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MONITORING

EUMeTrain LSA SAF Week Carla Barroso, IM. Vegetation Products Part 1 - Physical principles & algorithms. MONITORING. WEATHER. CLIMATE. SEISMIC ACTIVITY. TO. A SUSTAINABLE DEVELOPMENT AND A SAFER WORLD. Outline. S pectral signatures MSG channels for vegetation monitoring

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MONITORING

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  1. EUMeTrain LSA SAF Week Carla Barroso, IM Vegetation Products Part 1 - Physical principles & algorithms MONITORING WEATHER CLIMATE SEISMIC ACTIVITY TO A SUSTAINABLE DEVELOPMENT AND A SAFER WORLD

  2. Outline • Spectral signatures • MSG channels for vegetation monitoring • Using RGB images to identify vegetated areas • Vegetation Indices (NDVI) • Biogeophysical Parameters • FVC, LAI and fAPAR algorithms

  3. Who are you? 1- Where are you from? (use the )

  4. Who are you? 2- Which of you have already… (use the to answer)

  5. Spectral Signatures • Let’s start by the basics… • Different materials reflect and absorb differently at different wavelengths. • Have different spectral signatures. • Enables to distinguish the different features in remotely sensed data.

  6. Spectral Signatures Spectral Signature Or Spectral response curves Q. Which of the curves correspond to vegetation? (use the )

  7. Spectral Signatures Spectral Signature Or Spectral response curves Strong absorption However the reflectance peak in this region is located @ .55 m – that is why veg looks green

  8. Spectral Signatures Spectral Signature Or Spectral response curves High reflectance in the NIR (.8 m)

  9. Spectral Signatures Spectral Signature Or Spectral response curves The reflectance curves of non vegetated surfaces have smaller dependence on wavelength absorption by water, cellulose and lignin and several other biochemical constituents

  10. Spectral Signatures Spectral Signature Or Spectral response curves A material can only be identified from its spectral signature if the sensing system has enough spectral resolution to distinguish its spectrum from those of other materials!

  11. MSG channels for vegetation monitoring SEVIRI spectral response functions Ch 3 Ch 1 Ch 2 Source: http://www.eumetrain.org/resources/operational_use_rgb.html

  12. Spectral Signatures Q. Which of the images correspond to MSG 0.8m channel? (use the )

  13. MSG channels for vegetation monitoring Q. Which of the images correspond to MSG 0.8m channel? (use the ) 0.8 m 0.6 m 0.6 µm 0.8 µm

  14. RGB Techniques RGB Techniques – works by associating a colour to a particular channel Red - MSG 1.6 m Green - MSG 0.8 m Blue - MSG 0.6 m Source: http://www.eumetrain.org/resources/operational_use_rgb.html

  15. RGB Techniques Red - MSG 1.6 m Green - MSG 0.8 m Blue - MSG 0.6 m Q. Do you know the name of this RGB? (please use the text box to answer (F2) )

  16. RGB Techniques Meteosat-9 colour composite image Red - 1.6 μm; Green - 0.8 μm; Blue - 0.6 μm Natural colour Source: http://www.eumetrain.org/resources/operational_use_rgb.html

  17. Vegetation Indices • Combine information from different channels to enhance the 'vegetation signal'. NDVI (15 June 2008) SEVIRI/MSG Channels 1 e 2 reflectances NDVI daily composite of SEVIRI full disk, June 15, 2008 (Source: Yu,Y. et al. Development of Vegetation Products for U.S. GOES-R Satellite Mission, Presented on 4th Global Vegetation Workshop Univ. of Montana, Missoula, June 16-19 2009

  18. Vegetation Indices For bare soil NDVI ~0 NDVI (15 June 2008) For vegetated areas NDVI ranges between 0.2 and 0.6 NDVI daily composite of SEVIRI full disk, June 15, 2008 (Source: Yu,Y. et al. Development of Vegetation Products for U.S. GOES-R Satellite Mission, Presented on 4th Global Vegetation Workshop Univ. of Montana, Missoula, June 16-19 2009

  19. Vegetation Indices NDVI – Normalized Difference Vegetation Index • Advantages • Very easy to compute – it is a transformation of reflectance in 2 channels; does not need additional information (like the type of soil/vegetation); • Allows the analysis of long time series – using satellite data from NOAA, we can analyze data with more than 20 years.

