The growing complexity and diversity of 5G network architecture (e.g., power grid 5G network) have made security risk assessment and root cause analysis increasingly challenging. Recent advances in large language models (LLMs) have the potential to transform this landscape. However, existing LLMs-based solutions primarily focus on understanding the language of 5G telecommunications, while overlooking potential security vulnerabilities in the data flows. To facilitate LLMs' in-depth application, this paper presents RCA-LLM, a novel fault root cause analysis framework for 5G networks developed from tailored LLMs-based solutions. In explicit terms, RCA-LLM is trained by inputting processed and organized fault information for fine-tuning, and combined with retrieval-augmented generation (RAG) technology to significantly improve the accuracy of 5G fault analysis. Our experimental results indicate that RCA-LLM performs well in fault analysis, effectively supporting users in diagnosing and resolving fault issues. Model evaluation results further demonstrate that the model significantly improves fault analysis accuracy and has high practical value. In addition, RCA-LLM provides important reference value for efficient operation and maintenance management of 5G and future power grid networks, while also offering new ideas for advancing intelligent fault analysis.

Satellite-based cloud computing cybersecurity threats have long posed significant challenges, particularly for cloud infrastructure operators.While prior research has partially addressed these issues by mitigating threats and enhancing human response efficiency, this paper proposes a novel AI-Driven Threat Analysis and Response (TAR) framework.The study progresses in three main phases: (1) redefining urgent threats through a novel formula; (2) implementing a triage and analysis framework using augmented Large Language Models (LLMs); and (3) automating incident response via a Security Orchestration, Automation, and Response (SOAR) platform.Our prototype, tested in a simulated public cloud environments using real production threats, demonstrated a 17% improvement in handling low-and medium-urgency threats.Experimental results show our approach achieves 97.8% coverage in automatic threat classification, significantly outperforming traditional manual methods, which achieve 77.8% coverage.With high recall and precision in managing low-and medium-urgency threats, our method enhances manual efficiency through SOAR-enabled automation.Furthermore, * Corresponding Author.our augmented method surpasses the state-of-the-art GPT-4 Turbo model in addressing security threats containing Chinese characters.

A constellation of high-resolution thermal infrared (TIR) missions is expected to be launched in the upcoming years. Land surface temperature (LST), as a key parameter retrieved from TIR observations, constrains the variations in energy and water exchanges in the surface-atmosphere continuum. The widely used temperature and emissivity separation (TES) algorithm stands as a promising candidate for LST retrieval from these future missions due to the availability of$\ge 3$TIR bands. To explore the possibilities of further refinements of TES, a revisit of the TES algorithm in terms of the error propagation from different sources is necessary. Until now, the respective uncertainties introduced by the three modules in TES (i.e., the normalized emissivity method (NEM), the ratio algorithm (RATIO), and the maximum minimum difference (MMD) module) remain unclear. In addition, the controversy over the performances of TES on gray and nongray bodies is still unresolved. To address these research gaps, a comprehensive simulation analysis was conducted for the ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) sensor to quantify the independent impact of each error source in TES on LST retrieval accuracy, including: 1) sensor measurement noise; 2) atmospheric correction errors; 3) the NEM and RATIO modules; and 4) the MMD module. The respective responses of gray and nongray bodies to these factors were also compared based on the simulation dataset. Furthermore, the influence of the calibration scale of the minimum emissivity ($\varepsilon _{\min }$)–MMD relationship (i.e., cavity effect within vegetation canopies) was evaluated using the ECOSTRESS observations at 11 vegetated ground sites. The simulation analyses revealed that the error in atmospheric correction is the dominant impact factor significantly affecting the performances of all the other modules in TES, followed by the deviation from the regressed relationship in the MMD module. The measurement noise has minor impacts when it is well-controlled (e.g., NEdT$\le 0.1$K), and uncertainties caused by the NEM and RATIO modules are negligible. The performance discrepancy of TES over gray and nongray bodies is insignificant under low measurement noise, a condition anticipated to be met by the majority of current and future sensors. Regarding the calibration scale, the benefit of cavity effect correction is not evident according to the evaluation using the ground measurements. Based on the analyses, it is recommended that more efforts should be put into refining the atmospheric correction module and improving the fitting of emissivity samples to the$\varepsilon _{\min }$–MMD curve. In contrast, the expected benefits of refining the NEM and RATIO modules appear minimal.

