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The increase in greenhouse gas concentrations, particularly CO2, has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges such as low retrieval efficiency and insufficient retrieval accuracy. To address these challenges, this study focuses on enhancing the retrieval of column-averaged dry air mole fraction of carbon dioxide (XCO2) using spectral data from the OCO-2 satellite. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrieval. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of traditional optimization models, which are released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion and prediction performance of the machine learning model for XCO2 is compared, analyzed, and discussed for the observed region. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model achieves rapid predictions (<1 ms) with high precision (2 ppm or approximately 0.5 %). The accuracy of the machine learning model's retrieval results is validated against reliable data from TCCON sites, demonstrating its capability to capture CO2 seasonal variations and annual growth trends effectively. [ABSTRACT FROM AUTHOR]

Top-down inversions of China's terrestrial carbon sink are known to be uncertain because of errors related to the relatively coarse resolution of global transport models and the sparseness of in situ observations. Taking advantage of regional chemistry transport models for mesoscale simulation and spaceborne sensors for spatial coverage, the Greenhouse Gases Observing Satellite (GOSAT) retrievals of column-mean dry mole fraction of carbon dioxide (XCO2) were introduced in the Models-3 (a flexible software framework) Community Multi-scale Air Quality (CMAQ) and ensemble Kalman smoother (EnKS)-based regional inversion system to constrain China's biosphere sink at a spatiotemporal resolution of 64 km and 1 h. In general, the annual, monthly, and daily variation in biosphere flux was reliably delivered, attributable to the novel flux forecast model, reasonable CMAQ background simulation, well-designed observational operator, and Joint Data Assimilation Scheme (JDAS) of CO2 concentrations and natural fluxes. The size of the assimilated biosphere sink in China was - 0.47 PgCyr-1 , which was comparable with most global estimates (i.e., - 0.27 to - 0.68 PgCyr-1). Furthermore, the seasonal patterns were recalibrated well, with a growing season that shifted earlier in the year over central and south China. Moreover, the provincial-scale biosphere flux was re-estimated, and the difference between the a posteriori and a priori flux ranged from - 7.03 TgCyr-1 in Heilongjiang to 2.95 TgCyr-1 in Shandong. Additionally, better performance of the a posteriori flux in contrast to the a priori flux was statistically detectable when the simulation was fitted to independent observations, indicating sufficient to robustly constrained state variables and improved fluxes estimation. This study serves as a basis for future fine-scale top-down carbon assimilation. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space Carbon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturised GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry–Pérot imaging interferometer with a 2.3×2.3 km 2 spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO 2 and CH 4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms and provide their systematic and random errors, column vertical sensitivities, and degrees of freedom as a function of five scattering-error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two-thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5–1 ppm and 4–6 ppb, respectively, compliant with their respective 1 ppm and 6 ppb precision objectives. Finally, these Level 2 performance results are parameterised as a function of the explored scattering-error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO 2 and CH 4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]

The retrieval of solar-induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and Orbiting Carbon Observatory-3 (OCO-3) platforms, which are provided for each platform in daily netCDF files (Frankenberg, 2022, 10.22002/D1.8771; OCO-2 Science Team et al., 2020, 10.5067/XO2LBBNPO010; OCO-3 Science Team et al., 2020, 10.5067/NOD1DPPBCXSO). We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users with the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution of spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes. [ABSTRACT FROM AUTHOR]

Several single-platform satellite missions have been designed during the past decades in order to retrieve the atmospheric concentrations of anthropogenic greenhouse gases (GHG), initiating worldwide efforts towards better monitoring of their sources and sinks. To set up a future operational system for anthropogenic GHG emission monitoring, both revisit frequency and spatial resolution need to be improved. The Space CARBon Observatory (SCARBO) project aims at significantly increasing the revisit frequency of spaceborne GHG measurements, while reaching state-of-the-art precision requirements, by implementing a concept of small satellite constellation. It would accommodate a miniaturized GHG sensor named NanoCarb coupled with an aerosol instrument, the multi-angle polarimeter SPEXone. More specifically, the NanoCarb sensor is a static Fabry-Perot imaging interferometer with a 2.3 x 2.3 km² spatial resolution and 200 km swath. It samples a truncated interferogram at optical path differences (OPDs) optimally sensitive to all the geophysical parameters necessary to retrieve column-averaged dry-air mole fractions of CO2 and CH4 (hereafter XCO2 and XCH4). In this work, we present the Level 2 performance assessment of the concept proposed in the SCARBO project. We perform inverse radiative transfer to retrieve XCO2 and XCH4 directly from synthetic NanoCarb truncated interferograms, and provide their systematic and random errors, column vertical sensitivities and degrees of freedom as a function of five scattering error-critical atmospheric and observational parameters. We show that NanoCarb XCO2 and XCH4 systematic retrieval errors can be greatly reduced with SPEXone posterior outputs used as improved prior aerosol constraints. For two thirds of the soundings, located at the centre of the 200 km NanoCarb swath, XCO2 and XCH4 random errors span 0.5 – 1 ppm and 4 – 6 ppb, respectively, compliant with their respective 1-ppm and 6-ppb precision objectives. Finally, these Level 2 performance results are parameterized as a function of the explored scattering error-critical atmospheric and observational parameters in order to time-efficiently compute extensive L2 error maps for future CO2 and CH4 flux estimation performance studies. [ABSTRACT FROM AUTHOR]