Satellite remote sensing of greenhouse gases (GHGs), such as carbon dioxide and methane, is one of most rapidly growing fields of remote sensing technology. The pioneering measurements by SCIAMACHY onboard European ENVISAT satellite and Japanese GOSAT series have paved the way to the commercial satellites for GHGs measurements and the use of satellite data in climate change policy relevant applications including the verifications of national GHGs emission inventories. This manuscript will give an overview of satellite remote sensing projects for GHGs starting from early 2000's and provide some insights into future missions.

With the TANSO-3 sensor onboard, the Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) greenhouse gas (GHG) observing mission will make the first global, space-based observations of the column-averaged carbon dioxide (CO₂) and methane (CH₄) dry air mole fraction (XCO₂ and XCH₄, respectively) and vertical column density of tropospheric nitrogen dioxide (NO₂) in the Earth’s atmosphere at a horizontal/spatial resolution of less than or equal to 3 km by the single satellite platform. The objectives of the GOSAT-GW’s GHG observing mission include (1) monitoring of whole atmosphere global-mean concentrations of GHGs, (2) verification of national (or country-specific) anthropogenic emissions inventory of GHGs, and (3) detection of GHGs emissions from large emission sources, such as megacities, power plants, and permafrost. With a nominal lifetime of 7 years, the GOSAT-GW will provide space-based constraints on the anthropogenic GHG emissions, contributing to the mitigation of climate change, in particular, supporting the Global Stocktake (GST) mechanism, a key element in the Paris Agreement.

Data obtained by the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) on board the Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be processed in stages and distributed to users as TANSO-3 products. In order to start data processing immediately after the launch of GOSAT-GW, ground systems are currently being developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute for Environmental Studies (NIES). NIES is responsible for making Level 2 products from Level 1 products from JAXA, and for storing these products and delivering them to users. The NIES ground system consists of three systems: 1) the GOSAT 3rd-generation Data Processing/operating System (G3DPS) for product handling, storage and distribution, and for arranging observation requests; 2) the GOSAT-GW NO2 Data Processing System (GNDPS), which is responsible for processing Level 2 product (NO2) on the commercial cloud service; and 3) the GOSAT Operational and research Computing Facility (GOCF), which provides large-scale computing resources for Level 2 product (greenhouse gas, GHG) processing. In the future, TANSO-3 products will be available to general users through the GOSAT-GW TANSO-3 Product Archive (G3PA) website constructed on G3DPS. Dealing with the explosive increase in computational workload due to the increase in observation points from TANSO-3 for calculating column-averaged dry-air mole fractions of carbon dioxide (XCO₂) and methane (XCH₄) is one of the challenges for the NIES ground system and retrieval algorithms. At the time of writing, in FY2023, ground system tests are underway in coordination with the optimization of Level 2 product processing programs.

The Global Observation SATellite for Greenhouse gases and Water cycle (GOSAT-GW) is planned to be launched in FY2024. Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) on GOSAT-GW has three bands in the range from visible to shortwave infrared spectral regions, including the carbon dioxide (CO₂) and methane (CH₄) absorption bands. The GOSAT-GW project will provide column-averaged dry air mole fractions of CO₂ and CH₄ (XCO₂ and XCH_4, respectively) estimated from the observed spectra as the TANSO-3 Level 2 greenhouse gas (L2GHG) product. This product is processed by the algorithm called GOsat Retrieval ALgorithm (GORAL), which includes two different greenhouse retrieval techniques; the full physics and proxy methods. The full physics method simultaneously estimates XCO₂ and XCH₄ with explicit consideration of multiple scattering by aerosols in the radiative transfer calculations. The proxy method estimates XCH₄ utilizing CO₂ as a proxy to resolve contamination by multiple scattering using the close absorption bands of CO₂ and CH₄ near 1.6 μm.

The Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be launched in the Japanese fiscal year 2024. The Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3), one of the instruments onboard GOSAT-GW, is an imaging grating spectrometer that measures backscattered sunlight, from which the column-averaged dry-air mole fractions of carbon dioxide (XCO₂) and methane (XCH₄) and the total and tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO₂) will be derived (TANSO-3 Level 2 products). TANSO-3 will have two observation modes: Wide Mode, with a ~900 km swath width and a ~10 km footprint size; and Focus Mode, with ~90 km × 90 km observation areas and a < 3 km footprint size. The TANSO-3 Level 2 products will be validated primarily with data from global ground-based remote sensing networks. Total Carbon Column Observing Network (TCCON) and COllaborative Carbon Column Observing Network (COCCON) data will be used to validate XCO₂ and XCH₄, while Pandonia Global Network (PGN) and Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) data will be used for NO₂ VCD. Airborne in situ measurements, satellite measurements other than GOSAT-GW, and simulated concentration fields from atmospheric transport models will complement the ground-based data. In order to validate the Focus Mode data in urban areas, it is crucial to evaluate small-scale spatial gradients in XCO₂, XCH₄, and NO₂ VCD. Therefore, we are developing an urban operational observation network in the Tokyo metropolitan area, Japan, by deploying co-located EM27/SUN (COCCON instruments) and Pandora (PGN instruments) spectrometers.

