Simonetta Paloscia , Emanuele Santi, Simone Pettinato, Marco Brogioni CNR-IFAC, Florence Paolo Ferrazzoli, Rachid Rahmoune...
<ul><li>Microwave satellites demonstrated to be good sensors for investigating land surface features, and in particular so...
<ul><li>AMSR-E data (55°) at  C  (6.8GHz),  X  (10GHz),  Ku  (19GHz), and  Ka  (37GHz) bands, were collected during one ye...
<ul><li>The following 3 forest areas, have been studied by using the AMSR-E & SMOS sensors:  </li></ul><ul><ul><ul><li>A N...
<ul><li>Russian forest  (Evergreen) </li></ul><ul><li>Jageda q i forest (China) </li></ul><ul><li>Foreste Casentinesi (Ita...
<ul><li>PI  ( X   &   Ku )  shows a decreasing behavior in summer, due to the increase in leaf biomass, and an increasing ...
<ul><li>PI(Ku)=0.01- 0.0015 LAI (R 2 =0.59) </li></ul><ul><li>FI(Ku-Ka)=0.73-1.34 (R 2 =0.6) </li></ul><ul><li>Winter data...
<ul><li>Tn=0.986- 0.0023 R (R 2 =0.79) </li></ul><ul><li>Monthly rainfall data were recorded at a nearby meteo station and...
<ul><li>SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity (Tn) </li>...
<ul><li>The snowfalls in winter affect both PI and FI.  </li></ul><ul><li>FI   shows a great sensitivity to snow but even ...
<ul><li>PI(Ku)=0.007- 0.0012 LAI (R 2 =0.56) </li></ul><ul><li>Winter data (snow) were not considered </li></ul>PI LAI
<ul><li>Tn=0.99- 0.0003 R (R 2 =0.57) </li></ul><ul><li>Monthly rainfall data were recorded at a nearby meteo station and ...
<ul><li>In winter (until DoY 80) SMOS surface emissivity, Tn, shows values close to 1, when the soil is frozen and covered...
<ul><li>A mixed dense forest located in Tuscany, was selected as a temperate test area, where snowfalls are rather excepti...
<ul><li>Seasonal trends of the  PI(Ku) ,   FI ,  and   LAI  from 2006 to 2008. The annual trend of FI is in phase with the...
<ul><li>PI(Ku)=0.012- 0.0009 LAI (R 2 =0.4) </li></ul><ul><li>FI(Ku-Ka)=0.98-1.44 (R 2 =0.65) </li></ul>LAI PI LAI FI
<ul><li>Emissivity data at L band collected with an airborne sensor on some dense forests in Tuscany showed a fairly high ...
<ul><li>Temporal trends of brightness temperature and related microwave indexes from AMSR-E & SMOS satellites were analyze...
 
<ul><li>SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity  </li></ul...
 
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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

