Key Issues for EO of Land Surface Processes

Transcription

1 Key Issues for EO of Land Surface Processes Bernard Pinty EC-JRC Institute for Environment and Sustainability, Ispra, Italy Seconded to the Earth Observation Directorate, ESA-ESRIN, Frascati, Italy JRC Ispra 1 st EO Land Data Assimilation System Workshop November 16-17, 2009, ESRIN Frascati, Italy

2 Key Issues/Challenges for assimilating EO in the Solar domain to better quantify Land Surface Processes Bernard Pinty EC-JRC Institute for Environment and Sustainability, Ispra, Italy Seconded to the Earth Observation Directorate, ESA-ESRIN, Frascati, Italy JRC Ispra 1 st EO Land Data Assimilation System Workshop November 16-17, 2009, ESRIN Frascati, Italy

3 How does radiation redistribute energy between the atmosphere and the biosphere? The surface corresponds to the boundary condition of RT atmospheric problem Need to understand and represent the albedo of that surface. The energy absorbed below that surface controls the sensible and latent heat fluxes to the PBL Surface radiation budget The processes underpinning the heat fluxes are generally represented explicitly or parameterized in SVAT models Ref: Sellers et al. (1997) Science, 275,

4 Energy partitioning between the vegetation and the soil layer The surface corresponds to the upper boundary condition of the vegetation plus soil RT and other problems Need to understand and represent the RT processes yielding the distribution of energy below that surface, e.g., transmitted fluxes. The remaining energy in the soil layer is used to solve the heat conduction equation and soil hydrology, e.g., snow melting, evaporation. The energy left into the vegetation layer is used to drive the water, e.g., evapotranspiration, and the carbon cycle, e.g., NPP, NEP,.. Ref: Bonan, G. B. (2002) Cambridge Univ. Press

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6 Some key issues

7 Some key issues Are the radiative fluxes and state variables retrieved from remote sensing useful for Climate and/or NWP models? 2 RT fluxes: albedo, FAPAR 1 state variable: LAI.

8 Some key issues Are the radiative fluxes and state variables retrieved from remote sensing useful for Climate and/or NWP models? 2 RT fluxes: albedo, FAPAR 1 state variable: LAI. Series of proxies e.g., Land cover, % tree cover

9 Some key issues Are the radiative fluxes and state variables retrieved from remote sensing useful for Climate and/or NWP models? 2 RT fluxes: albedo, FAPAR 1 state variable: LAI. Series of proxies e.g., Land cover, % tree cover How Climate/NWP models can (must) adapt themselves to this new situation where accurate global land products are available?

10 Some key issues Are the radiative fluxes and state variables retrieved from remote sensing useful for Climate and/or NWP models? 2 RT fluxes: albedo, FAPAR 1 state variable: LAI. Series of proxies e.g., Land cover, % tree cover How Climate/NWP models can (must) adapt themselves to this new situation where accurate global land products are available? Adjusting (improving) their RT surface shemes

11 Challenges for the EO Land community

12 Challenges for the EO Land community 4 identified ECVs namely Albedo, FAPAR, LAI and ultimately Land Cover are linked via radiation and phenological processes: They MUST be retrieved consistently and then specified in the same manner in host models

13 Challenges for the EO Land community 4 identified ECVs namely Albedo, FAPAR, LAI and ultimately Land Cover are linked via radiation and phenological processes: They MUST be retrieved consistently and then specified in the same manner in host models Multiple datasets of these ECVs are available from different institutions: They MUST be analyzed and exploited to establish a coherent set of information across platforms and institutions.

14 Adequacy of RS products for further assimilation by Climate/NWP models

15 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves?

16 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty?

17 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty? Are they accurate enough so that the models can benefit?

18 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty? Are they accurate enough so that the models can benefit? Do they fit large-scale model s expectations?

19 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty? Are they accurate enough so that the models can benefit? Do they fit large-scale model s expectations? Do Climate/NWP models use the appropriate modeling tools to represent the available products?

20 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty? Are they accurate enough so that the models can benefit? Do they fit large-scale model s expectations? Do Climate/NWP models use the appropriate modeling tools to represent the available products?

21 Adequacy of RS products for further assimilation by Climate/NWP models Are they consistent between themselves? Are they delivered with documented uncertainty? Are they accurate enough so that the models can benefit? Do they fit large-scale model s expectations? Do Climate/NWP models use the appropriate modeling tools to represent the available products?

