This work is licensed under the Creative Commons Attribution 4.0 International License.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M.A., Baldocchi, D., Bonan, G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F.I., Papale, D. 2010. Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. – Science, 329(5993), 834–838. https://doi.org/10.1126/science.1184984.BeerC.ReichsteinM.TomelleriE.CiaisP.JungM.CarvalhaisN.RödenbeckC.ArainM.A.BaldocchiD.BonanG.B.BondeauA.CescattiA.LasslopG.LindrothA.LomasM.LuyssaertS.MargolisH.OlesonK.W.RoupsardO.VeenendaalE.ViovyN.WilliamsC.WoodwardF.I.PapaleD.2010Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate3295993834838https://doi.org/10.1126/science.1184984.10.1126/science.1184984Search in Google Scholar
Bonan, G.B. 1995. Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. – Journal of Geophysical Research Atmospheres, 100(D2), 2817–2831. https://doi.org/10.1029/94JD02961.BonanG.B.1995Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model100D228172831https://doi.org/10.1029/94JD02961.10.1029/94JD02961Search in Google Scholar
Chen, J.M., Liu, J., Cihlar, J., Goulden, M.L. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. – Ecological Modelling, 124(2–3), 99–119. https://doi.org/10.1016/s0304-3800(99)00156-8.ChenJ.M.LiuJ.CihlarJ.GouldenM.L.1999Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications1242–399119https://doi.org/10.1016/s0304-3800(99)00156-8.10.1016/S0304-3800(99)00156-8Search in Google Scholar
Estonian Environment Agency. 2020. Keskkonnaagentuur (KAUR). [WWW document]. – URL https://www.keskkonnaagentuur.ee/en. [Accessed 26 December 2020].Estonian Environment Agency2020[WWW document]. – URL https://www.keskkonnaagentuur.ee/en. [Accessed 26 December 2020].Search in Google Scholar
Estonian Weather Service. 2021. Ilmateenistus. [WWW document]. – URL https://www.ilmateenistus.ee/?lang=en. [Accessed 17 April 2021].Estonian Weather Service2021[WWW document]. – URL https://www.ilmateenistus.ee/?lang=en. [Accessed 17 April 2021].Search in Google Scholar
Feng, X., Liu, G., Chen, J.M., Chen, M., Liu, J., Ju, W.M., Sun, R., Zhou, W. 2007. Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing. – Journal of Environmental Management, 85(3), 563–573. https://doi.org/10.1016/j.jenvman.2006.09.021.FengX.LiuG.ChenJ.M.ChenM.LiuJ.JuW.M.SunR.ZhouW.2007Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing853563573https://doi.org/10.1016/j.jenvman.2006.09.021.10.1016/j.jenvman.2006.09.021Search in Google Scholar
He, L., Chen, J.M., Pisek, J., Schaaf, C.B., Strahler, A.H. 2012. Global clumping index map derived from the MODIS BRDF product. – Remote Sensing of Environment, 119, 118–130. https://doi.org/10.1016/j.rse.2011.12.008.HeL.ChenJ.M.PisekJ.SchaafC.B.StrahlerA.H.2012Global clumping index map derived from the MODIS BRDF product119118130https://doi.org/10.1016/j.rse.2011.12.008.10.1109/IGARSS.2011.6049427Search in Google Scholar
Heiskanen, J., Rautiainen, M., Stenberg, P., Mõttus, M., Vesanto, V.-H., Korhonen, L., Majasalmi, T. 2012. Seasonal variation in MODIS LAI for a boreal forest area in Finland. – Remote Sensing of Environment, 126, 104–115. https://doi.org/10.1016/j.rse.2012.08.001.HeiskanenJ.RautiainenM.StenbergP.MõttusM.VesantoV.-H.KorhonenL.MajasalmiT.2012Seasonal variation in MODIS LAI for a boreal forest area in Finland126104115https://doi.org/10.1016/j.rse.2012.08.001.10.1016/j.rse.2012.08.001Search in Google Scholar
Ju, W., Chen, J.M., Black, T.A., Barr, A.G., Liu, J., Chen, B., 2006. Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest. – Agricultural and Forest Meteorology, 140(1–4), 136–151. http://dx.doi.org/10.1016/j.agrformet.2006.08.008.JuW.ChenJ.M.BlackT.A.BarrA.G.LiuJ.ChenB.2006Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest1401–4136151http://dx.doi.org/10.1016/j.agrformet.2006.08.008.10.1016/j.agrformet.2006.08.008Search in Google Scholar
