Search Results (191)

Despite a growing number of Earth Observation satellites, a critical observational gap persists for timely, high-resolution flood mapping, primarily due to infrequent satellite revisits and persistent cloud cover. To address this issue, we propose a novel framework that synergistically fuses complementary data from three public sensor types. Our methodology harmonizes these disparate data sources by using surface water fraction as a common variable and downscaling them with flood susceptibility and topography information. This allows for the integration of sub-daily observations from the Visible Infrared Imaging Radiometer Suite and the Advanced Himawari Imager with the cloud-penetrating capabilities of the Advanced Microwave Scanning Radiometer 2. We evaluated this approach on the February 2022 flood in Brisbane, Australia using an independent ground truth dataset. The framework successfully compensates for the limitations of individual sensors, enabling the consistent generation of detailed, high-resolution flood maps. The proposed method outperformed the flood extent derived from commercial high-resolution optical imagery, scoring 77% higher than the Copernicus Emergency Management Service (CEMS) map in the Critical Success Index. Furthermore, the True Positive Rate was twice as high as the CEMS map, confirming that the proposed method successfully overcame the cloud cover issue. This approach provides valuable, actionable insights into inundation dynamics, particularly when other public data sources are unavailable. Full article

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article

Fagaceae trees dominate in the temperate and subtropical forests in East Asia. Understanding the spatial patterns of their carbon contents and the influencing factors can support high-precision forest carbon accounting. A comprehensive understanding of the changes in carbon in multiple organs of trees such as Fagaceae trees is still lacking at a large scale. This study investigated the inter-tissue variation, spatial patterns, and climatic drivers of carbon fraction across nine tissues (leaves, branches, bark, sapwood, heartwood, stump, coarse roots, medium roots, and fine roots) in 41 Fagaceae species (5 genera) from 12 sites across China’s major forest biomes. The sampling sites ranged from northern temperate to northern-tropical and covered an elevation range of 1200 m. The carbon fraction was measured with dry combustion after dried at 60 °C. Variance decomposition revealed that geographical location was the dominant source of variation (16%–55%), outweighing differences at the species and genus levels. Significant disparities in carbon fraction were observed among tissues, following a general pattern of leaves (517 mg g⁻¹) ≈ fine roots (516 mg g⁻¹) > heartwood (510 mg g⁻¹) > sapwood (504 mg g⁻¹) > branches (501 mg g⁻¹) ≈ medium roots (500 mg g⁻¹) > bark (495 mg g⁻¹) > coarse roots (488 mg g⁻¹) ≈ stump (487 mg g⁻¹). This indicated a “high-at-both-ends” arcuate pattern from leaves to fine roots. Spatially, carbon fractions in most tissues exhibited significant declining trends with increasing latitude and eastward longitude. Generalized additive models identified mean annual temperature and precipitation as the most influential factors for most above-ground tissues, while fine roots were primarily regulated by temperature seasonality. These findings help us understand the differences in tree carbon fraction from an organ perspective, highlighting the critical importance of multi-tissue sampling protocol. We recommend integrating the spatial and climatic drivers for refining forest carbon accounting. More species should be included to separate the species and climatic effects in the future. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

The use of aircraft for cloud seeding to enhance rainfall serves as an effective meteorological intervention and plays a vital role in ensuring ecological security within the context of the low-altitude economy. This study utilized ground-based precipitation observations from the Shiyang River Basin, in conjunction with Landsat satellite remote sensing imagery (2000–2024), regional historical regression, vegetation index retrieval, and spectral mixture analysis, to evaluate the effectiveness of aircraft-based cloud seeding for enhancing rainfall. The normalized difference vegetation index and the fraction of vegetation cover were calculated to examine the spatiotemporal dynamics and growth patterns of surface vegetation before and after the implementation of this rainfall enhancement measure, thus offering a quantitative assessment of the ecological restoration effect in the Shiyang River Basin. A novel application of cloud-seeding technology for ecological recovery has been developed. It provides one of the first quantitative assessments of aircraft-based cloud seeding in inland river basins of China, linking meteorological intervention directly to measurable ecological restoration outcomes. The findings indicate that: (1) Aircraft-based cloud seeding for rainfall enhancement has yielded significant results, with an average relative precipitation increase of 20.8% (p < 0.1%) in the operational area; (2) Following the commencement of this rainfall enhancement practice in 2010, normalized difference vegetation index and fraction of vegetation cover values within the study area have shown a marked increase, with the percentage of regions with low vegetation coverage declining from 30.36% to 25.21%; and (3) Since the implementation of this measure in 2010, vegetation conditions in the Shiyang River Basin have generally stabilized, demonstrating substantial improvement and a reduction in degradation. The percentage of regions classified as improved or slightly improved increased significantly, from 14.20% before the implementation of this measure to 36.24%, indicating a transition in the vegetation ecosystem from localized enhancement to overall improvement. These results demonstrate that ecological restoration efforts in the Shiyang River Basin have shown considerable improvement after the introduction of aircraft-based cloud-seeding operations, resulting in significant increases in vegetation coverage throughout extensive regions of the basin. The research connects scientific results to policy and management, suggesting that low-altitude economy-based cloud seeding can play a key role in water resource management, ecological stability, and climate resilience. Full article

