20 research outputs found
Passivation of α-Fe nanoparticle by epitaxial γ-Fe2O3 shell
No full textNanoparticles of iron prepared by inert gas condensation of plasma evaporated vapour exhibit remarkable resistance to oxidation. They remain rust free in air and in water for years. We have found by transmission electron microscopy and X-ray photoelectron spectroscopy, that all the passivated nanoparticles of iron are covered by an epitaxial shell of γ-Fe2O3 about 4 nm thick. The epitaxial relationship between the γ-Fe2O3 shell and the iron core is (001)γ-Fe(2)O(3)∥(001)α-Fe, and [110]γ-Fe(2)O(3)∥[100]α-Fe, [1̄10]γ-Fe(2)O(3)∥[010]α-Fe. The passivation of the nanoparticles of iron by an epitaxial oxide can be accounted for by the Caberra-Mott theory of oxidation of metal. The oxide layer grows rapidly at 420 K but slows down dramatically when the layer thickens. When the oxide layer thickens to 4 nm in a few hours, growth virtually stops. The 4-nm epitaxial oxide shell protects the iron core from further oxidation at room temperature.</p
Probabilistic calibration of stress-strain models for confined normal-strength concrete
No full textA probabilistic calibration for traditional deterministic stress-strain models of square confined concrete columns was conducted based on the proposed probabilistic stress-strain model and a large number of experimental data. The probabilistic models for both peak stress and peak strain (strain corresponding to peak stress) of confined normal-strength concrete (NSC) were established first based on the Bayesian theory and the Markov chain Monte Carlo method. Then, a probabilistic stress-strain model of confined NSC was established based on the proposed probabilistic models for peak stress and peak strain. Finally, the confidence level and computational accuracy of four typical deterministic stress-train models of confined NSC were calibrated based on the proposed probabilistic models and a large amount of experimental data. The proposed probabilistic models not only describe the probabilistic characteristics of peak stress, peak strain, and the stress-strain curve, but also provide an efficient approach to calibrate the confidence level and computational accuracy of traditional deterministic models.The financial support received from the National Natural Science Foundation of China (Grant Nos. 51668008 and 51738004), the Guangxi Science Fund for Distinguished Young Scholars (2019GXNSFFA245004), and the Natural Science Foundation of Guangxi Province (Grant No. 2018GXNSFAA281344) is gratefully acknowledged
Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS
No full textSurface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation
A Novel Land Surface Temperature Retrieval Algorithm for SDGSAT-1 Images
No full textLand surface temperature (LST) is a crucial parameter influencing Earth-atmosphere interactions and energy balance processes. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1) was recently launched to support the realization of the United Nations Sustainable Development Goals (SDGs), which provides worldwide three-spectrum wide-swath, high-resolution, and high-sensitivity thermal infrared (TIR) images. The objective of this study is to develop a modified three-channel split-window algorithm incorporating atmospheric water vapor content (W-TCSW) for LST retrieval from SDGSAT-1 images. This algorithm was developed from the existing split-window (SW) form. The parameters of the algorithm were determined based on the MODerate resolution atmospheric TRANsmission (MODTRAN) simulation results of 946 Thermodynamic Initial-Guess Retrieval (TIGR) atmospheric profiles. The W-TCSW algorithm was comprehensively compared with the SW and three-channel SW (TCSW) algorithms. The retrieval results of the three algorithms were validated with simulated datasets and in situ measurements from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites in China and the Surface Radiation Budget Network (SURFRAD) sites in USA. The SDGSAT-1 data retrieved by the W-TCSW algorithm was also intercompared with Landsat and ECOSTRESS LST products. The W-TCSW algorithm demonstrated the highest accuracy among the three retrieval algorithms (SW, TCSW, and W-TCSW). The influences of atmospheric water vapor content (AWVC) and land surface emissivity (LSE) as well as land use and land cover (LULC) on retrieval algorithms were discussed in a long-term time series. This study introduces a novel LST retrieval algorithm considering AWVC for SDGSAT-1 images and elucidates comprehensive validation and comparative assessment, expanding the application of high-spatial resolution TIR remote sensing data
Quantitative Assessment of the Impacts of Climate Change and Human Activity on the Net Primary Productivity of Subtropical Vegetation: The Case of Shaoguan, Guangdong, China
