Sea Surface Temperature Observation by Global Imager (GLI)/ADEOS-II: Algorithm and Accuracy of the Product

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1 Journal of Oceanography, Vol. 62, pp. 311 to 319, 2006 Sea Surface Temperature Observation by Global Imager (GLI)/ADEOS-II: Algorithm and Accuracy of the Product FUTOKI SAKAIDA 1 *, KOHTARO HOSODA 2, MASAO MORIYAMA 3, HIROSHI MURAKAMI 2, AKIRA MUKAIDA 4 and HIROSHI KAWAMURA 1 1 Center for Atmospheric and Oceanic Studies, Faculty of Science, Tohoku University, Aramaki-Aza-Aoba, Aoba-ku, Sendai, Miyagi , Japan 2 Earth Observation Research and application Center (EORC), Japan Aerospace Exploration Agency (JAXA), Harumi, Chuo-ku, Tokyo , Japan 3 Computer and Information Science Department, Faculty of Engineering, Nagasaki University, Bunkyo-machi, Nagasaki , Japan 4 Remote Sensing Technology Center of Japan (RESTEC), Harumi, Chuo-ku, Tokyo , Japan (Received 2 June 2005; in revised form 24 October 2005; accepted 27 October 2005) A sea surface temperature (SST) retrieval algorithm for Global Imager (GLI) aboard the ADEOS-II satellite has been developed. The algorithm is used to produce the standard SST product in the Japan Aerospace Exploration Agency (JAXA). The algorithm for cloud screening is formed by combinations of various types of tests to detect cloudcontaminated pixels. The combination is changed according to the solar zenith angle, which enables us to detect clouds even in the sun glitter region in daytime. The parameters in the cloud-detection tests have been tuned using the GLI global observations. SST is calculated by the Multi-Channel SST (MCSST) technique from the detected clear pixels. Using drifting buoy measurements, match-up data are produced to derive the coefficients of the MCSST equations and to examine their performance. The bias and RMSE of the GLI SST are 0.03 K and 0.66 K for daytime and, 0.01 K and 0.70 K for nighttime, respectively. Keywords: GLI/ADEOS-II, SST, cloud detection, MCSST. 1. Introduction Sea surface temperature (SST) is one of the major parameters for examining the status of the ocean. An SST chart indicates not only thermal conditions of the ocean surface, but also kinematical ocean states, such as geostrophic currents, fronts, and eddies. Many SST chart applications have been developed for practical usage, e.g., by fisheries, in ship navigations, etc. Satellite infrared (IR) measurements for SST retrieval have been conducted by operational polar and geostationary orbiting satellites since around The Advanced Earth Observation Satellite (ADEOS)- II was launched in December 2002 and functioned until October It carried Global Imager (GLI) which is a visible/infrared radiometer. The specification of GLI is summarized in Table 1. GLI had 36 channels in a spectral range between visible to thermal infrared and could observe ocean color as well as SST. The spatial resolution * Corresponding author. toki@ocean.caos.tohoku.ac.jp Copyright The Oceanographic Society of Japan/TERRAPUB/Springer of the GLI bands for generating the SST products is 1000 m. This paper introduces the GLI SST algorithm and shows its performance through validation of the global GLI SST products. The SST retrieval needs cloud detection, atmospheric correction and quality control processes. The algorithms used in these processes are explained in Section 2. The validation of the GLI SST is performed by comparison with drifting buoy measurements. The validation result is described in Section Algorithms of Cloud Detection, Atmospheric Correction and Quality Control 2.1 Algorithm versions There are three different version algorithms for the GLI SST retrieval. One is the pre-launch algorithm (V0). After the ADEOS-II launch, the GLI SST algorithm was changed twice. The first after-launch algorithm (V1) has been used to produce the GLI SST product since December 24, 2003 and the second (V2) since November 1, In the upgrade from V1 to V2, the cloud detection algo- 311

2 Table 1. Characteristics of the GLI bands. Noise equivalent differential temperature (NEdT) of MTIR bands is cited from Nakajima et al. (1998). Band No. Wavelength (nm) Spatial resolution (m) NEdT (K) (at 300 K) Band for cloud detection VNIR Band for atmospheric correction SWIR MTIR VNIR: Visible and near infrared. SWIR: Short-wave infrared. MTIR: Middle and thermal infrared. rithm was improved for screening thin cloud. Figure 1 shows example images of the GLI SST products from V1 and V2 with a Band 19 ( µm) image. It is seen that the clear pixels are reduced in the V2 image (Fig. 1(c)) when compared with the V1 image (Fig. 1), which is due to successful detection of thin clouds (see Fig. 1) by the V2 cloud-detection algorithm. Hereafter, we mainly describe the V2 algorithm, which is now used for generation of the standard GLI SST products. 2.2 Cloud detection The cloud-detection scheme is formed by several threshold tests for identifying a pixel as cloud-free when the pixel passes all the tests. Table 2 shows the threshold 312 F. Sakaida et al.

