Toshiyuki Fujito

The Japanese space mission JAXA/GCOM (Global Change Observation Mission-Climate)-C launched in 2017 carries a second-generation global imager (SGLI). The SGLI contains 19 channels from near-UV to thermal infrared (IR), including red (674 nm named PL1 band) and near-IR (869 nm; PL2 band) polarization channels. The SGLI's spatial resolution is fine as the instantaneous field-of-view (IFOV) is 250 m from the near-UV to short-IR wavelength range for radiometry and 1 km for the polarization measurements. This work presents the retrieval algorithms for BBA (biomass burning aerosol) using the advantages of the SGLI features. The BBA generated by wildfires causes long-range advection migration. Severe wildfires, which now frequently occur around the world, have become a major environmental problem. Using as an example the large scale wildfires in September of 2020 around western North America, we show that the SGLI's simultaneous observation of radiance and polarization is useful for the characterization of BBA. Furthermore, a regional numerical model SCALE (Scalable Computing for Advanced Library and Environment) is used to consider meteorological conditions and/or topography. The objective of this study is to better understand the BBA events through the integration of space-borne and ground-based measurements as well as the regional meteorological model.

The boundary between high-concentration aerosols (haze) and clouds is ambiguous and the mixing of aerosols and clouds is complex in terms of composition and structure. In particular, the contribution of biomass burning aerosols (BBAs) to global warming is a source of uncertainty in the global radiation budget. In a previous study, we proposed a method to detect absorption aerosols such as BBAs and dust using a simple indicator based on the ratio of violet to near-ultraviolet wavelengths from the Global Change Observation Mission-Climate/Second-Generation Global Imager (GCOM-C/SGLI) satellite data. This study adds newly obtained SGLI data and proposes a method for the direct detection of severe biomass burning aerosols (SBBAs). Moreover, polarization data derived from polarization remote sensing was incorporated to improve the detection accuracy. This is possible because the SGLI is a multi-wavelength sensor consisting of 19 channels from 380 nm in the near-ultraviolet to thermal infrared, including red (674 nm) and near-infrared (869 nm) polarization channels. This method demonstrated fast SBBA detection directly from satellite data by using two types of wavelength ratio indices that take advantage of the characteristics of the SGLI data. The SBBA detection algorithm derived from the SGLI observation data was validated by using the polarized reflectance calculated by radiative transfer simulations and a regional numerical model—scalable computing for advanced library and environment (SCALE). Our algorithm can be applied to the detection of dust storms and high-concentration air pollution particles, and identifying the type of high-concentration aerosol facilitates the subsequent detailed characterization of the aerosol. This work demonstrates the usefulness of polarization remote sensing beyond the SGLI data.