Non-photosynthetic vegetation fractional cover (f_NPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R²) for f_NPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of f_PV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R² of 0.7825 for f_NPV and 0.8725 for photosynthetic vegetation fractional cover (f_PV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status. Full article

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article

Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LST_g) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (T_s), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated T_s values and sun-shielded air temperatures (T_a). In contrast, relationships of T_a to tree coverage, impervious area, or the LST_g of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article

Solar radiation is a first-order essential climate variable like temperature and precipitation. Its significant spatiotemporal variability, mainly due to atmospheric conditions, makes modelling particularly challenging, especially in regions with complex atmospheric dynamics and sparse meteorological stations. This study evaluates 6 solar radiation models (SARAH, PVGIS, Constant Atmospheric Conditions, Physical Solar Model, CAMS Worldwide, and InsolMets) using monthly measurements from 141 ground-based stations across the Iberian Peninsula from 2004–2020. Although all models consistently captured intra-annual variability, discrepancies in absolute values arise due to factors such as the differences in their functional designs and input parameters. InsolMets, which integrates cloud optical thickness, cloud fractional cover, the diffuse radiation component, and enhanced solar illumination geometry, was the most robust model, showing relevant improvements (61.5% in January, 59.7% in November, and 52.0% in December) compared to the worst-performing model (constant atmospheric conditions). Using as a threshold three times the root-mean-square error (RMSE) proposed by the Global Climate Observing System, InsolMets achieved the highest number of months (10) under this limit, also achieving the best overall result, with only 1 month showing non-significant correlations over the same time span. Nevertheless, SARAH and PVGIS matched InsolMets’ performance during March, November, and December. The results provide insights for selecting and improving solar radiation estimations, highlighting the need to incorporate remote sensing atmospheric data to minimize uncertainties. While all models that account for atmospheric effects enhance accuracy, InsolMets stands out as the most accurate model for estimating solar radiation across the Iberian Peninsula throughout the year, achieving the lowest RMSE and normalized RMSE values. Full article

Wetland vegetation is vital for ecological purification and climate mitigation. This study analyzes the spatiotemporal characteristics and influencing factors of water areas, fractional vegetation cover (FVC), and land use types in Poyang Lake wetland across wet and dry seasons (1990–2022) using remote sensing technology. The results showed that the water area remained overall stable during the wet seasons but decreased significantly in the dry seasons (19.27 km²/a). FVC exhibited an overall increasing trend, with vegetation expanding from lake margins to central areas. The land use areas of shallow water, bare ground, and Phalaris arundinacea–Polygonum hydropiper (P. arundinacea–P. hydropiper) communities showed interannual fluctuating decreases, while other land use types areas increased. From 1990 to 2020, land use changes were mainly characterized by the transformation of shallow water into deep water and bare ground, other vegetation into Carex cinerascens (C. cinerascens) community and bare ground, bare ground into deep water, as well as P. arundinacea–P. hydropiper community to C. cinerascens community. Rising temperatures enhanced FVC in both seasons, stimulated the expansion of C. cinerascens community area and total vegetation area, and reduced the dry season water area. Decreasing accumulated precipitation exacerbated water area loss and the decline of P. arundinacea–P. hydropiper communities. These findings provide critical insights for wetland ecological conservation and sustainable management. Full article

Fractional vegetation cover (FVC) is a key indicator of plant growth. Unmanned aerial vehicle (UAV) imagery has gained prominence for FVC monitoring due to its high resolution. However, most studies have focused on single phenological stages or specific crop types, with limited research on the continuous temporal monitoring of creeping plants. This study addresses this gap by focusing on Thymus mongolicus Ronniger (T. mongolicus). UAV-acquired visible light and multispectral images were collected across key phenological stages: green-up, budding, early flowering, peak flowering, and fruiting. FVC estimation models were developed using four algorithms: multiple linear regression (MLR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN). The SVR model achieved optimal performance during the green-up (R² = 0.87) and early flowering stages (R² = 0.91), while the ANN model excelled during budding (R² = 0.93), peak flowering (R² = 0.95), and fruiting (R² = 0.77). The predictions of the best-performing models were consistent with ground truth FVC values, thereby effectively capturing dynamic changes in FVC. FVC growth rates exhibited distinct variations across phenological stages, indicating high consistency between predicted and actual growth trends. This study highlights the feasibility of UAV-based FVC monitoring for T. mongolicus and indicates its potential for tracking creeping plants. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 μm) and near-infrared bands (0.865 μm). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes. Full article