Land-use change is an important cause of carbon emissions (CEs). In the context of achieving carbon peaking and carbon neutrality goals, understanding the coupling mechanisms between land-use change and CEs is of great significance for fostering regional low-carbon sustainable development. In this study, the land-use net carbon emissions (LCN) calculation and evaluation model was built based on the perspective of land-use change. The land-use variation matrix, standard deviation ellipse, and spatial autocorrelation analysis were used to analyze the spatio-temporal evolution of land-use and the LCN in the Chengyu urban agglomeration (CUA) from 2000 to 2020. Meanwhile, the economic contribution coefficient and ecological support coefficient were applied to evaluate the alignment among the CEs, socio-economic development, and the ecological environment. In addition, the modified Kaya and Logarithmic Mean Divisia Index (LMDI) models were used to quantitatively analyze the drivers and underlying influence mechanisms of the LCN. The results showed the following: (1) The area of built-up land and forest land expanded rapidly, mainly transforming grassland and farmland to built-up land and forest land in the CUA during the study period. The built-up land was the main source of the regional CEs. The land-use changes led to the migration of the LCN center and the variations in spatial clustering. (2) The growth rate of the LCN decreased after 2010, and the disparities in carbon productivity and the carbon compensation rate among the cities gradually narrowed from 2000 to 2020. The alignment among the regional CEs, socio-economic development, and ecological environmental governance was effectively improved. (3) The economic development level and energy consumption intensity were the primary facilitator and inhibitor of the LCN, respectively. The results could offer valuable references and insights for formulating regional carbon reduction strategies and policies.

Microwave occultation using centimeter and millimeter wave links between low Earth orbit (LEO) satellites provide a potential way to estimate the moist atmospheric profiles based on refraction and attenuation measurements, which is called the LEO-LEO microwave occultation (LMO) technique. It has not yet been implemented in orbit. In this paper, we analyzed the attenuation properties at different heights of centimeter and millimeter waves based on simulations. The observing capabilities with different frequency combinations at the X, K, and M bands were analyzed. The results show that LMO may improve the retrieval accuracy of bending angles above 35 km. By using several appropriate frequencies at the X+K+M band, water vapor profiles from the near-surface to the lower stratosphere (~24 km) can be obtained. When the M-band frequencies were added, the temperature retrieval accuracy does not change obviously, but the accuracy of water vapor retrieval can significantly improve above 15 km, especially at about 17–24 km, and the RMS errors decrease from over 20% to less 10%. For promoting the LMO mission in the real world, a frequency combination at the X+K band is proposed, which can provide the potential to observe the temperature profiles at about 2.5–50 km and water vapor profiles at about 2.5–15 km accurately under clear and cloudy conditions. This study demonstrates that LMO can greatly extend the capabilities of the radio occultation technique and improve our ability to measure the moist atmospheric profiles globally.

Utilizing CloudSat/CALIPSO satellite data and ERA5 reanalysis data from 2007 to 2016, this study analyzed the distributions of optical and physical characteristics and change characteristics of ice clouds over the Tibetan Plateau (TP). The results show that the frequency of ice clouds in the cold season (November to March) on the plateau is over 80%, while in the warm season (May to September) it is around 60%. The average cloud base height of ice clouds in the warm season is 3–5 km, and mostly around 2 km in the cold season. The average cloud top height in the warm season is around 5–8 km, while in the cold season it is mainly around 4.5 km. The average thickness of ice clouds in both seasons is around 2 km. The statistical results of microphysical characteristics show that the ice water content is around 10⁻¹ to 10³ mg/m³, and the effective radius of ice clouds is mainly in the range of 10–90 μm. Both have their highest frequency in the west of the TP and lowest in the northeast. A comprehensive analysis of the change in temperature, water vapor, and ice cloud occurrence frequency shows that the rate of increase in water vapor in the warm season is greater than that in the cold season, while the rates of increase in both surface temperature and ice cloud occurrence are smaller than in the cold season. The rate of increase in temperature in the warm season is around 0.038 °C/yr, and that in the cold season is around 0.095 °C/yr. The growth rate of thin ice clouds in the warm season is around 0.15% per year, while that in the cold season is as high as 1% per year. The results suggest that the surface temperature change may be related to the occurrence frequency of thin ice clouds, with the notable increase in temperature during the cold season possibly being associated with a significant increase in the occurrence frequency of thin ice clouds.