Estimating evapotranspiration (ET) beyond the local or point scale is essential for many water-related studies. By exploiting the relationship between surface biophysical parameters and thermal emission, continuous ET at such larger spatial scales can be obtained. In this study, we applied the EVASPA tool, which provides an ensemble of ET estimates, over southern France. This was done using MODIS data, including Land Surface Temperature, NDVI, and albedo, resulting in 243 ET estimates. Initial evaluations using in-situ flux data yielded reasonable results even when a simple average was used, with a broad absolute and performance range between the member estimates being observed. Additionally, our uncertainty analyses indicated that ensemble-based contextual modelling can provide sufficient spread for enhanced flux simulations. As EVASPA is intended for operational use, this work aims to guide the establishment of an optimal weighting criteria for the members to improve ET estimates.

Posting programs to code hosting platforms such as GitHub is common for developers, but it will lead to privacy leakage issues in hosted programs. Though there are some detection methods for privacy leakage, they are difficult to be applied in practice. First, existing works mainly focus on detection algorithms, while ignoring the complete detection system from a holistic perspective. Second, the system will be blocked when acquiring programs because code hosting platforms usually have protection mechanism. Third, high-performance privacy detection algorithms need hardware devices in practice, and their effectiveness in real scenarios has not been verified since there is no public real hosted program privacy dataset.To address the above problems, we implement and commercialize a user-friendly privacy information leakage detection system for actual hosted programs. Firstly, we provide a system frame-work that can automatically complete "program acquisition-privacy detection-alert", allowing subscribers receive alerts if there is a privacy information leakage. Secondly, we propose a novel multi-random crawler scheme that can flexibly cope with the limitations of GitHub when acquiring hosted programs. Thirdly, we skillfully apply a cost-effective detection approach based on fuzzy matching, which can detect the subscriber customized privacy information with high performance. Based on this, we further provide a high-quality dataset obtained in real scenarios. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of our crawler scheme and detection approach in providing a user-friendly system.

The European ECOSTRESS Hub (EEH) funded by European Space Agency targets at generating land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP) from the high-resolution ECOSTRESS observations. In Phase 1 (2020-2022), EEH LST obtained using the split-window and temperature and emissivity separation algorithms achieved good accuracy with an overall RMSE around 2 K. Evaluation of three ET estimates using different models, namely the Surface Energy Balance System (SEBS), Two Source Energy Balance (TSEB) parametric models, and the non-parametric Surface Temperature Initiated Closure (STIC) model, indicated that STIC ET had the highest accuracy (RMSE of ~70 W m-2). In Phase 2 (2023-2026), the surface energy balance will be coupled with photosynthesis through canopy-stomatal conductance. Overall, EEH is promising to advance the science of terrestrial ecosystem processes and facilitate the preparation for the future high-resolution thermal missions.

With the rapid development of the cutting edge cloud computing technology, millions of vulnerabilities have been identified, there is a growing concern that organizations should devote plenty of time and lots of resources to secure. The overarching objective of remediation is to prioritize the vulnerabilities. Hence, define the severity and the urgency of the vulnerabilities and remediate them automatically is very important. Although the recognized Common Vulnerability Scoring System (CVSS) 4.0 method addresses this issues partly, they are difficult to be implemented in practices on the cloud because of the complication and lack of risk based factors.To this end, we propose a Cost-effective Massive Automation Method of Vulnerability Analysis and Remediation Based on Cloud Native Framework. Specifically, considering that the current CVSS is more like a severity of vulnerabilities, we design a novel formula to define the urgency of vulnerabilities. The formula takes the advantaged of the capabilities of modern cloud-based infrastructure and simplifies the CVSS. Besides, we propose an algorithm of risk reduction by leveraging the cloud native security capabilities, which cut down unnecessary patching time and workload. Particularly, in order to remediation the risk on the cloud, we implement an automatic scheme to harden the vulnerabilities by invoking the cloud native APIs based on the Security Orchestration, Automation and Response (SOAR) platform. Finally, we conduct comprehensive experiments to evaluate our system. Experimental results demonstrate the effectiveness of ours approach has a high ratio of urgency risk recognition of 99.24%. Meanwhile, ours approach shows a maximum risk reduction by downgrade the fixable vulnerability with a average of 79% risk reduction rate in application level and 99% of risk reduction rate in operating system level respectively. As a result, our approach lightens the workload of patching greatly in the real cloud computing environment.

