Spectral surface albedo derived from GOME-2/Metop measurements

Spectral surface albedo derived from GOME-2/Metop measurements Bringfried Pflug* a, Diego Loyola b a DLR, Remote Sensing Technology Institute, Rutherfordstr. 2, 12489 Berlin, Germany; b DLR, Remote Sensing Technology Institute, 12489 Wessling, Germany ABSTRACT Spectral surface albedo is an important input for GOME-2 trace gas retrievals. An algorithm was developed for estimation of spectral surface albedo from top-of-atmosphere (TOA)-radiances measured by the Global Ozone Monitoring Experiment GOME-2 flying on-board MetOp-A. The climatologically version of this algorithm estimates Minimum Lambert-Equivalent Reflectivity (MLER) for a fixed time window and can use data of many years in contrast to the Near-real time version. Accuracy of surface albedo estimated by MLER-computation increases with the amount of available data. Unfortunately, most of the large GOME pixels are partly covered by clouds, which enhance the LER-data. A plot of LER-values over cloud fraction is used within this presentation to account for this influence of clouds. This cloud fraction plot can be applied over all surface types. Surface albedo obtained using the cloud fraction plot is compared with reference surface albedo spectra and with the FRESCO climatology. There is a general good agreement; however there are also large differences for some pixels. Keywords: Surface albedo, MLER, GOME-2, cloud fraction, remote sensing, satellite observation 1. INTRODUCTION Surface albedo has a significant influence on vertical column trace gas retrieval via the change of air mass factors. A higher albedo causes higher AMFs due to increased surface reflection and multiple scattering. Trace gas retrieval algorithms usually take surface albedo from a suitable climatologically database, which can considerably deviate from the actual surface albedo. An algorithm was developed for estimation of the actual spectral surface albedo as input for trace gas retrieval algorithms. Spectral surface albedo is estimated from top-of-atmosphere (TOA)-radiances measured by the Global Ozone Monitoring Experiment GOME-2 flying on-board MetOp-A. The first version of this algorithm developed for near-real-time and off-line processing has been integrated into DLR s operational Universal Processor for Atmospheric Spectrometer (UPAS) system. This near-real-time version of the algorithm estimates Minimum Lambert-Equivalent Reflectivity (MLER) for a moving time window of variable width. It can use only data of the actual year. The Near-realtime version fails at some ground-pixels due to lack of data. Surface albedo must be provided by a consistent climatologically database for these ground pixels, which is generated by the climatologically version of the algorithm The climatologically version of the algorithm estimates Minimum Lambert-Equivalent Reflectivity (MLER) for a fixed time window and can use data of many years in contrast to the Near-real time version. The climatologically version is introduced within this paper. Examples of spectral surface albedo estimated with the climatologically version of the algorithm are presented in the present paper and compared with reference data. *bringfried.pflug@dlr.de; phone +49 (30) 6 70 55-655; fax +49 (30) 6 70 55-642; dlr.de Remote Sensing of Clouds and the Atmosphere XIV, edited by Richard H. Picard, Klaus Schäfer, Adolfo Comeron, Evgueni Kassianov, Christopher J. Mertens, Proc. of SPIE Vol. 7475, 747518 2009 SPIE CCC code: 0277-786X/09/$18 doi: 10.1117/12.830368 Proc. of SPIE Vol. 7475 747518-1

