Creating a regional MODIS satellite-driven net primary production dataset for European forests

emo e sensing A icle C ea ing a Regional MODIS Sa elli e-D i en Ne P ima y P oduc ion Da ase o Eu opean Fo es s Ma hias Neumann 1,*, Adam Mo eno 1, Ch is ophe Thu nhe 1, Volke Mues 2, Sanna Hä könen 3,4, Ma eo Mu a 5,6, Oli ie Bou iaud 7, Mai Lang 8, Giuseppe Ca dellini 9, Alain Thi olle-Caza 10, Ka ol B onisz 11, Jan Me ganic 12, Icia Albe di 13, Rasmus As up 14, F i s Moh en 15, Maosheng Zhao 16 and Hube Hasenaue 1 1Ins i u e o Sil icul u e, Depa men o Fo es and Soil Sciences, Uni e si y o Na u al Resou ces and Li e Sciences, Vienna 1190, Aus ia; [email p o ec ed] (A.M.); C hu nhe@g oupwise.boku.ac.a (C.T.); [email p o ec ed] (H.H.) 2Cen e o Wood Science, Wo ld Fo es y, Uni e si y o Hambu g, Hambu g 21031, Ge many; olke [email p o ec ed] 3 Depa men o Fo es Sciences, Uni e si y o Helsinki, Helsinki 00014, Finland; [email p o ec ed] 4Finnish Fo es Resea ch Ins i u e, Joensuu 80101, Finland 5 Depa men o Bioscience and Te i o y, Uni e si y o Molise, 86090 Pesche (IS), I aly; [email p o ec ed] 6geoLAB—Labo a o y o Fo es Geoma ics, Depa men o Ag icul u al, Food and Fo es y Sys ems, Uni e si à degli S udi di Fi enze, Fi enze 50145, I aly 7Facu y o Fo es y, Uni e si a ea S e an del Ma e, Sucea a 720229, Romania; [email p o ec ed] 8Ta u Obse a o y, Tõ a e e 61602, Es onia; [email p o ec ed] 9Di ision Fo es , Na u e and Landscape, Depa men o Ea h and En i onmen al Sciences, KU Leu en—Uni e si y o Leu en, Leu en 3001, Belgium; [email p o ec ed] 10 Technological Ins i u e, Fu ni u e, En i onmen , Economy, P ima y P ocessing and Supply, Champs su Ma ne 77420, F ance; Alain.THIVOLLECAZA[email p o ec ed] 11 Labo a o y o Dend ome y and Fo es P oduc i i y, Facul y o Fo es y, Wa saw Uni e si y o Li e Sciences, Wa saw 02-776, Poland; ka ol.b [email p o ec ed].pl 12 Facul y o Fo es y and Wood Sciences, Czech Uni e si y o Li e Sciences, P ague 16521, Czech Republic; [email p o ec ed] 13 Depa amen o de Sel icul u a y Ges ión de los Sis emas Fo es ales, INIA-CIFOR, Mad id 28040, Spain; [email p o ec ed] 14 No wegian Ins i u e o Bioeconomy Resea ch, Ås 1431, No way; asmus.as [email p o ec ed] 15 Fo es Ecology and Fo es Managemen G oup, Wageningen Uni e si y, Wageningen 6700, The Ne he lands; i s.moh en@wu .nl 16 Depa men o Geog aphical Sciences, Uni e si y o Ma yland, Collage Pa k, MD 20742, USA; [email p o ec ed] *Co espondence: [email p o ec ed]; Tel.: +43-1-47654-4059 Academic Edi o s: La s T. Wase and P asad S. Thenkabail Recei ed: 5 Ap il 2016; Accep ed: 25 June 2016; Published: 29 June 2016 Abs ac : Ne p ima y p oduc ion (NPP) is an impo an ecological me ic o s udying o es ecosys ems and hei ca bon seques a ion, o assessing he po en ial supply o ood o imbe and quan i ying he impac s o clima e change on ecosys ems. The global MODIS NPP da ase using he MOD17 algo i hm p o ides aluable in o ma ion o moni o ing NPP a 1-km esolu ion. Since coa se- esolu ion global clima e da a a e used, he global da ase may con ain unce ain ies o Eu ope. We used a 1-km daily g idded Eu opean clima e da a se wi h he MOD17 algo i hm o c ea e he egional NPP da ase MODIS EURO. Fo e alua ion o his new da ase , we compa e MODIS EURO wi h e es ial d i en NPP om analyzing and ha monizing o es in en o y da a (NFI) om 196,434 plo s in 12 Eu opean coun ies as well as he global MODIS NPP da ase o he yea s 2000 o 2012. Compa ing hese h ee NPP da ase s, we ound ha he global MODIS NPP da ase di e s om NFI NPP by 26%, while MODIS EURO only di e s by 7%. MODIS EURO also ag ees wi h NFI NPP ac oss scales ( om con inen al, egional o coun y) and g adien s (ele a ion, loca ion, ee age, dominan species, e c.). The ag eemen is pa icula ly good o ele a ion, dominan Remo e Sens. 2016,8, 554; doi:10.3390/ s8070554 www.mdpi.com/jou nal/ emo esensing Remo e Sens. 2016,8, 554 2 o 18 species o ee heigh . This sugges s ha using imp o ed clima e da a allows he MOD17 algo i hm o p o ide ealis ic NPP es ima es o Eu ope. Local disc epancies be ween MODIS EURO and NFI NPP can be ela ed o di e ences in s and densi y due o o es managemen and he na ional ca bon es ima ion me hods. Wi h his s udy, we p o ide a consis en , empo ally con inuous and spa ially explici p oduc i i y da ase o he yea s 2000 o 2012 on a 1-km esolu ion, which can be used o assess clima e change impac s on ecosys ems o he po en ial biomass supply o he Eu opean o es s o an inc easing bio-based economy. MODIS EURO da a a e made eely a ailable a p://palan i .boku.ac.a /Public/MODIS_EURO. Keywo