Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NO_x), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NO_x. The Exponentially Modified Gaussian (EMG) model is validated for estimating NO_x emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NO_x emissions for the majority of the studied cities.

Carbon dioxide (CO ₂ ) is the most important atmospheric contributor for global warming. Satellite remote sensing is a commonly used method for high-precision CO ₂ detection, but it often suffers from striping issues, which hinders its ability to achieve full coverage. Therefore, the limited availability of data poses challenges for global carbon accounting. In this paper, we generate a global seamless and high-resolution dataset of column-averaged dry air CO ₂ mole fractions (XCO ₂ ) from 2017 to 2020 by integrating multiple data sources using deep learning techniques Feedforward Neural Network (FNN). The data sources primarily include satellite, ground-based, and reanalysis XCO ₂ products, satellite vegetation index data, and meteorological data. The spatial resolution of the dataset is 0.1°, and the temporal resolution is one day. Moreover, this paper also investigated the importance of features in deep learning models and examined the spatiotemporal variations of global XCO ₂ . The results demonstrate that the FNN approach with time series features yields an improved dataset (R=0.98, RMSE=0.82 ppm) compared to the other methods. In an FNN, the known model data (CT XCO ₂ ) are considered as the most important features. We find that the global XCO ₂ has increased by approximately 7.5 ppm from 2017 to 2020. This seamless and fine-scale dataset provides valuable support for understanding global carbon cycling and formulating carbon emission reduction policies.

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product.

Space-borne multi-angle polarization remote sensing is considered to be one of the most important tools to obtain global aerosol parameters in assessment of climate change. Accurate calibration is a prerequisite for quantitative polarization remote sensing. However, most research focuses on the polarization calibration methods and the monitoring of the calibration coefficient stability. Few studies investigate the polarization calibration verification of the subsequent new satellite sensors. The Directional Polarimetric Camera (DPC) onboard the Chinese Terrestrial Ecosystem Carbon Inventory Satellite (abbreviated as TECIS, with the Chinese name ”Gou Mang”) is a brand-new polarization sensor. To evaluate the polarization calibration of this sensor, we propose a verification scheme for polarization calibration, which can verify both the degree of polarization (DoP) and polarized reflectance. The accuracy of DoP is verified by using sunglint on the ocean. When detecting the sunglint, constraints such as observational geometry, cloud identification, and wind speed are introduced, and the polarized reflectance is atmospherically corrected according to the marine aerosol model and aerosol optical depth from MODIS. The accuracy of polarized reflectance at the Top of Atmosphere (TOA) is verified based on AERONET inversion products and the Bidirectional Polarization Distribution Functions (BPDF). Experiments show that the accuracies of DoP of the three polarization channels (490, 670, 865 nm) of DPC are 5.57%, 2.07%, and 1.97% respectively, and the average accuracy of the multi-angle polarized reflectance at the TOA of the 865nm channel is 0.209%.