This study simulated carbon dioxide (CO₂) using regional Weather Research and Forecasting coupled with greenhouse gas modules (WRF-GHG) based on a central grid over Japan and at 27 km spatial resolution for the year 2019. We analyzed the Total Carbon Column Observing Network (TCCON) total column of CO₂ dry air mole fraction (XCO₂) using both global and regional modeling frameworks. XCO₂ was found to be significantly influenced by the CO₂ concentration at higher atmospheric pressure levels (> 400 hPa). We made use of the global Model for Interdisciplinary Research on Climate, version 4.0 based Atmospheric Chemistry-Transport model (MIROC4-ACTM) with a well-resolved stratosphere to better represent variabilities in XCO₂. We analyzed observations from three TCCON sites over Japan: Saga (130.3^oE, 33.2^oN), Tsukuba (140.1^oE, 36.0^oN), and Rikubetsu (143.7^oE, 43.4^oN), finding that correlation improved between observed and model-simulated XCO₂ profiles by using CO₂ concentration profile data produced by a hybrid model that combines WRF-GHG and MIROC4-ACTM; we used MIROC4-ACTM CO₂ concentrations at higher altitudes (< 400 hPa) with the WRF-GHG CO₂ output at lower altitudes. The correlation improvement between observed and simulated XCO₂ concentrations was most prominent over Saga (~35 %), which is near high CO₂ emission/sink regions such as China, Korea and southeast Asia. WRF-GHG simulations show a significant underestimation of XCO₂ over Saga during April and May of 2019. An analysis of WRF-GHG CO₂ spatial plots shows a tropopause fold that brings more depleted CO₂ air to Saga through stratosphere-troposphere exchange (STE) than suggested by the observations. The analysis was also conducted with XCO₂ data from Orbiting Carbon Observatory (OCO)-2 satellite observations, WRF-GHG, MIROC4-ACTM, and the hybrid model for the initial four and six months. The findings indicate that during the initial six months, WRF-GHG slightly underperformed compared to MIROC4-ACTM XCO₂. This suggests the need for precise tuning of the land biosphere in WRF-GHG, as the inclusion of the active land-biospheric period appears to deteriorate XCO₂ calculations from WRF-GHG.

A method based on the divergence theorem was developed to estimate emissions from high-frequency and high-horizontal-resolution satellite observations of atmospheric trace gases (NO₂ and CO₂), by accounting for the expected performance of the upcoming TANSO-3 / Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW). Pseudo satellite observations to which the divergence method was applied were constructed from atmospheric chemistry transport model simulations over the Kanto region with a 1-km horizontal resolution. Observational errors were then added as normal distributions with the standard deviations of 1.8 ppm for CO₂ total column dry-air mole fraction (XCO₂) and 3×10¹⁵ molecules/cm² for NO₂ tropospheric column density (NO₂VCD). The results showed that our central emission estimates for the Kashima Industrial Zone were within 10-24 % of the true values for CO₂ and NO_X, confirming the performance of the method. Uncertainties in the emission estimates propagated by observational errors (noise) were evaluated to be ±32 % and ±2 % for CO₂ and NO_X emissions of 16.6 MtCO₂/year and 25 ktNO_X/year, respectively, after averaging over 31 ideal observations, expected to occur within a 3-month period at best for GOSAT-GW.

To conserve seaweed beds in shallow waters, it is important to understand their time series change using accurate distribution data. Although remote sensing techniques can be used for regular observation in wide areas, the estimation of seaweed bed distribution using satellite image analysis requires manual correction of analysis results due to the noise generated on the water surface and the attenuation of light underwater. To improve the accuracy of this estimation, we used a depth estimation method that takes the seaweed flourishing season into consideration and applied it to seaweed bed distribution analysis. Our results show an overall accuracy of 78% and a tau coefficient of 0.53 under the application of satellite-derived bathymetry (SDB) estimated from the image taken during seaweed non-flourishing season to the analysis of the image taken during flourishing season. These results indicate that applying a water depth estimation method that takes seaweed flourishing season into consideration can improve the accuracy of seaweed bed distribution analysis.