The satellite observation of clouds has been contributing to meteorology and climate change science for more than 50 years. Remote sensing techniques have been developed and improved to obtain quantitative data regarding cloud properties. Quantitative estimation of cloud physical parameters depends on the observation technique used, and even basic cloud properties such as cloud amount and top height are different according to the technique. Therefore, to use the obtained data it is necessary to understand the principle of observation. Active remote sensing provides vertical profiles of clouds, although the coverage is limited to the nadir of the satellite orbit. On the other hand, passive remote sensing with a radiometer can scan a wide swath. The combination of these two sensors produces valuable information on the cloud, as well as on the use of multi-wavelength measurements. A comparison of satellite remote sensing with results of numerical simulation, and data assimilation of satellite data are powerful methods for advancing the understanding of cloud properties, which then impacts meteorology and climate change science.
Special Issue for Validation of Satellite Products :
Several ocean algorithms have been developed for the Second-Generation Global Imager (SGLI) on the Global Climate Observation Mission - Climate (GCOM-C) satellite (planned launch, 2016). Here we present verification of the ocean algorithms designed to retrieve the inherent optical properties, phytoplankton functional types and primary productivity. The satellite algorithm verification is defined here to evaluate accuracy of target variables using input parameter(s) obtained from in situ measurements rather than from satellite measurements. The verification of inherent optical properties (IOP) algorithms showed RMSE of 0.12, 0.22, and 0.05 for the absorption coefficient of phytoplankton, detrital materials plus colored dissolved organic materials, and the backscattering coefficient of suspended particles, respectively. Verification of the primary production algorithm indicated that it almost satisfied the values measured in situ by a factor of 2. Other algorithms such as phytoplankton functional types (PFTs) and size classes (PSCs) algorithms, which can be derived from the optical properties of phytoplankton rather than from chlorophyll a concentration, showed RMSE of 10.1-11.6 % in a relative abundance of PFTs/PSCs. Towards validation of the ocean algorithms, a radiometer called the Compact-Optical Profiling System (C-OPS), as well as another compact radiometer system specifically designed for turbid waters, were configured for in situ observation. The latter was found to reduce shelf-shading error to within 10 %. Furthermore, Ultra-High Performance Liquid Chromatography systems (UHPLC) have been developed for rapid measurements (7 min) of phytoplankton pigments in a water sample (conventional HPLC takes 30 min). This new system significantly increases spatio-temporal coverage of in situ data required for algorithm validation.
Special Issue for Validation of Satellite Products :
We studied validation methods for the space lidar ATLID onboard the EarthCARE satellite planned for launch in 2016. ATLID will be the first high-spectral-resolution lidar (HSRL) in space able to provide the extinction coefficient, backscattering coefficient and depolarization ratio at 355 nm without the assumption of the lidar ratio. First priority in the validation experiment will be the direct comparison of these parameters with the ground-based 355-nm Raman and HSR lidars. A unique data product from JAXA is the aerosol component retrieval using ATLID or the synergy of ATLID and Multi-Spectral Imager (MSI). Ground-based multi-wavelength Raman-scattering lidars and multi-wavelength HSRLs with the extended aerosol component retrieval method and the method used synergistically with sun-photometers and sky-radiometers are useful for validating aerosol component products. An important focus of the validation is confirming the validity of the optical models for aerosol components used in the retrievals. Comparing of the retrievals using the different parameter sets obtained with ground-based multi-parameter lidars and radiometers will be useful for testing the validity of the optical models. The aerosol component analysis approach will be useful for establishing continuity of data from the different space lidars. In addition, the reliable aerosol optical models will be also useful in calculating optical properties with chemical transport models, and in data assimilation. Development of multi-wavelength Raman-scattering lidars in the Asian dust and aerosol lidar observation network (AD-Net) are reported, along with development of HSRLs in the lidar network in South America in a JICA-JST SATREPS project.
Polar stratospheric clouds (PSCs) generated during the Antarctic winter were detected using the CO2 slicing method with thermal infrared band (band 4) data observed by the main sensor of the Greenhouse gases Observation SATellite (GOSAT), Thermal infrared And Near infrared Sensor for Observation Fourier Transform Spectrometer (TANSO-FTS). This method uses pseudo-channels consisting of actual channels in the spectral range of 700-750 cm-1 with almost identical sensitivity of peak altitudes. The best combination of these pseudo-channels is selected for each analysis of observed data based on simulation studies. This method was applied to analyze GOSAT data obtained during June-September 2010. The occurrences of PSCs were compared with those from CALIPSO data. Results show that horizontal distributions analyzed from data from these two sensors are particularly consistent over the Antarctic Peninsula and the eastern area of the peninsula to within the difference in sensitivities of the sensors. However, results show that this method is ineffective in the highlands, areas with extremely low surface temperatures, and ocean areas where the temperature lapse rate is extremely low. Although the vertical distribution of PSCs detected by the method for GOSAT data shows different features from those by CALIPSO, they show a similar tendency by which altitudes of PSC decrease gradually during the observation period. The method presented herein is also effective to obtain optical properties in the atmospheric window region for various types of PSCs, and it provides us with important information on atmospheric radiation field with PSCs.
In recent years, people have become concerned about the health impacts of PM 2.5 through cross-border pollution. Although measurement stations for PM 2.5 in Japan are insufficient, it is very difficult to increase observation stations because of budget constraints. The objective of this study was therefore to estimate the overland PM 2.5 distribution using GOSAT CAI data (Level 1 B data product) for free, high spatial / quantization resolutions (500 m/12 bits), and quick repeat cycle (3 days). A significant correlation was observed between the monthly average reflectance after the cloud screening and topographical correction at CAI Band 1 (380 nm) and the monthly average PM 2.5 (PM 2.5ave). Finally, the characteristics of PM 2.5ave distribution in the Chugoku-Shikoku and Kita-Kyushu regions of Japan are discussed using the proposed method. Relatively high PM 2.5 distribution in spring and summer within the limits of Japanese environmental standards was demonstrated in these study areas. GOSAT CAI data is an effective tool for monitoring monthly average PM 2.5 distribution.
Asian countries are responsible for approximately 90 % of the world’s rice production and consumption. As a result, rice is the most significant cereal crop in Asia. The Japan Aerospace Exploration Agency (JAXA) developed a work plan for monitoring the Asian rice crop in GEO Global Agriculture Monitoring (GEO-GLAM) as the Asia-Rice Crop Estimation and Monitoring (Asia-RiCE) project. In 2013, the GEO-GLAM project began to implement its Phase 1 proof of concept in selected countries, and the Asia-RiCE team set up 100 km × 100 km technical demonstration sites in Asia-RiCE team locations. This paper presents an overview of Asia rice crop monitoring in GEO-GLAM and Phase 1 implementation.
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