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A fiber strain sensor which is formed by cascading an optical microfiber coupler and a Sagnac loop is proposed and studied. The optical microfiber coupler, and the Mach-Zehnder interferometer formed between the optical microfiber coupler and the Sagnac loop can serve as different strain sensing elements, used for different types of strain measurement. The optical microfiber coupler is mainly responsible for precisely sensing small scale strain variations, and the Mach-Zehnder interferometer focuses on measuring strain in large operation range. The operating principle of this sensor is theoretically analyzed, and the experimental sensing spectra are in consistent with the simulated results.

Land surface temperature (LST) is crucial to wide varieties of environmental issues, whereas its low tolerance to cloud contamination greatly challenges its applications. Passive microwave (PMW) measurements are employed to retrieve LST due to its great capability of penetrating clouds. Despite great efforts from previous studies, their further applications are limited by coarse resolution of PMW, uncertainty from cloudy coverage, and less attention on large-scale applications. To address these problems, this article proposes a method combining adjustment from annual temperature circle to reconstruct LST based on downscaled microwave observations over mainland China. In order to conduct a comprehensive and solid accuracy evaluation, the proposed method is validated according to three strategies namely comparing with moderate resolution imaging spectroradiometer (MODIS) LST, ground surface temperature, and in situ observations from the Heihe Watershed Allied Telemetry Experimental Research, respectively. In the strategy 1, mean root mean square error (RMSE) of predicted LST is 2.39 K and 1.33 K at day and night. According to the strategy 2, mean RMSE of MODIS, predicted and merged LST is 4.97 K, 5.07 K, and 4.93 K at daytime, and 2.32 K, 4.57 K, and 3.39 K at nighttime. In the strategy 3, they are 3.75 K, 3.90 K, and 2.87 K at day, and 2.80 K, 3.61 K, and 2.79 K at night. Furthermore, adjustments from annual temperature cycle are discussed. The proposed method is promising in future research for reconstructing LST with high spatiotemporal resolution at large scales thanks to its simple process, high accuracy, and seamless reconstruction.

From the past analyses of Core Disruptive Accidents (CDA) for sodium-cooled fast reactors, it has been recognized that with the assumption of very pessimistic conditions, a large-scale molten-fuel pool, which contains sufficient amount of fuel to achieve the prompt criticality by fuel compaction, might be formed. Due to the potential entrapment of liquid sodium into the fuel pool, local Fuel-Coolant Interaction (FCI) in the pool is considered to be one of the various initial factors resulting in such compactive fluid motions. To ascertain the mechanisms underlying this interaction more reliably, in our earlier publication a number of simulated experiments was carried out by releasing a given amount of liquid water into a molten pool (comprised of a low-melting-point alloy) using the PMCI (Pressurization characteristics in Melt-Coolant Interaction) facility recently developed at the Sun Yat-sen University. In this work, motivated to acquire more enhanced understanding, the effect of interaction mode on local FCIs is further investigated by adding new experimental data within the Melt-Injection (MI) mode. From detailed analyses, it is found that except the existence of a similar impact of melt temperature, much difference in the influence of other experimental parameters such as the liquid volume delivered, water subcooling along with the pool depth is observed. In addition, the performed analyses in this work also confirm that compared to previous Coolant-Injection (CI) mode, the mechanical energy conversion efficiency is much lower within the MI-mode condition. This work provides a large palette of beneficial experimental data and evidence for the better understanding of CDAs and improved verifications of fast reactor safety accident analysis codes in China.

This study proposes a surrogate-based optimization framework (SBO) to help analyze the tradeoff between flood damages and investment while considering uncertainty originating from surrogates. The surrogate models were constructed based on the relationship between drainage specifications and simulated flood information and used to replace the numerical model in optimization, thereby reducing the computational burden. The bootstrapping approach was employed to quantify the uncertainty originating from surrogate models, which were incorporated into the NSGA-II optimization algorithm to seek the interval of optimal solutions. Through a case study, the results showed that the uncertainties caused by surrogate models have a significant influence on the reliability of the optimal solutions, but require lower computational efforts. Moreover, the local design conditions (i.e., various designed rainfalls) had an impact on the design and performance of the detention tanks. The proposed framework will facilitate cost-effective planning of flood mitigation systems with an awareness of associated uncertainty in order to resolve tradeoffs, particularly for large-scale problems.

