MERIS GPP/NPP product for Estonia: II. Complex meteorological limiting factor and optimum leaf area index / MERIS’e GPP/NPP tulem Eesti jaoks: II. Kompleksne meteoroloogiline piirangutegur ja optimaalne lehepinnaindeks

Tiit Nilson 1 , Mattias Rennel 2 , 3  und Mait Lang 1 , 4
  • 1 Tartu Observatory, 61602 Tõravere, Tartumaa, Estonia
  • 2 AS Regio, Riia 24, 51010 Tartu, Estonia
  • 3 Faculty of Science and Technology, Department of Bio- and Environmental Physics, Tartu University, Ülikooli 18, 51014 Tartu, Estonia
  • 4 Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia


The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.

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