3 Table 2. Cloud detection tests for the GLI SST product. Tests checked by are used in each scheme: Scheme-1, daytime and outside sun glint region; Scheme-2, daytime and sun glint region; and Scheme-3, nighttime. Overbars on the brightness temperature difference (BTD) terms in the BTD test group denote the average of 3 3 pixels excluding maximum. Group Cloud detection test Scheme-# Gross cloud test 2 BT φ BT Reflectance test R R > θ r R R > 048. R > θ r R > R > 02. and R R > BTD test BT BT > BT BT > exp BT ( ) + BT10. 8 BT12 > 43. ( ) > 15. BT BT BT BT 35. ( ) + < 15. BT 37. BT 108. BT 12 BT BT BT86. + BT10. 8 BT12 < 18. BT BT exp BT ( ) + Uniformity test mvbt10. 8 > 15. and mn( BT10. 8 BT12 ) > 25. mnr 124. > ( FR) mvbt37. > 20. ( LR) φ: Latitude (degree). θ r : Reflection angle (degree). R : Reflectance of band-19 (%). R : Reflectance of band-8 (%). R 1.38 : Reflectance of band-27 (%). BT 3.7 : Brightness temperature of band-30 (degk). BT 8.6 : Brightness temperature of band-34 (degk). BT 10.8 : Brightness temperature of band-35 (degk). BT 12 : Brightness temperature of band-36 (degk). mv : The difference between the maximum and center of a 3 3-pixel array. mn : Maximum-Minimum difference of a 3 3-pixel array. FR: Full resolution product. LR: Low resolution product. tests for cloud detection. This paper uses schemes to represent the combination of the threshold tests. Schemes- 1 and -2 are used for cloud detection in daytime when solar zenith angle (θ sun ) is less than 86.5 (θ sun < 86.5 ). Scheme-3 is used in nighttime (θ sun > 86.5 ). In daytime, scheme-1 is applied for the exterior of the sun glitter region and scheme-2 is used in the sun glitter region. To specify the sun glitter region, reflection angle (θ r ) is used according to Sakaida et al. (2000). The reflection angle is calculated by, cosθ r cosθsun + cosθ = 2 cosω where θ sat is satellite zenith angle and ω can be derived by, sat () 1 The GLI SST Product Algorithm and Accuracy 313

4 (c) Fig. 1. GLI image of band-19 ( µm), corresponding SST image by the V1 algorithm, and (c) corresponding SST image by the V2 algorithm. Data obtained at 06:11(UTC), June 24, 2004 are shown. Dark grey of SST image and (c) denotes cloud pixels judged by each cloud-detection algorithm. ( ) cos 2ω = cosθ cosθ + sinθ sinθ cos φ φ sun sat sun sat sun sat ( 2) where φ sun and φ sat are the azimuthal angles of the sun and the satellite, respectively. The limit value for specifying the sun glitter region is 30. As shown in Table 2, the cloud detection tests are categorized into four groups. Two gross cloud tests form the first group, in which a pixel is flagged as cloudy when the brightness temperature of band-35 (10.8 µm) is lower than the threshold value. Tanahashi et al. (2000) used a quadratic function of latitude as the threshold value for cloud detection using measurements of S-VISSR/GMS. We adopt their functional forms in the first gross cloud test. The coefficients in the quadratic function are derived empirically from the GLI data. Another gross cloud test uses a constant threshold value to identify clouds in high latitude regions. The threshold tests in the second group (Table 2) use visible and near-infrared reflectance values. It is widely known that the ratio of two different reflectances is effective for daytime cloud detection. Ackerman et al. (1997) showed that, for identifying cloud and clear ocean surface, reflection differences in wavelength above and below 0.72 µm are useful. In our preliminary studies of GLI cloud detection, we used the 0.87 µm reflectance divided by 0.66 µm reflectance (R /R ). However, it is found that R /R (band-19 divided by band-8) is more efficient for classifying cloud and clear ocean than R /R (band-19 divided by band-13). The 2-D histograms of GLI pixels in R /R and R /R versus reflection angle are shown in Figs. 2 and, respectively. The ratio R /R tends to be smaller No. of pixels No. of pixels Fig D histograms of pixels in the domains of R / R and R /R versus reflection angle. Color shows the frequency of appearance. 314 F. Sakaida et al.