A simple composite cavity fiber tip (CCFT) Fabry-Perot interferometer (FPI) is proposed and experimentally demonstrated. The composite cavity is composed of an air cavity and a silica cavity. The air cavity is an elliptical air hole embedded in the SMF. The silica cavity is a short section of SMF cascaded to the air cavity. To avoid the influence of other external physical quantities (refractive index, humidity, etc.) on the CCFT FPI, the CCFT was successfully encapsulated by a tapered-shaped silica capillary. The packaged CCFT FPIs were applied for high temperature sensing in the range of 100–800 °C. Temperature sensitivity of 14.31 pm/°C was obtained for the silica cavity. In addition, the composite cavity can be fabricated to be very small in size (dozens of micrometers) with extremely low-cost, thus the CCFT FPI can be a competitive candidate for local point temperature sensing.

Multi-Resolution Segmentation (MRS) is known to be a general segmentation algorithm for very-high-resolution (VHR) remote sensing applications. The critical problems of MRS are the optimization of ...

Surface urban heat island intensity (SUHII) widely acknowledged as the primary indicator has been extensively utilized in previous SUHI spatiotemporal research. However, diverse rural definitions reported in previous studies possibly affect rural temperature and consequently lead uncertainty and incomparability to SUHII dynamics. Despite of previous efforts, there are still some issues in systematic analysis of significance and spatiotemporal variations of SUHII interpretations resulting from these different rural extents. In order to address these issues, this paper quantitvely characterized the significance and spatiotemporal dynamics of RE-induced △SUHII (Rural-extent induced SUHII variations) in 100 major cities across China mainland. Major findings are summarized in the following three aspects. First, considerable SUHII variating with rural buffers extending and majority of △SUHII exceeding 0.5 K especially in the daytime illustrated that different rural definitions can induce significant uncertainty to SUHII studies. Second, RE-induced △SUHII is found to be unevenly distributed across the study area. Moreover, dense distributions of great △SUHII in city agglomerations suggest that city is an inappropriate study object for SUHII urban-cluster research since multi-influences from aggregated urban and rural areas without clear boundaries. Third, RE-induced △SUHII experiences evident seasonal variations especially in the daytime spring and summer, and spoon-shape (peaks in the begins and ends, and bottom in the middle) buffer variations with relatively low correlations among five subdivided buffer rings. Additionally, six potential factors are selected to discuss their possible effects on △SUHII spatiotemporal characters. On the whole, this paper illustrated that diverse rural extents do induce conspicuous uncertainty and incomparability to SUHII interpretations and highlight the necessity to develop uniform rural defining standards in the future SUHII research.

High-spatiotemporal-resolution land surface temperatures (LSTs) are required in various environmental applications. However, due to the trade-off between the spatial and temporal resolutions in remote sensing, such data are still unavailable. Many studies have been conducted to resolve this dilemma, but difficulties remain in generating high-spatiotemporal-resolution (i.e., diurnal, e.g., 30 m resolution) LSTs. Accordingly, this study proposes a weighted combination of kernel-driven and fusion-based methods (termed CKFM) to enhance the resolution of time series LSTs; the kernel-driven process can obtain abundant spatial details from visible bands, while the fusion-based process is applied for its spatiotemporal prediction ability. CKFM contains three parts. First, a kernel-driven method is applied to predict high-resolution LSTs via a regression relationship between simulated medium-resolution LSTs and kernels. Second, a fusion-based method is applied to predict the medium-resolution LST, the result of which is used for thin plate spline (TPS) downscaling. Finally, the results of the kernel-driven and fusion-based processes are combined via weights calculated from error estimations. Compared with existing thermal sharpening methods, CKFM has the following strengths: (1) it fully utilizes the available visible and thermal bands from multiple sensors, thereby obtaining spatial details in a variety of ways; (2) it downscales LSTs in a dynamic manner; and (3) it is suitable for heterogeneous regions. CKFM is tested with Landsat 8 and MODIS data and successfully downscales the 1 km resolution MODIS LSTs into 30 m resolution data. In both visual and quantitative evaluations, CKFM is more accurate and robust than the kernel-driven method with an improvement of 0.1–0.6 K, and it reconstructs more spatial details than the fusion-based process. Based on these characteristics, CKFM is a promising method for generating daily high spatial resolution LSTs for various environmental studies.

Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

Liver cancer is a kind of lethal and aggressive malignant neoplasm with a high rate of relapse and metastasis after therapy. An important cause for the relapse and metastasis is the existence of liver cancer stem cells (CSCs), which have high resistance to chemotherapy and high tumorigenic potential. Therefore, it is crucial to develop new methods to eradicate CSCs in tumors. Herein, we develop a photodynamic therapy (PDT) that features bimodal metallacage-loaded nanoparticles (MNPs) for integrated chemotherapy. This platform achieves chemo-photodynamic combinational therapy. Organoplatinum(ii) metallacage-loaded nanoparticles show excellent ability to kill liver CSCs, decreasing their mobility and sphenoid formation ability under near-infrared laser irradiation. Importantly, MNPs can successfully penetrate into 3D tumor spheroids, which display higher drug resistance compared to traditional 2D cultured cells. This destroys CSCs and prevents subsequent tumor formation in vivo. With the excellent combinational therapeutic results in hand, the working mechanisms of MNPs were then studied. MNPs under NIR light irradiation can generate reactive oxygen species (ROS), resulting in damage of mitochondrial membrane and subsequent cell apoptosis with chemotherapeutic platinum. This study proves the great potential of MNPs for combinational cancer therapy, providing a new insight for the next generation of nanomedicines.

Because waste gas from industrial burning has a significant effect on urban environment, it is important to detect industrial heat sources from remote sensing data. Given existing pyrometry, it is difficult to identify small factories with low burning temperatures. In addition, existing fire detection methods (such as the contextual algorithm) are cumbersome, complex, and contain multiple thresholds to be determined. With the purpose of detecting industrial heat sources efficiently and simply, we introduced a simple method based on the thermal anomaly index (TAI) to detect industrial heat sources. This index was constructed based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data with a detectable temperature of 400 K, which is lower than that used in most high-temperature detection methods. By confirming with the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and high-resolution images, the TAI confidently detected almost all hot spots with the VIIRS Nightfire product and detected many hot spots that were undetected by the VIIRS Nightfire product. Based on six images acquired over Tangshan, we determined that 54.52% of hot spots were undetected by the VIIRS Nightfire product, while the TAI method was able to detect these hot spots. With the MODTRAN 5 radiative transfer model, we simulated the high-temperature detection ability of the TAI. Compared with the VIIRS Nightfire product, the TAI is more sensitive when detecting hot spots below 700 K. Thus, this method can potentially detect family workshops engaged in the small-scale combustion of fuel.

Satellite-observed surface temperatures over heterogeneous urban pixels are generally determined by viewing and illumination angles, as well as by 3-D geometries and component temperature distributions of urban surfaces. This paper focuses on developing a Geometric model to simulate Urban Thermal Anisotropy considering overlaps of shadows on the ground for simplified neighborhoods (GUTA-osg). A Boolean model and projections for buildings are used to calculate the average fractions of urban components in a field of view. Simulation results derived from GUTA-osg are compared with those from the 3-D discrete anisotropic radiative transfer model. At view and solar zenith angles lower than 50°, GUTA-osg can be applied to urban scenes of $h/w (building-height-to-spacing ratio) with confidence where errors of the estimated fraction of walls and shaded ground are less than 0.1. As $h/w$ increases, GUTA-osg performs well when applied to urban scenes with smaller building plan area index values (e.g., $\lambda _{p} for $h/w = 2.0$ –4.0). GUTA-osg accurately simulates the anisotropy of airborne measured temperatures, with $R^{2} > 0.85$ and root mean square errors < 0.8 K. Biases of the model can result from: 1) the neglect of shadows on walls cast by adjacent buildings and 2) model simplifications, including constant building heights and random distributions of building location and orientation. GUTA-osg uses urban morphologic indexes as inputs, and coefficient values can be derived by fitting against multiangular thermal infrared observations. The model can be used to adjust the directional temperature to a common viewing geometry.

Remote measurements of land surface temperature are prone to significant directional anisotropy. This anisotropy is strong over urban surfaces because of three-dimensional structures and the resulting heterogeneous temperature distributions. However, the development of models that correct urban thermal anisotropy and consider the two factors is rare. In this study, with the assumption of random building orientations, we developed a Geometric model to simulate Thermal Anisotropy over a sparse Urban surface (GUTA-sparse), which does not consider the mutual shadowing effect. GUTA-sparse assumes that anisotropy is the sum of three parts: the vertical wall background temperature contribution, vertical wall orientation effects and shadow contribution. The simulation data provided by the 3-D Discrete Anisotropic Radiative Transfer (DART) model and airborne measurements over the city of Marseille were employed to evaluate model performance. The results show that in this model, the solar zenith angle influences the ability of the fitted coefficients to characterize surface features. GUTA-sparse is applicable over urban surfaces that have aspect ratios smaller than 1.0, where the mutual shadowing effect is negligible. The proposed model can also well simulate the airborne measured anisotropy with root mean square errors (RMSEs) of 0.44, 0.44, 0.56 and 0.40 K, and the anisotropy amplitudes at the four flight times all exceed 8 K. The model is independent of surface parameters that are difficult to obtain, which makes it suitable for remote sensing applications. Because the model is linear with respect to surface parameters, it can be applied to heterogeneous urban surfaces. This model aids in better understanding the relationships among urban surface geometry, component temperatures and thermal anisotropy, and this model may potentially correct the directional temperature values to a common viewing geometry.

