The Global Change Observation Mission (GCOM) consists of two satellite missions, GCOM-W (Water) and GCOM-C (Climate), and aims to provide comprehensive information of the Essential Climate Variables (ECVs) of atmosphere, ocean, land, cryosphere and ecosystem. The GCOM-W "SHIZUKU" satellite was launched in May 2012, which carries the Advanced Microwave Scanning Radiometer 2 (AMSR2). It achieved its 5-year designed mission life in May 2017 and is currently in extended mission phase. The GCOM-W satellite is joining in the A-train orbit to provide synergies with the other A-train satellites, such as Aqua and Cloudsat. The GCOM-C "SHIKISAI" satellite was launched in December 2017, which carries the Second generation Global Imager (SGLI). SGLI data has been released to public since December 20, 2018. Both AMSR2 and SGLI data are used in operational numerical weather prediction and environmental monitoring to reduce meteorological disasters. Because of its observation capability and observation local time, both GCOM satellites have synergies with the Joint Polar Satellite System (JPSS) program and can contribute to objectives of JPSS.

GCOM-C/SGLI is a multi-wavelength optical radiometer launched on December 23, 2017. The data provision has started from December 20, 2018. In this research, we briefly introduce standard Level 2 products and their validation results based on in situ data. Mean absolute percentage differences are 16.3 - 69.6% for NLW between 380 - 670 nm, 39 and 64.5% for AOT at 670 nm and 865 nm, 27.9% for CHL and 44.2% for aCDOM. Although the number of in situ validation data are still scare for a few ocean color products, the accuracy of GCOM-C/SGLI data will be improved by our future efforts of calibration/validation activities.

If learners cannot form an intention to take an appropriate action with reference to the knowledge of information ethics, it cannot be said that the learners have working knowledge of ethical principles. In the present study, the two experiments are conducted with high school students to examine if the knowledge-to-action gap can be confirmed by means of paperand-pencil tests consisting of knowledge and intention tasks of information ethics. The knowledge task is to inquire about appropriate behavior regarding the correct knowledge, while the intention task is an inquiry whether the respondents consciously select the action or not. The experiments 1 and 2 indicated that participants take unethical behaviors even though they know appropriate behavior. In addition, the experiment 2 demonstrated that the participants estimated the number of classmates with unethical behavior is higher than the actual number. These results indicate the importance of education and teaching methods, which need to be carefully designed to reduce the gap between knowledge and action to comply with information ethics.

Hazard maps provide knowledge about evacuation behavior, in addition to graphical indications of areas influenced by a disaster. However, without prerequisite knowledge or map reading literacy, it is not easy for citizens to fully understand hazard maps. Learning support systems for disaster education must thus to be effective in lightening the learning load and enhancing motivation for learning. If the content is not held in memory, learners would need to repeat the learning session, which would lessen their motivation for learning. Moreover, natural disasters do not happen often, and cannot always be predicted. Therefore, it is not easy to enhance citizens’ motivation for disaster prevention learning. This can result in a vicious circle between degraded motivation and insufficient prerequisite knowledge. In this paper, we propose a learning support framework that helps gain learners’ attention by gaming at two different processing levels and facilitates retention of disaster prevention knowledge on the basis of priming effect on implicit memory. The key idea behind this framework is to utilize two components: implicit learning by means of implicit memory and memory retention based on levels-of-processing effect, so that the vicious circle between degraded motivation and insufficient prerequisite knowledge can be disrupted.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water (GCOM-W or “SHIZUKU”) was launched on May 18, 2012 (JST), and continuous its observation from the ATrain orbit more than one year successfully. Validation results of AMSR2 standard products satisfied required release accuracy, and JAXA has released AMSR2 geophysical parameters to public though the GCOM-W1 Data Providing Service System (https://gcom-w1.jaxa.jp) since May 17, 2013. Definition of AMSR2 research products are currently discussed under the AMSR2 Science Team and some candidate algorithms are being processed and tested for operation utilization in some user agencies.

