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Modeling GPP in the Nordic forest landscape with MODIS time series data - comparison with the MODIS GPP product

Schubert, Per LU ; Lagergren, Fredrik LU ; Aurela, Mika; Christensen, Torben LU ; Grelle, Achim; Heliasz, Michal LU ; Klemedtsson, Leif; Lindroth, Anders LU ; Pilegaard, Kim and Vesala, Timo, et al. (2012) In Remote Sensing of Environment 126. p.136-147
Abstract
Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500 m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1 km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled... (More)
Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500 m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1 km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500 m reflectance data and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R2 = 0.45–0.86, Coniferous: R2 = 0.49–0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1 km daytime LST (land surface temperature) (R2 = 0.55–0.81, 0.57–0.77) and between GPP and EVI2, PPFD and daytime LST in multiple linear regressions (R2 = 0.73–0.89, 0.65–0.93). One year of data was collected from all coniferous sites to derive a general empirical model for GPP versus (1) the product of EVI2 and PPFD (R2 = 0.70), (2) the product of EVI2 and daytime LST (R2 = 0.62) and (3) EVI2, PPFD and daytime LST (R2 = 0.72). These three models were then validated at six sites for the remaining years by linearly relating eddy covariance GPP to modeled GPP, which resulted in fairly strong to strong relationships for most sites (R2 = 0.49–0.91, RMSE = 0.63–1.22 g C m− 2 day− 1, R2 = 0.53–0.73, RMSE = 0.90–1.43 g C m− 2 day− 1, R2 = 0.56–0.87, RMSE = 0.79–1.11 g C m− 2 day− 1). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1 km GPP product (R2 = 0.59–0.88, RMSE = 0.80–1.16 g C m− 2 day− 1). The main conclusion is that the suggested empirical models driven by MODIS 500 m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1 km GPP product. (Less)
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keywords
Moderate Resolution Imaging Spectroradiometer (MODIS), Light use efficiency (ε), Land surface temperature (LST), Gross primary productivity (GPP), Two-band enhanced vegetation index (EVI2)
in
Remote Sensing of Environment
volume
126
pages
136 - 147
publisher
Elsevier
external identifiers
  • wos:000310388600012
  • scopus:84865826031
ISSN
0034-4257
DOI
10.1016/j.rse.2012.08.005
project
BECC
MERGE
language
English
LU publication?
yes
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c2a86cda-b141-4ea5-bb83-bc715adbe8a0 (old id 3128768)
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http://dx.doi.org/10.1016/j.rse.2012.08.005
date added to LUP
2012-10-25 11:51:36
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2017-08-27 03:27:50
@article{c2a86cda-b141-4ea5-bb83-bc715adbe8a0,
  abstract     = {Satellite sensor-derived data are suitable for regional estimations of several important biophysical variables. Data with a finer spatial resolution should improve regional estimations of GPP (gross primary productivity), since they better capture the variation in a heterogeneous landscape. The main objective of this study was to investigate if MODIS 500 m reflectance data can be used to drive empirical models for regional estimations of GPP in Nordic forests. The performance of the proposed models was compared with the MODIS 1 km GPP product. Linear regression analyses were made on 8-day averages of eddy covariance GPP from three deciduous and ten coniferous sites in relation to MODIS 8-day composite data and 8-day averages of modeled incoming PPFD (photosynthetic photon flux density). Time series of EVI2 (two-band enhanced vegetation index) were calculated from MODIS 500 m reflectance data and smoothed by a curve fitting procedure. For most sites, GPP was fairly strongly to strongly related to the product of EVI2 and PPFD (Deciduous: R2 = 0.45–0.86, Coniferous: R2 = 0.49–0.90). Similar strengths were found between GPP and the product of EVI2 and MODIS 1 km daytime LST (land surface temperature) (R2 = 0.55–0.81, 0.57–0.77) and between GPP and EVI2, PPFD and daytime LST in multiple linear regressions (R2 = 0.73–0.89, 0.65–0.93). One year of data was collected from all coniferous sites to derive a general empirical model for GPP versus (1) the product of EVI2 and PPFD (R2 = 0.70), (2) the product of EVI2 and daytime LST (R2 = 0.62) and (3) EVI2, PPFD and daytime LST (R2 = 0.72). These three models were then validated at six sites for the remaining years by linearly relating eddy covariance GPP to modeled GPP, which resulted in fairly strong to strong relationships for most sites (R2 = 0.49–0.91, RMSE = 0.63–1.22 g C m− 2 day− 1, R2 = 0.53–0.73, RMSE = 0.90–1.43 g C m− 2 day− 1, R2 = 0.56–0.87, RMSE = 0.79–1.11 g C m− 2 day− 1). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1 km GPP product (R2 = 0.59–0.88, RMSE = 0.80–1.16 g C m− 2 day− 1). The main conclusion is that the suggested empirical models driven by MODIS 500 m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1 km GPP product.},
  author       = {Schubert, Per and Lagergren, Fredrik and Aurela, Mika and Christensen, Torben and Grelle, Achim and Heliasz, Michal and Klemedtsson, Leif and Lindroth, Anders and Pilegaard, Kim and Vesala, Timo and Eklundh, Lars},
  issn         = {0034-4257},
  keyword      = {Moderate Resolution Imaging Spectroradiometer (MODIS),Light use efficiency (ε),Land surface temperature (LST),Gross primary productivity (GPP),Two-band enhanced vegetation index (EVI2)},
  language     = {eng},
  pages        = {136--147},
  publisher    = {Elsevier},
  series       = {Remote Sensing of Environment},
  title        = {Modeling GPP in the Nordic forest landscape with MODIS time series data - comparison with the MODIS GPP product},
  url          = {http://dx.doi.org/10.1016/j.rse.2012.08.005},
  volume       = {126},
  year         = {2012},
}