ALBEDOMAP -Validation Report

Transcription

1 ALBEDOMAP -Validation Report ESA AO/1-4559/04/I-LG Jürgen Fischer and Rene Preusker FU Berlin Jan-Peter Muller University College London Marco Zühlke Brockmann Consult 1

2 1. Introduction 3 2. Intermediate Products: Atmospheric Correction and Cloud Detection 4 3. MERIS Global Albedo Products Qualitative Assessment of Global Albedo Products Quantitative inter-comparison of MERIS 16-day with MODIS 12 equivalent for Day-of-Year Intercomparison with MISR and POLDER products Spectral characteristics of albedo Spectral albedo shape BRDF effect on spectral albedos Spectral albedos cf. ground truth measurements MERIS Albedo and Impact on Atmospheric Products Cloud optical thickness Cloud top pressure Water vapour above land Conclusions 29 References cited 29 2

3 1. Introduction Validation of the albedo products, derived within the ALBEDOMAP project is essential for users to gain confidence in the application of the developed datasets, tools und procedures. An exhaustive validation exercise could not be performed due to the lack of resources for the immense amount of work necessary and the restricted time schedule of the project, however A first step of validation has been performed. It was focussed on four different approaches: (1) qualitative assessment of the individual products including (i) atmospheric correction; (ii) cloud detection; (iii) albedos (2) quantitative assessment and verification by inter-satellite comparisons between MERIS, MODIS, MISR and POLDER products. We focus here just on the MERIS vs. MODIS inter-comparisons (3) Spectral albedo products have also been compared to ground based measurements, taken from archived data as well as an assessment of the effect of BRDF correction (4) assessment of the impact of the land surface albedo on retrievals of MERIS atmospheric products which was one of the major motivations of the ALBEDOMAP project Our results suggest that there is confidence that a more accurate implementation of the surface albedo has significantly increased the accuracy of the derived MERIS atmospheric products. Overall lessons learnt: A. The MERIS albedo product appears to be of excellent quality over high-albedo (nonsnow/ice) regions but of medium to poor quality over dense vegetation. B. There are many gaps due to the lack of any retrieval in the source MODIS BRDFs and the presence of persistent clouds in the MERIS SDRs. Currently it is not possible to differentiate between these two error sources. C. There are significant numbers of erroneous areas due to inadequate cloud detection. D. There is some evidence of swath-related artefacts, most likely due to poor aerosol correction. E. There is reasonably good agreement between MERIS and the (magnitude inversion and full inversion portions of the) gap-filled MODIS albedo product. F. The agreement is better over higher albedo areas with full inversions in the MODIS BRDF product and poorer over vegetated areas. This appears to be due to the difficulties in cloud detection and poorer aerosol quality from the MODIS aerosol product. G. The agreement of MERIS vs. MODIS is closer than that between MISR (level 3 monthly composite) and a monthly composite of MODIS gap-filled products (not shown here). Once again, cloud contamination in both MISR and MODIS and the better aerosol quality from MISR appears to be responsible. Recommendations for future improvements 1. Attempt to replace MOD04 (MODIS aerosol products) using in order of priority: a. MERIS-only aerosol products b. AATSR-derived aerosol product c. MISR-only aerosol product d. MODIS-only aerosol product 3

4 2. Perform quantitative assessments of the aerosol correction by examining reflectances in overlapping orbital swath pixels and if possible after applying the BRDF correction to produce nadir-equivalent reflectances for each orbital swath. 3. Continue the improvement of the cloud detection algorithm to eliminate sub-pixel clouds and to better discriminate snow/ice and clouds. 4. Cloud shadows might be a source of errors and should be studied in more detail. 5. Differentiate between snow/ice and clouds, if possible using the MODIS daily snow/ice product. 6. Produce better monthly composites than just using weighted averaging of the constituent 16-day periods within a month, preferably by using weights of determination. This will require processing MOD43B3 into a MOD43C1 equivalent product rather than using MOD43C1 as the latter product does not include weights of determination in the magnitude and full inversions and so do not enable alternative strategies to be tested. 7. Extend the inter-comparison to all 16-day and all derived monthly products. 8. Improvement of the spectral extrapolation of the MODIS BRDF coefficients to the all MERIS channels, based on fundamental statistical investigations. 2. Intermediate products: Atmospheric correction and Cloud Detection Jürgen Fischer and Rene Preusker The processing chain to estimate MERIS spectral albedo includes the following components: 1. an improved cloud detection algorithm, 2. an atmospheric correction scheme which relies on the use of external aerosol optical thickness and Angstrom coefficient, and 3. applies a BRDF correction. The improved cloud detection algorithm and the atmospheric correction schemes and their verification are described in the ATBDs of the ALBEDOMAP project. 4

