Ground-based remote sensing, as a critical technique for monitoring atmospheric greenhouse gas column density and calibrating satellite data, provides robust scientific support for establishing carbon accounting systems, studying regional carbon cycles, assessing the effectiveness of carbon reduction policies, and analyzing the dynamics of carbon sources and sinks. We have developed a novel greenhouse gas remote sensing device, hyperspectral atmospheric greenhouse gas remote sensor (HAGRES), which uses a grating spectrometer and a home-built solar tracker to determine atmospheric CO₂ column density by analyzing solar spectra in the 1597.4–1618 nm band. The CO₂ column density results were validated against temporally coinciding on-site measurements taken with the Total Carbon Column Observing Network (TCCON) IFS125HR spectrometer. Over a year of comparative observations, the mean difference between the device and TCCON data was (0.12±0.23) %, with a correlation coefficient of R=0.98. Additionally, CO₂ data from the GOSAT satellite around Hefei was compared with ground-based results, demonstrating correlation coefficients of 0.95 with TCCON and 0.92 with HAGRES. HAGRES has operated automatically and stably outdoors for 12 months, showing strong optical stability and environmental adaptability after applying waterproof protection treatment. These results suggest that HAGRES is a cost-effective tool for satellite validation and carbon budget studies, supporting effective emission reduction strategies and contributing to China’s carbon neutrality goals.

Monitoring total column water vapor (TCWV) with high accuracy and full coverage is critical for understanding Earth’s hydrological cycle. Satellite TCWV observations provide broad spatial coverage but often lack accuracy. Ground-based TCWV measurements such as Global Navigation Satellite System (GNSS) have high accuracy and temporal resolution but low spatial coverage. To address these limitations, we developed an interpretable machine learning (ML) approach to correct TCWV biases in satellite-based blue-band spectral retrievals. Original TCWV retrievals from two Chinese Environmental Trace Gases Monitoring Instruments (EMI), with morning and afternoon overpasses, respectively, along with other influencing factors, are used as input features for the model, with GNSS measurements serving as target variables. The corrected dataset provides high-accuracy and full-coverage global TCWV observations twice daily. It overcomes the limitations of individual monitoring techniques and eliminates systematic biases both between different satellites and between satellite and ground-based measurements. The cross-validation against 488 GNSS sites shows an excellent agreement, with a determination coefficient (R2) of 0.89, a mean bias of -0.17~0.05 kg/m2, representing an improvement of ~48.8% compared to uncorrected satellite retrievals. The ML interpretability also reveals the impact of physical spectral fitting results, atmospheric mass factors, geographic information, cloud information, and surface albedo on TCWV retrieval biases. These results provide valuable guidance for enhancing and utilizing satellite TCWV retrievals. Overall, this study enables seamless and accurate global TCWV monitoring, offering crucial insights into the dynamics of the global hydrological cycle and its climatic impacts.

To cope with global climate change and monitor global CO2concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO2retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum$A$PosterioriDifferential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO2spectral retrieval. The global terrestrial CO2total vertical column density (VCD) and column-averaged dry-air mole fractions of CO2($\text{X}_{\text {CO2}}$) were simultaneously retrieved from TanSat GL spectral observations. Then, a comprehensive verification was performed between TanSat CO2retrieval and other measurements including Total Carbon Column Observing Network (TCCON), the Japanese Greenhouse gases Observing SATellite (GOSAT), and the US Orbiting Carbon Observatory-2 (OCO-2). Further comparisons between our TanSat CO2retrieval and ground-based FTIR measurements from TCCON indicated a good correlation with the mean bias of −0.78 ppm, the standard deviation at 1.75 ppm, and the Pearson correlation coefficient of 0.81. In addition, cross-satellite CO2validations of TanSat with GOSAT and OCO-2 showed consistently spatiotemporal trends for both CO2VCD and$\text{X}_{\text {CO2}}$. In summary, we can conclude that the presented CO2retrieval scheme has achieved global CO2retrieval from TanSat GL mode spectra with high precision and accuracy, as suggested by the results of independent ground-based and satellite validations.

