Difference between forward- and backwardlooking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data

International Journal of Remote Sensing ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: https://www.tandfonline.com/loi/tres20 Difference between forward- and backwardlooking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data Yu Oishi, Takashi Y. Nakajima & Tsuneo Matsunaga To cite this article: Yu Oishi, Takashi Y. Nakajima & Tsuneo Matsunaga (2016) Difference between forward- and backward-looking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data, International Journal of Remote Sensing, 37:5, 1115-1126, DOI: 10.1080/2150704X.2016.1145822 To link to this article: https://doi.org/10.1080/2150704x.2016.1145822 2016 The Author(s). Published by Taylor & Francis. Published online: 14 Mar 2016. Submit your article to this journal Article views: 890 View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalinformation?journalcode=tres20

INTERNATIONAL JOURNAL OF REMOTE SENSING, 2016 VOL. 37, NO. 5, 1115 1126 http://dx.doi.org/10.1080/2150704x.2016.1145822 Difference between forward- and backward-looking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data Yu Oishi a, Takashi Y. Nakajima b and Tsuneo Matsunaga c a Research & Information Centre, Tokai University, Tokyo, Japan; b School of Information Science & Technology, Tokai University, Hiratsuka, Japan; c Centre for Environmental Measurement and Analysis, National Institute for Environmental Studies, Tsukuba, Japan ABSTRACT Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2017. GOSAT-2 will be equipped with two Earth-observing instruments: the thermal and near-infrared sensor for carbon observation Fourier transform spectrometer 2 (TANSO-FTS-2) and TANSO-cloud and aerosol imager 2 (CAI-2). The FTS-2 data will be used to determine atmospheric concentrations of greenhouse gases, such as CO 2 (carbon dioxide), CH 4 (methane) and CO (carbon monoxide). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands for observing the optical properties of aerosols and clouds, and for monitoring the status of urban air pollution and transboundary air pollution over oceans. An important role of CAI-2 is to perform cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA) will be used for cloud discrimination with CAI-2. The Multi-angle Imaging Spectroradiometer (MISR) aboard the Terra spacecraft provides radiometrically and geometrically calibrated images for spectral bands at nine widely spaced angles. In this study, we examined the difference between forward and backward cloud discrimination by using CLAUDIA with Terra MISR data. The results were as follows: (1) in land areas and polar regions, cloud discrimination results obtained with either band could be used; and (2) in sea areas, cloud discrimination results that include no-sun-glint regions should be used. ARTICLE HISTORY Received 26 October 2015 Accepted 18 January 2016 1. Introduction Greenhouse Gases Observing Satellite-2 (GOSAT-2), which will be launched in fiscal year 2017, will continue the work of GOSAT and gather improved space-borne measurements of major greenhouse gases to monitor the effects of climate change and human activities on the carbon cycle and to contribute to climate science and climate change-related policies (NIES GOSAT-2 Project 2014). GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS- 2) and TANSO-cloud and Aerosol Imager 2 (CAI-2). The FTS-2 data will be used to determine global atmospheric concentrations of greenhouse gases, such as CO 2 (carbon dioxide), CH 4 CONTACT Yu Oishi oishi.yu@tokai-u.jp Research & Information Center, Tokai University, Tokyo, Japan 2016 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