Water body, as water cool islands (WCIs), is an effective factor of mitigating the urban heat island (UHI) effects through the evaporation and absorption of solar shortwave radiance. In this study, a combination of the range, amplitude, efficiency and capacity of the WCI effect (WCI_R, WCI_A, WCI_E and WCI_C as descriptors) within water buffers, is proposed to evaluate the interannual variation of the seasonal WCI effects within the fifth loop of Beijing from 2000 to 2018, further construct optimal regression models by seven impact factors with the four WCI descriptors through all-subset regression, as well as calculate the independent contribution rate (CR) of these factors by hierarchical partitioning (HP) analysis. The results indicate that (1) In terms of the interannual variation of WCI effects, the most significant trends that rise over fall produced by four descriptors generate in summer after 2009, while relatively stable states exist in spring and winter. (2) As for the impact factors, the water temperature (WT) and the percentage of vegetation (PV) contribute most for WCI_R, WCI_A and WCI_E in reaction to the interannual average, while the water temperature capacity (WTC) maintains significant impact on WCI_C from spring to autumn over 18 years but shares power with water temperature (WA) and WT in winter. (3) Considering the factor changes yearly, the dominance relationships of external ones, mainly the percentage of impervious surface (PI) and PV, gradually surpass that of WA and WTC in four seasons especially after 2009. On this basis, urban designers could propose suitable plans to better exert the cooling effect of water bodies, thereby reducing the UHI effect.

The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research.

For delta area once groundwater is over-exploited, it may trigger a series of geological disasters, especially ground subsidence, which made the city along coastal regions have an increased risk of sea-level rise. During recent years at Yangtze Delta ground subsidence has got been controlled primarily via re-pumping of seawater into land region, but the subsidence is still continuous. TCPInSAR(Temporarily Coherent Point Synthetic Aperture Radar Interferometry) technique as an improvement of PSInSAR can generate the settlement time series with high accuracy. We adopted this technique to process ENVISAT ASAR images spanning from 2007 to 2009 and TerraSAR-X images spanning from 2009 to 2010 to obtain the land surface subsidence in Shanghai. And then we compared the settlement of this area with the groundwater level measured by the in-situ wells, and found the two are in good agreement. Finally, we estimated the storage coefficients of the aquifer. This study showed that InSAR technique is very useful to monitor and model groundwater efficiently.

Water is an indispensable part of urban development and ecological environment protection. Xiong'an New Area is a state-level new areas established to adjust and optimize the urban layout and spatial structure of Beijing-Tianjin-Hebei. Exploring the water changes of the Xiong'an New Area is critical to local development. In this paper, the Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method—an effective approach for complex water extraction was used to extract the waters of spring in 1984, 1989, 1993, 2000, 2008, 2014 and 2018 and combined with the water extract results of 28 years to analyze the water degraded and restored areas of the Xiong'an New Area. The largest water area (280 km2) in Xiong'an New Area appeared in 1989. Water degraded areas is distributed in strong change zone and strong change zone and water restored areas is distributed in weak change zone and micro-change zone.

The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.

