The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R² = 0.91, RMSE = 35.13 m³ ha⁻¹, n = 321) and in the test phase (i.e., R² = 0.58, RMSE = 65.03 m³ ha⁻¹, and n = 138). This model was followed by the SVR model (R² = 0.54, RMSE = 65.60 m³ ha⁻¹, n = 321 in training; R² = 0.54, RMSE = 66.00 m³ ha⁻¹, n = 138 in testing), which was slightly better than the MLR model (R² = 0.38, RMSE = 75.74 m³ ha⁻¹, and n = 321 in training; R² = 0.49, RMSE = 70.22 m³ ha⁻¹, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10⁸ m³) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10⁸ m³). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

Three parallel Visible/Infrared Imager Radiometer Suite (VIIRS) aerosol products (SOAR, NOAA, and AERDT) provided data since 2012. It is necessary to study the performances and advantages of different products. This study aims to analyze the accuracy and error of these products over the ocean and compare them with each other. The results show that the three VIIRS ocean aerosol retrievals (including total aerosol optical depth (AOD), fine mode fraction, Ångström exponent (AE), and fine AOD (AODF)) correlate well with AErosol RObotic NETwork (AERONET) retrievals (e.g., correlation >0.895 for AOD and >0.825 for AE), which are comparable to the newest moderate-resolution imaging spectro-radiometer (MODIS) retrievals. Overall, the SOAR retrievals with quality filtering have the best validation accuracy of all parameters. Therefore, it is more recommended to use. The differences in the annual AOD spatial patterns of different products are small (bias < 0.016), but their AE spatial patterns are evidently different (bias > 0.315), indicating the large uncertainty of VIIRS AE. Error analysis shows that the scattering angle and wind speed affect aerosol retrieval. Application of the non-spherical dust model may reduce the dependence of retrieval bias on the scattering angle. Overall, this study provides validation support for VIIRS products usage and possible algorithm improvements.

As the second most abundant greenhouse gas after carbon dioxide (CO₂), methane not only plays an important role in global and regional photochemical reactions, but also has an important impact on energy balance and climate change. To explore the long-term trends and spatiotemporal variation of methane concentration over China, we verified the accuracy of the column-averaged, dry air-mixing ratio of CH₄ (abbreviated as XCH₄ hereafter) merged by SCIAMACHY and GOSAT products, utilizing the data of six surface observation stations in China and the surrounding areas. The root mean square error (RMSE) was mostly less than 2.5%, and the correlation coefficients (r) were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75. Furthermore, we analyzed the temporal and spatial variation patterns of the XCH₄ concentration over China from 2003 to 2020. The results showed that the XCH₄ concentration had an increasing trend over China from 2003 to 2020; the average growth rate was 6.64 ppb·a⁻¹, and the value range of the increase rate was from 4.66 ppb·a⁻¹ to 8.46 ppb·a⁻¹. The lowest XCH₄ concentration was located over Tibet (1764.03 ppb), and the high values were located in the Sichuan Basin, Central China (Hunan, Hubei, and Henan) and East China (Anhui and Jiangxi) (1825–1845 ppb). The XCH₄ concentration was higher in autumn and summer, low in winter and spring, and had obvious seasonal variations. Human factors such as population density, GDP and energy consumption have a significant impact on the XCH₄ concentration over China.

Urban landscapes exhibit significant regional differences shaped by geography, history, and culture, yet how these variations influence human perception remains underexplored. This study investigates the impact of street scene characteristics on human perceptions in East Asian and European cities by analyzing the large-scale MIT Place Pulse 2.0 dataset. We employ DeepLab v3+ and Mask R-CNN to extract multidimensional physical and visual features and utilize logistic regression to model their association with six subjective perceptions. The findings reveal significant cultural differences: streets in East Asian cities are characterized by higher compactness and brightness, whereas European city streets exhibit greater levels of greening and openness. While perceptions of aesthetics and liveliness show cross-cultural consistency, the mechanisms influencing safety and wealth perceptions diverge significantly; for instance, East Asian cities associate safety with road openness, while European cities favor greater enclosure. The study provides practical insights for creating urban environments that resonate with local cultural identities, enhancing well-being and supporting sustainable urban development.