The Global Change Observation Mission (GCOM) aims to achieve global and long-term monitoring of the Earth by using two polar orbiting satellite observing systems with three consecutive generations. The GCOM-W1 (or “SHIZUKU”) satellite is the first satellite of GCOM-W (water) series, and was launched on May 18, 2012 (JST). The Advanced Microwave Scanning Radiometer-2 (AMSR2) on board GCOM-W1 continues its observation successfully more than one year. Basic characteristics of AMSR2 are similar to those of AMSR-E on board the Aqua satellite to continue the AMSR-E observations. During the initial calibration and validation period, AMSR2 brightness temperature values are being evaluated and characterized through methodologies such as the inter-calibration among similar microwave radiometers including the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with the help of radiative transfer computations and global analysis field generated by meteorological agencies. Validation activities have been done to compare AMSR2 eight geophysical parameters with in-situ observations and/or other satellite instruments to evaluate accuracy of AMSR2 products. These results show that all products satisfied required release accuracy. JAXA has released AMSR2 brightness temperature (TB) and geophysical parameter (GEO) products to public though the GCOM-W1 Data Providing Service System since January 2013 for TB and May 2013 for GEO. AMSR2 products will be operationally utilized in various applications, such as numerical weather prediction, information of fishery fields, and so on. In future, AMSR2 data are expected to contribute in wider research and operational areas by archiving long term dataset and combining with data of AMSR-E and other instruments.
Passive microwave sensor AMSR2 was successfully launched by JAXA in May 2012 onboard GCOM-W1 satellite. The antenna diameter of AMSR2 is 2.0 m which provide highest spatial resolution as a passive microwave sensor in space. This paper describes about the outline of sea ice monitoring with AMSR2 and introduces some of the initial result acquired from AMSR2 observation. The sea ice concentration images derived from AMSR2 data allow us to see more detailed sea ice distributions compared with those of SSM/I. The sea ice concentration estimated from AMSR2 data were evaluated using MODIS data observed from Aqua satellite within few minutes after AMSR2 observation from GCOM-W1. The procedures of the evaluation are as follows. Firstly, MODIS ch2 data were binarized to discriminate sea ice from open water and sea ice concentration of each pixel size of AMSR2 were calculated. Then the AMSR2 sea ice concentration of each pixel was compared with the sea ice concentration calculated from MODIS data. The result suggested the possibility of estimating sea ice concentration from AMSR2 data with less than 10 % error under good weather condition. On 16 September 2012, the minimum sea ice extent in Northern Hemisphere was recorded by AMSR2 in the history of passive microwave sensor observation from space. The annual sea ice extent graph produced from the historical passive microwave sensor data strongly suggested the trend of sea ice extent reduction in the Northern Hemisphere. The importance of sea ice monitoring with AMSR2 is increasing.
Japan Meteorological Agency (JMA) plans to use the observation data from Advanced Microwave Scanning Radiometer 2 (AMSR2) on board Global Change Observation Mission 1st - Water “Shizuku” (GCOM-W1) for its Numerical Weather Prediction (NWP) models from summer 2013. Data quality of AMSR2 was investigated and the data assimilation tests were conducted in advance. Although the result showed that the radiances have positive bias against the first guess produced from JMA’s NWP models, it can be corrected by a bias correction scheme in JMA’s data assimilation system. The data assimilation test result showed that AMSR2 contributes to fill the gap in the current microwave imager data constellation and its assimilation can contribute for the early prediction of heavy rain.