Accurately understanding the vertical distribution of major global atmospheric gases is a critical issue in climate change research and response. The Low Earth Orbit-to-Low Earth Orbit (LEO-LEO) infrared laser occultation (LIO) detection technology enables three-dimensional, all-time, and high vertical-resolution simultaneous detection of multiple atmospheric composition (CO2, CH4, H2O, O3, N2O, CO, etc.) and line-of-sight wind speed. This approach is expected to complement existing greenhouse gas column total measurement methods in the future. The LIO system consists of a transmitter and a receiver. It employs eleven carefully selected infrared laser signals within the shortwave infrared (SWIR) spectral region of 2–2.5 µm. Based on the differential absorption lidar (DIAL) principle, the system retrieves vertical profiles of greenhouse gases and further derives line-of-sight wind speed via spectral Doppler frequency shift. During an occultation event, the laser signal emitted by the transmitter is attenuated by the atmosphere before reaching the receiver. The transmitter realizes differential absorption atmospheric spectral detection through multiple laser channels. Each detection element adopts dual-channel detection, and the receiver performs high-sensitivity detection for each spectral channel. To ensure precise laser wavelength control, the LIO system adopts optical frequency comb stabilization technology. Additionally, a spatial heterodyne spectrometer is used to achieve extremely high spectral resolution within a narrow field of view. By scanning the Earth's atmosphere from top to bottom, the system allows for high-precision retrieval of trace gases profiles. Currently, no LEO-LEO occultation mission has been deployed in space. Research has been focused on frequency selection evaluation, inversion algorithm refinement, occultation orbit design, and detection performance simulations. The continued development of infrared laser occultation technology can provide essential vertical atmospheric datasets for future global climate change research.

As the dominant greenhouse gas, the spatiotemporal distribution of carbon dioxide (CO2) concentration is a key parameter in global climate change research. Satellite remote sensing has become a crucial means for detecting CO2 on a global scale. The infrared laser occultation technology employed by Low-Earth Orbit (LEO) satellites offers a novel technical approach for obtaining vertical profiles of atmospheric CO2, capitalizing on its advantages of high vertical resolution, high precision, and strong global coverage capability. This paper systematically investigates the CO2 retrieval method for this technology. The core principle involves a transmission-reception mode based on a LEO-LEO dual-satellite constellation. An infrared laser source onboard the transmitting satellite emits laser pulses at specific wavenumbers, which traverse the atmosphere and are captured by the receiving satellite. Leveraging the selective absorption characteristics of CO2 in specific infrared bands and the principle of differential absorption, concentration retrieval is achieved. Firstly, an infrared laser occultation signal link model is constructed to optimize the selection of detection wavenumbers. Through simulation analysis of the sensitivity of CO2 detection precision at different wavenumbers, an optimal wavenumber pair is determined. This wavenumber pair effectively mitigates interference from other atmospheric components, ensuring the specificity of the CO2 absorption signal. Secondly, a complete retrieval calculation procedure is established. The dual-wavelength transmittance is calculated from the ratio of transmitted power to received signal strength, subsequently enabling the solution of the differential optical depth along the entire optical path. Utilizing the Abel integral transform, the differential optical depth is converted into the CO2 differential absorption coefficient at the atmospheric path tangent point. Combined with the ideal gas law and the atmospheric quasi-static equation, concentration conversion is performed, ultimately yielding the vertical CO2 concentration profile. The retrieval method proposed in this study effectively addresses the issues of low vertical resolution and uneven regional coverage inherent in traditional satellite remote sensing for CO2 profile detection. It provides core algorithmic support for the engineering implementation of spaceborne infrared laser occultation CO2 detection systems. The high-precision global CO2 concentration data obtained can offer significant scientific data support for carbon source/sink assessment, climate change prediction, and related policy formulation.

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions.