Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO₂, CO, CH₄, NMHCs, NOx, NH₃, SO₂, BC, OC, PM_2.5, and PM₁₀ were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO₂ as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO₂ emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO₂ emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO₂ emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion.

Mangroves play a crucial role in supporting the biodiversity of coastal wetlands, acting as a vital link between terrestrial and marine ecosystems. In mainland China, Sonneratia apetala, an invasive mangrove species, has recently become dominant in these environments. While it contributes to the stability of mangrove ecosystems and is widely used in coastal restoration efforts, its rapid growth poses a significant threat to the survival of native mangrove species. However, the spatiotemporal growth dynamics and landscape impacts of Sonneratia apetala remain underexplored in scholarly research. This study employs remote sensing and GIS techniques to analyze the growth patterns of Sonneratia apetala over a 14-year period along the eastern coast of the Leizhou Peninsula in China. The analysis revealed the following key findings: (1) The mangrove area expanded from 274.17 hm² to 383.42 hm², with an average annual growth rate of 2.84%. (2) The area of Sonneratia apetala increased from 115.15 hm² in 2010 to 254.81 hm² in 2023, with an average annual growth rate of 1.29%. The area of local mangrove species declined from 163.02 hm² to 125.06 hm² (a decrease from 22.11% to 16.96%), with an average annual growth rate of −1.66%. (3) The number of Sonneratia apetala patches increased from 139 to 324, while the area-weighted shape index rose from 3.4 to 7.81. The decline of native mangrove species, driven by the rapid spread of Sonneratia apetala, suggests that this species is encroaching on native mangrove habitats. Through geospatial analysis, this study provides valuable insights into how introduced species can reshape mangrove landscape structures and the broader implications for regional biodiversity. These findings clearly demonstrate that Sonneratia apetala is encroaching upon local mangrove habitats, highlighting the urgent need for strategic management and conservation efforts to mitigate the ecological impacts of the proliferation of this species. Furthermore, this research is important for coastal sustainability management strategies that balance ecological restoration with the preservation of native biodiversity, ensuring long-term ecosystem health and resilience.

Accurate estimation of tree Above-Ground Biomass (AGB) is critical for the timely monitoring of forest dynamics. However, the scarcity of high-quality in situ measured data introduces considerable uncertainty into the precise spatial mapping of AGB. In this study, a novel method for AGB mapping is proposed to enhance the accuracy of AGB estimation based on the Semi-Supervised Ensemble Learning (SSEL) strategy. By expanding the sample set via an iterative self-training approach based on an Inverted Query-by-Committee (I-QBC) strategy, the model significantly enhances the accuracy of AGB estimation. Using Sentinel-2 data, the experimental results show that: (1) The I-QBC-driven SSEL model demonstrated significantly higher estimation accuracy for AGB compared to conventional tree-based ensemble models. Optimal stability (${\mathrm{R}}^2$ = 0.80) and peak accuracy (${\mathrm{R}}^2$ = 0.88) were achieved at sample increments of 20 and 30, respectively. (2) Among various feature types, Recursive Feature Elimination with Cross-Validation (RFECV) identified GNDVI, PSSRa, slope and texture correlation as the most critical predictors for AGB estimation in the study area. (3) The total AGB stock in the study area is estimated to range from 1.46 × 10⁷ Mg to 1.71 × 10⁷ Mg. The SSEL model provides a valuable reference for AGB estimation under sparse ground-truth sample conditions, while offering a novel approach for large-scale AGB mapping.

Long-term monitoring of wetland degradation, recovery, and spatial resilience requires methods that can jointly exploit multi-temporal remote sensing observations and spatially heterogeneous environmental information. This study develops an intelligent remote sensing framework for monitoring the disturbance–recovery dynamics and spatial resilience of typical wetland landscapes in Northeast China by coupling Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) with geographically weighted random forest (GWRF). Annual wetland landscape state sequences for 2000–2022 are constructed from multi-temporal Landsat observations and associated geospatial variables. LandTrendr time-series segmentation is used to identify disturbance timing, disturbance magnitude, recovery rate, and recovery duration, thereby converting long-term wetland change trajectories into quantitative indicators of degradation and recovery. At the GIS grid scale, remotely sensed vegetation and wetness indicators are integrated with wetland patch structure, hydrological conditions, topography, land-cover change, and anthropogenic pressure. GWRF then models local nonlinear relationships under spatial-distance weighting to generate a spatially explicit wetland resilience index and reveal geographically varying dominant controls. The LandTrendr–GWRF model achieved an R² of 0.872, an RMSE of 0.057, and an MAE of 0.039, outperforming RF, GWR, XGBoost, and standalone GWRF benchmarks. The mean wetland resilience index was 0.641, with low-, medium-, and high-resilience areas accounting for 27.86%, 44.37%, and 27.77% of the study area, respectively. Significant spatial clustering was detected (Moran’s I = 0.421, Z = 6.92, p = 0.001). Hydrological regulation, landscape connectivity, fragmentation constraint, and anthropogenic disturbance pressure emerged as the four dominant spatial response types. High-resilience cells formed contiguous clusters where hydrological recharge and landscape connectivity were strong, while low-resilience cells clustered where fragmentation and construction pressure were concentrated. Disturbance magnitude and recovery duration differed markedly among the four response types, with connectivity-dominated cells returning to the pre-disturbance state within 4.4 years and anthropogenic pressure-dominated cells requiring 11.7 years. The proposed framework extends wetland remote sensing from static condition mapping toward process-oriented monitoring of degradation, recovery, connectivity, and resilience, providing a practical approach for long-term wetland restoration assessment and spatial management.