No full textVegetation net primary productivity (NPP) is critical to maintaining and enhancing the carbon sink of vegetation. Shaoguan is a characteristic forest city in the subtropical region of South China and an ecological barrier in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), playing an instrumental role in protecting water resources, purifying air, and maintaining ecological balance. However, studies that quantify subtropical vegetation NPP dynamics in Shaoguan under the influence of climate and human drivers are still incomplete. In this research, vegetation NPP at 30 m resolution was estimated from 2001 to 2020 using the enhanced CASA model based on the GF-SG algorithm in Shaoguan. The RESTREND method was then utilized to quantify climatic and human effects on NPP. The results indicated that the vegetation NPP in Shaoguan increased rapidly (4.09 g C/m(2)/yr, p < 0.001) over the past 20 years. Climate and human drivers contributed 0.948 g C/m(2)/yr and 3.137 g C/m(2)/yr to vegetation NPP, respectively. Human activity plays a major role in vegetation restoration through ecological projects, whereas vegetation deterioration is primarily attributable to the combined action of climate change and human activity, such as urban expansion, deforestation, and meteorological disasters. The results emphasize the importance of ecological projects for the restoration of vegetated ecosystems and ecological construction in Shaoguan
Epitaxial NiO hillocks on truncated octahedral nanoparticles of passivated Ni
No full textEpitaxial NiO hillocks on the {111} and {001} facets of truncated octahedral nanoparticles of Ni have been directly observed by high-resolution transmission electron microscopy. These nanometer hillocks form a rough shell enclosing the Ni nanoparticle. The epitaxial relationships of NiO on nanoparticles of Ni are the same as those of NiO on bulk Ni {111} and {001} surfaces. The formation of hillocks is related to the relaxation of the compressive stress in NiO arising from the very large lattice mismatch between NiO and Ni. The compressively stressed epitaxial NiO shell provides effective protection to the nanoparticles of Ni against further oxidation.</p
Cropland Inundation Mapping in Rugged Terrain Using Sentinel-1 and Google Earth Imagery: A Case Study of 2022 Flood Event in Fujian Provinces
No full textSouth China is dominated by mountainous agriculture and croplands that are at risk of flood disasters, posing a great threat to food security. Synthetic aperture radar (SAR) has the advantage of being all-weather, with the ability to penetrate clouds and monitor cropland inundation information. However, SAR data may be interfered with by noise, i.e., radar shadows and permanent water bodies. Existing cropland data derived from open-access landcover data are not accurate enough to mask out these noises mainly due to insufficient spatial resolution. This study proposed a method that extracted cropland inundation with a high spatial resolution cropland mask. First, the Proportional–Integral–Derivative Network (PIDNet) was applied to the sub-meter-level imagery to identify cropland areas. Then, Sentinel-1 dual-polarized water index (SDWI) and change detection (CD) were used to identify flood area from open water bodies. A case study was conducted in Fujian province, China, which endured several heavy rainfalls in summer 2022. The result of the Intersection over Union (IoU) of the extracted cropland data reached 89.38%, and the F1-score of cropland inundation achieved 82.35%. The proposed method provides support for agricultural disaster assessment and disaster emergency monitoring
Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints
No full textMonitoring heavy metal concentrations in soils is central to assessing agricultural production safety. Satellite observations permit inferring concentrations from spectrum, thereby contributing to the prevention and control of soil heavy metal pollution. However, heavy metals exhibit weak spectral responses, particularly at low and medium concentrations, and are predominantly influenced by other soil components. Machine learning (ML)driven modelling can produce predictions but lacks interpretability. Here, we present an interpretable ML framework for concentration quantification modelling and investigated the contributions of spectral and environmental factors-pH and organic carbon-to the estimation of metals with multiple concentration gradients, as analysed through SHAP (SHapley Additive exPlanations) data derived from four learning-based scenarios. The results indicated that scenarios SHC (spectral, pH, and organic carbon) and SH (spectral and pH) were the most optimal for chromium (Cr) [RPD = 1.42, Adj R2 = 0.62], and cadmium (Cd) [RPD = 1.80, Adj R2 = 0.80]. Under environmental constraints, the spectral predictability for Cr and Cd was improved by 67 % and 87 %, respectively. We concluded that interpretable modelling, utilising both spectral and soil environmental factors, holds significant potential for estimating heavy metals across concentration gradients. It is recommended that samples with higher organic carbon content and lower pH be selected to enhance Cr and Cd predictions. An advanced grasp of interpretable predictions facilitates earlier warning of heavy metal contamination and guides the formulation of robust sampling strategies
Design of fault diagnosis system for inertial navigation system based on virtual technology
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