1116 Y. OISHI ET AL. Figure 1. Observation direction angles of GOSAT-2 CAI-2 and Terra MISR. (a) CAI-2 has forward- (+20 ) and backward-looking ( 20 ) bands. (b) MISR has nine cameras (nadir, ±26.1, ±45.6, ±60.0 and ±70.5 forward and backward of the local vertical). (methane) and CO (carbon monoxide). CAI-2 is a push-broom imaging sensor that has forward- (+20 ) and backward-looking ( 20 ) bands (Figure 1(a)) for observing the optical properties of aerosols and clouds and for monitoring the status of urban air pollution and transboundary air pollution over oceans. In contrast, the existing GOSAT TANSO-CAI takes measurements at a fixed angle close to the nadir (NIES GOSAT-2 Project 2014). An important role of CAI-2 is to perform cloud discrimination to identify and reject cloud-contaminated FTS-2 data because the presence of clouds in the instantaneous field of view (IFOV) of the FTS-2 leads to incorrect estimates of the atmospheric greenhouse gas concentrations. GOSAT-2 has three instruments which can be used for cloud discrimination. (1) On-board camera (CAM) is a RGB camera. It points in the direction of FTS-2 viewing and it has higher spatial resolution than CAI-2. FTS-2 has an intelligent pointing system. This system judges the presence or absence of clouds in IFOV of the FTS-2 using the CAM in on-board processing, and automatically changes the pointing angle of the FTS-2 if there are clouds. By this system, an increase in the probability of obtaining cloud-free FTS-2 is expected. (2) CAI-2 has a near infrared (NIR, 0.87 μm) band and shortwave infrared (SWIR, 1.60 μm) band, which are effective in cloud detection. The cloud and aerosol unbiased decision intellectual algorithm (CLAUDIA) (Ishida and Nakajima 2009) used with CAI is employed by GOSAT for cloud discrimination (Ishida, Nakajima, and Kikuchi 2011). And cloud discrimination processing time using CLAUDIA-CAI is very short (Oishi et al. 2015a). (3) FTS-2 measures high-resolution spectra of the 2.0 μm and the molecular oxygen (O 2 ) A-band, near 0.76 μm. 2.0 μmiseffective in detecting thin cirrus (Yoshida et al. 2011). Meanwhile the A-band oxygen cloud screening algorithm (ABO2) was developed (Taylor et al. 2012). Although ABO2 can judge the presence or absence of clouds in IFOV of the FTS-2 without CAI-2, the processing time of cloud discrimination using the O2A-band is longer than using CAI-2. Furthermore, there is a possibility to take advantage of the aerosol optical properties derived using CAI-2 to reduce greenhouse gas retrieval uncertainty (Frankenberg et al. 2012). The derivation of aerosol optical properties needs CAI-2 cloud discrimination. Under this situation, the CAM is used for the intelligent pointing system, the CAI-2 for cloud discrimination except thin cirrus, and the

INTERNATIONAL JOURNAL OF REMOTE SENSING 1117 Table 1. Specifications of GOSAT CAI and GOSAT-2 CAI-2. Specifications of CAI Band Central Wavelength (nm) Spatial Resolution (m) Swath (km) 1 380 2 674 500 1000 3 870 4 1600 1500 750 Specifications of CAI-2 Band Central Wavelength (nm) Spatial Resolution (m) View Angle ( ) Swath (km) 1 343 2 443 460 3 674 +20 (Forward) 4 869 5 1630 920 920 6 380 7 550 460 8 674 20 (Backward) 9 869 10 1630 920 2.0 μm of the FTS-2 for thin cirrus. CLAUDIA-CAI only uses the radiances of three (centre wavelengths of 0.67, 0.87 and 1.60 μm) of the four bands (Table 1). The 1.60 μmbandisused to discriminate clouds in bright desert areas (Ishida, Nakajima, and Kikuchi 2011) (Figure 2). Figure 2. Cloud discrimination results for desert with and without the 1.60 μm band. (a) Input GOSAT CAI L1B image (GOSAT path 22 frame 26 in Algeria on 3 October 2012). R: CAI band 2, G: CAI band 3, B: CAI band 1. (b) Cloud discrimination by CLAUDIA-CAI using the 1.60 μm band. White pixels were assigned as cloud and black pixels were assigned as clear. (c) Cloud discrimination result not using the 1.60 μm band. The bright surface is misidentified as clouds.

