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

Cai, Zhanzhang LU ; Junttila, Sofia LU ; Holst, Jutta LU orcid ; Jin, Hongxiao LU ; Ardö, Jonas LU orcid ; Ibrom, Andreas ; Peichl, Matthias ; Mölder, Meelis LU ; Jönsson, Per and Rinne, Janne LU , et al. (2021) In Remote Sensing 13(3).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
EVI2, Gross primary productivity, MODIS, Nordic region, Sentinel-2 MSI
in
Remote Sensing
volume
13
issue
3
article number
469
pages
18 pages
publisher
MDPI AG
external identifiers
  • scopus:85100065669
ISSN
2072-4292
DOI
10.3390/rs13030469
project
Upscaling carbon fluxes to a landscape
language
English
LU publication?
yes
id
a2f13b62-4a4c-4659-82c3-56ea29efb533
date added to LUP
2021-02-08 08:33:50
date last changed
2023-02-27 05:27:34
@article{a2f13b62-4a4c-4659-82c3-56ea29efb533,
  abstract     = {{<p>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 (R<sup>2</sup> = 0.84 for Sentinel-2; R<sup>2</sup> = 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.</p>}},
  author       = {{Cai, Zhanzhang and Junttila, Sofia and Holst, Jutta and Jin, Hongxiao and Ardö, Jonas and Ibrom, Andreas and Peichl, Matthias and Mölder, Meelis and Jönsson, Per and Rinne, Janne and Karamihalaki, Maria and Eklundh, Lars}},
  issn         = {{2072-4292}},
  keywords     = {{EVI2; Gross primary productivity; MODIS; Nordic region; Sentinel-2 MSI}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{Modelling daily gross primary productivity with sentinel-2 data in the nordic region–comparison with data from modis}},
  url          = {{http://dx.doi.org/10.3390/rs13030469}},
  doi          = {{10.3390/rs13030469}},
  volume       = {{13}},
  year         = {{2021}},
}