ds: NPP; bioeconomy; o es in en o y; NFI; clima e; ca bon; biomass; downscaling; inc emen ; MOD17 1. In oduc ion Ne p ima y p oduc ion (NPP), he di e ence be ween G oss P ima y P oduc ion (GPP) and plan au o ophic espi a ion, is he ne ca bon o biomass ixed by ege a ion h ough pho osyn hesis. NPP ep esen s he alloca ion a e o pho osyn he ic p oduc s in o plan biomass and can be used o measu e he quan i y o goods p o ided o socie y by ecosys ems [ 1 – 3 ]. NPP o o es ecosys ems is essen ial o es ima e he po en ial supply o biomass o bioene gy, ibe and imbe supply. NPP is also a key a iable o assess en i onmen al change impac s on ecosys ems [ 4 ] since any a ia ion in he g owing condi ions in luences he ca bon cycle due o changes in ca bon up ake and/o espi a ion. As in e es g ows in u ilizing o es s o a “bio-based economy” [ 5 , 6 ], mo e accu a e and ealis ic o es p oduc i i y es ima es become inc easingly impo an . In addi ion, compe ing o es ecosys em se ices, such as biodi e si y o and na u e conse a ion, need o be conside ed o ensu e sus ainable use o ou o es s and o a oid unsus ainable o e -exploi a ion o enewable esou ces. Wi hin he EU-28 160.9 million ha o 37.9% o he o al land a ea a e co e ed wi h o es s [ 7 ]. These o es s p o ide esou ces o he imbe indus y, he ene gy sec o (24.3% o he ene gy in he EU-28 is gene a ed om enewable sou ces o which 64.2% consis s o o es biomass and was e [ 8 ]), bu also o non- imbe ecosys em se ices such as clean ai , wa e , biodi e si y o p o ec ion agains na u al haza ds. Accu a e and consis en o es in o ma ion is a p econdi ion o assessing he p oduc ion and ha es ing po en ial o o es esou ces in Eu ope. The e a e concep ually di e en da a sou ces and me hods o assess o es p oduc i i y like: (i) The MODIS algo i hm MOD17 uses emo ely sensed sa elli e-da a and clima e da a o p edic spa ially and empo ally con inuous NPP and GPP (G oss P ima y P oduc ion o ca bon assimila ion) based on an ecophysiological modelling app oach [ 2 ]. In addi ion o sa elli e e lec ance da a and clima e da a, i equi es he biophysical p ope ies o land co e ypes, which a e s o ed in he Biome P ope y Look-Up Tables (BPLUT) [9]. (ii) Na ional o es in en o y da a can be used o assess he imbe olume s ocks as well as olume inc emen and emo al, i epea ed obse a ions a e a ailable [ 10 ]. This e es ial bo om-up app oach collec s o es in o ma ion by measu ing sample plo s a anged on a sys ema ic g id design ac oss la ge a eas. In combina ion wi h biomass expansion ac o s o biomass unc ions, olume o ee in o ma ion can be con e ed in o biomass o ca bon es ima es o accoun o di e ences in wood densi ies, he ca bon ac ion and di e en alloca ion in o compa men s [11,12]. (iii) Flux owe s eco d he gas-exchange in plan -a mosphe e in e ac ions [ 13 ], which can be used o de i e GPP om Ne Ecosys em exchange (NEE). NEE is es ima ed using eddy co a iance da a, clima e measu emen s and o he ancilla y da a [14]. Remo e Sens. 2016,8, 554 3 o 18 Ne P ima y P oduc ion (NPP) om (i) op-down sa elli e-d i en MOD17 algo i hm and (ii) bo om-up NPP es ima es using e es ial o es in en o y da a we e compa ed in a pilo s udy o Aus ia on na ional scale [ 15 ]. Top-down and bo om-up e e o he le el o scaling o he p ima y eco ded in o ma ion ( o MOD17 1-km emo e sensing p oduc s and o Te es ial NPP single ee obse a ions). Ou de ini ion o op-down di e s om adi ional ca bon cycle modelling [ 16 ]. This s udy wan s o ex end and es his concep o Eu ope on a con inen al scale. Fo his pu pose, we ob ain wo wall- o-wall spa ially-explici and consis en MODIS NPP da ase s by acqui ing he global da ase using global clima e d i e and by c ea ing a egional da ase MODIS EURO using 1-km Eu opean clima e da a. We e alua e hese wo da ase s by compa ing wi h he NPP de i ed om o es in en o y da a om 12 Eu opean coun ies. We assess he eliabili y and po en ial disc epancies o he MODIS sa elli e-d i en op-down e sus he e es ial bo om-up NPP es ima es om con inen al o na ional scale and ac oss di e en g adien s like loca ion, ele a ion o s and densi y. This will p o ide a be e unde s anding o he eliabili y o emo e sensing based NPP es ima es, which could be used also o egions, whe e no e es ial measu emen s a e a ailable. 