5 3. MERIS global albedo products Jan-Peter Muller and Marco Zühlke The spectral albedo was estimated for 13 of the 15 MERIS channels with a spatial resolution of 0.05º x 0.05º (approximately 4*4.8 km 2 (4*4 pixels)) for the entire year 2003 (see Table 1). The individual Spectral Directional Reflectance (SDR) products were binned into 16 day averages to best match the input MODIS BRDFs, MOD43C1, used for the magnitude inversion of the 4 common spectral bands (see Table 1). The spectral albedo has been estimated from MERIS level 2 data, Rayleigh and ozone corrected reflectances, which was further processed for better cloud screening and aerosol corrections for the year Table 1: MERIS channels; channel 11 at is only used in the cloud detection scheme. Bands 3, 5, 7, and 13 are those used in the BRDF retrievals which are common to MODIS. Channel number Wavelength [nm] Qualitative Assessment of Global Albedo Products Most parts of the Earth s land surface were observed by MERIS within the 16 day periods in However, there are some areas which are missing including those with frequent cloud cover, such as tropical regions or mid-latitudes such as Ireland and western UK. The accuracy and reliability of the retrieved MERIS land surface albedos (known hereafter as albedomaps) is critically dependent on the accuracy of (a) cloud detection (including shadows) and (b) aerosol correction. One qualitative method of assessing how well the corrections have been applied is to display multi-spectral combinations in either natural colour (bands 7,5,3) or in false colour (13,7,5). A natural colour composite is shown in Figure 1 below. Examination of this figure demonstrates the green-up in the northern and southern hemisphere as well as highlighting one 16-day period with some missing swaths over South Africa and the Amazon. Some of these time periods are studied in greater detail below. These are indicated in the figure caption below by the Bold Day-of-Year titles. 16-day periods which are studied in even greater depth are highlighted Bold and underlined in the caption which follows. Many of the areas over dense vegetation and also semi-arid areas (such as Mongolia and Tibet) appear to have a variable magnitude greenish or bluish tint suggestive of either cloud contamination at the pixel or sub-pixel level or incorrect aerosol data. It is not possible given the resources available to pin down which cause is mainly responsible for the appearance of which pixel. Suffice it to say that very bright white pixels are likely to be strongly contaminated by clouds or in some cases snow. Another qualitative representation of the MERIS 16-day mosaics is to employ the NIR to replace the Red, Red to replace Green and Green to replace Blue. This is known as a false colour composite or FCC and is shown in Figure 2. 5

6 Figure 1: Mosaic of MERIS 16-day false-colour composites of channels 7,5,3 as Red-Green-Blue of white-sky albedo. Order is from the top-left corner going rightwards along columns and downwards along rows with the following 16-day time periods: D001, D017, D033 (row1), D049, D065, D081 (row 2), D097, D113, D129 (row 3), D145, D161, D177 (row 4), D193, D209, D225 (row 4), D241, D257, D273 (row 5),D289,D305,D321 (ROW 6), D337 (row 7). 6

7 Figure 2: Mosaic of MERIS 16-day false-colour composites of channels 13,7,5 as Red-Green-Blue of white-sky albedo. Order is from the top-left corner going rightwards along columns and downwards along rows with the following 16-day time periods: D001, D017, D033 (row1), D049, D065, D081 (row 2), D097, D113, D129 (row 3), D145, D161, D177 (row 4), D193, D209, D225 (row 4), D241, D257, D273 (row 5),D289,D305,D321 (ROW 6), D337 (row 7). 7

8 Figure 3: Mosaic of MERIS BRDF QA images of all 16-day periods. Order is from the top-left corner going rightwards along columns and downwards along rows with the following 16-day time periods: D001, D017, D033 (row1), D049, D065, D081 (row 2), D097, D113, D129 (row 3), D145, D161, D177 (row 4), D193, D209, D225 (row 4), D241, D257, D273 (row 5),D289,D305,D321 (ROW 6), D337 (row 7). Color legend: black: no MERIS observation at all due to too low solar zenith angle; blue: ocean; white: land with no albedo calculated; yellow: full inverse; red: magnitude inversion 8

