Haruma Ishida

Comparison of Cloud-Screening Methods Applied to GOSAT Near-Infrared Spectra

Several existing and proposed satellite remote sensing instruments are designed to derive concentrations of trace gases, such as carbon dioxide (CO2) and methane (CH4), from measured spectra of reflected sunlight in absorption bands of the gases. Generally, these analyses require that the scenes be free of cloud and aerosol, necessitating robust screening algorithms. In this work, two cloud-screening algorithms are compared. One applies threshold tests, similar to those used by the MODerate resolution Imaging Spectrometer (MODIS), to visible and infrared reflectances measured by the Cloud and Aerosol Imager aboard the Greenhouse gases Observing SATellite (GOSAT). The second is a fast retrieval algorithm that operates on high-resolution spectra in the oxygen A-band measured by the Fourier Transform Spectrometer on GOSAT. Near-simultaneous cloud observations from the MODIS Aqua satellite are used for comparison. Results are expressed in terms of agreement and disagreement in the identification of clear and cloudy scenes for land and non-sun glint viewing over water. The accuracy, defined to be the fraction of scenes that are classified the same, is approximately 80% for both algorithms over land when comparing with MODIS. The accuracy rises to approximately 90% over ocean. Persistent difficulties with identifying cirrus clouds are shown to yield a large fraction of the disagreement with MODIS.

Investigation of GOSAT TANSO-CAI Cloud Screening Ability through an Intersatellite Comparison

AbstractIn this work, the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near-infrared Sensor for Carbon Observation–Cloud and Aerosol Imager (TANSO-CAI) cloud screening results, which are necessary for the retrieval of carbon dioxide (CO2) and methane (CH4) gas amounts from GOSAT TANSO–Fourier Transform Spectrometer (FTS) observations, are compared with results from Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) in four seasons. A large number of pixels, acquired from both satellites with nearly coincident locations and times, are extracted for statistical comparisons. The same cloud screening algorithm was applied to both satellite datasets to focus on the performance of the individual satellite sensors, without concern for differences in algorithms. The comparisons suggest that CAI is capable of discriminating between clear and cloudy areas over water without sun glint and also may be capable of identifying thin cirrus clouds, which are generally difficult to detect without therma...

Cloud detection performance of spaceborne visible-to-infrared multispectral imagers

We investigate the cloud detection efficiency of existing and future spaceborne visible-to-infrared imagers, focusing on several threshold tests for cloud detection over different types of ground surfaces, namely, the ocean, desert, vegetation, semibare land, and cryosphere. In this investigation, we used the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA), which was developed for unbiased cloud detection. It was revealed that imagers with fewer bands than the Moderate Resolution Imaging Spectroradiometer tend to have cloudy shifts. An imager without any infrared bands could yield cloudy shifts up to 17% over the ocean. To avoid false recognition of Sun glint as clouds, the 0.905 and 0.935  μm bands are needed in addition to the infrared bands. In reflectance ratio tests, the 0.87 and 1.6  μm bands can effectively distinguish clouds from desert. In the case of desert, thermal-infrared bands are ineffective when the desert surface temperature is low during winter. The 3.9 and 11  μm bands are critical for distinguishing between clear and cloudy pixels over snow-/ice-covered areas. The results and discussions of this research can guide CLAUDIA users in the optimization of thresholds. Here, we propose a virtual imager called the cloud detection imager, which has seven or eight bands for efficient cloud detection.

Investigation of Low-Cloud Characteristics Using Mesoscale Numerical Model Data for Improvement of Fog-Detection Performance by Satellite Remote Sensing

AbstractThe comprehensive relationship between meteorological conditions and whether low water cloud touches the surface, particularly at sea, is examined with the goal of improving low-cloud detection by satellite. Gridpoint-value data provided by an operational mesoscale model with integration of Multifunction Transport Satellite-2 data can provide sufficient data for statistical analyses to find general parameters that can discern whether low clouds touch the surface, compensating for uncertainty due to the scarcity of observation sites at sea and the infrequent incidence of fog. The analyses reveal that surface-touching low clouds tend to have lower cloud-top heights than those not touching the surface, although the frequency distribution of cloud-top height differs by season. The bottom of the Γ > Γm layer (where Γ and Γm are the vertical gradient and the moist-adiabatic lapse rate of the potential temperature, respectively) with surface-touching low-cloud layers tends to be very low or almost attach...

Study on the characteristics of the Indonesian seas using satellite remote-sensing data for 1998–2007

This study analyses the characteristics of the Indonesian seas using satellite remote-sensing data for the 10-year period from 1998 to 2007. Statistical properties and monthly average data or climatological data of sea surface temperature (SST), wind speed (WS), wind direction (WD), chlorophyll (CH) and sea surface height anomaly (SSHA) for the Indonesian seas are investigated. The results indicate negative dependence between SST and WS. The correlation between SST and WS has a 1-month phase difference. CH and SSHA are considered local data and reveal no apparent characteristics for the Indonesian seas as a whole. Seasonal changes of the indices, coupled with a north–south change, are observed. This analysis confirms several characteristics of the Indonesian seas.

