Search Results (304)

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions. Full article

In this study, small-scale experiments were performed to examine fuel distribution effects on discrete flame spread behavior under natural convection and ambient wind. To this end, birch rod arrays with regularly varying column number (n) and array spacing (S) were designed. The results indicate that fuel distribution exerts a comparable influence on flame spread under both natural convection and ambient wind conditions. The flame spread rate (V_f), flame length (L_f), and mass loss rate (MLR) are insensitive to changes in S but have an exponential relationship with n. Based on the mass conservation law, prediction correlations for the mass loss rate based on S and n in the stable flame spread stage are proposed. We discovered that nondimensional mass loss has a power law dependence on the fuel coverage rate. In addition, radiative heat transfer dominates the flame spread process for the discrete array. Horizontal flame spread across discrete rod arrays exhibits critical spacing under natural convection. Finally, we established a comprehensive heat transfer model for flame spread under natural convection conditions and obtained a derivation of a critical sustainability criterion for the discrete flame spread process, which considers radiative and convective heat transfer. Full article

Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves ($C_b=0.90$) than for monocot leaves ($C_b=0.79$), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article

Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions. Full article

As one of China’s major megacities, Chongqing is highly vulnerable to rainfall-induced landslides, and the increasing frequency of extreme rainfall driven by climate change further exacerbates risks to infrastructure and public safety. Although numerous studies on landslide susceptibility, quantitative assessments of future landslide frequency under different climate scenarios remain insufficient. This study addresses this gap by integrating high-resolution climate projections with a landslide early-warning model to predict spatiotemporal variations in landslide hazard across Chongqing. Based on regional climate characteristics, the rainy season was divided into three periods: May–June, July, and August–September. Soil moisture variations, together with static geological and topographic factors, were integrated using the information value model to assess the semi-dynamic landslide susceptibilities. On this basis, a regional warning model was then established by linking rainfall thresholds to four geological subregions. High-resolution NEX-GDDP-CMIP6 projections and historical ERA5 0rainfall data were used to quantify changes in exceedance days under four shared socioeconomic pathways (SSPs) from 2021 to 2100. Results indicate a substantial increase in days exceeding the 30% landslide-triggering rainfall threshold, with maximum relative growth of 15.57%. Landslide frequency exhibits pronounced spatial and temporal heterogeneity: increases are observed in May–June and August–September, whereas July trends vary with radiative forcing-decreasing under low-forcing scenarios (SSP1-2.6, SSP2-4.5) and increasing under high-forcing scenarios (SSP3-7.0, SSP5-8.5). The largest increase in frequency reaches 72%, primarily affecting southwestern and central Chongqing. By linking climate projections with rainfall thresholds and semi-dynamic susceptibility assessment, the framework provides a scientific reference for landslide risk prevention and mitigation under future climate scenarios, and offers transferable insights for other mountainous urban regions facing similar hazards. Full article

This study numerically investigates the critical fuel concentration and flammable regions of methane–air and methane oxy-fuel counterflow diffusion flames. The goal is to determine the effects of strain rate, oxidizer composition, and radiative heat transfer models on flame extinction. Calculations were performed using the counterflow diffusion flame with the adiabatic (ADI), optically thin (OTM), and statistical narrow-band (SNB) radiation models at strain rates of 10 s⁻¹, 80 s⁻¹, and 200 s⁻¹. The key findings are as follows: For methane–air flames, radiation reabsorption has a negligible impact. The flammable region decreases with increasing strain rate (S_Low > S_Mid > S_High) across all models. In O₂/CO₂ flames, radiation plays a significant role. While the ADI and SNB models maintain the same trend as in air flames, the OTM yields a different order (S_Mid > S_High > S_Low). Reducing oxygen concentration increases the critical fuel concentration and shrinks the flammable region. When the oxygen concentration is between 0.35 and 0.40, the combustion characteristics of O₂/CO₂ flames resemble those of conventional air flames. In conclusion, this work highlights the critical influence of radiation modeling and oxidizer composition on oxy-fuel flame extinction limits, providing insights for combustion system design under CO₂ dilution. Full article

The study focuses on modeling the remote sensing reflectance (R_rs) for optically complex waters based on the absorption (a), scattering (b), and backscattering (b_b) coefficients measured at selected wavelengths (420 nm, 488 nm, 555 nm, and 620 nm). R_rs was calculated using both Morel’s proxy and Monte Carlo (MC) simulations. A comparison of the R_rs values obtained from the proxy and the MC simulations allowed us to determine the proxy factor (k). The results evidenced that this proxy parameter increases with wavelength. The findings demonstrate that R_rs can be computed from inherent optical properties (IOPs) using radiative transfer modeling, providing light independent reflectance estimates, unlike direct in situ R_rs measurements, which are affected by instantaneous lightening conditions. Full article