9 Inspection of this figure shows that generally speaking the green-up can be observed but there are some 16-day periods where the green-up suddenly disappears from one period to the next and then re-appears. This is probably most likely due to problems with the atmospheric correction. This can be observed easily in animated visualizations, especially over Siberia. A representation of the quality of the MERIS albedo product is shown in Figure 3 which combines the visualisation of those pixels which are due to MOD43 full BRDF inversions with those which are due to MOD43 magnitude inversions. For D001, the numbers of land pixels which are missing are also included due to no retrieval in MOD43C2 as well as the missing pixels due to unfavourable solar zenith angle conditions. A statistical analysis was performed, assuming that the number of land pixels that could be retrieved was constant (which is NOT the case given that the solar zenith angle changes) and the results are shown in Figure 4. Inspection of this figure reveals that the percentage of full inversions never reaches 50%, the percentage of magnitude inversions increases substantially in the wintertime (probably due to cloud conditions) and that the total percentage of inversions never reaches 100% so there are many places on the land surface which never have an albedo retrieval. Figure 4: Plot of the statistics of all inversions for the MERIS albedo assuming that the number of land pixels remain constant. Firstly, MODIS WS albedo images are shown in Figure 5 in the same format as the MERIS ones in previous figures with the difference that a pseudo-colour representation is employed and only the shortwave (broadband) albedo is shown. For comparison, an equivalent version using a similar colour LUT is shown for the MERIS SW albedo (see Figure 6). Given the fact that MODIS has SWIR bands (1.24µm, 1.64µm, 2.13µm) and hence better sampling over the full range whereas MERIS is restricted to the visible/nir region (see Table 1), the agreement 9

10 in overall albedo patterns appears reasonable. Unfortunately, due to the lack of human resources and time, it was not possible to assess anymore than a single 16-day period in detail. Day-of-Year 241 was chosen for this purpose and the results are described in the next subsection. Figure 5: Mosaic of MODIS 16-day pseudo-colour of entire shortwave (0.3-5µm) white-sky albedos. Order is from the top-left corner going rightwards along columns and downwards along rows with the following 16-day time periods: D001, D017, D033 (row1), D049, D065, D081 (row 2), D097, D113, D129 (row 3), D145, D161, D177 (row 4), D193, D209, D225 (row 4), D241, D257, D273 (row 5),D289, D305, D321 (row 6), D337 (row 7). 10

11 Figure 6: Mosaic of MERIS 16-day pseudo-colour of entire shortwave (0.3-5µm) white-sky albedos.. Order is from the top-left corner going rightwards along columns and downwards along rows with the following 16-day time periods: D001, D049 (row 1), D097, D145 (row 2), D193, D241 (row 3), D289, D337, (row 4). 11

12 3.2 Quantitative inter-comparison of MERIS 16-day with MODIS equivalent for Day-of-Year 241 Each MODIS spectral band albedo is some 1GB in size because they were calculated and stored at 2 arc-minute resolution whereas the MERIS albedo is some 1GB for all the 40 spectral bands (black-sky, white-sky, etc) stored in the MERIS product. The MODIS data was converted to floats from integer and missing data (value=-32,768) replaced by NaN which doubled the size of the product. The MERIS data, similarly had to have missing data (value=-1.0) replaced by NaN and a data layer stack (in ENVIv4.2) had to be created from the relevant MERIS bands chosen for analysis (black-sky albedo) together with the four MODIS channels in common with MERIS resampled to 0.05º. After the data layer stack was created, simple differences were calculated between corresponding MODIS and MERIS spectral band albedos (MERIS-MODIS). As well as simple differences, percentage differences were also calculated. It should be borne in mind by the reader that the % difference will highly exaggerate the differences in albedo. Figure 7 shows a plot (???) of the % differences between corresponding blue, green and red channels. It is interesting to compare this figure with the inversion plots shown in Figure 4. Most of the areas where full inversions took place in the MODIS BRDF show 5% differences between MERIS and MODIS albedos at all 3 wavelengths with two notable exceptions: South Africa and Western Australia. The cause for these differences is unknown. It should be noted that in all plots of % difference, white areas represent all those pixels with >100% difference between MERIS and MODIS. Extracts over Europe at full resolution are shown in Figure 8 and Figure 9. These indicate that most of the differences are in the ±10% range with values in Spain, Sicily, northern France and SE England having areas with 100%. In Figure 9, white areas include rugged topography, northern Germany and Poland all show differences of >100% which may be due to cloud contamination problems in these areas. Statistics of the simple differences are shown below in Table 2. The table shows very close agreement between the spectral albedos (standard deviation in the range ± ) but a significant bias in the blue. This suggests that it is aerosol correction that is most likely to blame for the differences. 12

