Search Results (175)

This work presents a multifactorial strategy for reusing waste thermoplastics and generating multifunctional filaments for additive manufacturing. Acrylonitrile–butadiene–styrene (ABS) waste and commercial poly(methyl methacrylate) (PMMA) were compounded with carbon black (CB), graphene (G), or graphene foam (GF) at different loadings and extruded into composite filaments. The aim is to couple filler-induced bulk modifications with atmospheric pressure plasma jet (APPJ) surface coatings of TiO₂ and graphene oxide (GO) to obtain waste-derived filaments with tunable morphology, wettability, and thermal stability for advanced 3D-printed architectures. The filaments were subsequently coated with TiO₂ and/or GO using an APPJ process, which tailored surface wettability and enabled the formation of photocatalytically relevant interfaces. Digital optical microscopy and SEM revealed that CB, G, and GF were reasonably well dispersed in both polymer matrices but induced distinct surface and cross-sectional morphologies, including a carbon-rich outer crust in ABS and filler-dependent porosity in PMMA. For ABS composites, static contact-angle measurements show that APPJ coatings broaden the apparent wettability window from ~60–80° for uncoated filaments to ~40–50° (TiO₂/GO) up to >90° (GO), corresponding to a ≈150% increase in contact-angle span. For PMMA/CB composites, TiO₂/GO coatings expand the accessible contact-angle range to ~15–125° while maintaining surface energies around 50 mN m⁻¹. TGA/DSC analyses confirm that the composites and coatings remain thermally stable within typical extrusion and APPJ processing ranges, with graphene showing only ≈3% mass loss over the explored temperature range, compared with ≈65% for CB and ≈10% for GF. Fused deposition modeling trials verify the printability and dimensional fidelity of ABS-based composite filaments, whereas PMMA composites were too brittle for reliable FDM printing. Overall, combining waste polymer reuse, tailored carbonaceous fillers, and APPJ TiO₂/GO coatings provides a versatile route to design surface-engineered filaments for applications such as photocatalysis, microfluidics, and soft robotics within a circular polymer manufacturing framework. Full article

Polybutylene adipate terephthalate (PBAT), a flexible biodegradable polyester, has gained widespread use in packaging applications due to its ability to degrade under controlled conditions, producing non-toxic substances. While this property makes PBAT particularly attractive for the development of transient electronic devices, this potential application remains unexplored. To address this research gap, we developed PBAT-based composites and modified their electrical properties through CO₂ laser functionalization. Although laser treatment of neat PBAT primarily resulted in material ablation, the incorporation of lignin and silica-based fillers enabled the formation of electrically conductive pathways. Among the various fillers tested, dealkaline lignin (DEALK) and glass fibers (GFs) provided the optimal combination of electrical conductivity, mechanical properties, and processability. Characterization techniques (electrical measurements, optical microscopy, SEM, EDX, and TGA) highlighted that by optimizing laser treatment and the filler concentration, it is possible to produce conductive tracks with remarkably low sheet resistance. Hybrid composites containing 10–15 wt% of GF and 20–25 wt% of lignin demonstrated the best electrical performance with values as low as 3.5 Ω/sq, which were further reduced to 1.72 Ω/sq after laser process optimization. These findings establish PBAT composites as promising candidates for sustainable transient electronics. Full article

Precise monitoring of rubber plantations is critical for effective management and ecological assessments, enabling optimal resource allocation, disease detection, and mitigation of environmental impacts. This study integrated multi-source remote sensing data—including Landsat 8, Sentinel-1/2, GaoFen-1 (GF-1) optical and SAR imagery, and DEM data of Hainan Island. The rubber plantation areas from 2021 to 2025 were extracted from the Google Earth Engine (GEE) platform by employing a multi-step threshold segmentation method, which utilized the Otsu algorithm to automatically determine optimal thresholds for distinguishing rubber plantations from other land covers. The overall accuracy of the extracted rubber plantations in this study was above 90%; the Kappa coefficient was greater than 0.85; and the F1-score surpassed 0.93. The resulting distribution maps reveal that rubber plantations on Hainan Island are predominantly concentrated in the northwestern and northern regions. The rubber plantation area of Hainan Island remained relatively stable from 2021 to 2023. During 2023–2024, the rubber plantation area experienced a decline. This reduction was particularly pronounced in 2024, when the area decreased by nearly 150 km² compared to the previous year. However, in 2025, this downward trend reversed sharply with an increase of approximately 300 km². These findings provide a critical scientific basis for sustainable rubber production, supporting informed decision-making in irrigation, pest control, and yield optimization. Furthermore, they offer valuable insights for strategic planning to balance economic returns with ecological conservation, thereby ensuring the long-term viability of the industry. Full article

This study comprehensively discusses the effect of multiple material recycling (five recycling cycles with the same technological conditions: injection molding → grinding → drying → injection molding → …) of commercial polypropylene-glass fiber composites (PPGF) (PP + 10, 20 and 30 wt.% GF) on the performance of the grinding process and the granulometric characteristics of the obtained regrinds, as well as selected surface, mechanical and thermal properties of the composites. An increase in mass (E_m) and volume (E_v) grinding efficiency was confirmed, along with an increase in GF content in the composite and the number of recycling cycles. Both the GF additive and the number of recycling cycles contributed to the deterioration of the aesthetic qualities of the composites (darkening and reduction in gloss). Slight changes in the surface hardness of the test materials were observed as a function of the number of recycling cycles, from 3 to 4% after five recycling cycles. The adverse effect of multiple recycling on the mechanical and thermal properties of PP and PPGF composites has been confirmed. The occurrence and increase in carbonyl index (CI) values, as a function of multiples recycling, was confirmed for a composite containing 20 wt.% GF (CI in the range from 0.045 to 0.092) and for PPGF containing 30 wt.% GF (CI in the range from 0.193 to 0.272). The effect of multiple material recycling on the glass fiber structure in the tested composites was also investigated using scanning electron microscopy (SEM) and optical microscopy. The issues of grinding and changes in the surface properties of PPGF composites in multiple material recycling processes discussed in this article may constitute a source of practical knowledge that will contribute to increasing the use of this type of secondary composite in industrial plastics processing processes. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks. Full article