22 Blunt and provocative thoughts!

23 Blunt and provocative thoughts! RT sfc schemes (when) implemented in climate/ NWP models are still immature wrt those adopted to interpret EO.

24 Blunt and provocative thoughts! RT sfc schemes (when) implemented in climate/ NWP models are still immature wrt those adopted to interpret EO. The former are (more than often) even NOT compatible with the models used in inverse mode (e.g., 1D versus 3D).

25 Blunt and provocative thoughts! RT sfc schemes (when) implemented in climate/ NWP models are still immature wrt those adopted to interpret EO. The former are (more than often) even NOT compatible with the models used in inverse mode (e.g., 1D versus 3D). EO products are FIRST useful for generating constraints e.g., albedos/fapar, that climate/nwp models should be assimilating.

26 Blunt and provocative thoughts! RT sfc schemes (when) implemented in climate/ NWP models are still immature wrt those adopted to interpret EO. The former are (more than often) even NOT compatible with the models used in inverse mode (e.g., 1D versus 3D). EO products are FIRST useful for generating constraints e.g., albedos/fapar, that climate/nwp models should be assimilating.

27 Blunt and provocative thoughts! RT sfc schemes (when) implemented in climate/ NWP models are still immature wrt those adopted to interpret EO. The former are (more than often) even NOT compatible with the models used in inverse mode (e.g., 1D versus 3D). EO products are FIRST useful for generating constraints e.g., albedos/fapar, that climate/nwp models should be assimilating.

28 Blunt and provocative thoughts! (cont )

29 Blunt and provocative thoughts! (cont ) Climate/NWP models must assimilate fluxes e.g., FAPAR (to access the absorbed property in vegetation) or better Albedos (to access in addition the scattering properties) rather than state variables e.g., LAI.

30 Blunt and provocative thoughts! (cont ) Climate/NWP models must assimilate fluxes e.g., FAPAR (to access the absorbed property in vegetation) or better Albedos (to access in addition the scattering properties) rather than state variables e.g., LAI. The EO community has not been able yet to propose a consistent set of products (e.g., among ECVs and across institutions) driving key land surface processes.

31 Blunt and provocative thoughts! (cont ) Climate/NWP models must assimilate fluxes e.g., FAPAR (to access the absorbed property in vegetation) or better Albedos (to access in addition the scattering properties) rather than state variables e.g., LAI. The EO community has not been able yet to propose a consistent set of products (e.g., among ECVs and across institutions) driving key land surface processes. Lack of concern from the EO community (at least at the EU level) to address key issues related to product consistency and accuracy documentation.

32 Blunt and provocative thoughts! (cont ) Climate/NWP models must assimilate fluxes e.g., FAPAR (to access the absorbed property in vegetation) or better Albedos (to access in addition the scattering properties) rather than state variables e.g., LAI. The EO community has not been able yet to propose a consistent set of products (e.g., among ECVs and across institutions) driving key land surface processes. Lack of concern from the EO community (at least at the EU level) to address key issues related to product consistency and accuracy documentation.

33 Blunt and provocative thoughts! (cont ) Climate/NWP models must assimilate fluxes e.g., FAPAR (to access the absorbed property in vegetation) or better Albedos (to access in addition the scattering properties) rather than state variables e.g., LAI. The EO community has not been able yet to propose a consistent set of products (e.g., among ECVs and across institutions) driving key land surface processes. Lack of concern from the EO community (at least at the EU level) to address key issues related to product consistency and accuracy documentation.

34 Some on-going initiatives

35 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR.

36 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR. The RAMI4PILPS JRC initiative to challenge and thus improve current RT schemes in Climate/NWP models.

37 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR. The RAMI4PILPS JRC initiative to challenge and thus improve current RT schemes in Climate/NWP models. Comparison exercises of EO products from different institutions and EO platforms.

38 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR. The RAMI4PILPS JRC initiative to challenge and thus improve current RT schemes in Climate/NWP models. Comparison exercises of EO products from different institutions and EO platforms. Feasibility study to highlight the current possibility to generate internally consistent set of products, including across platforms.

