Search Results (4,436)

To address the limitations of traditional forest above-ground biomass (AGB) retrieval methods—namely, the restricted accuracy of physical models and the limited generalization ability of purely data-driven models—this study proposes a differentiable physical modeling (DPM) approach for forest AGB estimation. The method adopts the water cloud model (WCM) as a physics-based framework, grounded in radiative transfer theory, and integrates C-band synthetic aperture radar (SAR) data with multispectral imagery. Within the PyTorch tensor computation framework, automatic differentiation (AD) is employed to seamlessly couple the WCM with the deep fully connected neural network (DFCNN), enabling a differentiable implementation of the WCM. Using mean squared error (MSE) as the loss function, the neural network parameters are optimized through backpropagation and gradient descent, thereby constructing an end-to-end trainable DPM model that effectively retrieves forest AGB while preserving physical interpretability and generalization capability. To validate the proposed method, two representative test sites were selected: Simao in Pu’er, Yunnan Province, and Genhe in Inner Mongolia. GF-3 PolSAR and RADARSAT-2 data were used to extract backscattering coefficients and compute the radar vegetation index (RVI), while Landsat 8 OLI imagery was employed to calculate the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and soil-adjusted vegetation index (SAVI). These datasets, together with ASTER GDEM, field-measured biomass, and other relevant datasets, were integrated to construct a multisource dataset combining remote sensing and ground observations. The performance of the DPM model was then compared with the traditional WCM and several data-driven models, including the fully connected neural network (FNN), generalized regression neural network (GRNN), RF, and Adaptive Boosting (AdaBoost). The results indicate that the DPM model achieved R² = 0.60, RMSE = 24.23 Mg/ha, Bias = 0.4 Mg/ha, and ubRMSE = 22.43 Mg/ha in Simao, and R² = 0.48, RMSE = 33.29 Mg/ha, Bias = 0.87 Mg/ha, and ubRMSE = 33.28 Mg/ha in Genhe, demonstrating consistently better performance than both the WCM and all tested data-driven models. The DPM model demonstrated consistent performance across ecologically contrasting forest regions. It alleviated the systematic overestimation bias of purely data-driven models and overcame the limitations in predictive accuracy resulting from the simplified structure of the WCM. The differentiability of the WCM enables the loss function errors to be backpropagated through the neural network, thereby allowing the optimization of the physical model parameters. Overall, the DPM framework integrates the advantages of both physical models and data-driven approaches, providing an estimation method with acceptable accuracy for forest AGB retrieval. It also offers theoretical and practical insights for the integration of deep learning and physical knowledge in other research fields. Full article

Bolivia is home to approximately 20% of the tropical glaciers in South America, which are sensitive indicators of climate change and critical water resources. Glaciers in the Cordillera Real supply meltwater to Bolivia’s administrative capital, La Paz, making it important to accurately assess their evolution. This study reassesses the trajectory of glacier loss in the Cordillera Real between 1992 and 2024. We construct a time-evolving glacier inventory utilizing remote sensing data (Landsat) and techniques to limit the impact of ephemeral snow cover. Our inventory is at a temporal resolution (5- to 8-year spacing) that allows us to assess the trajectory of glacier loss using statistical models. Between 1992 and 2024, the Cordillera Real lost 103.67 ± 9.97 km² of glacierized area, representing a 42.0 ± 2.1% reduction. We find that glaciers in the Cordillera Real have been retreating at a constant absolute loss rate of 2.99 [2.32, 3.67] km² yr⁻¹ and a constant fractional loss rate of 1.6 [1.3, 1.9]% yr⁻¹, contrasting with past studies that suggest accelerating or decelerating loss rates. Our findings provide new insights into the current extent of glaciers in the Cordillera Real and their longevity. The time-evolving inventory is available for use in future studies on the evolution of glaciers in the Cordillera Real and the impacts of their continued loss. Full article

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature (LST) from 1 March to 31 July 2025—a recent period marked by multiple heatwaves across the region. To assess the cooling performance of wetlands, data samples were collected within the buffer zones. A Light Gradient Boosting Machine was trained to characterize the relationship between cooling intensity and a set of influencing factors (e.g., distance to wetland boundary, land use/land cover, built-up density, and green space density). The model explains approximately 91% of the variation in cooling intensity around wetlands. Notably, a machine-learning-based simulation framework was proposed to attain insights into the cooling characteristics of the riparian zone. The result indicates a mean cooling effect of about 2 °C and an effective cooling distance of 210 m from the wetland boundary. Partial dependence analysis further reveals that increasing built-up density substantially weakens cooling performance and implies that, for the conditions observed in Hue City, maintaining built-up density near wetlands below roughly 45% is favorable for sustaining effective cooling of the blue space, as indicated by the model-based partial dependence analysis. Overall, the research findings provide a data-driven basis for informing urban planning and wetland management in Hue City to mitigate heat stress. Full article

Mountain ecosystems are susceptible to climate change, and alpine treeline ecotones (ATEs) represent one of the significant responsive indicators of climate-driven environmental change. This study examines long-term spatiotemporal dynamics of the ATE across the Eastern Slopes of the Canadian Rocky Mountains (ESCR) from 1984 to 2023, with the objective of assessing whether regional climate warming has influenced ATE extent and elevation across different aspects and watersheds. Multi-decadal Landsat imagery, ERA5-Land temperature data, and topographic variables were integrated within a Google Earth Engine (GEE) framework to map ATEs using the Alpine Treeline Ecotone Index (ATEI), a probabilistic approach designed to capture transitional vegetation zones. Temporal trends were evaluated using non-parametric statistics, correlation analyses, and watershed- and aspect-based comparisons. Results indicate that the total alpine treeline ecotone (ATE) area in the ESCR was approximately 13.3% larger in 2023 than in 1984. However, the temporal evolution of ATE extent and elevation was non-monotonic, and linear trend analyses did not detect statistically significant increasing or decreasing trends over the full study period. ATE elevation and expansion exhibited pronounced spatial heterogeneity, with greater changes occurring on north- and northwest-facing slopes and within selected watersheds. In contrast, summer (July–September) temperatures increased significantly (+2.84 °C), exceeding global land-only warming rates, and vegetation greenness (NDVI) showed a strong, statistically significant positive relationship with temperature. These findings show that while climate warming has clearly increased vegetation productivity, elevational ATE dynamics remain spatially heterogeneous and temporally non-synchronous with summer temperature trends. Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

Monitoring surface water quality over large river systems remains challenging due to sparse in situ sampling and the need for decision-ready indicators. This study aims to address this problem by developing and evaluating an integrated Landsat 8-based backpropagation neural network and Canadian Council of Ministers of the Environment Water Quality Index (L8-BPNN-CCME-WQI) for precise surface water quality assessment over the Saint John River (SJR), New Brunswick, Canada. The proposed approach combines atmospherically corrected Landsat 8 imagery, BPNN for estimating multiple surface water quality parameters (SWQPs), and CCME-WQI to translate SWQP fields into transparent water quality levels. The L8-BPNN-CCME-WQI models were trained using in situ measurements of turbidity, total suspended solids (TSS), total solids (TS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, electrical conductivity (EC), and temperature collected during our five field campaigns (from June 2015 to August 2016) and surface reflectance from five Landsat 8 scenes. The developed models achieved high performance during internal calibration and testing (R² ≥ 0.80 for all SWQPs) and demonstrated robust performance (R² ≈ 0.75–0.88) when applied to two independent surface water quality datasets from additional rivers across New Brunswick. Pixel-wise SWQP predictions were then input to the CCME-WQI formulation to derive reach-scale water quality levels, revealing that the lower Saint John River basin (below the Mactaquac Dam) is generally classified as “Fair” (CCME-WQI ≈ 67), whereas the middle basin upstream (above the Mactaquac Dam) is “Marginal” (CCME-WQI ≈ 59), reflecting stronger industrial and agricultural pressures. Overall, the L8-BPNN-CCME-WQI framework provides a scalable methodology for converting multi-parameter satellite-derived water quality information into spatially exhaustive CCME-WQI classes, supporting targeted regulation, prioritization of mitigation in critical reaches, and evaluation of management actions in large river systems. Full article