  1. 1. Simonetta Paloscia , Emanuele Santi, Simone Pettinato, Marco Brogioni CNR-IFAC, Florence Paolo Ferrazzoli, Rachid Rahmoune DISP, Tor Vergata University, Rome (Italy)
  2. 2. <ul><li>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. </li></ul><ul><li>The retrieval of information on forests is crucial for all studies concerning global changes and carbon balance. </li></ul><ul><li>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. </li></ul>
  3. 3. <ul><li>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 </li></ul><ul><li>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. </li></ul><ul><li>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: </li></ul><ul><ul><ul><li>Polarization Index: PI=(Tbv-Tbh)/0.5*(Tbv+Tbh) at both X- and Ku-bands; </li></ul></ul></ul><ul><ul><ul><li>Frequency Index: FI = [(TbvKu - TbvKa)+ (TbhKu + TbhKa)]/2; </li></ul></ul></ul><ul><ul><ul><li>Normalized Temperature: Tn=Tbh(C)/Tbv(Ka) or Tb(L)/Ts </li></ul></ul></ul>
  4. 4. <ul><li>The following 3 forest areas, have been studied by using the AMSR-E & SMOS sensors: </li></ul><ul><ul><ul><li>A Needle-leaved deciduous forest of Larix (Jiagedaqi) in China, characterized by cold winter with snowfalls (123°E/49.8°N); </li></ul></ul></ul><ul><ul><ul><li>A boreal Evergreen Spruce forest in Russia, with cold winters and snowfalls (60°E/50.5°N) </li></ul></ul></ul><ul><ul><ul><li>The Foreste Casentinesi in Italy, a mixed forest located in Central Italy and characterized by mild weather conditions (11.8°E/43.8°N) </li></ul></ul></ul><ul><ul><ul><li>The first 2 areas have already been selected in the past for investigations carried out by using SSM/I data </li></ul></ul></ul>
  5. 5. <ul><li>Russian forest (Evergreen) </li></ul><ul><li>Jageda q i forest (China) </li></ul><ul><li>Foreste Casentinesi (Italy) </li></ul>1 3 2
  6. 6. <ul><li>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. </li></ul><ul><li>The trend of LAI has an opposite trend with respect to these curves. </li></ul><ul><li>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. </li></ul>PI LAI FI
  7. 7. <ul><li>PI(Ku)=0.01- 0.0015 LAI (R 2 =0.59) </li></ul><ul><li>FI(Ku-Ka)=0.73-1.34 (R 2 =0.6) </li></ul><ul><li>Winter data (snow) were not considered </li></ul>PI LAI Late snowfall LAI FI
  8. 8. <ul><li>Tn=0.986- 0.0023 R (R 2 =0.79) </li></ul><ul><li>Monthly rainfall data were recorded at a nearby meteo station and compared to averaged Tb data </li></ul><ul><li>Winter data (snow) were not considered </li></ul>
  9. 9. <ul><li>SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity (Tn) </li></ul><ul><li>In winter (until DoY 80) the soil is frozen and covered by snow, with low permittivity and then emissivity is high. </li></ul><ul><li>Between DoY 90 and 120 there is a clear decreasing trend, associated to snow melting. </li></ul><ul><li>This effect is due to the strong variation of soil properties, from frozen to wet. </li></ul><ul><li>After this date, Tn increases again and shows variations partially related to soil moisture effects. </li></ul>Tn SMC Melting
  10. 10. <ul><li>The snowfalls in winter affect both PI and FI. </li></ul><ul><li>FI shows a great sensitivity to snow but even to the variations of LAI in summer and spring time. </li></ul><ul><li>The variations of PI at X and Ku band are similar to those in Jagedaqui. </li></ul>PI FI LAI
  11. 11. <ul><li>PI(Ku)=0.007- 0.0012 LAI (R 2 =0.56) </li></ul><ul><li>Winter data (snow) were not considered </li></ul>PI LAI
  12. 12. <ul><li>Tn=0.99- 0.0003 R (R 2 =0.57) </li></ul><ul><li>Monthly rainfall data were recorded at a nearby meteo station and compared to averaged Tb data </li></ul><ul><li>Winter data (snow) were not considered </li></ul>
  13. 13. <ul><li>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. </li></ul><ul><li>Between DoY 80 and 120 there is a clear decreasing trend, associated to snow melting. </li></ul><ul><li>This effect is due to the strong variation of soil properties, from frozen to wet. </li></ul><ul><li>However, the emissivity remains > 0.9 and does not show further variations related to soil moisture effects, due to the high forest density. </li></ul>Tn SMC Melting
  14. 14. <ul><li>A mixed dense forest located in Tuscany, was selected as a temperate test area, where snowfalls are rather exceptional. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>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. 15. <ul><li>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. </li></ul><ul><li>The X-band values were not used, since they were affected by strong RFI, probably originated by the radio transmitters close to this area. </li></ul>FI, LAI PIKu
  16. 16. <ul><li>PI(Ku)=0.012- 0.0009 LAI (R 2 =0.4) </li></ul><ul><li>FI(Ku-Ka)=0.98-1.44 (R 2 =0.65) </li></ul>LAI PI LAI FI
  17. 17. <ul><li>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. </li></ul><ul><li>These trends have been confirmed by model simulations (Della Vecchia et al. 2010) </li></ul>
  18. 18. <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
  19. 20. <ul><li>SMOS Tb, normalized to surface temperatures estimated by ECMWF, was transformed into surface emissivity </li></ul><ul><li>In winter (until DoY 80) the soil is frozen and covered by snow, with low permittivity and then emissivity is close to 1. </li></ul><ul><li>Between DoY 80 and 120 there is a clear decreasing trend, associated to snow melting. </li></ul><ul><li>This effect is due to the strong variation of soil properties, from frozen to wet. </li></ul><ul><li>However, the emissivity remains > 0.9 and does not show further variations related to soil moisture effects. </li></ul>Soil moisture

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