With advancements in solar-induced fluorescence (SIF) observation technology and the evolution of vegetation radiative transfer models, SIF signals can now be more effectively interpreted and leveraged from a mechanistic perspective. This, in turn, facilitates a deeper understanding of the mechanistic link between SIF and photosynthesis. Considering the impact of water stress on terrestrial ecosystems, this paper simulated SIF and gross primary productivity (GPP) values using the STEMMUS-SCOPE model at half-hour scales from 2017 to 2023 at the Daman site. The simulation results were compared and validated against flux tower observations and SCOPE model outputs. Taking advantage of irrigation events in the semi-arid irrigated farmland, we assessed the accuracy of STEMMUS-SCOPE in simulating SIF and GPP under drought stress, as well as its capability to quantitatively analyze the impacts of water stress on SIF and GPP. The results show that the accuracy of the SIF and GPP values simulated by the STEMMUS-SCOPE model is higher than that of the SCOPE model. The averaged R² and RMSE between the SIF simulated by STEMMUS-SCOPE model and the observed SIF values are 0.66 and 0.29 mW m⁻² nm⁻¹, and the averaged R² and RMSE between the GPP simulated by the STEMMUS-SCOPE model and the observed GPP values from 2017 to 2023 are 0.88 and 4.93 µmol CO₂ m⁻² s⁻¹, respectively. Especially under relatively drought conditions, the R² between the SIF simulated values and observed values is 0.84, and the R² between the GPP simulated values and observed values is 0.96. By further combining soil moisture content (SMC) and canopy conductance (Gs) analyses, we found that the response of the STEMMUS-SCOPE simulations under water stress was consistent with previous findings on the impacts of water deficits, thereby confirming the model’s reliability for drought conditions. Under drought stress, the decline in fluorescence emission efficiency (ΦF) with decreasing Gs and SMC was smaller than that of the light use efficiency (LUE). Therefore, the STEMMUS-SCOPE model is promising for investigating the SIF–GPP relationship under drought stress. Full article

Cloud feedback is a major source of uncertainty in climate projections. In particular, Arctic clouds, arguably one of the most poorly understood aspects of the climate system, strongly modulate radiative energy fluxes from the Earth’s surface to the top of the atmosphere. In situ and satellite observations reveal the existence of ubiquitous optically thin ice clouds (TICs) in the Arctic during polar nights, whose influence on atmospheric energy is still poorly understood. This study quantifies the effect of TICs on the atmospheric energy budget during polar winter. A reanalysis-driven simulation based on the Canadian Regional Climate Model version 6 (CRCM6) was used with the Predicted Particle Properties (P3) scheme (2016) to produce an ensemble of 3 km mesh simulations. This set is composed of three simulations: CRCM6 (reference, the original dynamically coupled cloud formation), CRCM6 (nocld) (clear-sky) and CRCM6 (100%cld) (overcast, 100% cloud cover as a forcing perturbation). Using the regional energetic equations (Nikiema and Laprise), we compare the three cases to assess TIC forcing. The results show that TICs cool the atmosphere, with the difference between two simulations (cloud/no clouds) reaching up to −2 K/day, leading to a decrease in temperature on the order of ~−4 KMonth⁻¹. The energetics cycle indicates that the time-mean enthalpy generation term G_M and baroclinic conversion dominate Arctic circulation. The G_M acting on the available enthalpy reservoir (A_M) increased by a maximum value of ~5 W·m⁻² (58% on average) due to the effects of TICs, increasing in energy conversion. TICs also lead to average changes of 9% in time-mean available enthalpy and −5.9% in time-mean kinetic energy. Our work offers valuable insights into the Arctic winter atmosphere and provides the means to characterize clouds for radiative transfer calculations, to design measurement instruments, and to understand their climate feedback. Full article

This study investigates the cooling potential of night ventilation and the climate adaptability of local vernacular buildings in the Turpan basin, aiming to identify passive energy-saving design strategies. A rural building with an air-drying shelter was selected for summer indoor environment measurements (two stages: all-day window closure vs. night ventilation), and a numerical model was established to simulate the impacts of window-to-wall ratio and window shading projection factor on the indoor environment. Results indicate that night ventilation introduces cool outdoor air to replace indoor hot air, cools building components, improves thermal comfort, and reduces cooling energy demand. Without additional cooling technology, increasing the window-to-wall ratio lowers nighttime temperatures but increases Degree Discomfort Hours, while appropriately sized shading devices mitigate daytime overheating from larger windows. Benefiting from the high thermal storage capacity of earth-appressed walls, semi-underground rooms offer better comfort with lower temperatures and higher humidity; for aboveground rooms, orientation is critical due to intense solar radiation. The air-drying shelter reduces solar radiant heat absorption and inhibits convective/radiative heat transfer on the roof’s external surface, significantly lowering its temperature from noon to midnight. This leads to notable reductions in the roof’s internal surface temperature (1.02 °C in the sealed stage, 2.09 °C during night ventilation) and the average indoor temperature (1.70 °C). Full article

