The Surface Water and Ocean Topography (SWOT) satellite, carrying a Ka-band Radar Interferometer, is designed to detect global hydrological, ecological, and climatic changes through high-precision measurements of water elevation level and to promote the sustainable use and conservation of water resources. The accuracy of water level elevations of inland from SWOT was verified globally in this paper by comparing SWOT data with Hydroweb and G-REALM data, so as to better utilize the advanced global remote sensing observation capabilities of SWOT in hydrology. Through spatiotemporal matching validation, this experiment validates the SWOT data by time series within different regions, confirming that SWOT observations have an accuracy of more than 99% for inland lakes and rivers globally in eight geographic subregions. Compared with Hydroweb and G-REALM, SWOT's lake products have an MAE of less than 0.5 and 0.3 m, as well as an RMSE of less than 1.5 and 2 m, respectively. SWOT's data reduce the error in the measurement of the lake by more than 0.1 m compared with the priori data and improves the accuracy by more than 10%. As for river measurements, SWOT's global average measurement error is less than 0.15 m, with an RMSE of less than 1.5 m, providing highly accurate measurements for rivers in most regions of the world. Overall, the product of SWOT can provide high-precision heights of terrestrial water bodies, which is of great significance for small inland water body measurements, global water quantity monitoring, and water circulation research.

Quantification of the distribution of the CO ₂ dry-air mixing ratio (XCO ₂ ) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO ₂ signals. This interference can result in a low yield of XCO ₂ retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO ₂ maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO ₂ curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO ₂ maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient ( $R^{2}$ ) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded $R^{2}$ and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained $R^{2}$ of 0.8690 and the RMSE of 0.9056 in Europe and $R^{2}$ of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

Rapid detection of oil tank targets has become a topic of significant and increasing interest because the quick acquisition of the distribution and volume of oil storage tanks has high economic and military value. However, research in the literature still faces many challenges in different scenarios. This letter presents a new oil tank detection approach for improved EfficientDet model. The proposed approach has three primary operations: 1) first, adding 3-D deformable convolution to the EfficientDet model as predetection to limit detection in the smaller area; 2) second, loading an attention mechanism appropriate for oil tank detection on the myNet model highlights tiny target information; and 3) lastly, training the myNet model repeatedly by focal loss function to seek better results. The result shows that the overall accuracy of mean average precision in the proposed approach increased by at least 8.25% compared with that of the tested conventional approaches. Therefore, it provides advantageous capabilities for monitoring oil tanks in high-resolution remote sensing imagery.

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO ₂ ) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO ₂ monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO ₂ in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO ₂ inversion. The 1.572 μm IPDA lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. And the experimental results show that the accuracy of the signal extracted by the 1.572 μm IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572 μm IPDA lidar column length. The optimized XCO ₂ was evaluated (standard deviation as an evaluation metric) and its XCO ₂ standard deviation reduced by 31%, 63% and 66% in the ocean, plains and mountains, respectively. Our algorithm can obtain the XCO ₂ with a consistent trend by using XCO ₂ from the OCO-2 satellite as a reference. The calculated XCO ₂ is more accurate in areas dominated by anthropogenic factors (plains), owing to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO ₂ inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.

A novel framework was proposed to measure atmospheric concentration of δ13C using a multi-wavelength integrated path differential absorption (IPDA) LIDAR. The spectroscopy range of the multi-wavelength IPDA LIDAR is recommended as from 2264.5 to 2265.5 cm-1. Using the proposed retrieving method, the relative error of δ13C retrievals would be within 0.16 % under reasonable settings. Moreover, the proposed method shows reliable performances in different circumstances. It would be of great significance for exploring the characteristic of δ13C in ecosystem and anthropogenic emissions in future.

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO₂, O₃, and CO₂ column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO₂ decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO₂, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO₂ and O₃ concentrations on changes in XCO₂. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO₂ concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O₃ concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO₂ concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O₃ concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO₂ concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O₃ concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO₂. Moreover, the results show that the effect of NO₂ concentration on XCO₂ is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO₂ and the O₃ concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO₂ fell by about 1.12 ppm in February. At last, the government should consider reducing XCO₂ and NO₂ concentration at the same time to make a synergistic reduction.