The vertical distribution profiles of NO2are essential for understanding the mechanisms, detecting near-surface emissions, and tracking pollutant transportation at high altitude. However, most of the published NO2studies are based on the surface 2-D measurements. The ground-based 3-D remote-sensing stations were recently built to measure vertical distribution profiles of NO2. However, the stations were spatially sparse due to the high cost and could not make the measurements without sunlight. In this study, we first developed a multimodel fusion network (MF-net) based on the sparse vertical observations from the Jing-Jin-Ji region. We achieved the 3-D profile prediction of NO2in the range of 39.005–41.405N and 115.005–117.905E with 24-h coverage. The MF-net significantly surpassed the conventional WRF-CHEM model and provided a more accurate evaluation of the NO2transmission between Beijing and the neighboring cities. Besides, the MF-net covers the monitoring of NO2to the whole study area and extends the monitoring time to the entire day (24 h), making it serviceable for continuous spatial-temporal estimation of NO2and its transmission in pollution events. The MF-net provides more robust data support to formulate reasonable and effective pollution prevention and control measures.

A ground-based high-resolution Fourier Transform Spectrometer (FTS) station has been established in Hefei, China to remotely measure CO2, CH4 and other greenhouse gases based on near-infrared solar absorption spectra. Total column measurements of atmospheric CO2and CH4have been successfully achieved from 2015 to 2020. Time series of individual measurements, daily averages of column-averaged DMF of CO2and CH4suggest that CO2and CH4variation showed a clear seasonal cycle. Also, CO2and CH4slowly increased from 2015 to 2020. Further, we made use of satellite measurements to compare with our data. The direct comparison of our observations with the Greenhouse gases Observing Satellite (GOSAT) data shows good agreement of daily median XCO2, with bias of −0.52 ppm and standard deviation of 1.63 ppm, respectively. The correlation coefficient (R) is 0.89 for XCO2between our FTS and GOSAT observations. Orbiting Carbon Observatory 2 (OCO-2) data produce a positive bias of 0.81 ppm and standard deviation of 1.73 ppm relative to our ground-based data, respectively. Our XCO2also show strong correlation with OCO-2 data, with correlation coefficient (R) of 0.91. For CH4, average differences between GOSAT and FTIR data were 1.60 ppb (0.09%) ± 13.0 ppb (0.70%), and the correlation coefficient (r) of the two dataset is 0.77. The results confirm the suitability of the observatory for ground-based long term measurements of greenhouse gases with high precision and accuracy.

The Environmental trace gas Monitoring Instrument (EMI) onboard the Chinese high-resolution remote sensing satellite GaoFen-5 is an ultraviolet-visible imaging spectrometer, aiming to quantify the global distribution of tropospheric and stratospheric trace gases and planned to be launched in spring 2018. The preflight calibration phase is essential to characterize the properties and performance of the EMI in order to provide information for data processing and trace gas retrievals. In this paper, we present the first EMI measurement of nitrogen dioxide (NO2) from a gas absorption cell using scattered sunlight as the light source by the differential optical absorption spectroscopy technique. The retrieved NO2column densities in the UV and Vis wavelength ranges are consistent with the column density in the gas cell calculated from the NO2mixing ratio and the length of the gas cell. Furthermore, the differences of the retrieved NO2column densities among the adjoining spatial rows of the detector are less than 3%. This variation is similar to the well-known “stripes-pattern” of the Ozone Monitoring Instrument and is probably caused by remaining systematic effects like a nonperfect description of the individual instrument functions. Finally, the signal-to-noise ratios of EMI in-orbit measurements of NO2are estimated on the basis of on-ground scattered sunlight measurements and radiative transfer model simulations. Based on our results, we conclude that the EMI is capable of measuring the global distribution of the NO2column with the retrieval precision and accuracy better than 3% for the tested wavelength ranges and viewing angles.