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TEMPORAL TRENDS OF MICROWAVE EMISSION FROM FOREST AREAS OBSERVED FROM SATELLITE.ppt
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TEMPORAL TRENDS OF MICROWAVE EMISSION FROM FOREST AREAS OBSERVED FROM SATELLITE.ppt

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  • 1. Simonetta Paloscia , Emanuele Santi, Simone Pettinato, Marco Brogioni CNR-IFAC, Florence Paolo Ferrazzoli, Rachid Rahmoune DISP, Tor Vergata University, Rome (Italy)
  • 2.
    • Microwave satellites demonstrated to be good sensors for investigating land surface features, and in particular soil moisture and vegetation cover, at both global and regional scales.
    • The retrieval of information on forests is crucial for all studies concerning global changes and carbon balance.
    • The temporal trends microwave emission measured by AMSR-E (Advanced Microwave Scanning Radiometer onboard Aqua) and ESA/ SMOS (Soil Moisture Ocean Salinity) satellites were analyzed on some forest plots in Russia, China and Italy.
  • 3.
    • AMSR-E data (55°) at C (6.8GHz), X (10GHz), Ku (19GHz), and Ka (37GHz) bands, were collected during one year from May 2007 to April 2008
    • SMOS LC1 data al L (1.4GHz) band were collected from January to December 2010 and averaged between 37.5° and 47.5°. Samples affected by RFI were removed.
    • Seasonal trends of brightness temperatures (Tb) at different frequencies, in both H and V polarizations, were analyzed on the 3 test areas, together with the following microwave indexes:
        • Polarization Index: PI=(Tbv-Tbh)/0.5*(Tbv+Tbh) at both X- and Ku-bands;
        • Frequency Index: FI = [(TbvKu - TbvKa)+ (TbhKu + TbhKa)]/2;
        • Normalized Temperature: Tn=Tbh(C)/Tbv(Ka) or Tb(L)/Ts
  • 4.
    • The following 3 forest areas, have been studied by using the AMSR-E & SMOS sensors:
        • A Needle-leaved deciduous forest of Larix (Jiagedaqi) in China, characterized by cold winter with snowfalls (123°E/49.8°N);
        • A boreal Evergreen Spruce forest in Russia, with cold winters and snowfalls (60°E/50.5°N)
        • The Foreste Casentinesi in Italy, a mixed forest located in Central Italy and characterized by mild weather conditions (11.8°E/43.8°N)
        • The first 2 areas have already been selected in the past for investigations carried out by using SSM/I data
  • 5.
    • Russian forest (Evergreen)
    • Jageda q i forest (China)
    • Foreste Casentinesi (Italy)
    1 3 2
  • 6.
    • PI ( X & Ku ) shows a decreasing behavior in summer, due to the increase in leaf biomass, and an increasing trend in winter, due to the simultaneous decrease of biomass and presence of snow.
    • The trend of LAI has an opposite trend with respect to these curves.
    • The FI (Ku-Ka) shows 2 peaks, one in agreement with the development of tree LAI in summer, and the second one with snowfall in winter.
    PI LAI FI
  • 7.
    • PI(Ku)=0.01- 0.0015 LAI (R 2 =0.59)
    • FI(Ku-Ka)=0.73-1.34 (R 2 =0.6)
    • Winter data (snow) were not considered
    PI LAI Late snowfall LAI FI
  • 8.
    • Tn=0.986- 0.0023 R (R 2 =0.79)
    • Monthly rainfall data were recorded at a nearby meteo station and compared to averaged Tb data
    • Winter data (snow) were not considered
  • 9.
    • SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity (Tn)
    • In winter (until DoY 80) the soil is frozen and covered by snow, with low permittivity and then emissivity is high.
    • Between DoY 90 and 120 there is a clear decreasing trend, associated to snow melting.
    • This effect is due to the strong variation of soil properties, from frozen to wet.
    • After this date, Tn increases again and shows variations partially related to soil moisture effects.
    Tn SMC Melting
  • 10.
    • The snowfalls in winter affect both PI and FI.
    • FI shows a great sensitivity to snow but even to the variations of LAI in summer and spring time.
    • The variations of PI at X and Ku band are similar to those in Jagedaqui.
    PI FI LAI
  • 11.
    • PI(Ku)=0.007- 0.0012 LAI (R 2 =0.56)
    • Winter data (snow) were not considered
    PI LAI
  • 12.
    • Tn=0.99- 0.0003 R (R 2 =0.57)
    • Monthly rainfall data were recorded at a nearby meteo station and compared to averaged Tb data
    • Winter data (snow) were not considered
  • 13.
    • In winter (until DoY 80) SMOS surface emissivity, Tn, shows values close to 1, when the soil is frozen and covered by snow, with low permittivity.
    • Between DoY 80 and 120 there is a clear decreasing trend, associated to snow melting.
    • This effect is due to the strong variation of soil properties, from frozen to wet.
    • However, the emissivity remains > 0.9 and does not show further variations related to soil moisture effects, due to the high forest density.
    Tn SMC Melting
  • 14.
    • A mixed dense forest located in Tuscany, was selected as a temperate test area, where snowfalls are rather exceptional.
    • Due to the small dimensions and the heterogeneity of the area, a preliminary analysis was carried out by using a RGB Landsat image in order to better identify and geolocate the forest site.
    • The dimensions of the image are 40kmx40km. In the image, the area of about 20 km x 20km, corresponding to the AMSR-E acquisition, was indicated.
    RGB Landsat image in the visible bands: R= Band 3 (0.63-0.69  m) G= Band 2 (0.53-0.61  m) B= Band 1 (0.45-0.52  m)
  • 15.
    • Seasonal trends of the PI(Ku) , FI , and LAI from 2006 to 2008. The annual trend of FI is in phase with the forest LAI, whereas the PI(Ku) is inversely related to it.
    • The X-band values were not used, since they were affected by strong RFI, probably originated by the radio transmitters close to this area.
    FI, LAI PIKu
  • 16.
    • PI(Ku)=0.012- 0.0009 LAI (R 2 =0.4)
    • FI(Ku-Ka)=0.98-1.44 (R 2 =0.65)
    LAI PI LAI FI
  • 17.
    • Emissivity data at L band collected with an airborne sensor on some dense forests in Tuscany showed a fairly high sensitivity to SMC at both H and V pol.
    • These trends have been confirmed by model simulations (Della Vecchia et al. 2010)
  • 18.
    • Temporal trends of brightness temperature and related microwave indexes from AMSR-E & SMOS satellites were analyzed over three forest areas characterized by different climatic conditions and tree species.
    • At the higher frequencies, the frequency index between Ku and Ka bands is sensitive to the snow cycle, whereas the polarization index at both X and Ku bands is sensitive to the leaf cycle. Direct relationships between PI(Ku) and LAI, derived from ECOCLIMAP database, confirmed a high correlation between these two quantities.
    • Looking at SMOS data, the emissivity, obtained normalizing L band (1.4 GHz) emission to the surface temperature derived from ECMWF, shows a clear decrease, at both polarizations, which can be associated to the snow melting process and therefore to a soil moisture increase.
  • 19.  
  • 20.
    • SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity
    • In winter (until DoY 80) the soil is frozen and covered by snow, with low permittivity and then emissivity is close to 1.
    • Between DoY 80 and 120 there is a clear decreasing trend, associated to snow melting.
    • This effect is due to the strong variation of soil properties, from frozen to wet.
    • However, the emissivity remains > 0.9 and does not show further variations related to soil moisture effects.
    Soil moisture
  • 21.