2. Ma e ials and Me hods We used wo concep ually di e en me hods o es ima e NPP, (i) he MODIS NPP algo i hm MOD17 and (ii) e es ial o es in en o y da a and ee ca bon es ima ion me hods. Bo h ha e hei espec i e s eng hs and weaknesses. MODIS NPP has he ad an age o p o iding spa ially con inuous es ima es wi h a consis en me hodology, which is impo an o any la ge-scale s udies. I inco po a es biogeochemical p inciples in mechanis ic modelling en i onmen and he ege a ion eedback o clima e condi ions h ough changes in Lea A ea Index and abso bed adia ion [ 17 ]. I does no dis inguish be ween di e en ege a ion apa om gene al Land Co e ypes, has a coa se spa ial esolu ion and migh no be able o ep esen speci ic local condi ions due o i s calib a ion o global condi ions. In con as , e es ial o es in en o y NPP assesses he ac ual ca bon alloca ion by ees and cap u es local small-scale e ec s (e.g., si e condi ions, ee age o o es managemen ) as well as egional di e ences in es ima ing ee ca bon [ 12 , 18 ]. I co e s only he inc emen o ees assessed by he in en o y sys em and migh no cap u e local speci ics o li e all and ine oo u no e e y well, since b oad model assump ions ha e o be used. 2.1. MODIS NPP Since he yea 2000, he MOD17 p oduc p o ides spa ially and empo ally con inuous NPP es ima es ac oss he globe [ 17 ]. The algo i hm behind uses he e lec ance da a om he senso MODIS (MODe a e esolu ion Imaging Spec o adiome e ) o he TERRA and AQUA sa elli es ope a ed by Na ional Ae onau ics and Space Adminis a ion o he Uni ed S a es (NASA). MOD17 p o ides GPP and NPP es ima es a a 1-km esolu ion [ 2 , 17 ] and inco po a es basic biogeochemical p inciples adop ed om Biome-BGC [ 19 ]. I in eg a es a ligh use e iciency logic using emo ely sensed ege a ion in o ma ion o es ima e GPP (Equa ion (1)) wi h a main enance and g ow h espi a ion module o de i e NPP (Equa ion (2)). GPP “LUEmax ˆ Tmin ˆ pd ˆ0.45ˆSW ad ˆFPAR (1) NPP “GPP´RM´RG(2) LUEmax is he maximum ligh use e iciency, which ge adjus ed by Tmin and pd o add ess wa e s ess due o low empe a u e (Tmin) and apo p essu e de ici (VPD). SW ad is sho wa e sola adia ion load, o which 45% is pho osyn he ically ac i e. FPAR is he ac ion o abso bed pho osyn he ic ac i e adia ion. R M is he main enance espi a ion and is es ima ed using LAI (Lea A ea Index), clima e da a and biome-speci ic pa ame e s. R G is he g ow h espi a ion and is es ima ed o be app ox. 25% o NPP. The comple e algo i hm is documen ed in [ 18 ] and mo e de ails a e ound in he ci ed li e a u e he ein. Remo e Sens. 2016,8, 554 4 o 18 The MOD17 algo i hm equi es clima e da a, FPAR and LAI (lea a ea index) da a as well as land co e da a, which is de i ed om MODIS e lec ance da a [ 20 ]. We ob ained he global MODIS NPP p oduc (MOD17A3 Ve sion 055) p o ided by he Nume ical Te adynamic Simula ion G oup (NTSG) a Uni e si y o Mon ana a ailable a p:// p.n sg.um .edu/pub/MODIS/NTSG_P oduc s/. This da a se (he ea e called MODIS GLOB) co e s he pe iod o 2000 o 2012, which is he ime pe iod co e ed by ou e es ial da a (see nex chap e ), and p o ides he annual NPP in gC¨m´2¨yea ´1. The sou ce o FPAR and LAI inpu is MODIS15 LAI/FPAR Collec ion 5, which was empo ally gap illed o close da a gaps due o un a o able a mosphe ic condi ions such as cloudiness o hea y ae osol p esence [ 9 ]. Fo Land co e , we used he land co e p oduc MOD12Q1 Ve sion 4 Type 2 [ 21 ] ep esen ing he condi ions in yea 2001. Clima e da a a e impo an inpu in o he MODIS NPP algo i hm and clima e da a ha e a s ong impac on he MODIS NPP esul s [ 15 , 22 ]. MODIS GLOB uses he global clima e da a se NCEP2 [ 23 ] desc ibed in he ollowing Sec ion 2.2. In Eu ope, we ha e high quali y daily clima e da a, he E-OBS da a se [24], which was ecen ly downscaled o a 1-km esolu ion [25]. We nex an he MOD17 algo i hm wi h he downscaled Eu opean clima e da a [ 25 ] and ob ained an addi ional MODIS NPP es ima e o he pe iod 2000–2012 (he ea e called MODIS EURO), which di e om MODIS GLOB p o ided by NTSG only in he used daily clima e inpu da a. We used he same FPAR, LAI and Land co e inpu , as used o he global NPP p oduc , MODIS GLOB. MODIS EURO co e s ou s udy egion, he EU-28 including No way, Swi ze land and he Balkan s a es (see Figu e 1) and is made a ailable unde p://palan i .boku.ac.a /Public/MODIS_EURO. Remo e Sens. 2016, 8, 554 4 o 18 The sou ce o FPAR and LAI inpu is MODIS15 LAI/FPAR Collec ion 5, which was empo ally gap illed o close da a gaps due o un a o able a mosphe ic condi ions such as cloudiness o hea y ae osol p esence [9]. Fo Land co e , we used he land co e p oduc MOD12Q1 Ve sion 4 Type 2 [21] ep esen ing he condi ions in yea 2001. Clima e da a a e impo an inpu in o he MODIS NPP algo i hm and clima e da a ha e a s ong impac on he MODIS NPP esul s [15,22]. MODIS GLOB uses he global clima e da a se NCEP2 [23] desc ibed in he ollowing Sec ion 2.2. In Eu ope, we ha e high quali y daily clima e da a, he E-OBS da a se [24], which was ecen ly downscaled o a 1-km esolu ion [25]. We nex an he MOD17 algo i hm wi h he downscaled Eu opean clima e da a [25] and ob ained an addi ional MODIS NPP es ima e o he pe iod 2000–2012 (he ea e called MODIS EURO), which di e om MODIS GLOB p o ided by NTSG only in he used daily clima e inpu da a. We used he same FPAR, LAI and Land co e inpu , as used o he global NPP p oduc , MODIS GLOB. MODIS EURO co e s ou s udy egion, he EU-28 including No way, Swi ze land and he Balkan s a es (see Figu e 1) and is made a ailable unde p://palan i .boku.ac.a /Public/MODIS_EURO. Figu e 1. Ou s udy egion sepa a ed in o ou egions, coun ies wi h o es in en o y da a o es ima ing e es ial Na ional Fo es In en o y (NFI) Ne P ima y P oduc ion (NPP) a e ma ked wi h do s. 2.2. Clima e Da a As ou lined, he wo MODIS NPP es ima es, MODIS GLOB and MODIS EURO, di e only in he daily clima e da a inpu : MODIS GLOB employs he global NCEP2 clima e da a se [23] and MODIS EURO uses Eu opean downscaled clima e da a [25]. We p o ide he e a b ie o e iew o he wo clima e da a se s. The NCEP2 da a se (NCEP-DOE Reanalysis 2) is a eanalyzed global daily clima e da a se wi h a spa ial esolu ion o 1.875° × 1.875°. This co esponds o app ox. 220 km a he equa o a la i ude 0° (app ox. 136 × 220 km a la i ude 50°). To compensa e he coa se spa ial esolu ion, o MODIS GLOB he clima e da a o he 1 km MODIS pixels was deduced wi h an bila e al in e pola ion me hod based on he neighbo ing NCEP2 pixels [9]. The downscaled clima e da a used o MODIS EURO p o ide daily clima e da a on a 0.0083° × 0.0083° esolu ion (app ox. 1 × 1 km a he equa o and app ox. 0.6 × 1 km a 50° la i ude) [25]. This da a Figu e 1. Ou s udy egion sepa a ed in o ou egions, coun ies wi h o es in en o y da a o es ima ing e es ial Na ional Fo es In en o y (NFI) Ne P ima y P oduc ion (NPP) a e ma ked wi h do s. 2.2. Clima e Da a As ou lined, he wo MODIS NPP es ima es, MODIS GLOB and MODIS EURO, di e only in he daily clima e da a inpu : MODIS GLOB employs he global NCEP2 clima e da a se [ 23 ] and MODIS Remo e Sens. 2016,8, 554 5 o 18 EURO uses Eu opean downscaled clima e da a [ 25 ]. We p o ide he e a b ie o e iew o he wo clima e da a se s. The NCEP2 da a se (NCEP-DOE Reanalysis 2) is a eanalyzed global daily clima e da a se wi h a spa ial esolu ion o 1.875 ˝ˆ 1.875 ˝ . This co esponds o app ox. 220 km a he equa o a la i ude 0 ˝ (app ox. 136 ˆ 220 km a la i ude 50 ˝ ). To compensa e he coa se spa ial esolu ion, o MODIS GLOB he clima e da a o he 1 km MODIS pixels was deduced wi h an bila e al in e pola ion me hod based on he neighbo ing NCEP2 pixels [9]. The downscaled clima e da a used o MODIS EURO p o ide daily clima e da a on a 0.0083˝ˆ0.0083˝ esolu ion (app ox. 1 ˆ 1 km a he equa o and app ox. 0.6 ˆ 1 km a 50˝la i ude) [25]. This da a se was de eloped ou o he E-OBS g idded clima e da a se (0.25˝ esolu ion , using da a om 7852 clima e s a ions) [ 24 ] in conjunc ion wi h he Wo ldClim da a se [26]. 2.3. Te es ial NFI NPP Te es ial o es da a such as na ional o es in en o y (NFI) da a assess accumula ed ca bon on a sys ema ic g id using a pe manen plo design. F om epea ed obse a ions o diame e a b eas heigh (DBH) and/o ee heigh (H) in combina ion wi h biomass unc ions o biomass expansion ac o s he ca bon accumula ion o ees is es ima ed. Since his me hod is based on single ee measu emen s and local biomass s udies, NPP de i ed om o es in en o y da a inco po a es local e ec s such as wea he pa e ns, clima e anomalies, s and age, di e ences in biomass alloca ion, si e and soil e ec s and di e en o es densi ies due o o es managemen [15,27]. We ob ained 196,434 o es in en o y plo s co e ing 12 Eu opean coun ies. In Eu ope, each coun y has i s own Na ional Fo es In en o y (NFI) sys em, which all ha e di e en measu emen pe iods, sampling designs and me hodologies [ 10 ] (Table S1 in he Supplemen a y Ma e ial). Thus, we i s had o de elop a ha monized and consis en e es ial da ase o es ima ing Te es ial NPP. We calcula ed NPP using he o es in en o y da a acco ding o Equa ion (3). NPP “CARBINC `FRTO `CLF (3) CARB INC is he ca bon inc emen o ees (gC ¨ m ´2¨ yea ´1 ). FR TO is he ca bon used o ine oo u no e [ 28 , 29 ]. Fine oo u no e FR TO is assumed o be equal o he ca bon low in o li e C LF [ 27 , 30 ]. Bo h p ocesses a e con olled by he same ac o s and he assump ion o simila i y be ween he abo e- and belowg ound u no e o sho -li ing plan o gans is suppo ed by