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of Photogrammetry and Remote Sensing, vol. 63, no. 4, pp. 409-426. Darvishzadeh, R, Skidmore, A, Schlerf, M & Atzberger, C 2008, ‘Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland’, Remote Sensing of Environment, vol. 112, no. 5, pp. 2592-2604. Fensholt, R, Sandholt, I & Rasmussen, MS 2004, ‘Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements’, Remote Sensing of Environment, vol. 91, no. 3-4, pp. 490-507. Fernandes, R, Butson, C


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.


Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.

/isprs2006-darvish.pdf>. [5 August 2015]. di Bella, CM, Paruelos, JM, Becerra, JE, Bacour, C & Baret, F 2004, ‘Effect of senescent leaves on NDVI-based estimates of fAPAR: experimental and modelling evidences’, International Journal of Remote Sensing, vol. 25, no. 23, pp. 5415-5427. Fourty, T, Baret, F, Jacquemoud, S, Schmuck, G & Verdebout, J 1996, ‘Leaf Optical Properties with Explicit Description of Its Biochemical Composition: Direct and Inverse Problems’, Remote Sensing of Environment, vol. 56, pp. 104-117. Gamon, J, Penuelas, J & Field, C 1992, ‘A Narrow

I, 1988. Atmospheric Dynamics (in Romanian). Tehnica Press, Bucharest. Giglio L., 2015. MODIS collection 6 Active Fire Product User’s Guide, Revision A. Gower S. T., Kucharik C. J., Norman J. M., 1999. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ., 70: 29-51. GTOS (2008), Terrestrial essential climate variables - for climate change assessment, mitigation and adaptation. Biennial report supplement GTOS, 44. Gu Y., Brown, J.F., Verdin J., Wardlow B., 2007. A five-year analysis of

.1029/2009JG001229. Seixas, J., Carvalhais, N., Nunes, C., Benali, A. 2009. Comparative analysis of MODIS-FAPAR and MERIS-MGVI datasets: Potential impacts on ecosystem modeling. - Remote Sensing of Environment, 113(12), 2547-2559. Statistical Yearbook of Estonia, 2013. - ISSN 1406-1783, ISBN 978-9985-74-526-7; 440pp. - URL http:// Turner, D.P., Ollinger, S., Smith, M.-L., Krankina, O., Gregory, M. 2004. Scaling net primary production to a MODIS footprint in support of Earth observing system product validation. - International Journal of Remote Sensing, 25

References Gower, S.T., Kucharik, C.J., Norman, J. M.1999. Direct and indirect estimation of leaf area index, fAPAR and net primary production of terrestrial ecosystems. - Remote Sensing of Environment, 70, 29-51. Heinsch, F.A., Zhao, M., Running, S.W., Kimball, J.S., Nemani, R.R., Davis, K.J., Bolstad, P.V., Cook, B.D., Desai, A.R., Ricciuto, D.M., Law, B.E., Oechel, W.C., Kwon, H.J., Luo, H., Wofsy, S.C., Dunn, A.L., Munger, J.W., Baldocchi, D.D., Xu, L., Hollinger, D.Y., Richardson, A.D., Stoy, P.C., Siqueira, M.B.S., Monson, R.K., Burns, S.P., Flanagan, L

References Aurdal, LRBH, Vikhamar, D & Solberg, A 2005, Use of hidden Markov models and phenology for multitemporal satellite image classification: applications to mountain vegetation classification. Available from: <> [12 April 2012]. Bacour, C, Baret, V, Beal, D, Weiss, M & Pavageau, K 2006, ‛Neural network estimation of LAI, fAPAR, fCover and LAIxCab, from top of canopy MERIS reflectance data: Principles and validation’. Remote Sensing of Environment, vol. 105, no. 4, pp. 313‑325. Brodsky, L, Vobora, V, Sourkova, L & Kodesova, R

.R., Running S.W., 1997, Estimation of global leaf area indes and absorbed PAR using radiative transfer models, IEEE Transactions on Geoscience and Remote Sensing , 35, 1380–1393. Myneni R.B., Ross J., 1990, Photon-Vegetation Interactions. Applications in Optical Remote Sensing and Plant Ecology , Springer-Verlag. Myneni R.B., Ross J., Asrar F., 1989, A review on the theory of photon transport in leaf canopies, Agricultural and Forest Meteorology , 45, 1–153. North P.R.J., 2002, Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery, Remote Sensing

University, Burnaby, British Columbia, and the Institute of Ecosystem Studies, Millbrook, New York. Gower, S.T., Kucharik, C.J., Norman, J.M. 1999. Direct and indirect estimation of leaf area index, fAPAR , and net primary production of terrestrial ecosystems. – Remote Sensing of Environment, 70, 29–51. Isenburg, M. 2007. LasTools – efficient tools for LiDAR processing. Version 150202. [WWW document]. – URL [Accessed 14 April 2015]. Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., Baret, F. 2004. Review of methods for in