2. GOME-2 INSTRUMENTS GOME-2 (Global Ozone Monitoring Experiment) was launched 2006 onboard the first of a series of 3 MetOp-satellites, which will be operated within the next 14 years. The GOME-2 spectrometer observes around nadir direction and detects the solar radiation, which was backscattered by the atmosphere or reflected at the earth surface. It detects solar radiation in the ultraviolet and visible spectral range (VIS-NIR). The main spectrometer consists of 4 optical channels. Two polarization units measure the linear polarized intensity in two orthogonal directions. Table 1. Main characteristics of GOME-2 instruments Swath width (maximum) Global coverage Main spectrometer: No. of spectral channels spectral range spectral resolution (FWHM) spatial resolution (cross to x within flight direction) Polarisation unit Spectral bands spatial resolution (cross to x within flight direction) GOME-1 960 km 3 days 4 237 794 nm 0.2 0.4 nm 320 x 40 km 2 3 fix defined (350, 490, 700 nm) 20 x 40 km 2 GOME-2 1920 km almost daily 4 240 790 nm 0.25 0.5 nm 80 x 40 km 2 12 programmable (312 790 nm; FWHM 2.8 40 nm) 10 x 40 km 2 3. ALGORITHM 3.1 Minimum Lambert-Equivalent Reflectivity (MLER) estimation MLER-estimation was successful used for generation of surface albedo climatology's 1, 2. MLER-processing starts with LER-estimation 3, 4 for all available data. LER-estimation works with the assumption, that the surface reflects Lambertian. Non-Lambertian reflection effects are present in the derived LER-values even for the large GOME pixels. However, by employing these LER-values with a Lambertian surface reflection in the trace gas retrievals using the same instrument, non-lambertian effects are, to first order, implicitly accounted for. Inhomogeneity of GOME-ground pixels with respect to surface types is of minor relevance for the large GOME footprints 5 and will be neglected for LER-computation. LER-estimation performs only Rayleigh-correction for each single pixel. Consequently, the derived LER includes the contribution of aerosols. LER-estimation requires input of geometrical observation parameters and a rough value for the vertical column ozone amount. Computation of LER is performed by inversion of a large look-up-table (LUT), which was computed with help of GOMETRAN 6. The grid of the different parameters oriented itself mainly on the grid used for the Differential optical absorption spectroscopy (DOAS)-algorithm, which serves for vertical column trace gas retrieval. LER-spectra are computed for 14 spectral bins of 0.5 nm width centered at wavelength [321.5, 336, 351, 369, 380, 416, 441, 462, 494.5, 555, 610, 670, 757, 777] nm. LER estimation is performed for GOME-pixels which have for each orbit a different footprint on earth surface. Therefore the results obtained for GOME-pixels must be tessellated into the fix database grid for MLER-estimation. The final grid size of database pixels of 0.36 x036 matches the smallest GOME-2 ground pixels. However, a coarser grid size of 1 x1 is used for the results presented within this paper. Proc. of SPIE Vol. 7475 747518-2

Minimum Lambert-equivalent reflectivity estimation is the extension of LER-estimation to multi-temporal measurements. A situation with low and consequently negligible aerosol scattering is selected to represent surface reflection within the time window of the multi-temporal measurements. Fixed time windows are used for generation of climtologically MLER-datasets as exemplary all data of the same month over several years of data. Computation of the minimum is realized with the median R3min of the smallest 3 LER-spectra within the time window. Using the median excludes single outliers from representing the surface albedo. The smallest spectra are selected by the vector length of LER-values at selected wavelengths. vector _ length = LER ( 380,416,495,610,777nm) 3.2 Cloud fraction plot Accuracy of surface albedo estimated by MLER-computation increases with the amount of available data. Unfortunately, most of the large GOME pixels are partly covered by clouds, which enhance the LER-data. Typically pixels with low cloud fraction are accepted for MLER computation to increase the data amount. A threshold defines the cloud fraction limit below which data points are included into computation. Resulting MLER is effected by residual cloud contamination and requires an extra correction step. A plot of LER-values over cloud fraction gives the opportunity to include partly cloudy pixels into MLER computation. This increases the amount of data useable for MLER-computation and avoids the influence of residual cloud cover on the resulting surface albedo. First the three smallest LER-spectra are determined. This is done by plotting the vector length over cloud fraction and transformation of all data points to cloud fraction zero. All transformed spectra are directly comparable and the three smallest LER-spectra at cloud fraction zero represent the clearest situations. They will be used computing the median R3min at all spectral points. The transformation to cloud fraction zero is realized by a fit through a reduced dataset assuming a linear relationship between LER-values and cloud fraction. The reduced dataset is given by the lower half of the spectra. When the 3 smallest spectra are determined, then a similar procedure is repeated wavelength after wavelength. Only the selected data points must be transformed to cloud fraction zero for calculation of R3min. However, the slope of the fitted line for the transformation must be determined for each wavelength individually as described above. The cloud fraction plot can be applied over all surface types. Examples of the cloud fraction plot are shown in Figure 1. The three clearest situations are found not only at cloud fraction zero. Transformation of the LER-values of these 3 clearest situations to cloud fraction zero give cloud fraction corrected LER-values, which serve for computation of MLER. If enough cloudless data points are available, then the result of the cloud fraction plot converges correctly to the median of the 3 smallest cloud free data points. The same spectra are used at all spectral points (wavelengths) for computation of R3min even if their LER-values are not one of the 3 smallest at each wavelength. This case occurs in the Atlantic Ocean Water example. There are several data points having smaller LER-values at 777 nm than the one selected near cloud fraction 0.18. However, taking into account other wavelengths by computing the vector length, this spectrum is one of the three smallest. Once this spectrum was found to represent one of the three clearest situations, then it has to be used at all wavelengths. 2 4. RESULTS 4.1 Comparison with reference spectra MLER spectra resulting from the cloud fraction plot over land and sea surface are compared with reference spectra (Fig. 2). Two reference spectra are from the FRESCO-climatology 2. Both the spectra resulting from the cloud fraction plot and the spectra from the FRESCO-climatology had been selected for the same locations: within the South American Rain forest at longitude -71 and latitude -4 and at the Atlantic Ocean at longitude -14 and latitude 34. Two more reference spectra are resulting from field measurements. We could use results of our own measurements over ocean water and a reference spectrum for grass 7 as a vegetation example. Unfortunately we did not found a reference spectrum for the rain forest. Therefore we selected this spectrum for grass for this comparison representing vegetation surface. Proc. of SPIE Vol. 7475 747518-3

