Search Results (44)

The Yangtze River Basin, one of China’s most vital watersheds, sustains both ecological balance and human livelihoods through its extensive lake systems. However, since the 1980s, these lakes have experienced significant ecological degradation, particularly in terms of aquatic vegetation decline. To acquire reliable aquatic vegetation data during the peak growing season (July–September), when clear-sky conditions are scarce, we employed Chinese domestic satellite imagery—Gaofen-1/6 (GF-1/6) Wide Field of View (WFV) and Huanjing-2A/B (HJ-2A/B) Charge-Coupled Device (CCD)—with approximately one-day revisit frequency after constellation networking, 16 m spatial resolution, and excellent spectral consistency, in combination with deep learning algorithms, to monitor aquatic vegetation across the basin. Comparative experiments identified the near-infrared, red, and green bands as the most informative input features, with an optimal input size of 256 × 256. Through visual interpretation and dataset augmentation, we generated a total of 5016 labeled image pairs of this size. The U-Net++ model, equipped with an EfficientNet-B5 backbone, achieved robust performance with an mIoU of 90.16% and an mPA of 95.27% on the validation dataset. On independent test data, the model reached an mIoU of 79.10% and an mPA of 86.42%. Field-based assessment yielded an overall accuracy (OA) of 75.25%, confirming the reliability of the model. As a case study, the proposed model was applied to satellite imagery of Lake Taihu captured during the peak growing season of aquatic vegetation (July–September) from 2020 to 2025. Overall, this study introduces an automated classification approach for aquatic vegetation using 16 m resolution Chinese domestic satellite imagery and deep learning, providing a reliable framework for large-scale monitoring of aquatic vegetation across lakes in the Yangtze River Basin during their peak growth period. Full article

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

The Chinese HuanjingJianzai-2 (HJ-2) A/B satellites are equipped with advanced sensors, including a Multispectral Camera (MSC) and a Polarized Scanning Atmospheric Corrector (PSAC). To address the challenges of atmospheric correction (AC) for the MSC’s wide-swath, wide-field images, this study proposes a pixel-by-pixel method incorporating Bidirectional Reflectance Distribution Function (BRDF) effects. The approach uses synchronous atmospheric parameters from the PSAC, an atmospheric correction lookup table, and a semi-empirical BRDF model to produce surface reflectance (SR) products through radiative, adjacency effect, and BRDF corrections. The corrected images showed significant improvements in clarity and contrast compared to pre-correction images, with minimum increases of 55.91% and 35.63%, respectively. Validation experiments in Dunhuang and Hefei, China, demonstrated high consistency between the corrected SR and ground-truth data, with maximum deviations below 0.03. For surface types not covered by ground measurements, comparisons with Sentinel-2 SR products yielded maximum deviations below 0.04. These results highlight the effectiveness of the proposed method in improving image quality and accuracy, providing reliable data support for applications such as disaster monitoring, water resource management, and crop monitoring. Full article

Methane (CH₄) emissions in coal-energy-rich regions are characterized by hidden emission point sources and highly variable emission rates. While the Matched Filter (MF) method for detecting the CH₄ point source using hyperspectral satellite sensors has been validated for high-emission concentrations, the accurate inversion of low-concentration emissions in complex environments remains challenging. In this study, an ‘end-to-end’ experiment—from emission simulations to satellite spectra and inversion results—has been designed to quantify the impact of internal payload parameters and environmental parameters for CH₄ emission inversions, and perform real-scenario calculations. The study reveals several key findings: (1) Under ideal conditions, 15% of satellite spectral noise contributes to a 13% bias in CH4 detection inversion, and a spectral resolution of 10–14 nm allows the detection of CH₄ emissions with concentrations as low as 350 ppb, above the background level of 1900 ppb. (2) For near-surface aerosols at 2100 nm, an aerosol optical depth (AOD) of 0.1 leads to a low bias of −51.6% with water-soluble aerosols and a strong bias of −69.2% with black carbon aerosols, while dust aerosols induce a medium bias of up to −60.7%. (3) The height of the aerosol layer affects the accuracy of methane inversion, which is up to 7.3% higher under aerosol conditions at 3 km than under aerosol conditions near the ground. (4) When the CH₄ emission source and its diffuse plume are located above a high-reflectance (bright) surface, while the background CH₄ concentration is associated with a low-reflectance (dark) surface, the significant reflectance contrast between the two surfaces leads to a rapid degradation in inversion accuracy. This contrast makes it impossible to effectively extract CH₄ signals when the reflectance difference reaches 0.2. (5) Under harsh conditions, where multiple parameters are present (AOD = 0.2, albedo = 0.2, aerosol layer height (ALH) = 2), the MF method is still able to detect CH₄ emissions, but with a significant error of 74.65%. (6) External environmental variables, particularly atmospheric pressure and water vapor content, significantly influence the inversion accuracy of methane (CH₄) concentrations. Variations in atmospheric pressure induce deviations in the CH₄ concentration distribution, resulting in an average inversion error of −12.06%. Similarly, elevated water vapor levels can lead to a maximum error of −16.2%. These findings highlight the substantial challenges in accurately detecting low-concentration CH₄ emissions. The results offer critical insights for refining CH₄ detection algorithms and enhancing the precision of satellite-based inversions for low-concentration CH₄ point-source emissions. Full article

Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection. Full article

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake’s water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake’s surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake’s water surface area decreased by 8.33 km², with a maximum surface area of 40.49 km² on 7 January 1986, and a minimum surface area of 10.64 km² on 20 April 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake’s surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake’s ecology. Full article

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems. Full article

The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R² = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R² = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R² of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images. Full article

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km². Full article

As the latest members of Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianzai-2 (HJ-2) series satellites were launched on 27 September 2020 by China and are usually abbreviated as HJ-2 A/B satellites. The polarized scanning atmospheric corrector (PSAC) is one of main sensors onboard HJ-2 A/B satellites, which is mainly used to monitor atmospheric components such as water vapor and aerosols. In this study, a columnar water vapor (CWV) retrieval algorithm using two bands (865 and 910 nm) is developed for PSAC. The validation results of PSAC CWV data based on ground-based CWV data derived from Aerosol Robotic Network (AERONET) show that PSAC CWV data has a high accuracy, and all statistical parameters of PSAC CWV data are better than those of Moderate-resolution Imaging Spectroradiometer (MODIS) CWV data released by NASA. Overall, there is no obvious overestimation or underestimation in PSAC CWV data. The root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and percentage of CWV data with error within $\pm \left(0.05+0.10\ast CW{V}_{AERONET}\right)$ (PER10) of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, the results of day-to-day comparisons between PSAC CWV data and AERONET data show that PSAC CWV data can effectively characterize the change trend of CWV. Full article

To determine whether mitigating the harmful effects of circulating microvesicle-associated inducible nitric oxide (MV-A iNOS) in vivo increases the survival of challenged mice in three different mouse models of sepsis, the ability of anti-MV-A iNOS monoclonal antibodies (mAbs) to rescue challenged mice was assessed using three different mouse models of sepsis. The vivarium of a research laboratory Balb/c mice were challenged with an LD₈₀ dose of either lipopolysaccharide (LPS/endotoxin), TNFα, or MV-A iNOS and then treated at various times after the challenge with saline as control or with an anti-MV-A iNOS mAb as a potential immunotherapeutic to treat sepsis. Each group of mice was checked daily for survivors, and Kaplan–Meier survival curves were constructed. Five different murine anti-MV-A iNOS mAbs from our panel of 24 murine anti-MV-A iNOS mAbs were found to rescue some of the challenged mice. All five murine mAbs were used to genetically engineer humanized anti-MV-A iNOS mAbs by inserting the murine complementarity-determining regions (CDRs) into a human IgG_1,kappa scaffold and expressing the humanized mAbs in CHO cells. Three humanized anti-MV-A iNOS mAbs were effective at rescuing mice from sepsis in three different animal models of sepsis. The effectiveness of the treatment was both time- and dose-dependent. Humanized anti-MV-A iNOS rHJ mAb could rescue up to 80% of the challenged animals if administered early and at a high dose. Our conclusions are that MV-A iNOS is a novel therapeutic target to treat sepsis; anti-MV-A iNOS mAbs can mitigate the harmful effects of MV-A iNOS; the neutralizing mAb’s efficacy is both time- and dose-dependent; and a specifically targeted immunotherapeutic for MV-A iNOS could potentially save tens of thousands of lives annually and could result in improved antibiotic stewardship. Full article

Hybrid joining (HJ) is the combination of two or more joining techniques to produce joints with enhanced properties in comparison to those obtained from their parent techniques. Their adoption is widespread (metal to metal joint, composite to composite and composite to metal) and is present in a vast range of applications including all industrial sectors, from automotive to aerospace, including naval, construction, mechanical and utilities. The objective of this literature review is to summarize the existing research on hybrid joining processes incorporating structural adhesives highlighting their field of application and to present the recent development in this field. To achieve this goal, the first part presents an introduction on the main class of adhesives, subdivided by their chemical nature (epoxy, polyurethane, acrylic and cyanoacrylate, anaerobic and high-temperature adhesives) The second part describes the most commonly used Hybrid Joining (HJ) techniques (mechanical fastening and adhesive bonding, welding processes and adhesive bonding) The third part of the review is about the application of adhesives in dependence of performance, advantage and disadvantage in the hybrid joining processes. Finally, conclusions and an outlook on critical challenges, future perspectives and research activities are summarized. It was concluded that the use of hybrid joining technology could be considered as a potential solution in various industries, in order to reduce the mass as well as the manufacturing cost. Full article

DNA lesions that impede fork progression cause replisome stalling and threaten genome stability. Bacillus subtilis RecA, at a lesion-containing gap, interacts with and facilitates DisA pausing at these branched intermediates. Paused DisA suppresses its synthesis of the essential c-di-AMP messenger. The RuvAB-RecU resolvasome branch migrates and resolves formed Holliday junctions (HJ). We show that DisA prevents DNA degradation. DisA, which interacts with RuvB, binds branched structures, and reduces the RuvAB DNA-dependent ATPase activity. DisA pre-bound to HJ DNA limits RuvAB and RecU activities, but such inhibition does not occur if the RuvAB- or RecU-HJ DNA complexes are pre-formed. RuvAB or RecU pre-bound to HJ DNA strongly inhibits DisA-mediated synthesis of c-di-AMP, and indirectly blocks cell proliferation. We propose that DisA limits RuvAB-mediated fork remodeling and RecU-mediated HJ cleavage to provide time for damage removal and replication restart in order to preserve genome integrity. Full article

In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation. Full article

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods. Full article