1118 Y. OISHI ET AL. Although cloud discrimination will be performed in each direction, the use of the forward and backward cloud discrimination in the processing for the calculation of the greenhouse gases concentration was discussed (Oishi et al. 2015b). Summarizing what is described in the article, it is necessary to make an addition of margins to the IFOV of the FTS-2 in a cloud discrimination result image depending on cloud moving by wind for the time difference between CAI-2 observation time and FTS-2 observation time. Thus, the difference between forward and backward cloud discrimination by CLAUDIA must be considered. Meanwhile, the multi-angle imaging spectroradiometer (MISR) aboard Terra provides radiometrically and geometrically calibrated images for spectral bands at nine widely spaced angles (nadir, ±26.1, ±45.6, ±60.0 and ±70.5 forward and backward of the local vertical, Figure 1(b)). The centre wavelengths of each of these bands are 0.45, 0.56, 0.67 and 0.87 μm. Therefore, it is possible to examine the difference in forward and backward CAI-2 cloud discrimination using the MISR 0.67 and 0.87 μm bands (CLAUDIA-MISR) at ±26.1 excluding bright desert areas. 2. Methods 2.1. CLAUDIA-MISR We applied CLAUDIA-CAI (Ishida, Nakajima, and Kikuchi 2011) to MISR. CLAUDIA-MISR primarily consists of two processes (Figure 3). (1) Calculation of minimum reflectance (R min ): First, calculate minimum and second-minimum top-of-atmosphere reflectance of each pixel using MISR data for 10 recurrent cycles. Next, correct for cloud shadows Figure 3. Flow chart for CLAUDIA-MISR.

INTERNATIONAL JOURNAL OF REMOTE SENSING 1119 (Fukuda et al. 2013) by using the minimum and second-minimum reflectance. Although R min for CAI is calculated with data from the past 30 days because the GOSAT recurrent cycle is 3 days, R min for MISR is calculated with data from the past 160 days because the Terra recurrent cycle is 16 days. (2) Calculation of integrated CCL: First, determine the clear-sky confidence level (CCL) in each discrimination test, which varies across sea areas, land areas, and polar regions. Next, integrate these CCL to maintain the neutrality of the integrated CCL. An integrated CCL of 0 means that the pixel is cloudy and 1 means that the pixel is cloud-free. Pixels with degrees of cloudiness between cloudy and cloud-free are expressed by numerical values from 0 to 1 (Ishida and Nakajima 2009). An integrated CCL threshold to decide whether the pixel is cloud-free or cloudy is determined after GOSAT FTS L2 processing (Yoshida et al. 2010). Assume the pixel is cloud-free when its integrated CCL is greater than 0.33. Otherwise, the pixel is cloudy. 2.2. Satellite data The orbits of the Terra platform repeat every 233 revolutions (233 paths) around Earth. Each path is divided into 180 blocks (ASDC 2008). We used MISR data on paths 1, 11, 21 221 and 231 (Figure 4) in 2013 2014 to perform cloud discrimination in 2014. The MISR products used were MISR level 1B2 MI1B2E ellipsoid-projected product for solar zenith angle, radiances, solar irradiances and Earth Sun distance; MISR level 1B2 MIB2GEOP geometric parameters product for glitter angles; and MISR ancillary MIANCAGP geographic product for latitude and land/sea flag. In addition, the National Institute for Environmental Studies (NIES), Japan, Global Land Cover Map (GLCM) at a latitude longitude resolution of 30 was used to classify the land cover of Earth into seven categories (Iwao et al. 2006) (Figure 5(a)) and CAI level 3 global reflectance distribution product, which is generated by using CAI L1B data from the past 30 days, to confirm seasonal variation of land cover, such as ice/snow in MISR images (Figure 5(b)) are also used. Figure 4. MISR data paths used and their locations.