The Impact of Different Support Vectors on GOSAT-2 CAI-2 L2 Cloud Discrimination

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.

On the cloud observations in JAXA's next coming satellite missions

The use of JAXA’s next generation satellites, the EarthCARE and the GCOM-C, for observing overall cloud systems on the Earth is discussed. The satellites will be launched in the middle of 2010-era and contribute for observing aerosols and clouds in terms of climate change, environment, weather forecasting, and cloud revolution process study. This paper describes the role of such satellites and how to use the observing data showing concepts and some sample viewgraphs. Synergistic use of sensors is a key of the study. Visible to infrared bands are used for cloudy and clear discriminating from passively obtained satellite images. Cloud properties such as the cloud optical thickness, the effective particle radii, and the cloud top temperature will be retrieved from visible to infrared wavelengths of imagers. Additionally, we are going to combine cloud properties obtained from passive imagers and radar reflectivities obtained from an active radar in order to improve our understanding of cloud evolution process. This is one of the new techniques of satellite data analysis in terms of cloud sciences in the next decade. Since the climate change and cloud process study have mutual beneficial relationship, a multispectral wide-swath imagers like the GCOM-C SGLI and a comprehensive observation package of cloud and aerosol like the EarthCARE are both necessary.

Cloud sciences using satellite remote sensing, cloud growth model, and radiative transfer.

In recent years, it has been revealed that the cloud microphysical properties such as cloud particle radii obtained from satellite remote sensing were of apparent values. A combined use of passive and active sensor has gradually revealed about what we observed using passive imager thorough the vertical information of clouds obtained from active sensors. For understanding the process of cloud growth in nature, model that simulates cloud droplet growth is also needed. Observation results obtained from the satellite remote sensing are used for validating model such as cloud resolving model and spectral-bin microphysics cloud model. Vice-versa, models are used for understanding the process that are hidden in satellite-remote sensing results. We are aiming consistent understanding of clouds with observation and modeling. In this paper, we will introduce a preliminary result of multi-sensor view of warm water clouds and we will review our research strategy of cloud sciences, using satellite remote sensing, the cloud growth model, and the radiative transfer.

A new Landsat 8 cloud discrimination algorithm using thresholding tests

ABSTRACT In this work, we propose a Cloud Discrimination Algorithm for Landsat 8 (CDAL8) to improve a high-frequency automatic land change detection system developed at the National Institute of Advanced Industrial Science and Technology (AIST), Japan for large-scale satellite image analysis. Although the land change detection system can process several kinds of satellite remote sensing data, improvements are needed to enable practical applications using Landsat 8 data. Cloud discrimination is a necessary pre-processing step for land cover change detection. Currently, most of the prediction errors on land change detection are caused by the false cloud discrimination results as a pre-processing step. Therefore, we introduce an improved cloud discrimination algorithm (CDAL8) in this study to improve the overall performance of our land change detection system. The algorithm was developed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm and Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). CDAL8 is distinct in that it switches judgment tests and their thresholds using a threshold brightness temperature and uses separate features in cloud judgment and clear-sky judgment. To evaluate the accuracy of the proposed algorithm, we compared it with the Automated Cloud-Cover Assessment algorithm (ACCA) and Function of Mask (Fmask) version 3.3 using US Geological Survey Landsat 8 cloud cover assessment validation data, which contain 96 cloud masks. Our proposed cloud discrimination algorithm (CDAL8) have promising results with an accuracy of 88.1%, which was greater than that of the ACCA (82.5%) and Fmask (84.6%). Furthermore, we also confirmed that the average accuracy of CDAL8 was approximately 91.2% when low solar elevation scenes were removed.

Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 10 and CH4 concentrations. GOSAT is equipped with two sensors: the thermal and near-infrared sensor for carbon observation (TANSO)-Fourier transform spectrometer (FTS) and TANSO-cloud and aerosol imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gases concentrations. This is 15 a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds 20 as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1+3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow and ice covered surfaces, where there is not enough spectral contrast to distinguish cloud. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared between CLAUDIA1-CAI and CLAUDIA3-CAI for various 25 land cover types, and evaluated the accuracy of CLAUDIA3-CAI by comparing the both of CLAUDIA1-CAI and CLAUDIA3-CAI against visual inspection of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1-CAI and CLAUDIA3-CAI for various land cover types indicated that CLAUDIA3-CAI had tendency to identify bright surface and optically thin clouds, however, misjudge the edges of clouds as compared with CLAUDIA1-CAI. The accuracy of CLAUDIA3-CAI was approximately 89.5 % in tropical rainforests, which is greater than that of 30 CLAUDIA1-CAI (85.9 %) for the test cases presented here.