0,6 South America Rain Forest example LER @ 777 nm 0,5 0,4 0,3 0,2 0,1 0,0 full dataset reduced dataset Points for minimum Resulting MLER 0,0 0,1 0,2 0,3 0,4 0,5 Cloud fraction LER @ 777 nm 0,6 0,5 0,4 0,3 0,2 0,1 Atlantic Ocean Water example full dataset reduced dataset Points for minimum Resulting MLER 0,0 0,0 0,1 0,2 0,3 0,4 0,5 Cloud fraction Figure 1: Examples of the cloud fraction plot for the spectral point at 777 nm over different surface types vegetation and ocean water. Proc. of SPIE Vol. 7475 747518-4

MLER 0,35 0,30 0,25 0,20 0,15 0,10 0,05 MLER-spectra over land and sea surface Cloud fraction plot Reference (FRESCO) Reference (field measurement) 0,00 300 400 500 600 700 800 Wavelength [nm] Figure 2: Example spectra resulting from application of the cloud fraction plot compared with reference spectra. References are from the FRESCO-climatology 2 for the same location (South america rain forest at lon=-71, lat=-4 and Atlantic Ocean at lon=-14, lat=34 ) and from field measurements (Vegetation (grass) 7 and ocean water). The vegetation spectrum of the cloud fraction plot and the grass spectrum are very close each to the other starting from the UV to the red edge around 700 nm. Then grass has a higher slope and ends more near to the vegetation spectrum given by the FRESCO climatology. The FRESCO spectrum is significantly higher then the other two vegetation examples for the whole range from the UV up to 700 nm. The spectrum resulting from the cloud fraction plot is less influenced by cloud contamination then the FRESCO spectrum. All three spectra are close each to the other for the ocean water example. The reference spectrum from field measurements is little higher than the other two. Below 440 nm the spectrum resulting from the cloud fraction plot goes down to zero with increasing uncertainty. Both reference spectra a slowly increasing with decreasing wavelength. Note, that the field measurement has no spectral points below 410 nm. 4.2 Comparison with the FRESCO climatology Figure 3 shows maps for comparison of the cloud fraction plot with the FRESCO climatology. Red areas in the MLERmaps of the cloud fraction plot are due to lack of data. This can be caused either by no coverage (polar regions) or because of insufficient data with cloud fraction less than 0.5. Data with larger cloud fraction are not used for the cloud fraction plot. General there is a good agreement, but there are also clear differences. Note that results of the cloud fraction plot are without any further correction whereas the FRESCO-climatology includes several corrections. Most outstanding differences are south of the equator over oceans. Here the cloud fraction plot gives higher surface albedo than the FRESCO-climatology. These differences are not caused by a failure of the cloud fraction plot, but probably by differences in the used LER-data. The lowest LER-data at 777 nm available for the cloud fraction plot are about 0.2 in that region, even for pixels free of clouds. The cloud fraction plot gives darker surface albedo than the FRESCO climatology over some land areas at 441 nm. Comparison with further references will show whether this darker surface albedo at short wavelengths is correct ore if there is again a problem with the computed LER-data. Proc. of SPIE Vol. 7475 747518-5