Building shadows (BSs) frequently occur in urban areas, and their area and distribution display strong seasonal variations that significantly influence the urban land surface temperature (LST). However, it remains unclear how BSs affect the LST at the city scale because it is difficult to extract the shaded area at the subpixel scale and to connect such areas with the LST at the pixel scale. In this study, we combined the sun angle, building height, building footprint and building occlusion to extract the seasonal spatial distribution of BSs in the central area of Beijing. The effect of BSs on the LST was analyzed using LST retrieved from Landsat-8 thermal infrared sensor data. First, the relationship between the LST patch fragmentation and proportion of BSs in the sample areas was modeled without vegetation. Then, we quantitatively studied the mitigated intensity of the LST in pure impervious surfaces (IS) and vegetation pixels covered by BSs; next, we analyzed the LST sensitivity of these pixels to BSs. The results showed that the existence of BSs influences the fragmentation of the low LST patches strongly from summer to winter. On the other hand, pure IS pixels totally covered by BSs experienced a greater cooling effect, with 3.16 K on 10 July, and the lowest cooling occurred between 14 and 25 December, with a mean of 1.24 K. Without considering the relationship in winter, the LST is nonlinearly correlated to the building shadows ratio (BSR) in pixels, and an approximate 10% increase in the BSR resulted in decreases in the LST of approximately 0.33 K (mean of 16 April and 10 May), 0.37 K (10 July) and 0.24 K (28 September) for pure IS pixels, and 0.18 K, 0.20 K and 0.15 K, respectively, for pure vegetation pixels. Further analysis indicates that the LST of pure IS pixels is more sensitive to BSs than that of vegetation because the self-regulation mechanism of vegetation reduces the cooling effect of BSs. These findings can help urban planners understand the cooling characteristics of BSs and design suitable urban forms to resist urban heat islands (UHIs).

In a postulated Steam Generator Tube Rupture (SGTR) accident of pool-type Lead-cooled Fast Reactor (LFR), highly-pressurized water from the secondary circuit will be discharged into the primary vessel containing low-pressure molten lead-based alloy (commonly pure lead or low-melting-point Lead-Bismuth Eutectic (LBE)). One of the dangers of such accident is the formation of a multiphase flow structure consisting of water droplets inside the molten metal pool, which could result in energetic Coolant-Coolant Interaction (CCI). In this study, to ascertain the pressurization characteristics underlying the CCI process, a series of experiments is conducted through releasing water lump of different volumes and shapes within a molten LBE pool based on the PMCI (Pressurization characteristics in Melt-Coolant Interaction) experimental facility at the Sun Yat-sen University. To obtain comprehensive understandings, a variety of experimental parameters are employed such as the temperature (subcooling), volume and geometry of water lump along with the temperature and depth of the molten LBE pool. After detailed analyses, it is verified that within the thermal interaction zone, there is the possibility for the steam explosion to occur that generally produces more violent pressure buildup. For the non-explosion cases, all our experimental parameters except the water subcooling are observed to have remarkable impact on the pressurization characteristics. Due to the restraining role of steam bubbles generated at the LBE-water interface, for given thermal condition (i.e. melt & water temperature) and LBE-pool depth, increasing water volume is confirmed to result in limited pressurization. Instead of LBE static pressure, the effect of pool depth on pressurization is supposed to be primarily caused by the varied easiness of the molten LBE penetration through the vapor interface. Regardless of steam explosion, the calculated mechanical energy conversion efficiency for present CCI experiments is found to be varied over a narrow scope of 2.5–5.5%. Current experiments provide a great amount of favorable database and insight for enhanced understanding on the mechanisms of SGTR accident as well as for the improved validations of LFR safety analysis codes in China.

Satellite observation of land surface temperature (LST) is an important tool for monitoring urban thermal environments but is prone to significant angular effects over non-isothermal pixels characterized by the substantial three-dimensional (3D) structure of urban areas. However, accurately characterizing the urban thermal anisotropy at the satellite pixel scale is still challenging. In this paper, comparing the simultaneous airborne observations and the temporal-aggregated signal from MODIS LST, we further investigate the seasonal and diurnal patterns of thermal anisotropy in urban areas with various simulation scenarios. The anisotropy was defined as the difference between off-nadir and nadir temperatures. First, a realistic morphological representation of urban surfaces as well as urban component temperatures measured by infrared thermometers (IRTs) were input into the discrete anisotropic radiative transfer (DART) model to simulate MODIS-scale anisotropy for Toulouse, France in summer. Extending this ‘IRT-DART’ method to other seasons was restricted because the IRTs' radiative source areas are affected by shadows when the sun elevation is low, meaning that measured sunlit temperatures are not reliably available. An energy balance model, TUF3D, and a sensor view model, SUM, for simplified urban geometry were coupled to replace ‘IRT-DART’ in winter. The cross-comparison shows that ‘TUF-SUM’ can simulate MODIS-scale anisotropy if urban structure and materials are relatively homogeneous, which provides a basis for assessing anisotropy when IRT sunlit temperatures are unavailable. The MODIS anisotropy is subject to a strong diurnal yet a discernable seasonal variation. For example, for a mid-latitude city, the anisotropy over all MODIS view angles reaches up to 6.6 K for Terra-Day (around 11:00 local time) and 4.9 K for Aqua-Day (around 13:00) in summer, and 6.1 K for Terra-Day and 4.0 K for Aqua-Day in winter. Nocturnal anisotropy is weak with values less than 0.2 K. The modeling methods can be used to quantify anisotropy of satellite LST in a broad range of urban environments and facilitate the use of multi-angular MODIS LST for a better assessment of urban thermal environments in the future.