5 Table 3. MCSST equations for the GLI SST product. Overbars on the brightness temperature difference terms in each equation denote the average of 7 7 pixels. Version MCSST equations ( ) + ( ) ( )( ) + ( )( ) Pre-launch (V0) SST = BT BT BT BT BT BT BT sec θ BT BT secθ 1 ( ) + ( ) ( )( ) V1 SST = BT BT BT BT BT ( ) ( ) BT BT sec θ BT BT secθ V2 (Daytime) SST = BT BT BT BT BT ( ) ( ) + ( ) ( )( ) ( ) + ( ) ( )( θ ) ( 7 )( θ 1) ( ) BT BT sec θ BT BT secθ (Nigttime) SST = BT BT BT BT BT ( ) ( ) ( ) BT BT sec θ BT BT sec ( ) BT BT sec θ BT BT. sec θ: Satellite zenith angle. BT 3.7 : Brightness temperature of band-30 (degk). BT 8.6 : Brightness temperature of band-34 (degk). BT 10.8 : Brightness temperature of band-35 (degk). BT 12 : Brightness temperature of band-36 (degk) than R /R in the clear ocean and even in the sun glitter region (θ r < 30 ). Both the reflectance ratios are almost the same in a cloudy region (~1). It is concluded that the ratio R /R performs better in cloud detection over the ocean even in the sun glitter region. In order to avoid the potential for masking clear pixels in the sun glitter region, the linear threshold function and sun glitter region (θ r < 30 ) are defined empirically. The second group comprises tests that do not use the reflectance ratio. The thresholds of these tests are determined empirically. The third group utilizes brightness temperature differences (BTDs) for detecting clouds. It is known that the BTD between 11 µm and 12 µm is effective for detecting relatively thin clouds. Saunders and Kriebel (1988) employed a similar BTD test for AVHRR/NOAA with a threshold which is a function of satellite zenith angle and BT 11 (BT of AVHRR Channel 4). On the other hand, Stowe et al. (1999) used the 5-th order function of BT 11 as the threshold for cloud classification. In view of these previous studies and given the character of the GLI data, the threshold function for GLI is determined as an exponential function of BT 10.8 (band-35) empirically. The test using the BTD between 8.6 µm (band-34) and 10.8 µm (band-35) is also useful to detect ice clouds (Strabara et al., 1994). Since the GLI data show that the temperature dependence on the threshold is small, the threshold of the test is determined as a constant value. In the nighttime cloud detection scheme (scheme- 3), the BTD between 3.7 µm (band-30) and other bands is used. This BTD test is effective for detecting low-level clouds at night. After examining the actual GLI data, we decided to use the combination of BT 3.7, BT 8.6, BT 10.8 and BT 12 and the coefficients and thresholds of the tests are determined empirically. As shown in Table 2, the BTDs in the several tests of this group are calculated as the value averaged over an array of 3 3 pixels around a pixel targeted for cloud detection; the maximum value in the array is not used in this average calculaton. The reason why the average of the 3 3 pixels is used as BTD is to avoid influences from the striping noise and the SST frontal region. The fourth group (Table 2) has three uniformity tests, which are based on the fact that brightness temperature or reflectance is spatially homogeneous in the cloud-free ocean. The uniformity test is very powerful for cloud detection, although its reliability declines in the coastal and frontal regions. Except for the uniformity calculation for BT 10.8 (band-35), the degree of uniformity is calculated as the maximum and minimum difference in a 3 3-pixel array. The uniformity of BT 10.8 (band-35) is obtained by the BTD between maximum value and center value in a The GLI SST Product Algorithm and Accuracy 315