  20. Vegetation NDVI – Normalized Difference Vegetation Index • Advantages • Very easy to compute – it is a transformation of reflectance in 2 channels; does not need additional information (like the type of soil/vegetation); • Allows the analysis of long time series – using satellite data from NOAA, we can analyze data with more than 20 years. • Disadvantages • Sensitive to fluctuations/noise in the observations (the lighting conditions of the surface, aerosols, contamination by clouds); • It saturates in high biomass situations; • Does not correspond to any property / physical characteristic of the surface/vegetation.

  21. Biogeophysical Parameters Parameters that represent the structural characteristics of vegetated surfaces: Fraction of Vegetation Cover [0 – 1]: fraction of the surface covered by vegetation (FVC) Leaf Area Index : total area occupied by the leaves per unit area [m2/m2] (LAI) It provides complementary information to the FVC, accounting for the surface of leaves contained in a vertical column normalized by its cross-sectional area.

  22. Biogeophysical Parameters 1 pixel 1 pixel FVC=0.8 LAI= 5 FVC=0.8 LAI= 5 FVC=0.6 LAI= 5 FVC=0.8 LAI= 7 ?

  23. Biogeophysical Parameters Parameter directly related with the state (and health) of vegetation: Fraction of Absorbed Photosynthetically Active Radiation: part of radiation used for photosynthesis (0.4 – 0.7m) absorbed by the green parts of the canopy (FAPAR)

  24. Channel 2 Channel 1 Biogeophysical Parameters FVC, LAI, FAPAR estimations from Remote Sensing are based in the analysis of vegetation signature in the 0.6 m, 0.8 m & 1.6 m channels. Starting with the characterization of reflectance in different channels for different types of vegetation (V1,V2, V3, ...) and soil (S1, S2, S3).

  25. Channel 2 Channel 1 LSA SAF FVC Algorithm  For any given observation () the algorithm assumes this value can be modelled by the pairs (ρvegetation, ρsoilsubclass) FVC ESTIMATION observation • the pure types,Vi and Si for each pixel are obtained by the prevalent Land-cover  • the distances between () and Vi,Si give the fractions of each vegetation and soil type within the scene  Number of vegetation subclasses present in the pixel Fraction of vegetation cover for vegetation type Vi

  26. LSA SAF LAI Algorithm LAI ESTIMATION - semi-empirical method proposed by Roujean and Lacaze (2002), in which LAI is related to FVC trough the following expression: Where b and G are constants and a0 and  coefficients depend on the LandCover Map.

  27. LSA SAF fAPAR Algorithm fAPAR ESTIMATION – the LSA SAF fAPAR is estimated from a NDVI-like vegetation index: Since the view-illumination geometry may have a large impact on reflectance observations, RDVI is computed from surface reflectances corrected to an optimal geometry. 

  28. EUMeTrain LSA SAF Week Carla Barroso, IM Vegetation Products Part 1 - Physical principles & algorithms MONITORING WEATHER CLIMATE SEISMIC ACTIVITY TO A SUSTAINABLE DEVELOPMENT AND A SAFER WORLD

  29. EUMeTrain LSA SAF Week Carla Barroso, IM Vegetation Products Part 2 - LSA SAF Products Characteristics & Applications MONITORING WEATHER CLIMATE SEISMIC ACTIVITY TO A SUSTAINABLE DEVELOPMENT AND A SAFER WORLD

  30. Outline • LSA SAF vegetation products • Overall quality • Products validation • Comparison to polar-orbiter derived products • Applications

  31. LSA SAF Vegetation Products Fraction of Absorbed Photosynthetically Active Radiation - fAPAR Leaf Area Index- LAI Fractional Vegetation Cover - FVC √ Available freely: Via Ftp (2006) Eumetcast (2009) √ Daily √SEVIRI resolution (3 x 3 km at sub-satellite point)

  32. LSA SAF Vegetation Products Besides the vegetation field, the FVC, LAI and fAPAR contain 2 more datasets each, comprising: Veg field; Error estimate field; quality control information

  33. LSA SAF Vegetation Products http://landsaf.meteo.pt

  34. LSA SAF Vegetation Products http://landsaf.meteo.pt

  35. Overall quality of LSA SAF Vegetation Products Mean value of the product error along year 2007 Q. Which of the following facts most () / do not () contribute to the optimal quality the LSA SAF veg products over Afr? • The temporal sampling of MSG • The spatial resolution of MSG • The geometry of acquisition

  36. Overall quality of LSA SAF Vegetation Products Mean value of the product error along year 2007 Q. Which of the following facts most () / do not () contribute to the optimal quality the LSA SAF veg products over Afr?  • The temporal sampling of MSG • The spatial resolution of MSG • The geometry of acquisition  

