GMES: calibration of remote sensing datasets

Transcription

1 GMES: calibration of remote sensing datasets Jeremy Morley Dept. Geomatic Engineering December 2006 Outline Role of calibration & validation in remote sensing Types of calibration Example 1 NDVI NDVI time series NOAA s AVHRR sensor series Inter-instrument calibration issues Example 2 Ground campaigns 1

2 Calibration and validation Both essential to scientific remote sensing Calibration: the process of converting an instrument reading to a physically meaningful measurement Particularly in this context, radiometric calibration From DN to radiance measurement Validation: experiments designed to verify instrument measurements using independent measurements Radiometric calibration methods 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) 2

3 Normalized Difference Vegetation Index (NDVI) Simple to compute value, based on radiances in red and very near infrared spectral regions NDVI = (L VNIR -L R ) / (L VNIR + L R ) Value range = -1 to +1 Strongly related to vigour of plant growth due to spectral response of plant leaves ( red edge ) NDVI Image Landsat bands 3,4 of Mount Etna 3

4 NDVI images MODIS Vegetation Index 1km Profiles TBRS team, The University of Arizona, 2002 Issues in NDVI calculation The biggest issue is the atmosphere Particularly: Rayleigh scattering ozone water vapour aerosols See van Leeuwen et al., 2006, and 4

5 Rayleigh scattering Scattering of light by gas molecules in atmos. Biased towards the short visible wavelengths (hence blue sky!) 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 Absorption ozone and water vapour Optical bands are weakly affected by ozone absorption. Water vapour absorption bands near 0.9 µm and 1.1 µm -> NIR is considerably affected. The water vapour reduces the observed near infrared reflectance observed at the satellite & hence NDVI. The longer path length from the sun - to the surface - to the satellite, the greater the effect that water vapour has. Difference in products when corrections introduced 5

6 From: NASA Earth Observatory website From: van Leeuwen et al., 2006 Aerosols More difficult! 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 6

7 Stratospheric aerosol optical depth from 1991 to This aerosol event was caused by the volcanic eruption of Mt Pinatubo in These data were used to correct AVHRR channel 1 and channel 2 data for the period, respectively (Vermote et al. 1997). Adapted from: Tucker et al., 2005 Advanced Very High Resolution Radiometer (AVHRR) Broad-band, 4- or 5-channel scanning radiometer Mounted on a series of polar orbiting spacecraft 17 instruments to date (though some failed) 2399 km (1491 mi) swath width (wide scan!) 14 orbits a day 833 km (517 mi) above Earth s surface. 4 or 5 channels: vis., NIR, TIR 7

8 AVHRR NDVI Channels From: van Leeuwen et al., 2006; see also Absorption ozone and water vapour AVHRR channels 1 ( µm) & 2 ( µm) are weakly affected by ozone absorption. Water vapour absorption bands near 0.9 µm and 1.1 µm -> AVHRR channel 2 is considerably affected. The water vapour reduces the observed near infrared reflectance observed at the satellite & hence NDVI. The longer path length from the sun - to the surface - to the satellite, the greater the effect that water vapour has. As a result, off nadir AVHRR channel 2 observations are more affected than near nadir observations. Bi-weekly composites constructed from daily obs. EDC uses the maximum NDVI compositing. In other words, the greenest pixel value over the biweekly period is chosen for the composite. Difference in products when corrections introduced 8

9 Water vapour correction example NDVI comparison between atmospherically corrected MODIS data (left; Terra; composite period: Nov 19 Dec 2, 2001), water-vapour-corrected AVHRR-16 data (middle; NOAA; composite period: Nov 16 29, 2001), and nonwater-vapour-corrected AVHRR-16 data (right; NOAA; composite period: Nov 16 29, 2001). Taken from: van Leeuwen et al AVHRR radiance calibration (See Roderick et al. 1996) The observed AVHRR digital counts (DN) in a given channel (i) can be related to the at-sensor spectral radiance (L*) using a linear calibration equation of the following form: L i * = α i DN i +β i, (W m -2 sr -1 µm -1 ) where α is the sensor slope and β the offset. I.e. a linear response model 9

10 AVHRR calibration albedo definition Spectral albedo (A) is defined as (Price, 1987) A i = (L i * / S i ) 100 (%) where S i = E 0i / π = solar radiance and E 0i = exoatmospheric spectral solar irradiance (W m -2 µm -1 ), derived from p i * = top of atmos. reflectance d = solar distance θ = solar zenith angle AVHRR calibration albedo definition (2) We can then redefine the calibration using albedo: A i = τ i DN i +δ i τ i and δ i are the prelaunch calibration constants measured & distributed by NOAA NB: deep space observations (DNs) used to assess constant term 10