This paper describes a new land use and land cover (LULC) classification method for classifying multi-temporal high-resolution satellite data in large areas. The classification method uses combined value of both reflectance of a pixel and its observation date as an input data, and calculates its probability distribution among all LULC classes via Bayesian inference based on a generative model estimated by kernel density estimation. This method can be easily applied to multi-temporal data to exploit phenological change information of vegetation, even if available multi-temporal data have a seasonal bias. In this paper, we conducted the classification over the entire land mass of Japan, using the multi-temporal data observed by the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) aboard the ALOS, and we evaluated its accuracy in comparison to conventional methods.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water 1st (GCOM-W1 or “SHIZUKU”) was launched on May 18, 2012 (JST), and continuous its observation from the A-Train orbit more than one year successfully. Validation results of eight geophysical parameters of AMSR2 standard products show that all products satisfied required release accuracy, and JAXA has released AMSR2 geophysical parameters to public though the GCOM-W1 Data Providing Service System (https://gcom-w1.jaxa.jp) since May 17, 2013.

The recognition of concepts that we human beings are able to locate within satellite imagery requires analysis based on the particular context using knowledge. In this paper, we present a supervised pixel-based classification approach toward utilization of the classification results in knowledgebased satellite data interpretation system. The proposed approach is based upon a generative model, which is able to output the classification results with their probabilities and subsequently utilize them in detailed analysis. The experiment of classification was performed to demonstrate characteristics of the approach.

This paper proposes a new framework for automatic interpretation, especially for change interpretation, in order to implement insightful and versatile interpretation like manual interpretation. The framework considers remote sensing data analysis as a knowledge information processing. For handling large amount of knowledge on computers consistently, the framework employs double-layered knowledge structure; ontology and heuristic. The ontology is a conceptual dictionary about remote sensing domain, and heuristic is experimental knowledge containing uncertainty. Additionally, this paper introduces the method for constructing Bayesian network using knowledge, and inferring target objects and changes using it. An experiment of change detection is conducted to demonstrate our framework.

This paper suggests a new automatic change detection approach using image-object-based contextual inference. The approach first detects changed areas, and then infers what happened there. The inference process uses knowledge based on a change detection process performed by humans. It enables flexible and intuitive description of the change detection process. In this paper, we describe the results of case studies on automatic change detection using the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”).

The Japan Aerospace Exploration Agency (JAXA) is pursuing the Global Change Observation Mission (GCOM) that will inherit the Advanced Earth Observing Satellite-II (ADEOS-II) mission and develop into long-term monitoring. GCOM is not the name of a single satellite, but of a mission that consists of two series of medium-size satellites, GCOM-W (Water) and GCOM-C (Climate), and three generations of each satellite series to continue the observations for 10 to 15 years. The Advanced Microwave Scanning Radiometer-2 (AMSR2) will be the single instrument on the GCOM-W1 satellite, which is the first satellite of the GCOM series. The second satellite will be GCOM-C1, which will carry the Second-generation Global Imager (SGLI). GCOM-W will mainly contribute to the observations related to global water and energy circulation, while GCOM-C will contribute to the measurements related to the carbon cycle and radiation budget. Current target launch years are calendar year 2011 for GCOM-W1 and 2014 for C1.

Conventional input devices such as the mouse and keyboard lack in intuitiveness when it comes to 3D manipulation tasks. In this paper, we explore the use of accelerometer and digital compass equipped mobile phones as 3-DOF controllers in a 3D object rotation task. We put the mobile phone up against the established standards, a mouse and a touch pen and compare their performance in a rotation task. Our preliminary evaluation indicates that for this type of task, with only 5 minutes of practice the mobile phone is significantly faster than both the mouse and the touch pen.

The proper depiction of water vapor distribution in the upper troposphere and the related radiative properties is critical for the accuracy of climate and atmospheric circulation model predictions. In the present study, differences between satellite observations (Terra-MODIS) and atmospheric models' simulations are examined. The models are: the National Center for Environmental Protection/Department Of Energy (NCEP-DOE) reanalysis-2 data and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The models' outputs (atmospheric temperature and humidity profiles) are used in this study to simulate the upper tropospheric brightness temperature (UTBT) and relative humidity (UTRH) at clear sky and low cloud areas. The results obtained show the contribution of atmospheric instability due to heat movements of the lower troposphere clear sky, cloud convection and low cloud radiative properties in the discrepancies between model and observation results. These differences tend to increase with the increase of the proportion of unstable pixels in the clear sky areas. Heat movements from cloud convection and low clouds examined through the convective clouds' distribution and the cloud effective emissivity respectively, show that cloud convection mostly affects the NICAM model results (existence of a positive correlation between the latter model error and the amount of convective clouds) and, discrepancies are stronger in broken clouds than continuous clouds.