Glaciers in the Canadian Arctic Archipelago (CAA) contribute significantly to sea-level rise, yet sparse in situ data limit regional climate assessments. This study presents the first decadal (2013–2024) satellite-derived time series of late-summer snowline altitude (SLA) for six CAA glaciers, utilising 9920 Landsat 8/9 and Sentinel-2 scenes. Glacier surface cover types (snow and bare ice) were mapped via machine learning, and SLA was extracted using elevation-binning and Snow-Elevation Histogram Analysis (SEHA). Elevation data were obtained from ArcticDEM v3; positive degree days (PDD) from Eureka, Pond Inlet, and Pangnirtung were used to characterize melt-season forcing. Satellite-derived SLA was validated against equilibrium-line altitude (ELA) observations from White Glacier. All glaciers exhibit a characteristic seasonal SCA cycle: maximum extent in June, minimum in August, and partial recovery in September, with extreme anomalies in 2020. Annual peak SLA correlates positively with summer warmth; sensitivities to PDD were 2.56, 0.67, and 0.83 m (°C d)⁻¹ for White, Highway, and Turner glaciers, respectively. Hypsometry strongly modulates climatic sensitivity: glaciers with limited high-elevation area (e.g., BylotD20s, Turner) frequently lose their accumulation zones in warm years. At White Glacier, SLA replicates interannual ELA variability with high correlation and lower error using the elevation-bin method (mean bias +53 m; RMSE 177 m) compared with SEHA (+165 m; 339 m). Meteorological records indicate significant summer and winter warming at Eureka, with increasing PDD; precipitation trends are spatially variable. A regionally calibrated, quality-assured elevation-bin method produces objective and transferable SLA time series, suitable for ELA estimation in data-sparse Arctic settings. The SLA–PDD relationship and hypsometry-dependent responses highlight increasing stress on accumulation zones under continued warming. Reporting SLA uncertainty and image quality, alongside expanded field observations, will enhance Arctic-wide glacier monitoring. Full article

Arctic regions are experiencing accelerated environmental change, yet integrated assessments of terrain-scale responses remain limited. This study quantifies the spatial-temporal variability of glaciers, shorelines, and outwash plains in Kaffiøyra, Svalbard, Norway, over four decades (1985–2023) using cross-evaluated Landsat and Sentinel imagery. Our results reveal systematic retreat across all eight glaciers (R² = 0.83–0.96), with tidewater glaciers experiencing substantially greater terminus area loss (62.8% and 72.1%) compared to land-terminating glaciers (34.5–69.0%). Coastal changes were highly variable: erosion (up to −3.2 m/yr) was most pronounced on shores exposed to southwesterly summer waves, while significant accretion (+13.0 m/yr) occurred near the tidewater glacier terminus. The insignificant outwash changes (−6.4% to +2.7%) despite substantial land-terminating glacier retreat indicate these systems respond to different controls. A moderate negative correlation between glacier terminus area and summer temperatures (r = −0.55 to −0.69) enabled a simple projection model. Diagnostic projections to 2020–2039 showed that both downscaled climate models and extrapolated local data overestimated retreat. However, extrapolated local data proved more accurate, with its projection gap averaging 11% for land-terminating and 46% for tidewater glaciers. The study provides crucial insights into Arctic terrain behaviors, highlighting complex and divergent responses. These findings emphasize the need for enhanced localized monitoring systems through ongoing high-resolution image surveys and planned modeling to understand accelerating polar environmental changes. Full article

This study aimed to analyze changes in vegetation, built-up areas, and population growth in Lahore city from 1990 to 2023. The data was acquired from Google Earth Engine, and the spectral bands were retrieved from Landsat 5 and Landsat 8. The decadal analysis of the landscape was conducted from 1993 to 2001, 2001 to 2012, and from 2013 to 2023. Further analysis was conducted in ArcGIS version 10.3 to evaluate the Normalized Difference Vegetation Index and the Normalized Difference Built-up Index to assess vegetation and built-up areas, respectively. To analyze the urban population of Lahore, data were obtained from the Global Human Settlement Layer for 1990, 2000, 2010, and 2020. Results revealed that the total vegetated area of Lahore city decreased from 1453.0 km² in 1993–2001 to 788.2 km² in 2013–2023. Moreover, the urban built-up area expanded from 319.6 km² in 1993–2001 to 966.8 km² in 2013–2023. Sub-district-level analysis indicated that Model Town and Raiwind areas of Lahore depicted better vegetation recovery in this decade. The population of Lahore has been increasing steadily, with the 2010s being a particularly rapid period of growth. The projections for 2030 also depict a continuous growth pattern. This study was further developed by integrating multi-decadal averaging coupled with selected-year analysis to distinguish gradual land transformation from relatively accelerated phases of urban expansion of Lahore. Also, by combining NDVI and NDBI values on both Lahore and its tehsil level, the research provides a collective sub-district- and district-level perspective into the spatial heterogeneity of peri-urban transformations. The findings of the study explain how major infrastructural projects shape the urban growth patterns of cities like Lahore and cause a decline in the green areas of fast-growing cities in South Asia. This study further highlights the consequences of unplanned urban expansion in regions where high population growth has compromised green infrastructure and threatened ecological balance. In addition, it supports several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land) by providing spatial evidence of urban expansion of the city and losses of its green spaces. The findings offer empirical insights to support climate-resilient developments. The study also demonstrates the necessity of integrating green infrastructure and providing robust strategies for forthcoming urban planning projects and policy development regarding urban expansion. Full article