13 Table 2: Simple differences between MERIS-MODIS spectral albedos. Outliers refer to points which were not masked out even though their values were >100%. N points = full + magnitude inversions. MERIS- MODIS Minimum Maximum Mean Std. dev. N points Outliers Blue simple difference Green simple difference Red simple difference ±0.05 4,118, ±0.05 4,118, ±0.05 4,118, NIR simple ±0.07 4,118, Finally, a correlation analysis has been performed between the MODIS and MERIS spectral albedos, albeit without calculation of correlation coefficients or student t-tests for 4 sub-areas shown in Figure These show a number of different aspects Firstly, for bright (high albedo) surfaces such as Africa and Australia the correlations are very high for the GREEN, RED and NIR bands but show MERIS BLUE values slightly higher than the corresponding MODIS ones. For Europe and North America, the GREEN and RED values have the closest correlation with blue showing much higher MERIS values and low correlation for the NIR. Readers should examine the coverage for full vs. magnitude inversion for Day-of-Year 241, 2003 shown in Figure 3. These results suggest that where the surface is bright and full inversions as well as the lowest aerosol corrections in the MODIS-BRDF are performed, the MERIS values correspond most closely to the MODIS ones. On the other hand, over vegetated surfaces which are mostly magnitude inversions where the aerosol effects are larger, the agreement is poorer which may be a result of the poorer atmospheric correction. The poorer agreement consistently for all regions at BLUE wavelengths suggest that residual aerosol is likely to be present due to use of 8-day aerosol aggregates whereas experience would suggest that aerosols vary on a daily (even hourly) basis. This would suggest that future aerosol data should be collected either from MERIS and/or the dual-look AATSR instrument preferably or daily MISR and MODIS results if the former are of too poor quality. 13

14 Figure 7: Difference of (MERIS MODIS) albedo for the BLUE (MERIS: 490nm and MODIS: 470nm) in the upper image; for the GREEN (MERIS: 560nm and MODIS: 555nm) in the lower image and the RED (MERIS: 665nm and MODIS: 670nm). There are large areas in North America, Australia, South Africa and South America with more than 100% differences in albedo. 14

15 Figure 8: Difference of MERIS - MODIS albedo for the BLUE (MERIS: 490nm and MODIS: 470nm) in the upper image and for the GREEN (MERIS: 560nm and MODIS: 555nm) in the lower image; There are large areas with more than 100% differences in albedo. 15

16 Figure 9: Difference of MERIS - MODIS albedo for the RED (MERIS: 665nm and MODIS: 670nm) in the upper image and for the NIR (MERIS: 865nm and MODIS: 865nm) in the lower image; There are large areas with more than 100% differences in albedo. 16

17 Figure 10: MERIS RGB over North Africa for bands 7,5,3 (left) and corresponding MERIS v s MODIS 2D scatterplots for BLUE, GREEN, RED and NIR in rasterscan order from top-left to bottom-right for the 16-day period starting on Day-of-Year 241, Figure 11: MERIS RGB over Australia for bands 7,5,3 (left) and corresponding MERIS v s MODIS 2D scatterplots for BLUE, GREEN, RED and NIR in rasterscan order from top-left to bottom-right for the 16-day period starting on Day-of-Year 241, Figure 12: MERIS RGB for Europe for bands 7,5,3 (left) and corresponding MERIS v s MODIS 2D scatterplots for BLUE, GREEN, RED and NIR in rasterscan order from top-left to bottom-right for the 16-day period starting on Day-of-Year 241,

18 Figure 13: MERIS RGB for North America for bands 7,5,3 (left) and corresponding MERIS v s MODIS 2D scatterplots for BLUE, GREEN, RED and NIR in rasterscan order from top-left to bottomright for the 16-day period starting on Day-of-Year 241, Intercomparison with MISR and POLDER products Due to the lack of time, it was not possible to find the optimum method for creating monthly composites and complete an analysis for these monthly global products. Monthly composites based on a simple-minded weighting scheme based on the number of days within any 16-day period were present within a month was tested and were found wanting. Suffice it to say that for global MODIS (based on the simple-minded compositing scheme described above) vs. MISR inter-comparisons, the correlations are much poorer than those shown for MERIS to MODIS. Also, the geo-coding of the monthly POLDER-2 products made any meaningful inter-comparisons extremely difficult as well as very poor aerosol corrections. 3.4 Spectral characteristics of albedo Jürgen Fischer and Jan-Peter Muller Spectral albedo shape A typical spectrum is shown for D241 below all on the same colour LUT scale (Figure 14). The spectrum shape and the correlation with spectra for forested areas apears reasonable BRDF effect on spectral albedos An initial qualitative assessment was made of the impact of the BRDF on the resultant albedo. A separate processing run was performed on some of the selected orbits and an assumption of Lambertian reflectance was used to produce an isotropic albedo (i.e. one in which there were no directional effects). This isotropic albedo was then used to create a difference image (see Figure 15 and resulting profile above Europe and North Africa as well as South Africa (Figure 16). The largest differences correspond to the desertic regions of the Attacama desert 18