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison owing to its dual-angle viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature-based and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types. The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Pérez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42 K, 1.79 K and 2.05 K, respectively. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature. The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.

Land surface temperature (LST) is an important parameter that characterizes the energy balance of the land surface, and it is widely used in various research fields. This paper proposes an operational split-window (SW) algorithm for use with the Chinese Fengyun-3 (FY-3) series satellite data, with the purpose of generating long-term global LST products. The algorithm primarily involves three steps. First, the brightness temperatures of the FY-3 Visible and Infra-Red Radiometer (VIRR) were recalibrated using historical recalibration coefficients to improve the accuracy of the absolute radiometric calibration. Second, daily dynamic emissivity maps were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global emissivity dataset (GED) and vegetation/snow cover products based on the vegetation cover method. Finally, the coefficients of the SW algorithm were simulated using MODTRAN 5 combined with the SeeBor V5.0 atmospheric profile library and ASTER spectral library, and then the coefficients were stratified by the view zenith angle and atmospheric water vapor content to improve the fitting accuracy. The proposed SW algorithm was integrated into the MUlti-source data SYnergized Quantitative (MUSYQ) remote sensing production system to then generate FY-3 VIRR LST products. Ten land surface sites from the HiWATER and SURFRAD networks and nine water surface sites from the National Data Buoy Center (NDBC) were used to evaluate the accuracy of the FY-3 VIRR LST products. The results demonstrated that the accuracy of the historical recalibration coefficients of the FY-3A/B VIRR is higher than that of the operational calibration coefficients for LST retrieval. The evaluation results revealed that the FY-3A VIRR LST products (2009-2013) had a bias of 0.13 K and an RMSE of 2.77 K, and the FY-3B VIRR LST products (2011-2020) had a bias of -0.07 K and an RMSE of 2.83 K. These results demonstrate that the proposed operational SW algorithm has reasonable accuracy and can be used to produce global LST products from the FY-3 VIRR data.

Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.

Rice variety identification is important for genetic breeding classification and crop yield estimation. Traditional identification methods are time-consuming and inaccurate. This paper proposes a method for rice variety identification based on the hyperspectral characteristics of leaves. Hyperspectral data of rice leaves were collected using a geophysical spectrometer imaging system. To reduce the redundance among the hyperspectral data and save the identification cost, locality preserving projections (LPP) is first applied to extract low-dimensional representative features from the leaf hyperspectral data. Then, support vector machine (SVM) is combined for conducting the identification of rice varieties. The experimental results show that the identification rate of 10 varieties of early rice was found to be 91.67% and the identification rate of 10 varieties of late rice was 97.33%.

