Remote Sensing

Biomass burning aerosols (BBA) released from large-scale wildfires pose a serious threat worldwide, necessitating a comprehensive understanding of their plume characteristics. To address this challenge, this study used satellite data provided by the Second-generation Global Imager (SGLI) aboard the Global Change Observation Mission-C and regional-scale numerical chemical transport model (CTM) simulations to characterize BBA plumes. The SGLI data and CTM simulations were compared and verified, and the 3D characteristics of BBA plumes, including concentration, diffusion range, spatial variation in optical properties, plume top height, and vertical profile, were subsequently derived. In this study, we focused on large-scale forest fires that occurred in western North America in September 2020 and Indonesia in September 2019. In both cases, Aerosol optical thickness (AOT) and Ångström Exponent (AE) values show a positive correlation with the height of the BBA plume top. The results showed that the higher the BBA plume top, the thicker the plume and the smaller the aerosol size. This point is what we particularly wish to highlight in this study. The SGLI polarization data proved useful for characterizing the upper layers of the BBA plumes. By understanding the detailed characteristics at the top of the plume, it is possible to predict the BBA plume’s advection and lifetime.

Global warming profoundly affects hydrological processes and regional aridity. However, the shifts in the arid–humid transition zone and its relationship to divergent surface and subsurface hydrological responses remain not fully understood. This study investigates the spatiotemporal aridity changes in China using hydroclimate datasets (1950–2022) and examines associated hydrological responses via remote sensing (RS) since the early 2000s. The results reveal that: (1) a pronounced ~32-year oscillatory pattern governs both the expansion and contraction of drylands and non-drylands, with China currently in a wetting phase; (2) a distinct climatic transitional zone is identified, and a distinct boundary emerges separating drylands and non-drylands, here referred to as China’s Arid–Humid Divide, reflecting the climatic equilibrium shaped by multiple monsoon systems and local topography; and (3) the nationwide expansion of surface water bodies, following the increase of groundwater storage in partial areas, was detected via recent RS data. These findings provide new insights into the mechanisms driving long-term aridity transitions and support climate adaptation and sustainable land management in China.

The US Southwest is one of the driest and hottest regions, with a recent upsurge in land surface temperature (LST). Further, with land-use changes and global warming, anthropogenic pollution also significantly contributes to the rise in surface temperatures. While the impact of pollution on LST has been studied only in specific urban regions, insights from a broader, more diverse topography remain limited. This research incorporates LST with land cover parameters (NDBI, MNDWI, NDBSI, SAVI, WET), surface albedo, air pollutants (NO₂, SO₂, O₃, CO), aerosol particles, urban nighttime light, and digital elevation model to evaluate the non-linear spatial dependence of these variables for the summer (from June to August 2025) and winter (from December 2024 to February 2025) seasons in the US southwest. All multi-resolution inputs were harmonized by projecting to WGS84 and applying a ~11 km fishnet sampling grid commensurate with the coarsest-resolution dataset (Sentinel-5P), ensuring each sample captures a unique pixel value across all layers. AutoML was applied to benchmark learning algorithms, and we found that CatBoost, Extra Trees, LightGBM, HistGradientBoosting, and Random Forest were among the optimal models for predicting LST. After tuning these models using Bayesian optimization, we achieved a mean R² of 0.86 during summer and 0.84 during winter. After developing the hyperparameter-optimized model, explainable AI, e.g., SHAP, was employed to understand the complex nonlinear dynamics and top contributing features. Landcover variables had a more dominant impact on the spatial distribution of summer LST, while winter LST was more influenced by pollutant parameters. Partial Dependency Plot and Accumulated Local Effect were further incorporated to examine the marginal effects of the top-contributing features on spatial LST prediction. By extending the study area to the entire US Southwest, this study effectively captures urban–rural contrasts, climate- and land-cover–dependent pollutant responses, and regional climatic influences. It presents explicit spatial dependencies among LST, pollutants, land cover, topography, and nighttime activity that will aid future researchers and policymakers in effectively developing sustainable thermal planning for urban activities.