19 and the arid regions of Botswania at NIR wavelengths and the lowest to North Africa and Southern Europe (referred to as Europe) in the BLUE. These results reflect the low sun elevation in South Africa during south hemispheric winter and thus the enhanced effect of non-isotropic reflection. Figure 14: MERIS pseudo-colour spectrum for each of the 13 wavelengths, the colour LUT used for the pseudo-colour display and a spectrum for a single pixel within the scene for Day-of-Year 241,

20 Figure 15: MERIS ISO-BRDF for the 16-day period commencing on Day-of-Year 193, 2003: the darker the higher the differences; MERIS channel 12 (NIR); for channel 3 (BLUE) it looks very similar. 20

21 0,4 0,35 0,3 0,25 0,2 0,15 0,1 0, iso-brdf_193 [Europe_ch3] iso-brdf_193 [Europe_ch12] iso-brdf_193 [S_Africa_ch3] iso-brdf_193 [S_Africa_ch12] Figure 16: MERIS ISO-BRDF spectra for the 2 regions indicated in the previous figure for the 16-day period commencing on Day-of-Year 193, Spectral albedo cf. ground truth measurements The spectral albedo is characteristic of different targets, such as deserts (pin 3) and vegetation (pin 6), shown in Figures 17 and 18. The pixel at pin 5 shows higher values in the blue and green, although a vegetation spectrum is expected. This might be caused by a poor atmospheric correction, either arising by an underestimation of the aerosol load estimated from MODIS or caused by an underlying soil. Pin 5 also represents pixels which are bluish, which are mainly located in the 70 northern regions. Most of the spectra 60 are reasonable ,4 0,5 0,6 0,7 0,8 0,9 1 Conifer grass Light yellowish brown loamy sand Decidous Dry grass Figure 17: Spectral albedo estimated from MERIS for different targets (upper image) and for groundbased measurements of different surfaces (lower image). The spectra taken above the Sahara (pin 3) are close to the ground-based measurements above light yellowish brown loamy sand (Figure 18). Since most of the measured spectra by MERIS with a spatial resolution of 0.05 exhibit a mixture of different surface types, a validation with those ground-based data is limited and can only be used for a verification of the derived product. 21

22 Figure 18: RGB image of the black-sky albedo using channel 8 (red), channel 4 (green), and channel 2 (blue) for day , In general the derived spectral albedo is in line with the ground based measurements (NASA, 2005). 22

23 4. MERIS albedo and impact on atmospheric products Réné Preusker and Jürgen Fischer There is a significant impact of the surface spectral albedo on the estimation of atmospheric products from MERIS measurements. The knowledge of the surface albedo has to be known to an accuracy that demands the use of the MERIS data themselves. However, as a first guess more rude albedo maps are used because of the lack of adequate data. The current MERIS cloud algorithms use a 1 x1 monthly database of the surface albedo at 0.76 µm for their lower boundary condition. This database has been generated from 1 year GOME data (Koelemeijer et al, 1996). Even if the database would be perfectly precise, the variations within a grid box could be huge. Figure 19 shows this variability for a typical scene above northern Germany. In this particular example the spatial variability is in the order of 0.1. We will use this value in the next two sections for a quantitative estimation of the sensitivity of the MERIS cloud algorithms, knowing, that on the one hand more homogeneous scenes exists (e.g. the huge boreal areas) and on the other hand temporal variations could be much higher (e.g. snow fall during the spring in the mid-latitudes). Nevertheless this value is a good starting point to decide if the currently used database of surface albedo is sufficient, or not. Figure 19: Variability of the surface albedo at 753 nm within a 1 x1 grid-box in northern. 4.1 Cloud optical thickness The cloud optical thickness is calculated from MERIS band 10 (see Fischer et al 1997, ATBD). The advantage of using this channel wavelength region is the relatively small influence by variations of the cloud microphysics, and that the measured reflectance is strongly correlated with the cloud optical thickness. However, the cloud droplet effective radius and the cloud phase are still an important source of ambiguity. This ambiguity can not be solved using MERIS, since only measurements in the SWIR (e.g. at 1.6 µm) could give the needed information about the liquid and ice water absorption. 23