In this study, two collection 6 (C6) Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 land surface temperature (LST) products (MYD11_L2 and MYD21_L2) from the Aqua satellite were evaluated using temperature-based (T-based) and radiance-based (R-based) validation methods over barren surfaces in Northwestern China. The ground measurements collected at four barren surface sites from June 2012 to September 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment were used to perform the T-based evaluation. Ten sand dune sites were selected in six large deserts in Northwestern China to carry out an R-based validation from 2012 to 2018. The T-based validation results indicate that the C6 MYD21 LST product has a better accuracy than the C6 MYD11 product during both daytime and nighttime. The LST is underestimated by the C6 MYD11 products at the four T-based sites during the daytime, with a mean bias of -2.82 K and a mean RMSE of 3.82 K, whereas the MYD21 LST product has a mean bias and RMSE of -0.51 and 2.53 K, respectively. The LST is also underestimated at night by the C6 MYD11 products at the four T-based sites, with a mean bias of -1.40 K and a mean RMSE of 1.72 K, whereas the MYD21 LST product has a mean bias and RMSE of 0.23 and 1.01 K, respectively. For the R-based validation, the MYD11 results are associated with large negative biases during both daytime and nighttime at three sand dune sites and biases within 1 K at the other seven sites, whereas the MYD21 results are more consistent at all ten sand dune sites, with a mean bias of 0.45 and 0.70 K for daytime and nighttime, respectively. The emissivities for these two products in MODIS bands 31 and 32 were compared with each other and then compared with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity and laboratory emissivity. The results indicate that the emissivities in MODIS bands 31 and 32 of MYD11 at the four T-based and three of the R-based validation sites are overestimated and result in LST underestimation, whereas the emissivities of MYD21 are more consistent with the laboratory emissivity. Besides, an experiment was carried out to demonstrate that the physically retrieved dynamic emissivity of the MYD21 product can be utilized to improve the accuracy of the split-window (SW) algorithm for barren surfaces, making it a valuable data source for retrieving LST from different remote sensing data.

Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and RL model, gave the best fitting results for all the scenes. The R2of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions.

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors.

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on non-vegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 W · m-2, respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 W·m-2, respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2 W · m-2, whereas orchard surfaces yield biases are between -2 and -3.5 W · m-2, and village surfaces yield biases exceeding -10 W · m-2. These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 W·m-2, respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature-emissivity algorithm (by 8.7 and 11.7 W · m-2, respectively).

In this study, a single-channel parametric model (SC-PM) algorithm were used to produce 300m LST product from HJ-1B IRS data. The NCEP atmospheric profiles and a parametric model were used for atmospheric correction. In order to improve the accuracy of the land surface emissivity (LSE), the 1km ASTER Global Emissivity Dataset (GED) and self-developed 5-day 1km vegetation cover product were used for estimating the LSE based on the Vegetation Cover Method. Two years of HJ-1B IRS LST product in Heihe River basin (Gansu province, China) from June 2012 to June 2014 were generated. The LST products were evaluated against ground observations collected during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites and ten vegetated sites were chosen for the evaluation. The results show that the produced HJ-1B IRS LST products demonstrate a good accuracy, with an average bias of 0.10 K and an average root mean square error (RMSE) of 2.43 K for all the sites during daytime. In addition, the biases are within 1K for the four barren surface sites. This indicate that using ASTER GED can produce reliable LST products from HJ-1B IRS data, especially for the barren surfaces.

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR.

This study analyzed the relationship between the spatial heterogeneity of land surface temperature (LST) and the spatial scale using the Thermal Airborne Hyperspectral Imager (TASI) and satellite-based Advanced Spaceborne Thermal Emission Reflection (ASTER) data. The spatial heterogeneity of LST was quantified using variogram modeling in univariate and multivariate method. The results show that in both methods, the spatial heterogeneity of LST in a landscape as quantified by the dispersion variance increases with the spatial scale until the scale is larger than the characteristic scale, and the land cover types have significant influences on the spatial heterogeneity of LST. Additionally, the spatial heterogeneity of the land surface decreases obviously as the wavelength increases in the multivariate model.

Wavelength division multiplexing technique can greatly increase the bandwidth of all-optical networks without the reconstruction of physical infrastructure. Optical power splitters can reduce the number of required wavelengths for multicast requests by dividing the incoming optical signal into multiple outcoming signals with equal optical power. This paper considered the problem of how to reduce the number of splitters on a directed fiber tree and not to decrease the quality of service too much, compared against the case that each node on the tree is equipped with one splitter. The proposed splitter placing strategy showed satisfying performance in the experiments.