An algorithm to estimate daily maximum Ta (Tamax) is presented in this study based on moderate resolution imaging spectroradiometer (MODIS) products and a daytime Ta variation model. The algorithm first estimates daytime Ta at the times of Terra and Aqua satellites overpass. Subsequently, the Tamax is inferred from the daytime Ta estimations using the daytime Ta variation model. This algorithm was applied to the area within the sixth ring road of Beijing from 2009 to 2010. A comprehensive validation experiment including a leave-one-out cross validation, a temporal validation, an overall comparison and a sensitivity analysis was conducted to evaluate the new algorithm. Results indicate that the spatial distribution of Tamax can be accurately generated using the algorithm with a root mean square error (RMSE) varying from 1.62 to 2.33 K and a coefficient of determination varying from 0.95 to 0.98. Sensitivity analysis shows that the algorithm is more sensitive to Ts at daytime of Aqua MODIS overpass ...

CCS (carbon capture and storage) is a technique that can effectively reduce CO 2 emissions released into the atmosphere from industrial activities and mitigate global climate warming. However, the captured CO 2 may leak from the underground storage, so a novel way of rapidly detecting points of CO 2 leakage in sequestration fields is needed. A field experiment was performed at the Sutton Bonington Campus of the University of Nottingham (52.8 N, 1.2 W), and grass ( cv Long Ley ) and bean ( Vicia faba cv Clipper ) were chosen as the test plants. Sixteen plots, eight plots with grass and eight plots with bean, were established, and four of each plant was randomly chosen for the controlled injection of CO 2 into the soil. The other plots acted as controls. The canopy spectra of the plants were measured, and the continuum removal method was used to process the spectral data. The spectral feature parameters of band depth and full width at half maxima of the canopy spectra were analysed, and the results showed that the band depth and full width at half maxima of the plants decreased with the elevation of CO 2 leakage stress levels. Therefore, the product of band depth and full width at half maxima was defined as the Area index, which can better identify the plants under CO 2 leakage stress throughout the entire growth period. To quantitatively evaluate the separability of the Area index, the Jeffries-Matusita distances were calculated among the Area indices of plants under different levels of CO 2 leakage stress. The results suggested that the Area index can stably and reliably identify the plants under CO 2 leakage stress. Therefore, in the future, the points of leakage on CCS projects can be detected by monitoring the growth of plants in the sequestration fields with hyperspectral remote sensing.

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (similar to -0.086K/yr, p

In a Core Disruptive Accident (CDA) of Sodium-cooled Fast Reactor (SFR), by supposing rather pessimistic condition, a large molten fuel pool would be possibly formed that contains enough fuel with the potential to exceed prompt criticality due to fuel compactive motion. Local fuel-coolant interaction (FCI) in the molten pool is identified as one of the typical inducing factors that may result in such compactive motion. To understand the characteristics of this interaction, owing to the PMCI (Pressurization characteristics in Melt-Coolant Interaction) facility recently established at the Sun Yat-sen University, in our earlier publications two series of simulated experiments, i.e. Coolant-Injection (CI) experiments and Melt-Injection (MI) experiments, have been conducted, in which water and low-melting-point Bi-Sn-In alloy (60% Bi, 20% In and 20% Sn) were used respectively as the simulant materials for sodium and molten fuel. In this study, aimed at checking the generality of the experimental findings as well as achieving further enhanced understanding on this interaction, a large number of experiments are newly performed through delivering water into a simulated molten fuel pool comprised of Lead-Bismuth Eutectic (LBE) alloy (another low-melting-point alloy). Through detailed comparisons (with the CI-mode experiments using Bi-Sn-In alloy), it is found that a similar trend in the transient pressure and temperate history as well as the overall effect of experimental parameters (such as water quantity, melt and water temperature, water-lump shape and melt depth) on the pressurization characteristics can be reproduced for the new LBE experiments. Despite a much lower thermal diffusivity, for the LBE experiments it is confirmed that the most likely reason resulting in the limited pressurization as water volume increases should be also primarily owing to the isolation effect of vapor bubbles generated at the melt-water interface. Possibly due to the enhanced melt density which facilitates the penetration of melt through vapor bubbles, under the same parametric condition the pressurization in LBE experiments is generally much higher than that in Bi-Sn-In experiments. Compared to melt static pressure, the pressurization under different melt depths is supposed to be more influenced by the varied easiness of melt penetration. Current work provides a large amount of favorable experimental database and insight for promoted understanding on actual CDAs and improved verifications of SFR severe accident codes in China.

Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap.

Competition among different uses for land is becoming acute under the process of urbanization, and conflicts related to this competition are becoming more frequent and more complex. This article presents a methodology for confronting this issue. By applying an integrated framework, we explore the implicit role of the Beijing–Tianjin–Hebei Regional Integration (BRI) policy in land use conflicts by focusing on the urban-rural interface, and try to address the research question: “How feasible is BRI for reconciling land use conflicts across the urban-rural interface?” An original structure of the analysis is developed based on the identification of three types of conflicts, namely, conflicts over land use structure, conflicts over land conversion and conflicts over landscape pattern. According to the interactions and relationships among these conflicts, we define broad categories of land use conflict areas. Indeed, these conflicts are all related to the unplanned use of agricultural land reserves, which competes with other more immediate uses, and the over-exploitation of land resources caused by unsustainable urban practices. This policy is clearly a critical objective for optimizing the land use structure. It, however, fails to reconcile the conflicts over land conversion and landscape pattern, especially for considerable agricultural land conversion to non-agricultural uses, and low-density development pattern with mixed residential and industrial land uses. Hence, alternative strategies involving public participation, spatial equity, rural revitalization, land-use system reform, and new type of urbanization, can be identified as viable solutions for land use conflict management, which may be complementary to regional integration. The findings of our paper may also contribute to the policy debate on BRI concerning land use planning and regional sustainability.

The anisotropy of directional radiative surface temperature measurements over urban building surfaces is expected to have strong temporal variation. However, a model with the capability to simulate the temporal variation of urban thermal anisotropy (UTA) and with inversion abilities has not yet been developed. In this paper, the relationship between the temporal variation of UTA and temperature contrasts among surface components is investigated. Based on a system of previously developed geometric models for simulation of thermal anisotropy in simplified urban neighborhoods of different densities, a system of advanced geometric models, GUTA-T, is proposed that incorporates the temporal variability of UTA through the dependence of temperature contrasts on solar zenith angle θs under clear skies. A 3-D microscale urban surface temperature model and a sensor view model are combined to generate a synthetic UTA dataset for a variety of simplified urban geometries and with which to evaluate GUTA-T. The results show that parameter inversions are sensitive to sun-surface-sensor geometries. The seasonal and hourly behavior of anisotropy is simulated by GUTA-T with mean absolute differences of 1.06 °C, 1.25 °C, 1.69 °C and 1.04 °C for the view zenith and azimuth angles (θv, φv) of (40°, 90°), (60°, 270°), (60°, 180°) and (40°, 130°), respectively. Evaluation using diurnal measurements over an urban scale model shows that the root mean square difference between simulated anisotropy and the reference anisotropy generated from observed directional temperatures and a high-accuracy computer model is 2.02 °C for θs ≤ 60°. If the proposed model is to be calibrated with satellite data, a second directional observation from the opposite side of the surface target area can improve model performance significantly. Calibrations using samples including only small (e.g., θs ≤ 30°) or large θs (e.g., θs ≥ 50°) tend to decrease the anisotropy time-series simulation accuracy. Due to its inversion abilities, the GUTA-T model has the potential to simulate and correct anisotropy in time-series thermal infrared remote sensing data, thereby improving the analysis either of spatial variation within an image, or of temporal variation of angular urban surface temperature.

The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the “regression-then-fusion” (R-F) and “fusion-then-regression” (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa.

Urban surface structures affect block surface temperatures, thermal loading of pedestrians, wind patterns and more. Comparisons of the building density and height impact on block surface temperature and optimization at a fixed floor area ratio (FAR) are rare. In this research, a 3-dimensional simulation model, ENVI-met, is used to simulate block surface temperature in different scenarios and evaluate the urban geometry impact on surface temperature of each component (roof, street and four-oriented walls). Results show that: (1) the increased building density (λp) and height (H) cool blocks at unfixed FARs; (2) buildings with λp and H of 0.36 and 10 m with a fixed FAR of 1.5 produce the highest summer surface temperatures for all studied components; (3) when FAR is fixed, among scenarios with λp ≤ 0.36 and H ≥ 10 m, building height dominates component surface temperature by reducing the solar radiation reaching the surface. Among scenarios with λp > 0.36 and H

Effective coupling between space light and optical fiber is one of the key technologies in free space optical communication. Few-mode fiber coupling is becoming a promising method to improve the performance of free space optical communication receiver. The dependence of the few-mode fiber coupling efficiency on the focal ratio, the central occlusion ratio and the numerical aperture are analyzed in this paper. The research results may be beneficial for the design of the receiver based on FMF coupling in the FSOC system.

