China will launch the Atmospheric Environment Monitoring Satellite, which is equipped with a CO2-IPDA LIDAR. It is of great significance to explore the feasibility and effectiveness of this novel technique and to identify potential differences among its CO2 concentration products and the passive remote sensing technique. In this work, we developed an initial data-processing procedure for an airborne CO2-IPDA LIDAR, which is the minified prototype of the forthcoming space-borne LIDAR. We tested the effectiveness of this procedure and evaluated the performance of the minified prototype in a flight test over ocean, urban and mountainous terrain. The column-weighted xCO2 (XXCO2) retrievals obtained by the airborne IPDA-LIDAR were considerably more sensitive to the gradients of the dry-air mixing ratio of CO2 (xCO2), compared with both the XCO2 products of OCO-2 and in-situ measurements of point xCO2. The mean XXCO2 over the ocean, urban and mountainous area are 412.02, 438.32 and 433.08 ppm with STDs of 1.35, 0.75 and 0.91 ppm, respectively. We also used altitude-dependent xCO2 obtained by a decline-climb flight to calculate a reference for XXCO2 over the ocean. The difference between XXCO2 obtained via two means was less than 0.5 ppm. Moreover, our performance evaluation model predicted that a relative random error of less than 0.3% would be very likely for the forthcoming satellite mission over land. However, measuring CO2 concentrations precisely over oceans is identified as a very challenging work. Thus, developing dedicated algorithms to address CO2 measurement over oceans by using the IPDA-LIDAR is necessary.

In view of the urgent need for high coverage and high-resolution atmospheric CO₂ data in the study of carbon neutralization and global CO₂ change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO₂ datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO₂ dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO₂ interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R² is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO₂ dataset.

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R² of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state.

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO₂) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO₂ from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO₂, CO, and NO₂ across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO₂ and CO₂ emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO₂ and CO emissions and a weaker correlation of 0.37 between CO and NO₂ emissions. Notably, we identify the NO₂ concentration as a reliable indicator of CO₂ emission levels, in which a 1% increase in NO₂ concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO₂ emissions. Enhancement ratios among NO₂, CO, and XCO₂ are also calculated, uncovering that high ΔNO₂: ΔXCO₂ ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO₂ ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making.

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring.

Atmospheric pollutant gases emitted from straw burning and forest fires can lead to air quality and human health problems. This work explored the evolutionary trends of atmospheric CO${}_2$ and other pollutant gases in five countries of Peninsular Southeast Asia (PSEA) over a long time series using various satellite remote sensing data. The research results indicate that a considerable number of fires occur in the region every spring, which negatively affects air quality. The concentration of CO${}_2$ increased every year, indicating a correlation coefficient of 0.57 with the number of fire points. The concentration of CO and NO${}_2$, respectively, showed a correlation coefficient of 0.87 and 0.95 with the number of fire points as well. Additionally, the AOD reflects the relationship between fire points and air quality. The study also used the meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) to simulate the fire season in March 2016. In this sensitivity study, we examined the impact of air pollutant gases on air quality in PSEA under a hypothetical scenario with and without fire emissions. The simulation results were also compared with satellite observations, which showed that the WRF-Chem model and the FINN (Fire INventory from NCAR) inventory could effectively simulate the spatial distribution and spatial–temporal variability characteristics of CO concentration in the fire, but the simulation result of NO${}_2$ was not satisfactory. This study suggests that spring wildfires affect not only air quality, but also short-term weather in the region.