ecen ly collec ed Eu opean da a on ine oo u no e [ 29 ] and li e all [ 31 ]. C LF is he low o ca bon in o li e (gC ¨ m ´2¨ yea ´1 ) es ima ed using a clima e-sensi i e and species-dependen model [ 31 ] and is calcula ed as: B oadlea -domina ed : CLF “CFexpp2.643 `0.726LnpT`10q ` 0.181LnpPqq (4) Coni e ous-domina ed : CLF “CFexpp2.708 `0.505LnpT`10q ` 0.240LnpPqq (5) CF is he ca bon ac ion o d y biomass which is se equal o 0.5 [ 11 ]. Tis he mean annual empe a u e om he yea 2000 o 2012 ( ˝ C). Pis he mean annual p ecipi a ion 2000 o 2012 [mm]. Fo empe a u e and p ecipi a ion we use he Eu opean clima e da a [ 25 ] o cap u e impo an small-scale egional e ec s such as ele a ion o opog aphy in a mo e ealis ic way. Equa ion (4) is applied o all plo s whe e b oadlea species con ibu e mos o o al basal a ea and Equa ion (5) is used o coni e ous-domina ed plo s (see Table S2 o he Supplemen a y Ma e ial). We used da a om nine Na ional Fo es In en o ies (Aus ia, Czech Republic, Ge many, F ance, Finland, No way, Poland, Romania, Spain), and h ee Regional Fo es In en o ies (Belgium, Es onia, I aly). We g ouped ou 12 coun ies in ou geog aphic egions, No h Eu ope, Cen al-Wes Eu ope, Cen al-Eas Eu ope and Sou h Eu ope [ 7 ], o add ess he la ge en i onmen al, ele a ional and clima ic Remo e Sens. 2016,8, 554 6 o 18 g adien s in Eu ope. Coun ies wi hin a egion should ha e simila clima ic and edaphic condi ions as well as simila ee allome ies and alloca ion pa e ns [32]. The o iginal loca ions o he in en o y plo s we e alsi ied o he nea es pixel o he MODIS g id o gua an ee he loca ions o he plo s emain unknown. Tempo al consis ency wi h he MODIS da a (a ailable since yea 2000) was ensu ed by using only in en o y da a, which p o ide CARB INC (Equa ion (3)) o he ime pe iod 2000 o 2012. Figu e 1shows ou s udy egion wi h he ou geog aphic egions comple ely co e ed by MODIS EURO, and he 12 coun ies, whe e we ha e NFI NPP. Al hough all ou e es ial o es in en o y da a assess p ope ies o ees, he e a e di e en sampling me hods and inc emen calcula ion by coun y in place, which may s ongly a ec he esul ing es ima es [ 33 , 34 ]. Fou di e en me hods o es ima e ee ca bon inc emen CARB INC a e used in ou da a: (1) epea ed obse a ions o ixed a ea plo s (used in No way, Poland, Belgium); (2) epea ed angle coun sampling ( o Aus ia, Ge many, Finland); (3) inc emen co es (F ance, Romania, I aly); as well as inc emen p edic ions om (4) ee g ow h models (Czech Republic, Es onia, I aly). T ee g ow h model p edic ions we e used i no inc emen obse a ions, ei he om epea ed obse a ions o om inc emen co es, we e a ailable. In he Supplemen a y Ma e ial, we p o ide all de ails o ou 12 in en o y da a se s, he local sampling sys em, he a ailable da a and he used inc emen me hod (Table S1 in he Supplemen a y Ma e ial). The ee ca bon esul s o de e mining ca bon inc emen CARB INC (Equa ion (3)) we e es ima ed using he ca bon calcula ion me hod applied by he local o es in en o y o ganiza ion and compiled in [ 32 ]. Local biomass unc ions and biomass expansion ac o s we e used o de i e ee biomass and ca bon ac ions o con e biomass in o ca bon. In he Supplemen a y Ma e ial, we p o ide a de ailed desc ip ion on p ocessing he NFI da a, he ee ca bon es ima es and s and a iables o desc ibe he ep esen ed o es s (e.g., mean age, basal a ea o s and densi y index). Using his me hodology, we p ocessed he o es in en o y da a om he 12 coun ies (Table S1) and de i ed ha monized ca bon s ocks o all in en o y plo s. The o es in en o y da a se consis s o 196.434 plo s, ha monized ac oss 12 Eu opean coun ies. We applied he ca bon inc emen me hod o each coun y and calcula ed NPP by in en o y plo (he ea e called NFI NPP) using Equa ions (3)–(5). 2.4. Analysis o NPP Resul s We hus ha e h ee NPP sou ces: wo using he MOD17 algo i hm wi h di e en daily clima e da a: (i) MODIS GLOB p oduced by he Nume ical Te adynamic Simula ion G oup (NTSG) a Uni e si y o Mon ana and (ii) MODIS EURO by unning he o iginal MOD17 algo i hm and he la es BPLUTs pa ame ized by [ 9 ] wi h downscaled daily clima e da a om Eu ope [ 25 ] as well as (iii) Te es ial NFI NPP using o es in en o y da a om he 12 coun ies (Table S1) and local ca bon es ima ion me hods [32]. We compa ed he h ee NPP da ase s ac oss Eu ope, by ou 4 egions (Figu e 1) and he 12 coun ies o analyze ou esul s ac oss di e en spa ial scaling. We ex ac ed o each o es in en o y plo a he co esponding MODIS cell he a e age NPP om MODIS GLOB and MODIS EURO o 2000 o 2012. We nex compu ed o all plo s he di e ence be ween