A temperature and refractive index simultaneous measurement sensor based on an optical microfiber coupler (OMC) combined with a polarization maintaining fiber (PMF) is proposed and experimentally demonstrated. The OMC-PMF sensor is fabricated by inserting a section of PMF between two output ports of the OMC. The interference spectrum of the OMC-PMF sensor has different responses to temperature and refractive index variations. Temperature and the refractive index can be measured by monitoring the dip intensity variation and the dip wavelength shift of the interference spectrum, respectively. The experimental results show that the refractive index sensitivity is 12 020 nm/RIU, and the temperature sensitivity is 0.88 dB/°C upon increasing temperature from 35 °C to 41 °C.

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous.

Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R2).

Change detection in multi-temporal remote sensing images has usually been treated as a problem of explicitly detecting land cover transitions. To date, multi-dimensional change vector analysis has been an effective solution to such problems. However, using change vector analysis makes it hard to calculate multiple directions or kinds of change. Through combining multi-feature object-based image analysis and change vector analysis, this paper presents a novel method for object-based change detection of multiple changes. Our technique, named self-adaptive weight-change vector analysis, carries out: (1) change vector analysis to determine magnitude and direction of changes; and (2) self-adaptive weight-based analysis of the standard deviation of image objects. Furthermore, a polar representation has been adopted to acquire visual change information for image objects. This paper proposes an automatic technique that can be applied to the field of multi-feature object-based change detection for very high resolution remotely sensed images. The two-step automatic detection strategy includes extraction of changed objects using an expectation-maximization algorithm to estimate the threshold under a Gaussian assumption, and identification of different kinds of changes using a K-means clustering algorithm. The effectiveness of our approach has been tested on both multispectral and panchromatic fusion images. Results of these two experimental cases confirm that this approach can detect multiple kinds of change. We found that self-adaptive weight-change vector analysis had superior capabilities of object-based change detection compared with standard change vector analysis, yielding Kappa statistics of 0.7976 and 0.7508 for Cases 1 and 2, respectively.

A new algorithm was developed in this research to minimize aerosol effects on the normalized difference vegetation index (NDVI). Simulation results show that in red-NIR reflectance space, variations in red and NIR channels to aerosol optical depth (AOD) follow a specific pattern. Based on this rational, the apparent reflectance in these two bands of neighboring pixels were used to reduce aerosol effects on NDVI values of the central pixel. We call this method the neighboring pixels (NP) algorithm. Validation was performed over vegetated regions in the border area between China and Russia using Landsat 8 Operational Land Imager (OLI) imagery. Results reveal good agreement between the aerosol corrected NDVI using our algorithm and that derived from the Landsat 8 surface reflectance products. The accuracy is related to the gradient of NDVI variation. This algorithm can achieve high accuracy in homogeneous forest or cropland with the root mean square error (RMSE) being equal to 0.046 and 0.049, respectively. This algorithm can also be applied to atmospheric correction and does not require any information about atmospheric conditions. The use of the moving window analysis technique reduces errors caused by the spatial heterogeneity of aerosols. Detections of regions with homogeneous NDVI are the primary sources of biases. This new method is operational and can prove useful at different aerosol concentration levels. In the future, this approach may also be used to examine other indexes composed of bands attenuated by noises in remote sensing.

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

A simple and highly sensitive temperature sensor based on an isopropanol-sealed optical microfiber coupler (OMC) is reported. Due to the high thermo-optic coefficient of isopropanol, the temperature sensitivity of the OMC can be effectively improved by immersing the OMC into isopropanol. The refractive index change of isopropanol is caused by the surrounding temperature variation which in turn allows highly sensitive temperature sensing. Theoretical analysis demonstrates that the temperature sensitivity increases quickly with the decreasing OMC waist diameter. Temperature sensitivities at different waist diameters exhibit high consistency with the theoretical results. When used for temperature sensing, the transmission spectrum blueshifts as the surrounding temperature increases. The highest sensitivity of −5.89 nm/°C is achieved at the waist diameter of 2.2 μm. This is the most sensitive OMC based temperature sensing device reported so far. Such a compact size, low cost, and highly sensitive device may widen the application range of OMC.