24 Additionally to the cloud microphysics, the albedo of the surface below the cloud is the most important source of uncertainty for the estimation of the cloud optical thickness, since clouds above bright surfaces appear brighter and therewith optically thicker, than clouds above dark surfaces. Figure 20 quantifies this effect. It shows the contribution of the surface Φ to the top of atmosphere (toa) reflectance as a function of the cloud optical thickness and the surface albedo: Φ=(r-r b )/r Figure 20: Contribution of the surface to the top of atmosphere reflectance in MERIS band 10, as a function of cloud optical thickness and surface r is the toa reflectance, the subscript b refers to a black surface below the cloud. For the case of a moderately thin cloud of (a typical stratocumulus) and a surface albedo of (a typical range for vegetation) 20% to 30% of the signal originates from the surface. If the albedo of the surface is only known with an accuracy of 0.1 ( 20%-30%), then the reflectance of the cloud and therewith its optical thickness could only estimated with an accuracy of 6% ( 0.25*0.25). This effect decreases with an increasing optical thickness, and vanishes for clouds thicker than 50. However, the majority of the clouds covering the earth are thinner than Cloud top pressure The cloud top pressure is finally calculated from the ratio of MERIS band 11 and band 10. Band 11 is affected by absorption due to atmospheric oxygen and the amount of measured absorption is directly related to the mean photon path length in the atmosphere, whereas short photon paths (weak absorption) belong to high clouds and long photon paths (strong absorption) belong to low clouds. The amount of absorption is calculated, as already mentioned, by rationing MERIS band 11 and band 10. However, not only the photon path is determining the band ratio, but also the albedo of the surface below the cloud is important. The reason is that the Figure 21: Contribution of the surface to the top of atmosphere reflectance in MERIS band 11, as a function of cloud optical thickness and surface albedo. 24 surface influences band 10 and band 11 differently. The contribution of the surface to band 10 has already been shown in the previous section and it can reach up to 40% for stratocumulus

25 clouds above vegetation. The contribution to band 11 is determined significantly by the strength of the absorption (a strong absorbing channel is e.g. unaffected by the surface). Figure 22 shows, analogue to Figure 21, the contribution Φ of the surface to the toa reflectance of MERIS band 11. Figure 22: Accuracy of the retrieved cloud top pressure as a function of the cloud optical thickness and the inaccuracy of the surface albedo. For the same case of a moderately thin cloud of (typical stratus or cumulus) and a surface albedo of (typical range for vegetation) 10% to 20% of the signal originates from the surface in contrary to the 20%-30% for the non-absorbing band. This example demonstrates the importance of the surface albedo and that it is a needed input parameter for the MERIS cloud top pressure algorithm. If the albedo of the surface is only known with an accuracy of 0.1 ( 20%-30%), then the reflectance ratio could estimated with an accuracy of 2% only in this case. Using, that the sensitivity of the ratio to the cloud top pressure is in the order of 0.2%/hPa (Fischer et al 1996, ATBD Cloud Top Pressure), the inaccuracy of the surface albedo can be translated into an inaccuracy of the retrieved cloud top pressure of 20hPa. This rough calculation has been generalized in order to calculate the effect of the inaccuracy of the surface albedo as a function of the cloud optical thickness and the surface albedo. Therefore the MERIS cloud top pressure algorithm has been applied to simulated toa reflectances for a variety of cloud top heights, cloud optical thicknesses and surface albedo to quantify the loss of accuracy of the retrieved cloud top pressure by inaccurate surface albedo knowledge. Figure 22 shows the accuracy as a function of the cloud optical thickness and the inaccuracy of the surface albedo. Two effects can be seen: 1.) the accuracy increases with increasing optical thickness and 2.) the accuracy of the 0,99 0,98 0,97 0,96 0,95 0,94 0,93 0, Figure 23: MERIS band ratio ch_10/ch_12 for D097 (left), figure right shows the ratio from west to east (blue curve), data above north-eastern Germany (red curve), where a ctp validation experiment took place in