1120 Y. OISHI ET AL. Figure 5. (a) NIES GLCM. (b) CAI L3 global reflectance distribution in January and August 2014.

INTERNATIONAL JOURNAL OF REMOTE SENSING 1121 2.3. Comparative approach It is impossible to compare the cloud discrimination results for the forward and backward MISR images pixel-by-pixel because the clouds move during the time that the data is collected (approximately 90 s). Therefore, we compared the clear-sky probabilities for every block. The clear-sky probability is defined as the ratio of the number of cloud-free pixels to all pixels in a block. The analysis procedure for the clear-sky probabilities is as follows. (1) Take the absolute value of the difference between both clear-sky probabilities by separating sea areas, land areas, and polar regions in a block, referred to hereafter as previous aggregate results. However, use only if the separated area is larger than half a block area because large changes in clear-sky probability calculated in a small area are caused by moving clouds. (2) Aggregate the absolute values of the difference between the clear-sky probabilities in 2014 for each path. 3. Results Figure 6 shows examples of input MI1B2E products and their cloud discrimination results using CLAUDIA-MISR. The input MISR MI1B2E images indicate that the forward and backward images differ in the latitude of sun-glint regions, the ranges of effective pixels fluctuate with season due to the change in solar elevation and the locations of ice/snow regions fluctuate with season. The cloud discrimination results show that the ice/snow and sun-glint, which arise from the specular reflection of light from water surfaces, tend to be misidentified as clouds. Figure 7 shows the comparative results obtained from the average and standard deviation of the absolute values of the difference between forward and backward clearsky probabilities for each path. There were slight differences between the forward and backward clear-sky probabilities excluding polar regions, where cloud discrimination is difficult. The differences in probabilities were less than 8% in sea areas and less than 4% in land areas. However, the standard deviations were relatively large in sea areas and on path 161 over land areas. Thus, we investigated the cause by using previous aggregate results on path 161 (Figure 8). Figures 5(a) and 8 show that the forward and backward clear-sky probabilities were similar on woodland, grassland or cropland (differences of less than 2%), whereas the differences in probabilities were larger on bare soil (3 10%). The reason for these observations is that CLAUDIA-MISR could not cope with bright desert areas because MISR does not have a 1.60 μm band. However, the difference between both clear-sky probabilities was large in sea areas because cloud discrimination in sun-glint regions is difficult (Figure 9). Cloud discrimination accuracy could be good in one direction, but poor in the other because the location of the sun-glint regions is different (Figure 10). 4. Discussion In land areas and polar regions, the forward and backward cloud discrimination results were similar for cropland, grassland, and woodland areas. Therefore, we can use cloud discrimination results from either band in these areas. In contrast, the forward and

1122 Y. OISHI ET AL. Figure 6. Examples of cloud discrimination results using CLAUDIA-MISR. Left-hand images are input MISR MI1B2E products (R: near-ir, G: green, B: blue) and right-hand images are their cloud discrimination results (white: cloudy, black: cloud-free, grey: exception) on path 108. Yellow ovals indicate the sun-glint regions and blue rectangles indicate ice/snow regions. (a) Forward looking on 6 January 2014. (b) Backward looking on 6 January 2014. (c) Forward looking on 2 August 2014. (d) Backward looking on 2 August 2014. backward cloud discrimination results were different in areas in which the accuracy of cloud discrimination was lower. In particular, cloud discrimination could be improved for desert areas because CAI-2 has a 1.60 μm band; however, there is currently no prospect of a solution for ice/snow areas. In sea areas, the cloud discrimination results that include no sun-glint regions should be used because clear sun-glint regions were sometimes determined as cloudy (overestimate) and thin clouds over sun-glint regions were often determined as clear

INTERNATIONAL JOURNAL OF REMOTE SENSING 1123 Figure 7. Average of the absolute values of the difference between forward and backward clear-sky probabilities in 2014 for each path. Orange, blue, and green symbols represent comparative results for polar regions, sea areas and land areas, respectively. The error bars represent the standard deviation of the absolute values. Figure 8. Average of the absolute values of the difference between forward and backward clear-sky probabilities in 2014 for each block on path 161. The error bars represent the standard deviation of the absolute values. Blue areas indicate polar regions. Green arrow ranges include woodland, grassland or cropland in Figure 5(a). Yellow arrow ranges include bare soil in Figure 5(a).