he wo MODIS NPP es ima es and he Te es ial NFI NPP ( ∆ NPP GLOB = MODIS GLOB minus NFI NPP and ∆ NPP EURO = MODIS EURO minus NFI NPP). We used each NFI plo sepa a ely and did no compu e a e age alues o MODIS pixels. This a oided smoo hing e ec s due o di e en spacing be ween in en o y g id poin s and he plo clus e s used in some coun ies (Table S1). To analyze he e ec o g adien s on he NPP esul s, we collec ed po en ially meaning ul me a-in o ma ion such as plo loca ion (Longi ude and La i ude in WGS1984), Ele a ion (EU-DEM 30 m esolu ion), MODIS Land Co e ype o o es cha ac e is ics (dominan ee species, mean age, s and densi y, ee heigh , e c.) and analyzed pa e ns o ∆ NPP GLOB and ∆ NPP EURO ac oss hese g adien s. Remo e Sens. 2016,8, 554 7 o 18 Te es ial and emo e sensing NPP es ima es exhibi ed disc epancies in p e ious esea ch [ 15 , 18 ] and as explana ion he au ho s sugges ed changes in s and densi y, which a e commonly caused by o es managemen and dis u bances [ 15 , 18 ]. Since majo pa s o he o es s in Eu ope a e managed [ 7 ] and a ec ed by na u al dis u bances such as wind damage o o es i e [ 35 ], hey should ha e expe ienced changes in s and densi y as compa ed o unmanaged o es s. S and densi y di ec ly a ec s e es ial NPP es ima es by i s impac on he de elopmen o DBH and H o he emaining ees a e o es managemen ope a ions un il canopy closu e is eached. On he o he hand MODIS NPP is based on he “big lea ” concep and assumes a ull co e age o o es a ea. We hus use S and densi y index (SDI) [36] in he analysis o ou NPP es ima es. 3. Resul s NPP es ima ed using he MOD17 algo i hm has he ad an age o p o iding spa ial- and empo al-con inuous NPP es ima es ac oss Eu ope on a 1-km esolu ion and Figu e 2illus a es his by showing MODIS EURO o he yea s 2000 o 2012. No e ha MODIS EURO also co e s no - o es land co e ypes such as c ops, sh ub- o g assland. Remo e Sens. 2016, 8, 554 8 o 18 Remo e Sens. 2016, 8, x; doi:10.3390/ www.mdpi.com/jou nal/ emo esensing Figu e 2. MODIS EURO NPP on 1-km esolu ion ep esen ing a e age NPP o he pe iod 2000–2012 using Eu opean daily clima e da a (a ailable unde p://palan i .boku.ac.a /Public/MODIS_EURO). Ou NFI da ase co e s he ull ele a ional and la i udinal ange o o es condi ions in Eu ope including di e en si e condi ions, ee species, de elopmen s ages o managemen p ac ices. Fo mos coun ies we ha e mo e han 5000 in en o y plo s (excep ion: Belgium wi h 512 plo s) and in mos cases a plo spacing o a leas 4 by 4 km (Table S1). This da ase also p o ides in o ma ion on o es p ope ies such as ee age, ca bon s ocks o s and densi y and Table 2 indica es ha hese cha ac e is ics a y ac oss Eu ope. Table 2. NPP and ∆NPP (always using median) o he whole da ase (“All Coun ies”), o each coun y sepa a ely and o each egion (MODIS NPP using global clima e da a—MODIS GLOB; MODIS NPP using local Eu opean clima e da a—MODIS EURO and NPP using o es in en o y da a—NFI NPP); ∆NPP and Rel. ∆NPP bo h o MODIS GLOB and MODIS EURO. Posi i e di e ences indica e ha MODIS NPP o e es ima es NFI NPP and ice e sa. NPP and ∆NPP (gC·m−2·yea −1) MODIS MODIS ∆NPP Rel. ∆NPP [%] GLOB EURO NFI NPP GLOB EURO GLOB EURO All Coun ies 680 577 539 141 38 26% 7% No h Eu ope Finland 471 399 414 57 −15 14% −4% No way 484 406 409 75 −3 18% −1% Es onia 534 504 492 42 12 9% 3% all 519 479 461 58 18 13% 4% Cen al-Wes Eu ope Aus ia 739 612 634 105 −22 17% −4% Belgium 732 599 644 88 −45 14% −7% F ance 787 666 604 183 62 30% 10% Ge many 692 602 716 −24 −114 −3% −16% all 759 645 615 144 30 23% 5% Cen al-Eas Eu ope Czech Republic 696 618 553 143 65 26% 12% Poland 641 571 659 −19 −88 −3% −13% Romania 713 562 565 148 −3 26% −1% all 677 592 595 82 −3 14% −1% Figu e 2. MODIS EURO NPP on 1-km esolu ion ep esen ing a e age NPP o he pe iod 2000–2012 using Eu opean daily clima e da a (a ailable unde p://palan i .boku.ac.a /Public/MODIS_EURO). Te es ial NFI NPP is d i en by o es in o ma ion collec ed by ield c ews. Thus i p o ides NPP and he ca bon accumula ion by o es s ands du ing a ce ain ime pe iod. Table 1gi es a summa y o he o es in en o y esul s by coun y, by egion and he whole da ase , wi h he e es ial NFI NPP a he igh side. Remo e Sens. 2016,8, 554 8 o 18 Table 1. Summa y o he o es in en o y esul s: Numbe o plo s wi h da a, Time pe iod co e ed by NFI NPP, Mean ele a ion ( ange Minimum–Maximum) in me e abo e sea le el (EU-DEM 30 m esolu ion). Fo he ollowing plo s a is ics we p o ide mean and s anda d de ia ion: Mean quad a ic DBH (cm), Mean T ee heigh (m), Basal a ea a 1.3 m heigh (m 2¨ ha ´1 ), S