26 surface albedo affects the accuracy of the retrieved cloud top pressure, even above optically thick clouds, if the accuracy of the used surface albedo is worse than 0.1. The ratio of channel 10 and channel 12 is shown in Figure 23 and it indicates that it is mainly below 1. In Europe, along the yellow line, it varies between 0.93 and 0.97 with a very few exceptions. In north-eastern Germany it varies only between 0,94 and 0,96. This results in an impact of 0.02 in the ratio of channel 10 and channel 11. Beside the influence of the absolute value of the albedo the spectral variations also has an impact on the retrieval of the cloud top pressure. Since the sensitivity of the ratio to the cloud top pressure is in the order of 0.2%/hPa as discussed above, the inaccuracy of the surface albedo slope, assuming a 10% transmission in the absorption channel through a stratocumulus cloud can be translated into an inaccuracy of the retrieved cloud top pressure of 1-2hPa. For a bias free retrieval of cloud top pressure and also to enhance its accuracy, the surface albedo and the spectral slope around the oxygen band have to be taken into account. 4.3 Water vapour above land Water vapour is the most effective greenhouse gas in the atmosphere. It influences weather and climate and is responsible for clouds development, precipitation, and modulates the atmospheric radiative energy transfer. Therefore it is necessary to know the water vapour with high accuracy and bias-free to use it in weather forecasts and climate studies. The MERIS instrument has two channels for water vapour measurements, located at 885 nm and 900 nm, both with 10 nm bandwidth (Figure 24). As previously demonstrated by Fischer (1988), this channel setting minimizes the effects of surface albedo slope, whereby the channel location at the shortwave edge of the VW absorption band yields a good sensor response. However, an impact of the spectral albedo slope is still present, but difficult to achieve, since the spectral slope in such narrow band are not known so far even on a local or regional scale. The common underlying method used is the differential absorption technique: the columnar water vapour content is related to the ratio of radiances measured in an absorption and a window channel, in this case the MERIS channels 14 centred at 885 nm (window channel) and MERIS channel 15, located at 900 nm (absorption channel). The general form of the retrieval algorithm is as follows: Figure 24: Transmission due to water vapour absorption via wavelegth; MERIS (blue) and MODIS filter-functions are displayed (red). 26 L L15 W = c + 1 c2 log c3 log (1) L14 L14 Where W denotes the columnar water vapour content and L 14 and L 15 are the radiances measured in MERIS channels 14 and 15, respectively. The coefficients c i are regression constants, derived by inverting the results of radiative transfer

27 calculations performed during the algorithm development and described in more detail in the later sections 1. This simple model is based on the assumption that an exponential relation exists between the absorber mass and extinction. It therefore reflects Lambert s law for an idealized non-scattering atmosphere, unsaturated absorption and monochromatic radiation. Since none of these 50 assumptions are given in reality an empirical ,85 0,9 0,95 quadratic correction term is introduced in Equation (1). It has to be outlined, though, that the model described in Equation (1) has been chosen due to its simplicity and its Figure 25: Spectral surface albedo of different surfaces (NASA, 1998). physical motivation. The relation between W and the radiance ratio may be fitted with several other non-linear functions, with similar results in terms of the retrieval error. Equation (1) implicitly assumes that the surface reflectivity do not differ significantly between the window and the absorption channel. Variations in the measured radiance ratio resulting from spectral variations of the surface albedo would be interpreted as varying water vapour contents. However, earlier studies showed that the choice of the MERIS water vapour channels with a very small spectral difference between the window and the absorption channel is optimal in order to minimize this effect. The assumption that these do not differ significantly between the two channels is fulfilled to a high accuracy. Additionally, a simple correction scheme for a possible surface reflectivity slope based on the radiance ratio between MERIS channels 10 (753.75) nm and channel 14 is applied to the measurements. The coefficient c i of Eq. (1) have been calculated by means of radiative transfer simulations. The expected algorithm accuracy is between 2 and 2.5 kg/m 2 for most geometries, only for sun zenith angles > 70 the error increases significantly (Fischer et al, 2005). Figure 26: Scatter plot of total precipitable water vapour from the Microwave Water Radiometer at the ARM-SGP site and from MERIS measurements. Grayish brown loam Reddish brown fine sandy loam L ig h t ye llo w is h brown loamy sand The radiance ratio of channels 15 and 14 should be corrected for possible spectral slopes of the surface albedo between the two channels. The correction could be based on the two window channels 13 and 14 and is of the following form: 1 The regression coefficients are derived individually for each sun- and viewing geometry for which radiative transfer simulations are performed. When applied to real measurements the appropriate coefficients are interpolated linearly following the actual sun and satellite angles. 27

28 ( s + s R s ) R c = +. 15/14 R15/ /13 2R15/14 R x / y denotes the radiance ratios between MERIS channels x and y, s i are regression constants derived from radiative transfer simulations. The upper index c denotes the slope corrected radiance ratio which is afterwards used for the water vapour retrieval following Equation 1. Validation of the MERIS water vapour product was so far performed by comparing MERIS results to measurements of the Microwave Water Radiometer (MWR) on the ARM-SGP (Atmospheric Radiation Measurement Program Southern Great Plains) site in Oklahoma / USA, to ground based GPS data over Germany and to radio soundings. Figure 27: MERIS band ratio ch_13/ch_14 for D193, see yellow curve in Fig The MWR is designed to measure the emissions of atmospheric water vapour and liquid water at specific frequencies of 23.8 GHz and 31.4 GHz. Measurements of sky brightness temperature at these two frequencies are converted into total water vapour content and integrated cloud liquid water path. It was used for the validation of MERIS measurements of total water vapour content above cloud free land surfaces. For this comparison, 61 MERIS overpasses for the period August 2002 to September 2003 were investigated, 36 of which showed the pixel closest to the ARM-SGP site identified as cloud free. For these pixels, the MERIS results were compared to the appropriate MWR measurements of total precipitable water vapour (averaged over 5 minutes) closest in time to the satellite overpass. The resulting scatter plot is showed in Figure 26. The root mean square deviation between MERIS and the MWR is 2.0 mm, the bias is -1.1 mm, equivalent to 2 g/cm 2 and -1.1 g/cm 2, 1,03 1,02 1,01 1 0,99 0,98 0, Figure 28: Ratio of channel_13/channel_14 above Europe (blue) and North Africa (yellow). respectively. The error bars in this Figure are for the MWR based on estimates provided by David Tobin (personal communication, 2003), They represent a combination of the absolute uncertainty of the sensitivity of the MWR water vapour measurements to increasing water vapour of 1.5 % and the uncertainty in the offset of 1 mm. An absolute error of 2.5 mm was chosen for