1124 Y. OISHI ET AL. Figure 9. Cloud discrimination results in sun-glint regions and no-sun-glint regions (MISR path 111 block 91 on 17 April 2014). Left-hand images are input images and right-hand images are their cloud discrimination results. White pixels were assigned as cloud and black pixels were assigned as clear. The red circles indicate thin clouds that were discriminated in (b), but not in (d) because it is difficult to discriminate thin clouds in sun-glint regions. (a) Forward-looking image in no-sun-glint regions. (b) Cloud discrimination results for (a). (c) Backward-looking image in sun-glint regions. (d) Cloud discrimination results for (c). Figure 10. Changes in sun-glint regions in forward- and backward-looking images depending on the date. The sun-glint regions are defined in this figure as having a glitter angle (Θ) of 10 or less. (overlook). CAI s sun-glint may be defined as the regions which the glitter angle (Θ) is 35 or less in GOSAT TANSO-CAI L2 cloud flag processing (Ishida, Nakajima, and Kikuchi 2011). Except for CAI s sun-glint regions, the cloud discrimination results for which the glitter angle is small should be used because brighter images are better for cloud discrimination. Θ is defined by the formula Θ ¼ cos 1 fcos z 1 cos z 2 sin z 1 sin z 2 cosða 1 a 2 Þg; (1) where z 1 is the solar zenith angle, z 2 is the sensor zenith angle, a 1 is the solar azimuth angle and a 2 is the sensor azimuth angle. The threshold of the glitter angle, irrespective of whether forward or backward cloud discrimination results are used, is a problem that must be solved in the future.

INTERNATIONAL JOURNAL OF REMOTE SENSING 1125 GOSAT-2 CAI-2 has forward- and backward-looking bands, whereas GOSAT CAI has bands at only the nadir direction; therefore, the best cloud discrimination results can be selected from the forward- and backward-looking bands with CAI-2. Our results show that selecting the bands improves the cloud discrimination quality considerably, particularly in sea areas. This increase in quality will result in the following benefits. (1) A decrease in overlooked clouds, which were determined as clear despite cloudy, in IFOV of the FTS-2, increasing the accuracy of estimates of atmospheric greenhouse gas concentrations. (2) A decrease in overestimating clouds, increasing the amount of estimated greenhouse gas concentration data that is not rejected as cloud-contaminated FTS-2 data. This will also decrease the standard error in the estimation accuracy of global greenhouse gas concentrations and global greenhouse gas flux by increasing the size of samples. Acknowledgements The authors would like to thank the GOSAT Project, GOSAT-2 Project and Dr H. Ishida for helpful comments. The MISR data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. Disclosure statement No potential conflict of interest was reported by the authors. Funding This research is supported by the GOSAT-2 Project at NIES, Japan (2016, 2017). References Frankenberg, C., O. Hasekamp, C. O Dell, S. Sanghavi, A. Butz, and J. Worden. 2012. Aerosol Information Content Analysis of Multi-Angle High Spectral Resolution Measurements and Its Benefit for High Accuracy Greenhouse Gases Retrievals. Atmospheric Measurement Techniques 5: 1809 1821. Fukuda, S., T. Nakajima, H. Takenaka, A. Higurashi, N. Kikuchi, T. Y. Nakajima, and H. Ishida. 2013. New Approaches to Removing Cloud Shadows and Evaluating the 380 Nm Surface Reflectance for Improved Aerosol Optical Thickness Retrievals from the Gosat/Tanso-Cloud and Aerosol Imager. Journal of Geophysical Research 118 (24): 13521 13531. Ishida, H., and T. Y. Nakajima. 2009. Development of an Unbiased Cloud Detection Algorithm for a Spaceborne Multispectral Imager. Journal of Geophysical Research 114 (D7): D07206. doi:10.1029/ 2008JD010710. Ishida, H., T. Y. Nakajima, and N. Kikuchi. 2011. Algorithm theoretical basis document for GOSAT TANSO-CAI L2 cloud flag. Accessed 10 October 2015. https://data.gosat.nies.go.jp/ GosatWebDds/productorder/distribution/user/ATBD_CAIL2CLDFLAG_V1.0_en.pdf Iwao, K., K. Nishida, T. Kinoshita, and Y. Yamagata. 2006. Validating Land Cover Maps with Degree Confluence Project Information. Geophysical Research Letters 33 (23): L23404. doi:10.1029/ 2006GL027768.

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