Air pollution is a social and transboundary environmental issue, and pollution-related problems are especially acute in China. With the rapid growth of the Chinese economy, massive sulfur oxide (SOx) and nitrogen oxide (NOx) air pollution is caused by fossil fuel consumption in the new “automobile society.” These airborne pollutants are transported by strong seasonal winds from source regions in China to neighboring countries, and they cause acid rain and photochemical smog. To deal with this problem, extensive observation by atmospheric observatories is essential. Several computer simulations using atmospheric models have also been developed. Remote sensing technology is useful for the near real-time monitoring of air pollutants, and the interpretation of moderate-resolution imaging spectroradiometer (MODIS) true-color images is a common technique for conducting the real-time monitoring of air pollutants. However, there are some difficulties in the MODIS decision process, particularly in discriminating between air pollutants and clouds. To overcome this problem, we propose a new MODIS false-color composite image method that is created by an aerosol enhancement (AE) reflectance index and a water index (WI). The false-color image distinctly depicts, in yellow, the air pollutants that are greater than 0.4 in aerosol optical thickness. In a case study of transboundary air pollution in February 2011, the movement of air pollutants was clearly understood when we conducted a time-series analysis of MODIS false-color images, a chemical analysis of the rimes of tree samples collected from Japan’s Mount Zao, and a data analysis of some atmospheric observatories in Yamagata Prefecture, Japan. The results confirmed that the air pollutants were transported by a strong wind from the North China Plain to Mt. Zao, and our findings confirmed that the proposed method employing false-color composite images is useful for monitoring and predicting transboundary air pollution.
Tree height is an important parameter in forestry for evaluating stem volume, selling trees, and scheduling tree felling. Current wood prices are low, and some forests are not managed, even in the traditional “Yoshino Forestry” area in Nara Prefecture. Recently, a new approach has been introduced to reduce cutting costs. To achieve cost savings, it is important to determine the distribution of tree heights. The Advanced Land Observing Satellite (ALOS) with the onboard Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) sensor was launched on January 24, 2006. PRISM observes land surfaces from three different directions, at a spatial resolution of 2.5 m and with a spectral range from 0.55 to 0.77μm. Digital surface models (DSMs) can be produced from PRISM data. For mountainous areas, the difference in height between the DSM and the ground height can be considered to be the crown height. This study estimated height differences using a DSM and a digital terrain model (DTM) for the Nara Prefecture area of Japan. Abnormal differences in height were recorded, including values lower than -10 m and higher than 70 m. The areas with negative height values corresponded to a lake formed by a dam, a river, and mountain slopes. Areas where heights were considered too high were affected by cloud. In Yoshino District, crown height was validated using two tree-height datasets, one consisting of tree heights measured in a field survey and the other composed of light-detection and ranging (LiDAR) data. Both datasets were compared with the forest stand database for Nara Prefecture. The crown height estimates were separated into two categories : areas with successful and failed estimates. Areas with successful estimates were often on sout-facing slopes. We hypothesize that brightness or texture differences between nadir and backward images resulted in mismatching when the DSM was calculated. For areas with successful estimates, there were differences of several meters between estimated crown heights and average tree heights in the smallest management areas. These results indicate that the estimated crown heights were not sufficiently accurate for estimating average tree heights in the smallest forest management areas. However, the crown height estimates can be used to extract areas of large-diameter trees within the cutting cycle and to note inconsistencies in tree heights compared with the forest management database. The pre-processing method needs to be improved to allow its use in areas where estimates failed.
In October 2006, a cyclone created very strong winds in the northern part of Hokkaido, Japan, causing large-scale windfall forest damage in the town of Shimokawa. We were asked to estimate the damage using remote-sensing from the perspective of organizations in Shimokawa. Therefore, we applied remote-sensing techniques using ALOS satellite images to estimate the distribution and area of the windfall damage and provided the results to relevant organizations. Subsequently, we investigated how the various organizations used the analysis results. The analysis contributed to reducing the effort exerted in field surveys and the rapid provision of results was more important than accuracy.
Several large wildfires occurred in Quebec, Canada in June, 2013. Especially on the eastern side of James Bay, the fires continued to burn for a month over a wide forested area. The GOSAT (Greenhouse gases Observing SATellite) payload includes a multispectral imager (TANSO-CAI : Thermal And Near infrared Sensor for carbon Observation - Cloud and Aerosol Imager) which is suitable for observing the smoke issuing from wildfires. We report the results of wildfire monitoring in Quebec by the GOSAT TANSO-CAI. Firstly, we show that the wildfire smoke started in Quebec but subsequently crossed the Atlantic Ocean and reached the Norwegian coast. Then, we estimate the spatial and temporal pattern of burned forest area on the eastern side of James Bay using NDVI (Normalize Difference Vegetation Index) calculated from CAI data and near infrared data from CAI. The burned area showed a gradually expanding pattern and eventually became as large as about 47,000 hectares.