39 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR. The RAMI4PILPS JRC initiative to challenge and thus improve current RT schemes in Climate/NWP models. Comparison exercises of EO products from different institutions and EO platforms. Feasibility study to highlight the current possibility to generate internally consistent set of products, including across platforms.

40 Some on-going initiatives Attempts at bridging the gap between RT schemes in Climate models (forward mode) and those used in inverse mode e.g., for albedos and FAPAR. The RAMI4PILPS JRC initiative to challenge and thus improve current RT schemes in Climate/NWP models. Comparison exercises of EO products from different institutions and EO platforms. Feasibility study to highlight the current possibility to generate internally consistent set of products, including across platforms.

41 RAMI4PILPS automate benchmarking process RAMI On-Line Model Checker (ROMC) reference data sets credible 3-D MC models RAMI-1 (1999) RAMI-2 (2002) RAMI-3 (2005) ROMC (2007)

42 RAMI4PILPS automate benchmarking process assess shortwave RT scheme of GCMs l Checker (ROMC) reference data sets credible 3-D MC models RAMI-2 (2002) RAMI-3 (2005) ROMC (2007) RAMI4PILPS (2008)

43 RAMI4PILPS: Forward mode

44 RAMI4PILPS: Inverse mode

45 RAMI4PILPS

46 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos.

47 MISR MODIS EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos.

48 EO product comparison MISR MODIS

49 MODIS EO product comparison MISR MISR

50 OK but.

51 OK but.

52 OK but.

53 EO product comparison

54 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos

55 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos but some issues to be investigated

56 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos but some issues to be investigated FAPAR products from different platforms show quite a significant scatter and strong biases.

57 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos but some issues to be investigated FAPAR products from different platforms show quite a significant scatter and strong biases...need to compare same physical quantities (Sun angle, incoming radiation, leaf color )

58 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos but some issues to be investigated FAPAR products from different platforms show quite a significant scatter and strong biases...need to compare same physical quantities (Sun angle, incoming radiation, leaf color )

59 EO product comparison Looks OK regarding the MODIS and MISR broadband surface albedos but some issues to be investigated FAPAR products from different platforms show quite a significant scatter and strong biases...need to compare same physical quantities (Sun angle, incoming radiation, leaf color )

60 JRC-Two-stream Inversion Package: JRC-TIP

61 JRC-Two-stream Inversion Package: JRC-TIP RS flux products from various sources + uncertainties Prior knowledge on model parameters + uncertainties Ancillary information, e.g. occurrence of snow and water

62 JRC-Two-stream Inversion Package: JRC-TIP RS flux products from various sources + uncertainties Prior knowledge on model parameters + uncertainties Ancillary information, e.g. occurrence of snow and water Time-independent operating mode JRC-TIP Radiation transfer model

63 JRC-Two-stream Inversion Package: JRC-TIP RS flux products from various sources + uncertainties Prior knowledge on model parameters + uncertainties Ancillary information, e.g. occurrence of snow and water Time-independent operating mode JRC-TIP Radiation transfer model Diagnostic fluxes + uncertainties Posterior model parameters + uncertainties

64 JRC-Two-stream Inversion Package: JRC-TIP RS flux products from various sources + uncertainties Prior knowledge on model parameters + uncertainties Ancillary information, e.g. occurrence of snow and water Re-analysis generating consistent dataset Time-independent operating mode JRC-TIP Radiation transfer model Diagnostic fluxes + uncertainties Posterior model parameters + uncertainties

65 Two-stream model to redistribute Sun energy between the atmosphere and the biosphere Scattered Fluxes by the surface Absorbed Fluxes in Vegetation Absorbed Fluxes in Soil Pinty etal., (2006): Journal of Geophysical Research, doi:

66 prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, doi:

67 prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, doi:

68 prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, doi:

69 prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, doi:

70 prior knowledge on model parameters Pinty etal., (2007): Journal of Geophysical Research, doi:

71 prior knowledge on model parameters a priori green leaves in case snow occurs Pinty etal., (2007): Journal of Geophysical Research, doi:

72 posterior knowledge on model parameters/independent variables 3 effective (stated by the model) state variables: 1. Optical depth: LAI amount of leaf material 2. single scattering albedo : Leaf (stem) [reflectance+ transmittance] leaf (stem) color 3. asymmetry of the phase function Leaf (stem) [reflectance/transmittance] 1 true (stated by the model) state variables: 4. background Albedo soil color Pinty etal., (2006): Journal of Geophysical Research, doi: /2005jd00