em numbe (ha ´1 ), T ee ca bon pe hec a e (gC ¨ m ´2 ), Median age class, SDI S and Densi y Index [ 36 ] ( o de ails on his a iables see Supplemen a y Ma e ial), NPP is he NFI Ne p ima y p oduc ion (gC ¨ m ´2¨ yea ´1 ) acco ding o Equa ion (3), Fo Czech Republic we only ha e coun y means. Emp y cells (-) indica e ha his a iable is no a ailable om he NFI da a se . A he end o each sec ion, s a is ics o he egion a e gi en and a he bo om o he able summa y s a is ics o whole Eu ope. Region Coun y Numbe o Plo s Time Pe iod Mean Ele a ion (min–max) (m) Mean DBH (cm) Mean T ee Heigh (m) Basal A ea (m2¨ha´1) S em Numbe (ha´1) T ee Ca bon (gC¨m´2) Median Age (Yea s) SDI NPP (gC¨m´2¨yea ´1) No h Eu ope Es onia 19930 2000–2010 66 (2–275) 17 ˘8 17 ˘7 19 ˘8 1540 ˘2554 5240 ˘2929 40–60 449 ˘192 509 ˘163 Finland 6442 2000–2008 141 (1–400) 18 ˘7 14 ˘5 18 ˘8 3522 ˘13251 4859 ˘3020 40–60 400 ˘236 446 ˘173 No way 9562 2000–2009 391 (0–1253) 15 ˘6 9 ˘3 15 ˘12 930 ˘682 4003 ˘3691 60–80 368 ˘265 442 ˘143 all 35379 2000–2010 161 (0–1253) 16 ˘7 14 ˘7 18 ˘9 1736 ˘5983 4856 ˘3199 40–60 419 ˘224 482 ˘162 Cen al-Wes Eu ope Aus ia 9562 2000–2009 912 (113–2299) 32 ˘14 21 ˘7 32 ˘19 987 ˘1070 10364 ˘6973 60–80 688 ˘396 681 ˘251 Belgium 512 2009–2013 39 (2–278) 29 ˘12 18 ˘6 30 ˘13 660 ˘446 11507 ˘6475 40–60 648 ˘279 671 ˘195 F ance 33152 2001–2011 444 (0–2707) 23 ˘11 15 ˘7 23 ˘15 778 ˘602 8083 ˘6457 60–80 512 ˘298 649 ˘254 Ge many 5894 2000–2008 344 (´5–1879) 28 ˘12 22 ˘7 31 ˘14 833 ˘814 11811 ˘6371 60–80 628 ˘302 754 ˘185 all 49120 2000–2013 514 (´5–2707) 25 ˘12 17 ˘8 25 ˘17 824 ˘749 9034 ˘6698 60–80 564 ˘328 667 ˘253 Cen al-Eas Eu ope Czech Rep. 13929 2001–2004 541 (138–1503) 25 20 33 812 17340 ˘10858 60–80 809 ˘441 643 ˘266 Poland 17281 2005–2013 193 (´4–1459) 23 ˘9 18 ˘5 29 ˘14 883 ˘614 10656 ˘6623 40–60 612 ˘263 720 ˘288 Romania 5509 2003–2011 542 (´1–1968) 24 ˘11 - 28 ˘15 878 ˘723 10355 ˘7256 40–60 582 ˘289 571 ˘164 all 36719 2001–2013 443 (´4–1968) 23 ˘10 18 ˘5 28 ˘15 881 ˘673 12376 ˘8793 40–60 652 ˘345 649 ˘248 Sou h Eu ope I aly 15183 2002–2009 860 (7–2891) 20 ˘8 12 ˘4 22 ˘13 839 ˘636 6315 ˘4897 20–40 497 ˘293 635 ˘179 Spain 60033 2000–2008 842 (1–2549) 23 ˘13 10 ˘4 13 ˘11 491 ˘516 4003 ˘3918 40–60 288 ˘246 606 ˘293 all 75216 2000–2009 831 (1–2891) 22 ˘12 10 ˘4 15 ˘12 561 ˘560 4469 ˘4237 40–60 330 ˘269 578 ˘275 All coun ies - 196434 – 548 (´5–2891) 22 ˘11 13 ˘7 20 ˘15 900 ˘2646 7298 ˘6916 40–60 469 ˘325 597 ˘252 Remo e Sens. 2016,8, 554 9 o 18 Ou NFI da ase co e s he ull ele a ional and la i udinal ange o o es condi ions in Eu ope including di e en si e condi ions, ee species, de elopmen s ages o managemen p ac ices. Fo mos coun ies we ha e mo e han 5000 in en o y plo s (excep ion: Belgium wi h 512 plo s) and in mos cases a plo spacing o a leas 4 by 4 km (Table S1). This da ase also p o ides in o ma ion on o es p ope ies such as ee age, ca bon s ocks o s and densi y and Table 2indica es ha hese cha ac e is ics a y ac oss Eu ope. Table 2. NPP and ∆ NPP (always using median) o he whole da ase (“All Coun ies”), o each coun y sepa a ely and o each egion (MODIS NPP using global clima e da a—MODIS GLOB; MODIS NPP using local Eu opean clima e da a—MODIS EURO and NPP using o es in en o y da a—NFI NPP); ∆ NPP and Rel. ∆ NPP bo h o MODIS GLOB and MODIS EURO. Posi i e di e ences indica e ha MODIS NPP o e es ima es NFI NPP and ice e sa. NPP and ∆NPP (gC¨m´2¨yea ´1)MODIS MODIS ∆NPP Rel. ∆NPP [%] GLOB EURO NFI NPP GLOB EURO GLOB EURO All Coun ies 680 577 539 141 38 26% 7% No h Eu ope Finland 471 399 414 57 ´15 14% ´4% No way 484 406 409 75 ´3 18% ´1% Es onia 534 504 492 42 12 9% 3% all 519 479 461 58 18 13% 4% Cen al-Wes Eu ope Aus ia 739 612 634 105 ´22 17% ´4% Belgium 732 599 644 88 ´45 14% ´7% F ance 787 666 604 183 62 30% 10% Ge many 692 602 716 ´24 ´114 ´3% ´16% all 759 645 615 144 30 23% 5% Cen al-Eas Eu ope Czech Republic 696 618 553 143 65 26% 12% Poland 641 571 659 ´19 ´88 ´3% ´13% Romania 713 562 565 148 ´3 26% ´1% all 677 592 595 82 ´3 14% ´1% Sou h Eu ope I aly 862 657 635 227 22 36% 4% Spain 632 555 503 129 52 26% 10% all 691 584 519 172 65 33% 13% 3.1. NPP Es ima es ac oss Di e en Scales Compa ing all ou h ee NPP es ima es on a Eu opean scale allowed us o explo e he gene al beha iou and e alua e he ag eemen o he wo emo e sensing d i en NPP p oduc s, MODIS GLOB and MODIS EURO, wi h he e es ial d i en NFI NPP es ima es (Figu e 3). Re- unning he MOD17 algo i hm wi h local clima e da a educed he emo ely sensed MODIS NPP in e ms o median, mean and a ia ion as compa ed o he global clima e d i e (Figu e 