6 3 3-pixel array, which is used in Rossow and Garder (1993). This is to reduce the error of a cloud-free pixel being flagged as cloudy. In addition, the uniformity of BTD (BT 10.8 BT 12 ) is combined with the uniformity of BT 10.8 to avoid masking the frontal region as cloud. The nighttime scheme (scheme-3) uses the uniformity of BT 3.7 (band-30) with different thresholds for FR (full-resolution) and LR (low-resolution) products since noise in GLI band-30 is so great that the spatial homogeneity of BT 3.7 in a clear region of LR is less than that in FR. All the thresholds of the uniformity tests are also determined empirically through careful examination of the global GLI measurements. Fig. 3. Dependence of bias and RMSE on the pixel array size (see the text). Thin line denotes the nighttime case, and dotted line the daytime one. 2.3 Atmospheric correction The GLI SST is calculated by the Multi-Channel SST (MCSST) technique for atmospheric correction. Table 3 shows the MCSST equations from the pre-launch to the latest. The coefficients of the pre-launch equation (V0) were derived by the numerical simulation of an atmospheric radiation-transfer model. Merchant and Le Borgne (2004) showed that the offset coefficient of the SST retrieval equation derived by the radiative transfer model often requires an empirical adjustment. Since our V0 equation also showed a negative bias, the first (V1) and second (V2) version coefficients of the post launch equation are derived from the match-up data generated using the in situ SSTs from drifting buoys. The V1 coefficients were determined from 546 match-ups obtained on 3/19 22 and 4/ while ADEOS-II was functioning. The match-up data collected on 4/2 7/31 were used to derive the V2 daytime and nighttime equations. Their numbers are 5880 (daytime) and 5327 (nighttime), respectively. When the match-up data are collected, the time difference between the satellite and buoy observation is within 2 hours and the clear-pixel-fraction (the fraction of clear pixels in an pixel array) around the buoy observation point is set to more than 95%. As shown in Table 3, the MCSST equations use the mean brightness temperature difference of a 7 7-pixel array. This is to reduce the noise effects in the GLI data. Wu et al. (1999), who used the GOES data, pointed out that the optimal box size of averaging brightness temperature differences is as small as a 3 3-pixel array for a representative of a buoy measurement, since the RMSE (root-mean-square-error) of satellite-estimated and buoymeasured SSTs (buoy SST) becomes minimal for the array size. Figure 3 shows the relationship between the bias and RMSE and the pixel array size to calculate the BTD average. The RMSE decreases monotonically with the array size. The bias between the GLI SST and the buoy SST approaches zero at an array size of 7 in daytime. At night, the bias is close to zero when an array size of 8 is used. However, it is difficult to change the array size during the day and at night because of the restrictions of the GLI SST data processing system. We therefore decide to use the 7 7-pixel array for calculating the BTD average. Figure 4 shows the biases and RMSEs of several MCSST equations as a function of clear-pixel-fraction. In daytime (Fig. 4), the triple-window equation gives the smallest RMSE; there is little relation between the clear-pixel-fraction and the RMSE. The triple-window equation also has a smallest change in the bias. Thus, the triple-window equation is able to obtain a better estimation of SST than the split-window (BT 11 and BT 12 ) or dualwindow (BT 8.6 and BT 11 ) equations (these equations are omitted in this paper). In nighttime, the four-band equation gives the smallest RMSE and a smallest dependence of the bias on the clear-pixel-fraction. The estimated SST from the equation including 3.7 µm (green in Fig. 4) is considered to be accurate even if the clear-pixel-fraction is low, or, near a cloud region. It can be concluded that the accuracy of the GLI SST is improved by including BT 3.7 and BT 8.6 in the MCSST equations (V2) as shown in Table Quality control Generally, satellite-derived SST products have inherent contamination due to the cloud influences. Therefore the quality control (QC) process is needed to decrease the cloud contaminated pixels after the SST retrieval processes mentioned above. In the QC process for the GLI SST Level 2 product, the monthly climatological SST by 316 F. Sakaida et al.

7 Fig. 4. Relationships between bias and RMSE of the tested MCSST equations and the clear-pixel-fraction (clear) around the buoy observation point. Daytime and nighttime. Each colored line denotes the different MCSST equations and the bands used for each are indicated beside the color bars. The triple window equation (BT 8.6, BT 10.8 and BT 12 are used) of daytime and the four-bands equation (BT 3.7, BT 8.6, BT 10.8 and BT 12 ) of nighttime are shown in Table 3 but the other equations are omitted. No. of points Fig. 5. Locations of the match-up data points. Color denotes the number of the match-up data. The GLI SST Product Algorithm and Accuracy 317

