Presentation is loading. Please wait.

Presentation is loading. Please wait.

GEOGG142 GMES Calibration & validation of EO products Dr. Mat Disney Pearson Building room 113 020 7679 0592

Similar presentations

Presentation on theme: "GEOGG142 GMES Calibration & validation of EO products Dr. Mat Disney Pearson Building room 113 020 7679 0592"— Presentation transcript:

1 GEOGG142 GMES Calibration & validation of EO products Dr. Mat Disney Pearson Building room 113 020 7679 0592

2 2 Outline Calibration Example: AVHRR NDVI across time Multiple AVHRR (and different) sensors: calibration, drift etc. Validation Example: MODIS NPP product Time, space, measurements? Scaling?

3 3 Calibration & validation? Calibration: –process of converting an instrument reading to a physically meaningful measurement –Particularly radiometric calibration –i.e. from DN to radiance measurement Validation: –experiments designed to verify instrument measurements using independent measurements Both essential to scientific remote sensing Material from J. Morley

4 4 Example: calibration of AVHRR NDVI Calibration: –We observe a known target, and relate output DNs to target radiance –Known targets: prelaunch, lab targets (e.g. AVHRR) on-board lamps (e.g. CZCS) astronomical objects (Sun, Moon, space E.g., SeaWIFS) invariant surfaces (e.g. deserts) Material from J. Morley

5 5

6 6

7 7 Example: calibration of AVHRR NDVI Normalised Difference Vegetation Index (NDVI): –Simple to compute value, based on radiances in red and near infrared spectral regions –NDVI = (L_NIR – L_R) / (L_NIR + L_R) –Value range = -1 to +1 –EMPIRICALLY related to vegetation amount due to spectral response of plant leaves (red edge) Material from J. Morley

8 8 Example: MODIS EVI GLobal EVI winter/spring 2001 0000/a002300/a002317/index. html

9 9 Issues in NDVI calibration The biggest issue is the atmosphere Particularly: – Rayleigh scattering – ozone – water vapour – aerosols See van Leeuwen et al., 2006 Different versions of NDVI product (c4 NOT comparable w c5) –Saleska et al. (2005) Amazon Forests Green-Up During 2005 Drought, Science –Samanta et al. (2010) Amazon forests did not green up during the 2005 drought, GRL –??? Material from J. Morley

10 10 Rayleigh scattering Scattering of light by gas molecules in atmos. Biased towards the short visible wavelength & adds radiance to the red channel Quite easily calculated based on surface altitude (hence surface pressure) Reference values for Rayleigh optical depths for standard pressure and temperature conditions are available Vegetated areas have low red reflectance, so Rayleigh scat. can substantially decrease NDVI Material from J. Morley

11 11 Ozone and water vapour absorption Optical bands weakly affected by ozone absorption. Water vapour absorption bands near 0.9 μm and 1.1 μm -> NIR is considerably affected. Water vapour reduces the observed NIR & hence NDVI The longer path length from the sun - to the surface - to the satellite, greater effect of water vapour has –Off-nadir views more affected Difference in products when corrections introduced Material from J. Morley

12 12 Ozone and water vapour absorption

13 13 Aerosols Effects vary depending on particle size e.g. difference between volcanic and forest fire aerosols Note particularly El Chichon and Mount Pinatubo eruptions left aerosol in atmos. for ~2 years each Need better spectral resolution for correction, e.g. MODIS, or modelling Material from J. Morley

14 14 Material from J. Morley AVHRR?

15 15 Aerosols Material from J. Morley

16 16 Material from J. Morley

17 17 Material from J. Morley

18 18 Material from J. Morley

19 19 Material from J. Morley

20 20 Material from J. Morley

21 21 Material from J. Morley

22 22 Material from J. Morley Empirical mode decomposition (EMD)

23 23 Material from J. Morley

24 24 Material from J. Morley

25 25 Material from J. Morley

26 26 Material from J. Morley Sensor intercomparison?

27 27 Validation example: MODIS NPP Productivity recap: Net Primary Productivity (NPP) annual net carbon exchange quantifies actual plant growth Conversion to biomass (woody, foliar, root) –i.e. not just C0 2 fixation (GPP) –NPP = GPP – Ra (plant respiration) MODIS product example used here –MOD17 GPP/NPP ATBD –Turner et al (2005)

28 28 Productivity recap GPP/NPP from MODIS Requirements? MOD17 ATBD Running et al. (2004) Turner et al. (2005) Zhao et al. (2005) Heinsch et a. (2006)

29 29 MODIS GPP/NPP + QC??

30 30 MOD17 validation approach Need to address time (days to years) and space (local to global) Permanent network of ground validation sites Quantify seasonal and interannual dynamics of ecosystem activity (cover time domain) EO to quantify heterogeneity of biosphere Quantify land cover, land cover change dynamics Models to: Quantify, understand unmeasured ecosystem Provide predictive capability (in time AND space)

31 31 How on earth…..???? …can we validate an EO-derived estimate of something that depends on soil, climate, land cover etc.? Given that it requires various models to go from a satellite observation (radiance), to reflectance, to LAI/FAPAR, to PSN, to GPP to NPP At 500m-1km pixels. Globally. And how do you even measure NPP on the ground??

