricultural Sciences
  • Malmö University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Physical Geography

Keywords

  • EVI2, Gross primary productivity, MODIS, Nordic region, Sentinel-2 MSI
Original languageEnglish
Article number469
Number of pages18
JournalRemote Sensing
Volume13
Issue number3
Publication statusPublished - 2021
Publication categoryResearch
Peer-reviewedYes