8 Reynolds and Smith (1994) is used as the reference SST (SST clim ). Given the standard deviation of SST, SST clim, the pixel is flagged if the following equation is satisfied. GLI SST SST clim > 2 SST clim. (4) GLI SST (K) GLI SST (K) Buoy SST (K) Buoy SST (K) Fig. 6. Comparison between the GLI and buoy SSTs. Daytime and nighttime. Another QC process is further applied to mask the suspect data in the GLI SST Level 3 products. A past 10 days Level 3 daily-mean SST product (L3STAMAP) is used as an ancillary data. The past 10 days data are smoothed by a spatial running average over 5 5 degrees. If the center pixel is missing in the past 10 days product or the number of valid pixels located in spatial averaging area is less than 10%, the QC process at the pixel is skipped. When the ancillary data are obtained, the SST value is flagged if the difference between the GLI and ancillary SSTs at the same point is larger than an acceptable difference of SST (6 K in daytime, 4 K in nighttime). 3. Validation Result In order to validate the GLI SST product s accuracy, match-up data of the GLI SST with the drifting buoymeasured SST (buoy SST) have been produced. The match-up data were collected from Apr. 02 to Oct. 24 under the condition that the time difference between buoy and GLI observations is less than 3 hours. The position difference is set within ±1 pixel of GLI (about 1 km at nadir). For this validation, we selected the match-up data that have clear pixels more than 110 within an pixel array around a buoy point. The numbers of matchup data are 7108 (daytime) and 8767 (nighttime), respectively. Their locations are shown in Fig. 5. The match-up data for the validation do not include the match-ups to derive the MCSST coefficients shown in Table 3. RMSE (K) RMSE (K) Bias (K) Bias (K) Fig. 7. Time variations of bias and RMSE. Daytime and nighttime. 318 F. Sakaida et al.

9 Figure 6 shows the comparisons between the GLI and the buoy SSTs. The biases and RMSEs are 0.03 K and 0.66 K for daytime, 0.01 K and 0.70 K for nighttime, respectively. The RMSE of the nighttime GLI SST is somewhat larger, which may be caused by the cloud-detection error. Hosoda et al. (2006) performed an intercomparison study between the GLI SST and the microwave SST derived by Advanced Microwave Scanning Radiometer (AMSR) aboard ADEOS-II. They showed that a large difference tends to occur in the high-latitude region mainly due to the nighttime cloud detection algorithm of GLI. There is room for improvement in the nighttime cloud detection algorithm. Figure 7 shows the temporal variations of the bias and RMSE; the monthly biases and RMSEs are shown against months when the GLI were functioning. The RMSE increases in the period of June to August. This tendency (up to 1.0 K) is clearly found in the daytime RMSE (Fig. 7). The biases also have a seasonal variation, although the change in the bias is within ±0.1 K. The cause of these RMSE and bias variations is not clear. According to Hosoda et al. (2006), the difference between the GLI and AMSR SST depends on the condition of water vapor and aerosol concentrations. This suggests that the water vapor and aerosol variations affect the variation of the GLI SST accuracy. Further research will be necessary to elucidate these points. 4. Summary and Conclusion This paper describes the algorithm for the sea surface temperature (SST) retrieval from Global Imager (GLI) aboard the ADEOS-II satellite. The algorithm for the GLI SST product by JAXA was changed three times and the latest one is called the version 2 algorithm (V2). Its cloud-detection algorithm is applicable for the sun glitter regions in the GLI scenes. It is found that R / R (band-19 divided by band-8) is more efficient for classifying cloud and clear ocean than R /R (band- 19 divided by band-13), even in the sun glitter region. The nighttime MCSST equation uses the four infrared bands (3.7, 8.6, 10.8, and 12 µm). To validate the GLI SST product accuracy, match-up data of the GLI SST and the drifting buoy-measured SST (buoy SST) were produced. The numbers of match-up data are 7108 (daytime) and 8767 (nighttime), respectively. The biases are 0.03 K (daytime) and 0.01 K (nighttime) and the RMSEs, 0.66 K (daytime) and 0.70 K (nighttime). Although the GLI SST products possess comparable accuracy to the other satellite SSTs, the latest (V2) algorithm for the GLI SST should be examined to show its accuracy and effectiveness under a variety of cloud and atmospheric conditions. Further study will improve the GLI SST s accuracy. Acknowledgements The authors express thanks to anonymous reviewers for their helpful comments. 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