32 32 So, how might we validate? Need to consider scale Relate measurements at the small scale to 1km pixels?? Flux tower approach Eg BIGFOOT approach, FLUXNET etc. Measurements and validation at many scales Models to bridge time/space scales –(but how good are models…?) Fig from MOD17 ATBD

33 33 Ecosystem measurements: FLUXNET Fig from MOD17 ATBD

34 34 Ecosystem measurements: FLUXNET 1999

35 35 Ecosystem measurements: FLUXNET 2009

36 36 Ecosystem measurements: FLUXNET

37 37 Ecosystem measurements: FLUXNET by biome Some distribution of biome types, but clearly biased in location Even considering only limited biomes

38 38 BigFoot approach to validating MODIS NPP E.g. Turner et al. (2005), 6 sites spanning range of vegetation and climate Crops, forest, tundra, grassland 5 x 5 km site at each plot (25 MODIS pixels) Flux tower & 100 (25x25m) sample plots within each area, seasonally measured for LAI and above-ground (A)NPP (from harvested leaf and wood material) Land cover from high res EO Use measured data at sample plots to calculate NPP, GPP Spatially distribute across site using (vegetation-calibrated) BiomeBGC model Requires daily met data, land cover, LAI Gives measured estimate from ground AND flux tower

39 39 BigFoot v flux tower GPP Turner et al. (2005)

40 40 BigFoot v MODIS GPP Turner et al. (2005) Not such good agreement as for flux tower (not surprisingly)

41 41 Comparison of MODIS NPP with flux data Turner et al. (2005) Differences due to Ra (autotrophic i.e. plant respiration)? PAR, VPD differences between those from DAO and actual? (VPD = deficit between the amount of moisture in the air and how much moisture the air can hold when it is saturated)

42 42 DAO PAR, VPD? Turner et al. (2005) Clearly some sites better agreement than others PAR generally good (relatively easy to measure) VPD less so e.g. SEVI (desert grassland site) VPD Other issues?

43 43 MODIS-estimated v BigFoot FPAR Turner et al. (2005) How do you measure FPAR even on the ground?? Requires models to interpret measurements of radiation

44 44 MODIS-estimated v BigFoot LUE (light use efficiency) Turner et al. (2005) LUE inferred from flux data Again, hard to even measure this on the ground…..

45 45 Zhao et al. (2005) Heinsch et al. (2006)

46 46 Process/SVAT (soil-veg-atm-transport) models Fig from MOD17 ATBD

47 47 From Running et al. (2004) MOD17 ATBD Biome-BGC model predicts the states and fluxes of water, carbon, and nitrogen in the system including vegetation, litter, soil, and the near- surface atmosphere i.e. daily PSN Process models: how do we test/validate?

48 48 Process models: how do we test/validate? Fig from MOD17 ATBD

49 49 Canadell et al. 2000 Data-Model Fusion [Using multiple streams of datasets with parameter optimization] C stock and flux measurements Inventory analyses Process-based information Climate data Remote sensing information CO 2 column from space Inverse modeling Process-based modeling Retrospective and forward analyses

50 50 Multi-level model/data validation MOD17 ATBD: Synergy of various carbon measurement programs Fig from MOD17 ATBD

51 51 Summary Calibration Needed to allow comparison data from multiple sensors of over time with another, even for simple empirical NDVI Can be done on-board, or via sensor intercomparison etc. Validation example: NPP Far removed from EO measurement & spatially, temporally variable Requires: observation networks over time and space and measurement of met. & biophysical data Models to interpolate spatially from ground-based, site-scale measurements Testing and intercomparison of models Ideally: optimal combinations of models + data across scales (e.g. via data assimilation)

52 52 References: calibration Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from multiple Sensors, RSE, 112, 4318-4332 (Part II) and 4333-4343 (Part I)

53 53 References: calibration

54 54 References: calibration

55 55 References: validation NPP Running et al. (2004) A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, Bioscience 54(6), 547-560. Ganguly et al. (2008a, b) Generating vegetation leaf area index earth system data record from multiple Sensors, RSE, 112, 4318-4332 (Part II) and 4333-4343 (Part I) Turner et al. (2005) Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring, Glob Change Biol, 11, 666-684. Zhao et al. (2005) Improvements of the MODIS terrestrial net and gross primary production data sets, RSE, 95, 164-176. Heinsch et al. (2006) Evaluation of Remote Sensing Based Terrestrial Productivity From MODIS Using Regional Tower Eddy Flux Network Observations, IEEE TGRS, 44(7), 1908-1925. General validation Morisette et al. (2002) A framework for the validation of MODIS Land products, RSE, 83, 77-96. Disney et al. (2004) Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales, IJRS, 25(23), 5297-5317.

56 56 Other cal/val links NPP: Cal/val programs CEOS-WFGCV (Committee on EO Working Group on Cal/Val) SAFARI2000: VALERI: NCAVEO: JAXA: Etc etc etc

57 57 - Carbon sinks/sources using AVHRR data to derive NPP - Carbon pool in woody biomass of NH forests (1.5 billion ha) estimated to be 61 20 Gt C during the late 1990s. - Sink estimate for the woody biomass during the 1980s and 1990s is 0.68 0.34 Gt C/yr. -From Myneni et al. PNAS, 98(26),14784- 14789

58 58 Dominant Controls water availability 40% temperature 33% solar radiation 27% Total vegetated area: 117 M km2 Limiting factors

59 59 Bottom line - half the vegetated lands greened by about 11% - 15% of the vegetated lands browned by about 3% - 1/3 rd of the vegetated lands showed no changes. Since the early 1980s about, These changes are due to easing of climatic constraints to plant growth.

60 60 Example: MODIS core val sites Justice et al. (1998) Privette et al. (2002) and RSE 83, 1-2, 1-359

Download ppt "GEOGG142 GMES Calibration & validation of EO products Dr. Mat Disney Pearson Building room 113 020 7679 0592"

Similar presentations

Ads by Google