Kljun, N., Calanca, P., Rotach, M.W., Schmid, H.P. 2015. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). – Geoscientific Model Development, 8(11), 3695–3713.KljunN.CalancaP.RotachM.W.SchmidH.P.2015A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)8113695371310.5194/gmd-8-3695-2015Search in Google Scholar
Li, X., Zhu, Z., Zeng, H., Piao, S. 2016. Estimation of gross primary production in China (1982–2010) with multiple ecosystem models. – Ecological Modelling, 324, 33–44. https://doi.org/10.1016/j.ecolmodel.2015.12.019.LiX.ZhuZ.ZengH.PiaoS.2016Estimation of gross primary production in China (1982–2010) with multiple ecosystem models3243344https://doi.org/10.1016/j.ecolmodel.2015.12.019.10.1016/j.ecolmodel.2015.12.019Search in Google Scholar
Liang, S., Shuey, C.J., Russ, A.L., Fang, H., Chen, M., Walthall, C.L., Daughtry, C.S.T., Hunt, R. Jr. 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. – Remote Sensing of Environment, 84(1), 25–41. https://doi.org/10.1016/S0034-4257(02)00068-8.LiangS.ShueyC.J.RussA.L.FangH.ChenM.WalthallC.L.DaughtryC.S.T.HuntR. Jr.2003Narrowband to broadband conversions of land surface albedo: II. Validation8412541https://doi.org/10.1016/S0034-4257(02)00068-8.10.1016/S0034-4257(02)00068-8Search in Google Scholar
Liu, J., Chen, J.M., Cihlar, J., Chen, W. 1999. Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data. – Journal of Geophysical Research Atmospheres, 104(D22), 27735–27754. https://doi.org/10.1029/1999JD900768.LiuJ.ChenJ.M.CihlarJ.ChenW.1999Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data104D222773527754https://doi.org/10.1029/1999JD900768.10.1029/1999JD900768Search in Google Scholar
Liu, J., Chen, J.M., Cihlar, J., Chen, W. 2002. Net primary productivity mapped for Canada at 1-km resolution. – Global Ecology and Biogeography, 11(2), 115–129. https://doi.org/10.1046/j.1466-822X.2002.00278.x.LiuJ.ChenJ.M.CihlarJ.ChenW.2002Net primary productivity mapped for Canada at 1-km resolution112115129https://doi.org/10.1046/j.1466-822X.2002.00278.x.10.1046/j.1466-822X.2002.00278.xSearch in Google Scholar
Liu, J., Chen, J.M., Cihlar, J., Park, W.M. 1997. A process-based boreal ecosystem productivity simulator using remote sensing inputs. – Remote Sensing of Environment, 62(2), 158–175. https://doi.org/10.1016/S0034-4257(97)00089-8.LiuJ.ChenJ.M.CihlarJ.ParkW.M.1997A process-based boreal ecosystem productivity simulator using remote sensing inputs622158175https://doi.org/10.1016/S0034-4257(97)00089-8.10.1016/S0034-4257(97)00089-8Search in Google Scholar
Liu, S., Zhuang, Q., He, Y., Noormets, A., Chen, J., Gu, L. 2016. Evaluating atmospheric CO2 effects on gross primary productivity and net ecosystem exchanges of terrestrial ecosystems in the conterminous United States using the AmeriFlux data and an artificial neural network approach. – Agricultural and Forest Meteorology, 220, 38–49. https://doi.org/10.1016/j.agrformet.2016.01.007.LiuS.ZhuangQ.HeY.NoormetsA.ChenJ.GuL.2016Evaluating atmospheric CO2 effects on gross primary productivity and net ecosystem exchanges of terrestrial ecosystems in the conterminous United States using the AmeriFlux data and an artificial neural network approach2203849https://doi.org/10.1016/j.agrformet.2016.01.007.10.1016/j.agrformet.2016.01.007Search in Google Scholar
Lloyd, J., Taylor, J.A. 1994. On the temperature dependence of soil respiration. – Functional Ecology, 8(3), 315–323. https://www.jstor.org/stable/2389824.LloydJ.TaylorJ.A.1994On the temperature dependence of soil respiration83315323https://www.jstor.org/stable/2389824.10.2307/2389824Search in Google Scholar
Lõhmus, E. 2004. Forest site types in Estonia. (Eesti metsakasvukohatüübid). Tartu, Eesti Loodusfoto. 80 pp. (In Estonian).LõhmusE.2004TartuEesti Loodusfoto80 pp. (In Estonian).Search in Google Scholar
Luo, X., Croft, H., Chen, J.M., Bartlett, P., Staebler, R., Froelich, N. 2018. Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site. – Agricultural and Forest Meteorology, 248, 156–168. https://doi.org/10.1016/j.agrformet.2017.09.012.LuoX.CroftH.ChenJ.M.BartlettP.StaeblerR.FroelichN.2018Incorporating leaf chlorophyll content into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a forest site248156168https://doi.org/10.1016/j.agrformet.2017.09.012.10.1016/j.agrformet.2017.09.012Search in Google Scholar