29 the error bars for MERIS representing the average expected regression error. The mean MWR water vapour column amount over all measurements was 20.3 mm, yielding a relative rms deviation and bias of 9.9 % and 5.4 %, respectively. This bias can be explained by the albedo slope measured above the ARM-site. The ratio ch_13/ch_14 amounts and an albedo of 0.26 at ch_14 is quite low. Those values can be transformed into a bias of 3 % (Bennartz and Fischer, 2001). As all ratios <1 cause a dry bias of MERIS measurements, the ARM-site comparison shows also a dry-bias when compared with MWR-data. The ratio of ch_13/ch_14 above Europe and North Africa are shown in Figure 27 and Conclusions The spectral albedo products of the ALBEDOMAP project binned into 16 days, have been validated against MODIS 16-day and MISR monthly albedo products (latter not shown). There are areas and periods of good agreements between MERIS and MODIS and there are features which indicate that the atmospheric correction and the cloud detection need to be improved. There is a significant impact of the new surface albedo on MERIS atmospheric products. The use of the ALBEDOMAP products, the temporal surface albedo, will surely enhance the accuracy of the MERIS cloud optical thickness, the cloud top pressure and the water vapour product. In general, we can state that the agreement is good and the products of the MERIS ALBEDOMAP project can be used for a variety of different application, such as climate impact studies or vegetation studies. Nevertheless, a continuation is needed to address the remaining open queries. References cited P. Albert, R. Bennartz, and J. Fischer. Remote sensing of atmospheric water vapour from backscattered sunlight in cloudy atmospheres. Journal of Atmospheric and Oceanic Technology, 18: , 2001 P. Albert, K. M. Smith, R. Bennartz, D. A. Newnham, J. Fischer. Satellite- and groundbased atmospheric water vapour absorption in the 940 nm region. Journal of Quantitative Spectroscopy and Radiative Transfer, 84(2): , 2004 B. Bartsch and J. Fischer. Passive remote sensing of columnar water vapour content over land surfaces. MPI report No. 234, Hamburg, Germany, ISBN , 1997 R. Bennartz and J. Fischer. Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer. Remote Sensing of the Environment, 78: , 2001 J.-L. Bézy, S. Delwart, and M. Rast. MERIS, a new generation of ocean-colour sensor onboard envisat. Esa bulletin, 193:48-56, 2000 D. E. Bowker, R. E. Davis, D. L. Myrickm K. Stacy, and W. T. Jones. Spectral reflectances of natural targets for use in remote sensing studies. NASA Ref. Publ. 1139,

30 I. Chernykh and R. Eskridge. Determination of cloud amount and level from radiosonde soundings. Journal of Applied Meteorology, 35: , 1996 F. Fell and J. Fischer. Numerical simulation of the light field in the atmosphere-ocean system using the matrix-operator method. Journal of Quantitative Spectroscopy and Radiative Transfer, 1257, May J. Fischer. High resolution spectroscopy for remote sensing of physical cloud properties and water vapour. In: Current Problems in Atmospheric Radiation, Ed. J. Lenoble and J. F. Geleyn, Deepak Publishing: , J. Fischer, R. Preusker, and L. Schüller. ATBD cloud top pressure, Algorithm Theoretical Basis Document PO-TN-MEL-GS-0005, European Space Agency, Moody, E.G., King, M.D., Platnick, S., Schaaf, C.B. and Gao, F., Spatially complete global spectral surface albedos: Value-added datasets derived from terra MODIS land products. IEEE Transactions on Geoscience and Remote Sensing, 43(1): NASA, ASTER spectral library, URL: status March 2005 Y. Han and E. R. Westwater. Remote sensing of tropospheric water vapor and cloud liquid water by integrated ground-based sensors. Journal of Atmospheric and Oceanic Technology, 12(5): ,