73 posterior knowledge on model fluxes : Dependent canopy level Scattered (albedo) in any spectral band Absorbed by the vegetation layer in any spectral band Absorbed by the background layer in any spectral bands e.g., using broadband visible and near-infrared albedos as input generates estimates of FAPAR (absorbed by vegetation in the visible domain) plus other flux quantities Pinty etal., (2006): Journal of Geophysical Research, doi: /2005jd00

74 Application over Yakustk Forest: a deciduous needle-leaf larch forest Courtesy of Dr. R. Suzuki

75 Application over Yakustk Forest: a deciduous needle-leaf larch forest Courtesy of Dr. R. Suzuki

76 Application over Yakutsk: Measurements Specified uncertainty on BHRs is 5% relative

77 Application over Yakutsk: Measurements MODIS Snow indicator (MOD10A2) Specified uncertainty on BHRs is 5% relative

78 Application over Yakutsk: model parameters Pinty etal., (2008): Journal of Geophysical Research, doi:10.129/2007/jd00

79 Application over Yakutsk: model parameters Pinty etal., (2008): Journal of Geophysical Research, doi:10.129/2007/jd00

80 Application over Yakutsk: radiant fluxes Pinty etal., (2008): Journal of Geophysical Research, doi:10.129/2007/jd00

81 Application over Yakutsk: radiant fluxes a priori green leaves Pinty etal., (2008): Journal of Geophysical Research, doi:10.129/2007/jd00

82 Application over Yakutsk: radiant fluxes MERIS FAPAR JRC-TIP inversion based on TERRA albedos a priori green leaves Pinty etal., (2008): Journal of Geophysical Research, doi:10.129/2007/jd00

83 Application over Finland: Agriculture Background Albedo in Near-InfraRed Leaf Area Index Leaf Albedo in Visible

84 Application over Finland: Agriculture Background Albedo in Near-InfraRed Leaf Area Index Leaf Albedo in Visible Leaf Area Index Background albedo (visible)

85 Application over Portugal: Burnt vegetation Leaf Area Index Leaf Area Index

86 Application over the Okavando delta: hydrology Background Albedo in Near-InfraRed Leaf Area Index Leaf Albedo in Visible Leaf Area Index Background albedo (NIR)

87 Application over Kenya: Drying (February)

88 Application over Rondonia: Deforestation

89 January 2005 Soil Albedo in NIR Leaf Area Index Leaf Albedo in Visible No solution found High residual cost-function Snow Prior used

90 January 2005 Soil Albedo in NIR Leaf Area Index Leaf Albedo in Visible No solution found High residual cost-function Snow Prior used

91 January February March April May June July August September October November December Soil Albedo in NIR No solution found Leaf Area Index High residual cost-function Leaf Albedo in Visible Snow Prior used

92 Benefits from a JRC-TIP approach 1. Derive consistent set of radiation fluxes in vegetation and soil layers (R, A and T + uncertainties) optimized against albedo products available from different sensors and by-products.

93 Benefits from a JRC-TIP approach 1. Derive consistent set of radiation fluxes in vegetation and soil layers (R, A and T + uncertainties) optimized against albedo products available from different sensors and by-products. 2. Derive associated land surface variables, e.g., LAI, leaf and soil colors to constrain and provide BCs to the climate/nwp models

94 Benefits from a JRC-TIP approach 1. Derive consistent set of radiation fluxes in vegetation and soil layers (R, A and T + uncertainties) optimized against albedo products available from different sensors and by-products. 2. Derive associated land surface variables, e.g., LAI, leaf and soil colors to constrain and provide BCs to the climate/nwp models 3. Ensure physical consistency between the RT schemes used in inverse and forward mode

95 Benefits from a JRC-TIP approach 1. Derive consistent set of radiation fluxes in vegetation and soil layers (R, A and T + uncertainties) optimized against albedo products available from different sensors and by-products. 2. Derive associated land surface variables, e.g., LAI, leaf and soil colors to constrain and provide BCs to the climate/nwp models 3. Ensure physical consistency between the RT schemes used in inverse and forward mode 4. Pave the way towards strong predictive capabilities for land surface monitoring needed for many applications.

96