Ma, L., Bicking, S., Müller, F. 2019. Mapping and comparing ecosystem service indicators of global climate regulation in Schleswig-Holstein, Northern Germany. – Science of The Total Environment, 648, 1582–1597. https://doi.org/10.1016/j.scitotenv.2018.08.274.MaL.BickingS.MüllerF.2019Mapping and comparing ecosystem service indicators of global climate regulation in Schleswig-Holstein, Northern Germany64815821597https://doi.org/10.1016/j.scitotenv.2018.08.274.10.1016/j.scitotenv.2018.08.274Search in Google Scholar
Nilson, T. 1971. A theoretical analysis of the frequency of gaps in plant stands. – Agricultural Meteorology, 8, 25–38. https://doi.org/10.1016/0002-1571(71)90092-6.NilsonT.1971A theoretical analysis of the frequency of gaps in plant stands82538https://doi.org/10.1016/0002-1571(71)90092-6.10.1016/0002-1571(71)90092-6Search in Google Scholar
Pisek, J., Lang, M., Nilson, T., Korhonen, L., Karu, H. 2011. Comparison of methods for measuring gap size distribution and canopy nonrandomness at Järvselja RAMI (RAdiation transfer Model Intercomparison) test sites. – Agricultural and Forest Meteorology, 151(3), 365–377. https://doi.org/10.1016/j.agrformet.2010.11.009.PisekJ.LangM.NilsonT.KorhonenL.KaruH.2011Comparison of methods for measuring gap size distribution and canopy nonrandomness at Järvselja RAMI (RAdiation transfer Model Intercomparison) test sites1513365377https://doi.org/10.1016/j.agrformet.2010.11.009.10.1016/j.agrformet.2010.11.009Search in Google Scholar
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. – Global Change Biology, 11(9), 1424–1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x.ReichsteinM.FalgeE.BaldocchiD.PapaleD.AubinetM.BerbigierP.BernhoferC.BuchmannN.GilmanovT.GranierA.GrünwaldT.HavránkováK.IlvesniemiH.JanousD.KnohlA.LaurilaT.LohilaA.LoustauD.MatteucciG.MeyersT.MigliettaF.OurcivalJ.-M.PumpanenJ.RambalS.RotenbergE.SanzM.TenhunenJ.SeufertG.VaccariF.VesalaT.YakirD.ValentiniR.2005On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm11914241439https://doi.org/10.1111/j.1365-2486.2005.001002.x.10.1111/j.1365-2486.2005.001002.xSearch in Google Scholar
Román, M.O., Schaaf, C.B., Woodcock, C.E., Strahler, A.H., Yang, X., Braswell, R.H., Curtis, P.S., Davis, K.J., Dragoni, D., Goulden, M.L., Gu, L., Hollinger, D.Y., Kolb, T.E., Meyers, T.P., Munger, J.W., Privette, J.L., Richardson, A.D., Wilson, T.B., Wofsy, S.C. 2009. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes. – Remote Sensing of Environment, 113(11), 2476–2498. https://doi.org/10.1016/j.rse.2009.07.009.RománM.O.SchaafC.B.WoodcockC.E.StrahlerA.H.YangX.BraswellR.H.CurtisP.S.DavisK.J.DragoniD.GouldenM.L.GuL.HollingerD.Y.KolbT.E.MeyersT.P.MungerJ.W.PrivetteJ.L.RichardsonA.D.WilsonT.B.WofsyS.C.2009The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes1131124762498https://doi.org/10.1016/j.rse.2009.07.009.10.1016/j.rse.2009.07.009Search in Google Scholar
Shi, H., Li, L., Eamus, D., Huete, A., Cleverly, J., Tian, X., Yu, Q., Wang, S., Montagnani, L., Magliulo, V., Rotenberg, E., Pavelka, M., Carrara, A. 2017. Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types. – Ecological Indicators, 72, 153–164. https://doi.org/10.1016/j.ecolind.2016.08.022.ShiH.LiL.EamusD.HueteA.CleverlyJ.TianX.YuQ.WangS.MontagnaniL.MagliuloV.RotenbergE.PavelkaM.CarraraA.2017Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types72153164https://doi.org/10.1016/j.ecolind.2016.08.022.10.1016/j.ecolind.2016.08.022Search in Google Scholar
Smith, R.B. 2010. The heat budget of the earth’s surface deduced from space. [WWW document]. – URL https://yceo.yale.edu/sites/default/files/files/Surface_Heat_Budget_From_Space.pdf. [Accessed 31 October 2021].SmithR.B.2010[WWW document]. – URL https://yceo.yale.edu/sites/default/files/files/Surface_Heat_Budget_From_Space.pdf. [Accessed 31 October 2021].Search in Google Scholar