Fire suppression in the upper U.S. Midwest has led to the expansion of flammable coniferous ladder fuels, necessitating precise tracking of conifer species basal area (BA) for fire risk management. This study benchmarks four subset-selection pipelines—xPLS, GA-xPLS, RF-xPLS, and SVR-xPLS—to optimize the fusion of high-dimensional, collinear data from Sentinel-2, Landsat-9, and LiDAR sensors. Using 141 field plots in Minnesota’s Kawishiwi Ranger District of the Superior National Forest, we evaluated 175 predictors against eight BA response variables. Results show that RF-xPLS provided the superior accuracy–parsimony trade-off, achieving the highest pooled R² (≈0.86) and lowest error with a compact 27-predictor block. GA-xPLS ranked second, excelling for specific species such as Pinus resinosa. The most effective predictors combined SWIR-based moisture indices, red-edge/NIR structure, and a single LiDAR-derived surface of vertical-structure (quadratic mean height). Our findings demonstrate that integrating machine learning selection engines with multi-sensor fusion substantially enhances the scalability and precision of forest inventory and fuels monitoring. This comparative framework offers practical insights for sustainable management and fire risk mitigation in northern temperate–boreal forests. Full article

This study examined the glacial lake of JiongpuCo in the southeastern Tibet region. According to satellite images obtained by Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) from 1995 to 2025, JiongpuCo’s area expanded from 1.92 ± 0.06 km² to 5.26 ± 0.02 km², which is a 174% increase over 30 years. The lake was in a state of dynamic equilibrium. The bathymetric data showed that JiongpuCo has a basin-like morphology. Its reservoir capacity curve was concave-up, with a maximum water depth of 237 m and total reservoir capacity of 6.35 × 10⁸ m³. A sequential HEC-RAS-MIKE 21 numerical modeling framework was constructed to simulate flood propagation. For three simulated scenarios (with breach volumes of 80%, 60%, and 30%), the peak discharge at the breach outlet was 28,368.45 m³/s, 25,451.67 m³/s, and 17,855.54 m³/s. Analysis of the simulation results shows that the glacier lake outburst flood (GLOF) has continuous attenuation of peak discharge and a gradual lag in arrival time along the flow path. Except for Bagai in Scenarios 2 and 3, all other target research towns and villages were flooded by floodwaters. These findings offer a solid scientific foundation for the reduction in GLOF disasters and the development of an early warning system for JiongpuCo. Full article

Accurately estimating urban above-ground vegetation carbon density (UAGVCD) is crucial for assessing urban carbon sinks, but it is difficult due to varying spatial patterns, complex land covers, and differences caused by terrain. This study measures UAGVCD in Shenzhen, China, using an explainable remote sensing and machine-learning approach. We combined Landsat 8 spectral bands, vegetation indices, texture metrics, and terrain-based variables with 195 field measurements of carbon density to develop an Extreme Gradient Boosting (XGBoost) model. We evaluated model performance with spatial block cross-validation, using block sizes of 2 km, 5 km, and 10 km to account for spatial autocorrelation. The results show that the XGBoost model performed reliably during spatially independent validation, with the 5 km block showing the best accuracy (train R²= 0.917 ± 0.086, RMSE= 5.53 ± 3.97 Mg ha⁻¹; validation R² = 0.617 ± 0.055, RMSE = 10.25 ± 1.39 Mg ha⁻¹). Smaller blocks gave more varied results, while larger blocks led to a significant drop in accuracy (validation R² = 0.380 ± 0.297 at 10 km). Predictions showed clear differences in UAGVCD, with higher values in mountainous and green areas and lower values in highly developed regions. SHapley Additive exPlanations (SHAP) analyses suggested that both spectral and topographic factors play a significant role in UAGVCD. Additionally, the relationships between spectral data and carbon density showed strong nonlinear responses affected by terrain. These findings highlight the importance of spatially explicit validation and explainable machine learning for reliable urban vegetation carbon mapping. Full article

