Cai, Zhanzhang and Junttila, Sofia and Holst, Jutta and Jin, Hongxiao and Ardo, Jonas and Ibrom, Andreas and Peichl, Matthias and Molder, Meelis and Jonsson, Per and Rinne, Janne and Karamihalaki, Maria and Eklundh, Lars
(2021).
Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS.
Remote Sensing. 13
, 469
[Research article]
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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 (R-2 = 0.84 for Sentinel-2; R-2 = 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.
Authors/Creators: | Cai, Zhanzhang and Junttila, Sofia and Holst, Jutta and Jin, Hongxiao and Ardo, Jonas and Ibrom, Andreas and Peichl, Matthias and Molder, Meelis and Jonsson, Per and Rinne, Janne and Karamihalaki, Maria and Eklundh, Lars | ||||||
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Title: | Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS | ||||||
Series Name/Journal: | Remote Sensing | ||||||
Year of publishing : | 2021 | ||||||
Volume: | 13 | ||||||
Article number: | 469 | ||||||
Number of Pages: | 18 | ||||||
Publisher: | MDPI | ||||||
Language: | English | ||||||
Publication Type: | Research article | ||||||
Article category: | Scientific peer reviewed | ||||||
Version: | Published version | ||||||
Copyright: | Creative Commons: Attribution 4.0 | ||||||
Full Text Status: | Public | ||||||
Subjects: | (A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing | ||||||
Keywords: | gross primary productivity, Sentinel-2 MSI, EVI2, MODIS, Nordic region | ||||||
URN:NBN: | urn:nbn:se:slu:epsilon-p-110962 | ||||||
Permanent URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-110962 | ||||||
Additional ID: |
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ID Code: | 22726 | ||||||
Faculty: | S - Faculty of Forest Sciences | ||||||
Department: | (S) > Dept. of Forest Ecology and Management | ||||||
Deposited By: | SLUpub Connector | ||||||
Deposited On: | 09 Mar 2021 14:43 | ||||||
Metadata Last Modified: | 09 Mar 2021 14:51 |
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