USGS. 2021. MCD15A3H product. [WWW document]. – URL https://doi.org/10.5067/MODIS/MCD15A3H.006. [Accessed 23 December 2021].USGS2021[WWW document]. – URL https://doi.org/10.5067/MODIS/MCD15A3H.006. [Accessed 23 December 2021].Search in Google Scholar
Wang, Z., Schaaf, C.B., Chopping, M.J., Strahler, A.H., Wang, J., Román, M.O., Rocha, A.V., Woodcock, C.E., Shuai, Y. 2012. Evaluation of moderate-resolution imaging spectroradiometer (MODIS) snow albedo product (MCD43A) over tundra. – Remote Sensing of Environment, 117, 264–280. https://doi.org/10.1016/j.rse.2011.10.002.WangZ.SchaafC.B.ChoppingM.J.StrahlerA.H.WangJ.RománM.O.RochaA.V.WoodcockC.E.ShuaiY.2012Evaluation of moderate-resolution imaging spectroradiometer (MODIS) snow albedo product (MCD43A) over tundra117264280https://doi.org/10.1016/j.rse.2011.10.002.10.1016/j.rse.2011.10.002Search in Google Scholar
Wang, Z., Schaaf, C.B., Strahler, A.H., Chopping, M.J., Román, M.O., Shuai, Y., Woodcock, C.E., Hollinger, D.Y., Fitzjarrald, D.R. 2014. Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods. – Remote Sensing of Environment, 140, 60–77. https://doi.org/10.1016/j.rse.2013.08.025.WangZ.SchaafC.B.StrahlerA.H.ChoppingM.J.RománM.O.ShuaiY.WoodcockC.E.HollingerD.Y.FitzjarraldD.R.2014Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods1406077https://doi.org/10.1016/j.rse.2013.08.025.10.1016/j.rse.2013.08.025Search in Google Scholar
Wang, Z., Schaaf, C.B., Sun, Q., Kim, J., Erb, A.M., Gao, F., Román, M.O., Yang, Y., Petroy, S., Taylor, J.R., Masek, J.G., Morisette, J.T., Zhang, X., Papuga, S.A. 2017. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF / NBAR / albedo product. – International Journal of Applied Earth Observations and Geoinformation, 59, 104–117. https://doi.org/10.1016/j.jag.2017.03.008.WangZ.SchaafC.B.SunQ.KimJ.ErbA.M.GaoF.RománM.O.YangY.PetroyS.TaylorJ.R.MasekJ.G.MorisetteJ.T.ZhangX.PapugaS.A.2017Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF / NBAR / albedo product59104117https://doi.org/10.1016/j.jag.2017.03.008.10.1016/j.jag.2017.03.008764116933154713Search in Google Scholar
Wu, C., Munger, J.W., Niu, Z., Kuang, D. 2010. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. – Remote Sensing of Environment, 114(12), 2925–2939. https://doi.org/10.1016/j.rse.2010.07.012.WuC.MungerJ.W.NiuZ.KuangD.2010Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest1141229252939https://doi.org/10.1016/j.rse.2010.07.012.10.1016/j.rse.2010.07.012Search in Google Scholar
Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., Reichstein, M. 2018. Basic and extensible post-processing of eddy covariance flux data with REddyProc. – Biogeosciences, 15(16), 5015–5030. https://doi.org/10.5194/bg-15-5015-2018.WutzlerT.Lucas-MoffatA.MigliavaccaM.KnauerJ.SickelK.ŠigutL.MenzerO.ReichsteinM.2018Basic and extensible post-processing of eddy covariance flux data with REddyProc151650155030https://doi.org/10.5194/bg-15-5015-2018.10.5194/bg-15-5015-2018Search in Google Scholar
Zhang, F., Chen, J.M., Chen, J., Gough, C.M., Martin, T.A., Dragoni, D. 2012. Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model. – Remote Sensing of Environment, 124, 717–729. https://doi.org/10.1016/j.rse.2012.06.023.ZhangF.ChenJ.M.ChenJ.GoughC.M.MartinT.A.DragoniD.2012Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model124717729https://doi.org/10.1016/j.rse.2012.06.023.10.1016/j.rse.2012.06.023Search in Google Scholar
Zhu, X., Pei, Y., Zheng, Z., Dong, J., Zhang, Y., Wang, J., Chen, L., Doughty, R.B., Zhang, G., Xiao, X. 2018. Underestimates of grassland gross primary production in MODIS standard products. – Remote Sensing, 10(11), 1771. https://doi.org/10.3390/rs10111771.ZhuX.PeiY.ZhengZ.DongJ.ZhangY.WangJ.ChenL.DoughtyR.B.ZhangG.XiaoX.2018Underestimates of grassland gross primary production in MODIS standard products10111771https://doi.org/10.3390/rs10111771.10.3390/rs10111771Search in Google Scholar