Monitoring the biophysical parameters of agricultural surfaces is a key issue for food security in the context of climate change. Since 2016, agricultural surfaces can be monitored from space at high spatial resolution (~10/30 m) in the microwave and optical domains owing to radiometer and SAR sensors onboard Sentinel-1, -2 and Landsat-8 satellites. This paper draws on multi-temporal acquisitions over a six-year period to analyze satellite time series for the main winter and summer crops (corn, sunflower, soybean, sorghum, rapeseed, wheat) grown in southwestern France and more widely cultivated around the world. From January 2016 to December 2021, satellite signals extracted at the field spatial scale offer a unique opportunity to monitor agricultural surfaces with a high temporal resolution (every 1 or 2 days) never achieved before thanks to the combination of multi-sensor and multi-orbit data. Analyses on the impact of the topography and satellites’ viewing angles showed that the NDVI values derived from Sentinel-2 and Landsat-8 are very close (r > 0.92) and can be merged to construct multi-annual time series. Angular sensitivity is much more pronounced for radar images; while it demonstrates a weaker cross-polarization and polarization ratio, it is greater for co-polarization. Optical and radar time series are modulated in time and amplitude depending on yearly climatic conditions and agricultural practices. The combined use of the ascending and descending orbits of the two Sentinel-1 satellites makes it possible to detect specific periods (harvest, flowering) for certain crops (wheat and sunflower). The long-term approach has enabled the modeling of satellite time series using double logistic functions with good performance (r > 0.92 on average), allowing the identification of interannual variations of crop development driven by climatic conditions and agricultural practices. Full article

Precise spatiotemporal monitoring of soil moisture is fundamental to the efficient regulation and sustainable utilization of agricultural water resources in arid and semi-arid irrigation districts. This study focuses on the Yichang Irrigation District within the Hetao Irrigation Area to elucidate the spatiotemporal dynamics of surface soil moisture during the crop growing season. Multi-year Landsat 8/9 remote sensing imagery (2022–2024) was integrated with the Temperature Vegetation Dryness Index (TVDI) framework to construct two feature spaces, namely Normalized Difference Vegetation Index–Land Surface Temperature (NDVI–LST) and Enhanced Vegetation Index–Land Surface Temperature (EVI–LST). A dual-index complementary inversion strategy was applied for soil moisture estimation, and the outputs were validated against Soil Moisture Active Passive (SMAP) soil moisture products and MOD16 evapotranspiration products. Results indicated that the dry edges of the feature spaces derived from both vegetation indices exhibited double-inflection-point characteristics, with optimal fitting intervals located between the inflection points. The inflection point positions shifted dynamically with variations in crop coverage. During bare-soil and low-vegetation-coverage periods (May, June, and September), the minimum thresholds for low NDVI and EVI values were 0.07 and 0.06, respectively, whereas during high-vegetation-coverage periods in July and August, the minimum thresholds for both indices increased to 0.15. NDVI demonstrated superior performance during May, June, and September, whereas EVI exhibited greater advantages during active crop growth periods in July–August. The optimized model achieved robust inversion accuracy, with a validation R² of 0.81 for the measured soil moisture in the 0–20 cm layer on 12 May 2024. The inversion results exhibited strong correlations with the SMAP soil moisture products (R² = 0.663 during low crop coverage; R² = 0.625 during high crop coverage) and MOD16 evapotranspiration data (R = 0.751). The spatiotemporal patterns of soil moisture were distinctly discerned. Following spring irrigation in May, abundant moisture in certain areas resulted in bimodal distribution patterns in the inversion results. June exhibited the lowest soil moisture content across the study area, with arid zones making up 36.67% of the total area. From July to August, concentrated precipitation coupled with summer irrigation reduced the proportion of extremely arid zones to below 0.98%. Full article

Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs a comprehensive framework coupling “Morphometric Stability–Ecological Security Reliability–Resource Use Efficiency” to delineate the suitable ecological interval for Ebinur Lake, the largest saltwater lake in Xinjiang. Using multi-source remote sensing data (Landsat, Sentinel, ICESat, CryoSat), we reconstruct the long-term hydrological dynamics from 2001 to 2023. Results indicate a significant shrinking trend in the lake area, driven primarily by reduced inflow. We jointly consider the lake morphometric breakpoint, the ecological security baseline, and the lower bound of ecosystem service water use efficiency (ESWUE) to determine a minimum suitable ecological area of 500 km²; the regulation upper limit is set at 740 km² based on the marginal peak of ESWUE. However, monitoring data reveal that the lake falls below the minimum 500 km² baseline in approximately 40% of months, highlighting a severe ecological deficit risk. Furthermore, ESWUE analysis shows a peak in April (10 CNY/m³), suggesting that, under current climate conditions, a “Spring Surplus and Autumn Deficit” regulation strategy—advancing the replenishment window to the spring windy season—can maximize dust suppression benefits at a lower evaporative cost. This study provides a theoretical basis and methodological paradigm that will contribute to the sustainable management of shrinking terminal lakes globally. Full article