The laser heterodyne radiometer (LHR) is a promising technique for atmospheric remote sensing due to its exceptionally high spectral resolution and sensitivity. A model based on a random forest algorithm is proposed to generate highly accurate prior atmospheric profiles using real-time meteorological parameters. In addition, a locally weighted scatter plot smoothing (LOWESS) method is applied for baseline correction during data preprocessing. An inversion algorithm is implemented using the Py4CAtS radiative transfer model, in which quadratic baseline parameters are included in the iterative process. Continuous measurements of the atmospheric ${\mathrm{CO}}_2$ absorption spectrum were made in our laboratory (Hefei, China, 31.9°N, 117.16°E), and the dry mixing ratio ($X_{{\mathrm{CO}}_2}$) was obtained after data processing and inversion. The results demonstrate that this research improves the accuracy of LHR signal inversion. The implemented Python-based framework shows potential for real-time atmospheric ${\mathrm{CO}}_2$ monitoring. Full article

The Ozone Monitoring Suite–Limb (OMS-L) carried by the Fengyun-3F (FY-3F) satellite, as China’s first effective payload using the limb observation mode to conduct hyperspectral atmospheric detection in the ultraviolet (UV) and visible (Vis) bands, was successfully launched on 3 August 2023. It mainly serves the research in the fields of climate change, atmospheric chemistry, and atmospheric environment. This study is the first to conduct the retrieval of the ozone profiles from OMS-L data. The retrieval scheme utilizes the radiances within the UV band, normalizing them to the radiance at the upper tangent height. To minimize the impact of aerosol scattering, the pair method is implemented, with seven carefully selected wavelength pairs fully exploiting ozone’s UV absorption characteristics. The weighted multiplicative algebraic reconstruction technique (WMART) is then applied to effectively integrate multi-wavelength information, in tandem with an iterative retrieval process using the radiative transfer model. This approach yields ozone concentration profiles in the altitude range of approximately 18–55 km. The retrieval errors resulting from the parameters are estimated to be 5–13% above 25 km, increasing to 10–30% in the upper troposphere. Comparison of OMS-L retrieved ozone profiles with the OMPS/LP v2.6 product reveals good consistency, with differences generally within 10% in the 20–50 km altitude range. However, biases are more pronounced at lower altitudes, particularly in tropical regions. This work conclusively demonstrates that OMS-L can accurately measure stratospheric ozone profiles with high vertical resolution, thereby contributing significantly to the field of atmospheric science. Full article

The use of semi-transparent photovoltaic (Solar PV) glass in buildings is an effective strategy for integrating renewable energy generation, solar control, and thermal comfort. However, conventional simulation models rely on global optical properties, neglecting spectral radiation and its propagation within the material. This limits the accurate assessment of thermal comfort, light distribution, and performance in complex systems such as multi-layer glazing. This study presents the development, implementation, and experimental validation of a numerical model that reproduces the thermal, electrical, and optical behaviour of semi-transparent Solar PV glass, explicitly incorporating radiative transfer. The model simultaneously solves the conduction, convection, and electrical generation equations together with the radiative transfer equation, solved via the finite volume method across two spectral bands. The refractive index and extinction coefficient, derived from manufacturer-provided optical data, were used as inputs. Experimental validation employed 10% semi-transparent a-Si glass, comparing surface temperatures and electrical power generation. The model achieved average relative errors of 3.8% for temperature and 3.3% for electrical power. Comparisons with representative literature models yielded errors between 6% and 21%. Additionally, the proposed model estimated a solar factor of 0.32, closely matching the manufacturer’s 0.29. Full article

Dust plays a significant role in the atmospheric radiative balance by altering both shortwave and longwave radiation fluxes. While deserts are the primary sources of dust emissions, atmospheric circulation can transport dust over long distances, impacting air quality and climate in remote regions. These transport episodes, commonly known as dust events, vary in intensity and effects. Despite extensive research, uncertainties persist regarding their precise radiative impacts. This study examines the direct radiative effects of dust events in 2024 (a year marked by heightened dust activity) over Limassol, Cyprus. A comprehensive approach is employed, integrating radiative transfer modelling, ground-based solar radiation measurements, and dust optical property analysis. The LibRadtran radiative transfer package is used to simulate atmospheric radiative transfer under dust-laden conditions, incorporating key dust optical properties such as Aerosol Optical Depth, Single Scattering Albedo, and the Asymmetry Parameter retrieved from the Limassol’s AERONET station. Observations from solar radiation station at the ERATOSTHENES Centre of Excellence serve as validation for the model. This study quantifies the radiative impact of dust by evaluating changes in surface irradiance, providing valuable insights into the role of dust in atmospheric energy balance. Full article