FRESCO-climatology2 cloud fraction plot Surface albedo @ 777 (772) nm surface albedo @ 441 (440) nm Figure 3: Comparison of cloud fraction plot with the FRESCO-climatology2 as a reference (Example month: January). Proc. of SPIE Vol. 7475 747518-6

Cloud fraction plot Cloud fraction plot Reference: FRESCO-climatology 2 Mean difference ± sdev = 0.011 ± 0.069 Reference: FRESCO-climatology 2 Mean difference ± sdev = -0.022 ± 0.078 Figure 4: Scatter plot of MLER estimated with the cloud fraction plot compared with the FRESCO-climatology 2 as a reference (Example month: January). Figure 4 shows a scatter plot of surface albedo estimated with the cloud fraction plot over the FRESCO-climatology as a reference. The mean difference between the cloud fraction plot and the FRESCO-climatology is small. The results are similar for many pixels as also concluded from the maps in figure 3. On the other side there are large differences for many pixels giving a large standard deviation. Differences should be present, but smaller. Most acceptable are differences with little smaller surface albedo resulting from the cloud fraction plot because of the cloud fraction plot account for partial cloud cover. However, the cloud fraction plot gives both higher and smaller surface albedo than the reference. Most of the pixels where the cloud fraction plot gives higher surface albedo are at low reflection, which occurs over the ocean. Over ocean the FRESCO climatology includes a correction for residual cloud contamination. The effect of this correction can be seen very clearly at 777 nm, where many MLER values of the FRESCO-climatology are limited to below 0.1. There are vertical lines in the scatter plot at high surface albedo at both example wavelengths. They are interpreted to result from setting the surface albedo to a fixed value for some snow covered pixels in the FRESCOclimatology. There are of course differences originating from differences in the used LER-databases. Results give reason to look for improvements in the LER-database which was computed for the cloud fraction plot. Proc. of SPIE Vol. 7475 747518-7

5. CONCLUSIONS The cloud fraction plot gives the opportunity to include partly cloudy pixels into MLER computation. He can be applied over all surface types. Example spectra show good agreement and demonstrate the great potential of the cloud fraction plot, most of all over land surfaces. Over land surface exists no other method for correction of partial cloud contamination. General there is a good agreement between surface albedo estimated with the cloud fraction plot and the FRESCO-climatology as a reference, but there are also clear differences. Part of these differences can be explained by correction procedures applied for the FRESCO-climatology, but not applied for the cloud fraction plot. Further differences probably originate from differences in the used LER-databases and give reason to look for improvements in the LER-database which was computed for the cloud fraction plot. REFERENCES [1] Herman, J.,R. and Celarier, E.A., "Earth surface reflectivity climatology at 340-380 nm from TOMS data, Journal of Geophysical Research, 102 (D23), 28003-28011 (1997) [2] Koelemeijer, R., B., A., de Haan, J., F. and Stammes, P., "A database of spectral surface reflectivity in the range 335-772 nm derived from 5.5 years of GOME observations, Journal of Geophysical Research, 108 (D2), 4070 (2003), doi:10.1029/2002jd002429 (pp. ACH 8-1 - 8-13) [3] Davé, J., V., "Effect of aerosols on the estimation of total ozone in an atmospheric column from the measurement of its ultraviolet radiance, J. Atmos. Sci. 35, 899-911 (1978) [4] Koelemeijer, R., B., A., Stammes, P. and Stam, D., M., "Spectral surface albedo derived from GOME data, Proc. 3rd ERS Symposium on Space at the service of our Environment, Florence, Italy, 17-21 March 1997, ESA SP-414 (II & III), 663-667 (1997) [5] Valks, P. and Loyola, D., G., "Algorithm Theoretical Basis Document for GOME-2 Total Columns of Ozone, Minor Trace Gases, and Cloud Properties. GDP 4.2, DLR/GOME-2/ATBD/01, Iss./Rev.:1/C, (2008) http://wdc.dlr.de/sensors/gome2/dlr_gome-2_atbd_1c.pdf [6]. Rozanov, V., V., Diebel, D., Spurr, R., J. and Burrows, J., P., "GOMETRAN: A radiative transfer model for the satellite project GOME, the plane-parallel version", J. of Geophysical Research, 102 (D14), 16,683-16,695 (1997) [7] Bowker, D.,E., Davis, R., E., Myrick, D., L., Stacy, K. and Jones, W., T., 1985, "Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, NASA Reference Publication 1139, Washington (1985) Proc. of SPIE Vol. 7475 747518-8