11 NDVI redefined Effects of varying d and θ are small, so can recast NDVI in terms of the albedo, A: NDVI = (A VNIR -A R ) / (A VNIR + A R ) AVHRR inter-mission calibration From: Roderick et al., See also Gutman, 1998 for further example. 11

12 AVHRR effects of orbital drift N-11 failed N-13 failed Solar zenith angles vs time for NOAA-6 (07:30 and 19:30 hours local solar overpass times) and the series of afternoon NOAA satellites from 1981 to 2003 adapted from Tucker et al Intra-instrument calibration (2) Tucker et al., NOAA-7 through NOAA-14 channel 1 and 2 data were processed using the Vermote and Kaufman (1995) channel 1 and channel 2 calibration (using ocean & cloud views) and the NDVI formed. Resulting NDVI fields were further adjusted using the technique of Los (1998), then decomposed and reconstructed using empirical mode decomposition to correct for solar zenith angle effects (Pinzon et al. 2005) 2. Data from NOAA-16 and NOAA-17 were processed using the preflight channel calibration values and formed into maximum value composites. An empirical mode decomposition and reconstruction was performed that ensures a zero slope with respect to time in desert areas and was also used to correct solar zenith angle artefacts. Overlapping SPOT Vegetation NDVI time series used as the means to intercalibrate or tie together the NOAA-14 and NOAA-16 and -17 NDVI time series (Pinzon et al. 2004). The NOAA-16 and -17 NDVI time series were adjusted by a constant offset to match up with a coincident-in-time and spatially aggregated 8-km SPOT Vegetation NDVI (Bi-linear gain for channel 1 and channel 2 of NOAA-16 s and -17 s AVHRR instruments complicate ex post facto calibration.) 12

13 EMD processing Portion Removed >98% correlated with SZA signal Before Before After After Taken from: Brown et al. Regions affected by drift Taken from: Brown et al. 13

14 Inter-instrument records e.g. differences in spectral response From: Tucker et al., 2005 Intercomparison of multi-sensor NDVIs van Leeuewn et al. have taken a modelling approach Model radiances at satellite based on canopy and atmos. radiative t fer models, incl. sensor responses 14

15 Effect of different red & NIR spectral response functions on NDVI For herbaceous vegetation and a bright soil and a range of LAI values. Wider NIR band for AVHRR means lower readings (MODIS, VIIRS tighter on red-edge peak), hence lower NDVI. Taken from: van Leeuwen et al., 2006 NDVI differences (van Leeuwen et al., 2006) The average difference between NDVI MODIS and NDVI AVHRR-16 is about 1.6%±1.4%. Highest average difference: NDVI MODIS and NDVI AVHRR-14 is about 4.3% ± 2.3%. If the translation to NDVI MODIS is not performed the differences among multi-sensor NDVI values are about 1% for low NDVI values for AVHRR-14/16. For the higher NDVI, values the difference between NDVI MODIS and NDVI AVHRR-16 about 3%, and between NDVI MODIS and NDVI AVHRR-14 about 7% 15

16 Results from Tucker et al., 2005 Bondville, Illinois time series plot with the NDVI from five sensors for (a) and (b) Solid blue line, the AVHRR data; black line, MODIS data; red dashed line, SPOT Vegetation data; solid red line, SeaWiFS land data; and points, Landsat-7 ETM data. References (NB: all refs. available on-line via library) M. Roderick, R. Smith, G. Lodwick, Calibrating Long-Term AVHRR-Derived NDVI Imagery. Remote Sens. Environ. 58:1-12 (1996) C.J. Tucker, J.E. Pinzon, M.E. Brown, D.A. Slayback, E.W. Pak, R. Mahoney, E.F. Vermote, N. El Saleous, An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. IJRS Vol. 26, No. 20, 20 October 2005, pp

17 References (2) W.J.D. van Leeuwen, B.J. Orr, S.E. Marsh, S.M. Herrmann, Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications, Remote Sensing of Environment 100 (2006) Gutman, G., Monitoring global vegetation using AVHRR, Geoscience and Remote Sensing Symposium Proceedings, IGARSS '98. Publication Date: 6-10 Jul Volume: 5, pp References (3) background information Emprical mode deconstruction / orbital drift Brown et al.: E. Vermote, Y.J. Kaufman, Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views, IJRS, vol. 16, no. 3, pp