This paper addresses the importance of cognitive accessibility and cognitive usability as critical requirements for universal design of information. Information should not be said to be accessed unless its content is cognitively internalized or understood by the user. Accessibility of information, therefore, should be evaluated not only for its perceivability but also for its understandability. We proposed a new cognitive walkthrough (CW) method whose CW questions were formulated based on an extended HCI model that distinguishes between perceiving and understanding. Applied to a Web design evaluation study, the extended CW was shown to be more effective in identifying accessibility and usability problems while remaining as efficient as the currently-practiced CW.

Results of measurements of the bidirectional reflection function of snow for the solar zenith angle close to 54/spl deg/ are compared with a recently developed snow optical model based on the representation of snow grains as fractal particles. The model has a high accuracy out of the principal plane for the observation zenith angles smaller than 60/spl deg/. However, the accuracy is reduced in the principal plane. Specular light reflection by partially oriented snow plates on the snow surface not accounted for by the model can play a role for measurements in the principal plane. The model discussed can be used for the grain size retrieval using both ground and spaceborne measurements of the snow reflectance. This is supported by a high accuracy of the model in a broad spectral range 545-2120 nm as demonstrated in this work.

We conducted vicarious calibration of the Global Imager (GLI) in visible to near-infrared channels over different targets. For calibration over the ocean, we used the normalized water-leaving radiance derived from the Marine Optical Buoy (MOBY) and the aerosol optical properties (aerosol optical depth, size distribution, and refractive index) obtained through the Aerosol Robotic Network (AERONET). For calibration over land, we used the ground-based measurement data at Railroad Valley Playa. The following GLI characteristics are recognized from the calibration results. First, GLI underestimates the radiance in channels 1, 2, 4, and 5. Next, in the near-infrared channels, there is good agreement between the observed and simulated radiance over bright targets. On the other hand, it is suggested that the GLI overestimates the radiance over dark targets (e.g., on the order of 15% at 4.0 W/m/sup 2///spl mu/m/sr in channels 18 and 19). Furthermore, we evaluated these calibration results over different targets taking into account the difference in the target radiance and in the accuracy between the two results. This combined evaluation of vicarious calibration results suggests the possibility that the GLI-observed radiance has offset radiance versus the simulated radiance.

The Global Imager (GLI) aboard the ADEOS-II satellite launched in December 14, 2002 observed sunlight reflection and infrared emission from the Earth's surface globally, and detected various geophysical parameters (e.g., snow and sea-ice cover extent, snow grain size and impurity). They contribute to the investigation of global hydrological cycle and radiation budget that are primal factors of the global climate change. Preliminary analysis results of the GLI snow products with 6-month GLI data and their validation results are presented.

Recently, the Advanced Earth Observing Satellite 2 (ADEOS-2) was launched (December 14, 2002) successfully, and the Global Imager (GLI) onboard the ADEOS-2 satellite became operational in April 2003. In a first calibration checkup, the radiometric performance of GLI was compared relatively to that of other sensors on different satellites with different calibration backgrounds. As a calibration site, a large snowfield near Barrow, Alaska, was used, where space sensors in polar orbits view the same ground target on the same day with small differences in the local crossing times. This is why GLI, the Moderate Resolution Imaging Spectroradiometer (Terra, Aqua), the Sea-viewing Wide Field-of-view Sensor, the Advanced Very High Resolution Radiometer (N16, N17), the Medium Resolution Imaging Spectrometer, and the Advanced Along Track Scanning Radiometer datasets were selected for the following clear-sky condition days: April 14 and 26, 2003. At the same time, ground-truth experiments (e.g., measurements of ground reflectance, bidirectional reflectance distribution function, aerosol optical thickness) were carried out. Thereinafter, top-of-atmosphere (TOA) radiance/reflectance was forward calculated by means of radiative transfer code for each sensor, each band, and each day. Finally, the vicariously retrieved TOA signal was compared to TOA sensor Level 1B data. As a result, GLI's performance is encouraging at that time of the mission. GLI and the other seven sensors deliver similar sensor output in the range of about 5% to 7% around the expected vicariously calculated TOA signal.