1. Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems* Maselli F. 1, Papale D. 2, Puletti N. 3, Chirici G. 4, Corona P. 2 1 CNR-IBIMET, Firenze, Italy 2 University of Tuscia, Viterbo, Italy 3 University of Firenze, Italy 4 University of Molise, Isernia, Italy *Remote Sensing of Environment 113(2009), 657-667

2. - Starting from a straightforward forest productivity model (C-fix, by Veroustraete et al. 1994, 2002, 2004), the aim was to develop a procedure of simple implementation (based on widely available EO data) and characterized by easy repeatability in the perspective of quick monitoring on a national level - The C-fix approach is between complex process models based on a large amount of field measurements and very simple empirical models merely based on remotely sensed data

3. Methodology The work is based on C-fix model, modified z = number of months = 12 i = number of forest and other wooded land categories = 12 GPP = Gross Primary Production (gC m -2 year -1 ) ε = radiation use efficienty (gC/MJ) T cor = temperature correction factor fAPAR = fraction of the Absorbed Photosyntetic Active Radiation G par = incoming photosynthetically active radiation (MJ m -2 year -1 ) Maselli, F., Barbati, A., Chiesi, M., Chirici, G., Corona, P., 2006. Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sensing of Environment 100, 563-575. Chirici, G., Barbati, A., Maselli, F., 2007. Modelling of Italian forest net primary productivity by the integration of remotely sensed and GIS data. Forest Ecology and Management 246, 285-295. NEP = 35.2 M t C y -1

4. Water stress index Cws = 0.5 + 0.5 AET/ PET Where AET and PET are actual and potential evapotranspiration, respectively. For simplicity, AET can be assumed to equal precipitation when this is lower than PET. Consequently, Cws can vary between 0.5 (when strong water shortage reduces photosynthesis to half of its potential value) to 1 (when there is no water shortage and photosynthesis reduction). PET = monthly mean of daily potential evapotranspiration (mm/day); Rs = monthly mean of daily global (total) solar radiation (kJ/m2/day); Ta = monthly mean of daily air temperature (°C). Monthly temperature and radiation maps were used to produce monthly PET maps for the whole study period (1999-2006).

5. Estimation of G par 1 - Estimation of theoretical radiation (G 0 ) based on astronomic formulas of Iqbal (1983) using latitude, slope and aspect from DEM 2 - Conversion from theoretical to real radiation to take into account radiation reduction due to cloud coverage (Benincasa et al. 1991) 3 – Comparison to real incoming solar radiation 4 – Conversion of total incoming solar radiation into G par

6. Estimation of fAPAR (fraction of the Absorbed Photosynthetically Active Radiation) for each month and for each forest category fAPAR is basically estimated linearly from NDVI (Myneni e Williams 1994, modified by Veroustraete et al. 2002) fAPAR zi = 0.8642 NDVI zi – 0.0814 The relationship is theoretically valid only for pure pixels To estimate the NDVI value of each forest category the locally calibrated end-member estimation method of Maselli (2001) was applied For each forest category and for each month a value of fAPAR was mapped

7. SPOT-VEGETATION NDVI - Downloading from VITO dekad MVC From 1999 to march 2006 - Low-pass filtering - Monthly maximum value compositing - Resampling to the project geographic system (UTM 32 N)

8. Selected forest categories Mapped in a national development of CORINE database to increase the thematic resolution for forest categories Mixed categories for the present project were aggregated to pure ones Original vector data were converted in fuzzy raster format Fir and spruce Dec. oaks Mount. pinesMed. pines (High) Maquis Evergreen oaks Meso. broadleaves Higro broadleaves Exotic broadleaves Beech Exotic coniferous Chestnut

10. Forest monthly GPP images of Italy obtained by the original C-Fix (a), by the modified C-Fix (b), and difference image (c) for August 2003

11. Forest annual GPP images of Italy obtained by the original C-Fix (a), by the modified C-Fix (b), and difference image (c) for 2002

12. Site nameCodeCoordinates Altitude (m) speciesAge (in 2003) Measurement period classReference CastelporzianoIT-Cpz 41.71° N 13.63° E 30Quercus ilex L.552000-2006Holm oak (Reichstein et al., 2007) CollelongoIT-Col 41.85° N 13.59° E 1560Fagus sylvatica Mill.1151999-2006Beech (Valentini et al., 2000) LavaroneIT-Lav 45.96°N 11.28°E 1353Picea abies (L.) Karst.90 2001-2002 2004, 2006 Norway spruce/ White fir (Cescatti and Marcolla, 2004) RenonIT-Ren 46.59° N 11.44° E 1737Picea abies (L.) Karst.0-1801999-2006 Norway spruce/ White fir (Marcolla et al., 2005) Roccarespampani 1IT-Ro1 42.39° N 11.92 ° E 224Quercus cerris L.32000-2006Other oaks (Tedeschi et al., 2006) Roccarespampani 2IT-Ro2 42.39° N 11.92 ° E 235Quercus cerris L.142002-2006Other oaks (Tedeschi et al., 2006) San RossoreIT-SRo 43.7° N 10.5° E 5Pinus pinaster Ait.541999-2006 Mediterranean pines (Ciais et al., 2005) TicinoIT-PT145.20° N 9.06° E 65Populus alba I 214132002-2004Hygrophylus broadleaves (Meroni et al., 2004)

13. Monthly GPP measured (full circles) and estimated by C-Fix (asterisks) and modified C-Fix (empty circles) at Castelporziano