In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. In the control experiment (EXP CTL), aerosols interact with radiation only, incorporating direct and semi-direct aerosol effects. The sensitivity experiment (EXP ACI) couples aerosols with both radiation and Thompson microphysics, accounting for aerosol indirect effects and fully interactive aerosol–cloud dynamics. Introducing aerosol and cloud interactions results in net cooling at the top of the atmosphere (TOA). Further analysis shows that the EXP ACI produces more liquid water at lower levels and less ice water at higher levels compared to the EXP CTL. The aerosol optical depth (AOD) shows a good linear relationship with cloud droplet number concentration, similar to other climate models, though with larger standard deviations. Including aerosol and cloud interactions generally enhances simulations of the Indian Summer Monsoon, stratocumulus, and diurnal cycles. Additionally, the study evaluates the impacts of aerosols on deep convection and cloud life cycles. Full article

The stray light suppression design aims to reduce the impact of stray light on optical systems. For high-resolution optical remote sensing systems, practical tests of stray light suppression performance are essential to ensure optimal functionality. However, due to system complexity and spatial constraints, physical test methods for evaluating the stray light suppression performance of large-aperture, long-focal-length remote sensing cameras remain scarce. To address this issue, a comprehensive test is conducted on the stray light suppression performance of the Jilin-1 GF04A satellite remote sensing camera by integrating multiple test methods, including the environmental light effect test, neighborhood point source response test, key surface response test, and sneak path of stray light test. The experimental results indicate that the stray light response ratios obtained from different test methods are all below 1%. The on-orbit performance of GF04A further validates the effectiveness of its stray light suppression design. Full article

Lake Taihu has highly turbid inland waters with complex optical properties. Due to the bottom effect of submerged aquatic plants in optically shallow waters, currently available phytoplankton chlorophyll-a retrieval algorithms tend to overestimate chlorophyll-a concentrations in the eastern part of Lake Taihu. This overestimation can distort the eutrophication evaluation of the entire lake. This paper identifies submerged and emergent plants, determines the retrieval models for the upwelling (K_u) and downwelling (K_d) irradiance attenuation coefficients, and proposes a phytoplankton chlorophyll-a retrieval model using a water depth optimization-based method to remove the bottom effect. The results show the following: (1) The normalized difference vegetation index (NDVI) method can distinguish the bottom mud (NDVI < −0.46) and submerged aquatic plants (−0.46 ≤ NDVI < 0.52) from the emergent plants (NDVI ≥ 0.52) with 90% accuracy. (2) The downwelling and upwelling irradiance attenuation coefficients are highly correlated with the suspended sediments, and retrieval models for these coefficients in three visible bands with high accuracy are presented. (3) Compared to traditional algorithms without bottom effect removal, the proposed chlorophyll-a concentration estimation algorithm based on the water depth-optimized bottom effect removal method efficiently reduces the bottom effect of the submerged aquatic plants. The root mean square error (RMSE) for the obtained chlorophyll-a concentrations decreases from 45.61 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ to 8.69 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$, and the mean absolute percentage error (MAPE) is reduced from 245.12% to 19.58%. In the validation step, the obtained RMSE of 10.89 ${\mathsf{\mu }\mathrm{g}·\mathrm{L}}^{-1}$ and MAPE of 17.52% are consistent with the proposed algorithm. This research provides a good reference for the determination of chlorophyll-a concentrations in phytoplankton in complex inland water bodies. The findings are potentially useful for the operational monitoring of harmful algal blooms in the future. Full article

Estimates of tree species diversity via traditional optical remote sensing are based only on the spectral variation hypothesis (SVH); however, this approach does not account for the vertical structure of a forest. The relative height (RH) indices derived from GEDI spaceborne LiDAR provide vertical vegetation structure information through waveform decomposition. Although RH indices have been widely studied, the optimal RH index for tree species diversity estimation remains unclear. This study integrated GF-1 optical imagery and GEDI LiDAR data to estimate tree species diversity in a warm temperate forest. First, random forest plus residual kriging (RFRK) was employed to achieve wall-to-wall mapping of the GEDI-derived indices. Second, recursive feature elimination (RFE) was applied to select relevant spectral and LiDAR features. The random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) methods were subsequently applied to estimate tree species diversity through remote sensing data. The results indicated that multisource data achieved greater accuracy in tree species diversity estimation (average R² = 0.675, average RMSE = 0.750) than single-source data (average R² = 0.636, average RMSE = 0.754). Among the three machine learning methods, the RF model (R² = 0.760, RMSE = 2.090, MAE = 1.624) was significantly more accurate than the SVM (R² = 0.571, RMSE = 2.556, MAE = 1.995) and kNN (R² = 0.715, RMSE = 2.084, MAE = 1.555) models. Moreover, mean_mNDVI, mean_RDVI, and mean_Blue were identified as the most important spectral features, whereas RH30 and RH98 were crucial features derived from LiDAR for establishing models of tree species diversity. Spatially, tree species diversity was high in the west and low in the east in the study area. This study highlights the potential of integrating optical imagery and spaceborne LiDAR for tree species diversity modeling and emphasizes that low RH indices are most indicative of middle- to lower-canopy tree species diversity. Full article

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. Full article

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features. Full article