Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product

Publication: Research - peer-reviewJournal article – Annual report year: 2012

  • Author: Schubert, Per

    Lund University, Sweden

  • Author: Lagergren, Fredrik

    Lund University, Sweden

  • Author: Aurela, Mika

    Finnish Meteorological Institute

  • Author: Christensen, Torben

    Lund University, Sweden

  • Author: Grelle, Achim

    Uppsala University, Sweden

  • Author: Heliasz, Michal

    Lund University, Sweden

  • Author: Klemedtsson, Leif

    University of Gothenburg, Sweden

  • Author: Lindroth, Anders

    Lund University, Sweden

  • Author: Pilegaard, Kim

    Ecosystems Programme, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Frederiksborgvej 399, 4000, Roskilde, Denmark

  • Author: Vesala, Timo

    University of Helsinki, Finland

  • Author: Eklundh, Lars

    Lund University, Sweden

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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 500m 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 1km 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 500m 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 1km 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=0.63–1.22gCm−2day−1, R2=0.53–0.73, RMSE=0.90–1.43gCm−2day−1, R2=0.56–0.87, RMSE=0.79–1.11gCm−2 day−1). In comparison, similar validation strengths were found for the latest collection 5.1 of the MODIS 1km GPP product (R2=0.59–0.88, RMSE=0.80–1.16gCm−2day−1). The main conclusion is that the suggested empirical models driven by MODIS 500m reflectance data can be used for regional estimations of Nordic forest GPP, while preserving a finer resolution than the MODIS 1km GPP product.

Original languageEnglish
JournalRemote Sensing of Environment
Pages (from-to)136-147
StatePublished - 2012
CitationsWeb of Science® Times Cited: 21


  • Gross primary productivity (GPP), Land surface temperature (LST), Light use efficiency (ε), Moderate Resolution Imaging Spectroradiometer (MODIS), Two-band enhanced vegetation index (EVI2)
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ID: 12377399