3). NFI NPP is close o MODIS EURO ega ding median and mean, bu show la ge a ia ion. In addi ion, Figu e 3con i ms ha ou da a is clea ly igh -skewed (NFI NPP in pa icula ). Zooming in and examining he di e en NPP es ima es by eco egion and coun y allowed us o analyze ou esul s on a highe spa ial esolu ion and o assess local e ec s such as di e en egional g owing condi ions, he impac o local biomass allome ies o ee species composi ion [ 32 ] as well as he po en ial e ec o di e en o es managemen p ac ices in Eu ope [7]. We p o ide in Table 2 he median NPP o he h ee NPP sou ces (MODIS GLOB, MODIS EURO and NFI NPP) and he di e ences be ween MODIS and NFI NPP ( ∆ NPP GLOB and ∆ NPP EURO ), bo h in absolu e alues in gC ¨ m ´2¨ yea ´1 and no malized in ela ion o NFI NPP (Rel. ∆ NPPi in %). Resul s a e gi en in Table 2 o Eu ope, by coun y and o he ou eco- egions [7]. A he Eu opean le el, he MODIS GLOB gi es an NPP o 680 gC ¨ m ´2¨ yea ´1 , he MODIS EURO esul ed in 577 gC ¨ m ´2¨ yea ´1 , and he NPP om he NFI da a exhibi a alue o 539 gC ¨ m ´2¨ yea ´1 . The di e ences in NPP ( ∆ NPP GLOB ) using he global da ase MODIS GLOB a e la ge han ∆ NPP EURO using he egional da ase MODIS EURO (+26% s. +7%). The same pa e n is e iden ac oss all ou Remo e Sens. 2016,8, 554 16 o 18 measu es o Eu opean o es s. Since he li e ime o he sa elli es ca ying he MODIS senso is unknown, we s ongly sugges he implemen a ion and es ing o his concep in he upcoming Eu opean sa elli e echnologies such as he Cope nicus P og amme o ensu e consis en and ealis ic p oduc i i y es ima es also in he u u e. MODIS EURO da a a e made eely a ailable o 2000 un il 2012 unde p://palan i .boku.ac.a / Public/MODIS_EURO. Supplemen a y Ma e ials: The ollowing a e a ailable online a www.mdpi.com/2072-4292/8/7/554/s1, Table S1: Summa y o he p ope ies o he di e en o es in en o y da ase s, Table S2: T ee species g oups used in his s udy, desc ip ion and selec ed ee species, Figu e S1: Di ec pixel- o-plo compa ison o MODIS EURO and NFI NPP, Figu e S2: Fo No h Eu ope ∆ NPP g ouped by Ele a ion, La i ude and Longi ude, Figu e S3: Fo Cen al-Wes Eu ope ∆ NPP g ouped by Ele a ion, La i ude and Longi ude, Figu e S4: Fo Cen al-Eas Eu ope ∆ NPP g ouped by Ele a ion, La i ude and Longi ude, Figu e S5: Fo Sou h Eu ope ∆ NPP g ouped by Ele a ion, La i ude and Longi ude, Figu e S6: Di e ence ∆ NPP g ouped by age classes, Figu e S7: Di e ence ∆ NPP g ouped by ee heigh classes, Figu e S8: Di e ence ∆ NPP g ouped by MODIS Land co e ypes, Figu e S9: Di e ence ∆ NPP g ouped by dominan species, Figu e S10: MODIS EURO and NFI NPP by S and densi y Index (SDI) classes. Acknowledgmen s: This wo k was conduc ed as pa o he collabo a i e p ojec “FORes managemen s a egies o enhance he MITiga ion po en ial o Eu opean o es s” (FORMIT). The esea ch leading o hese esul s has ecei ed unding om he Eu opean Union Se en h F amewo k P og amme unde g an ag eemen n ˝ 311970. Special hanks o all he ield c ews collec ing he sample da a o he in en o y plo s. We a e also g a e ul o he esponsible people om he a ious o es in en o y o ganiza ions o p o iding us wi h he da a o hei o es in en o y sys ems and he eby making his wo k possible in he i s place. We also wan o acknowledge he open da a policy o NASA and he wo k o he MODIS land p oduc science eam p o iding us wi h he FPAR and LAI p oduc s. We u he wan o hank Lo e a Mo eno o p oo eading he manusc ip . We wan o hank in pa icula he edi o and he anonymous e iewe on hei help ul commen s on an ea lie d a o he manusc ip . Au ho Con ibu ions: M.N. concei ed and designed he s udy, coo dina ed compiling he NFI NPP da ase , calcula ed he o es in en o y esul s o Aus ia and w o e he i s d a o he manusc ip , A.M. and C.T. de eloped he code o compu ing MODIS EURO and main ain he p-se e , V.M. calcula ed he o es in en o y esul s o Ge many, S.H. o Finland, M.M. o I aly, O.B. o Romania, M.L. o Es onia, G.C. o Belgium, A.T. o F ance, K.B. o Poland, J.M. o Czech Republic, I.A. o Spain, R.A. o No way, M.Z. p o ided he o iginal MOD17 code and helped in p epa a ion o he inpu da a, F.M. and H.H. coo dina ed and supe ised he analysis and he manusc ip w i ing, all au ho s con ibu ed equally in w i ing and e ising he manusc ip . Con lic s o In e es : The au ho s decla e no con lic o in e es . Re e ences 1. Gowe , S.T.; Kucha ik, C.J.; No man, J.M. Di ec and Indi ec Es ima ion o Lea A ea Index, APAR, and Ne P ima y P oduc ion o Te es ial Ecosys ems. Remo e Sens. En i on. 1999,70, 29–51. [C ossRe ] 2. 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