  37. Products Validation • The quality of the vegetation products (FVC, LAI, FAPAR) is regularly monitored through: • Comparisons with similar parameters obtained from other satellites (MERIS (ENVISAT),MODIS (EOS), VEGETATION (SPOT)); • Comparisons with in situ data;

  38. Products Validation LAI SEVIRI/ MERIS/ MODIS • High spatial consistency; • LSA SAF LAI presents less gaps in vegetated areas. LSA SAF team (F. Camacho, J. Garcia-Haro)

  39. LAI Products Validation • Comparison with in situ measurements Dahra (Senegal) Q. Which of the products shows less gaps? MODIS MSG MERIS Land-SAF team (F. Camacho, J. Garcia-Haro) Land-SAF team (F. Camacho, J. Garcia-Haro)

  40. LAI Products Validation • Comparison with in situ measurements Dahra (Senegal) • All products are consistent with ground measurements; • LSA SAF LAI better follows the seasonality of the vegetation activity during 2006 and 2007; • The peak of the green season is well captured by LSA SAF LAI; Land-SAF team (F. Camacho, J. Garcia-Haro) Land-SAF team (F. Camacho, J. Garcia-Haro)

  41. Comparison to Polar-Orbiter Derived Products • The vegetation products (FVC, LAI, FAPAR) obtained from geostationary satellites (like SEVIRI/MSG), while compared with polar-orbiter derived products (MODIS, VGT): • have coarser spatialresolution; • Reproduce the seasonal cycle in a more realistic way (without spurious variations; time series show less noise); • Have fewer gaps in the data

  42. Applications - Phenological parameters • Vegetation products from LSA SAF • Generate spatially coherent images of phenological parameters. E.g: • The start, length & amplitude of the growing season; • the timing of phenological stages (onset of greenness, maximum development, senescence);

  43. Applications - Phenological parameters • The date of the SOS (start of the growing season) is a critical parameter for food security monitoring. C.Afr – Guinea Gulf  FVC product from VEGETATION (VGT – JRC)  FVC product from SEVIRI (LSA SAF) Q. Can you indicate the SOS in 2007? LSA SAF team (F. Camacho, J. Garcia-Haro)

  44. Applications - DMP • Dry Matter Productivity – representsthe increase in dry matter biomass. • It is a function of (Monteith, 1972): • Incoming solar radiation (0.2-3.0 μm); • Surface Temperature; • fAPAR The Joint Research Center (JRC) is computing this parameter for crop monitoring using meteorological information and the fAPAR product from the LSA SAF (LSA SAF 3rd User Training Workshop).

  45. Applications - DMP • Dry Matter Productivity 2008, February, Dekad 3 over Africa SPOT MSG • Very good spatial consistency; A North-South transect through Africa (from Lybia to Cape Town) also highlights the good correspondence between SPOT and MSG:

  46. Applications – Veg Monitoring Oman The south east monsoon affects the Dhofar region from about June to early September. 15 Aug 2010 Effects of the khareef in vegetation Oman 15 Sep 2010 Oman 15 Oct 2010 Sept Sept

  47. Applications – Drought Monitoring Severe Drought in Kenya 2009 Ethiopia Ethiopia Somalia Somalia Q. Can you tell the name of this RGB? Kenya Kenya July 2009 August 2009 Blue colors – cloud free/no rain areas Ethiopia Ethiopia Red colors – high coverage of cold ice clouds & precipitation Somalia Somalia Kenya Kenya Sequence of images from http://oiswww.eumetsat.org/WEBOPS/iotm/iotm/20091001_drought/20091001_drought.html September 2009

  48. Applications – Drought Monitoring Severe Drought in Kenya 2009 Ethiopia Ethiopia Somalia Somalia Blue colors – cloud free/no rain areas Kenya Kenya Red colors – high coverage of cold ice clouds & precipitation October Ethiopia Ethiopia clouds and rains developed also in Somalia, Ethiopia and the coastal/central areas of Kenya. Somalia Somalia Kenya Kenya Sequence of images from http://oiswww.eumetsat.org/WEBOPS/iotm/iotm/20091001_drought/20091001_drought.html

  49. Applications – Drought Monitoring Which from bellow is from November 2009? LSA SAF FVC LSA SAF FVC

  50. Applications – Drought Monitoring Severe Drought continued up to 2011… source: http://www.eumetsat.int/Home/Main/News/Features/809364?l=en

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