Search Results (198)

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

Silver nanoclusters that are confined inside zeolites can give off intensive tunable emission across the visible region under UV excitation. In this research, a series of silver nanoclusters loaded with R-FAU/Ag (R = Li, Na, K) zeolites were synthesized and then applied as phosphors for LEDs. The XRD and SEM measurements showed the R-FAU/Ag (R = Li, Na, K) zeolites have high crystallinity and a size distribution of 0.7–1.25 μm. Under excitations of 310–330 nm ultraviolet radiation, Li-FAU/Ag, Na-FAU/Ag, and K-FAU/Ag exhibit monotonically declining emission intensities and red-shifted emissions with peak wavelengths of 520, 527, and 535 nm, respectively. By using silicone-based epoxy resin as the packaging material, a series of LEDs were fabricated by mixing R-FAU/Ag (R = Li, Na, K) phosphors. It is indicated that the Li-FAU/Ag-LED shows the strongest intensity of 94.9 mcd, much higher than that of the LEDs made from Na-FAU/Ag (63.7 mcd) and K-FAU/Ag (74.2 mcd) phosphors. Additionally, the chromaticity coordinate of the Li-FAU/Ag-LED is located at (0.2651, 0.4073) and has a high color temperature of 7873 K. Thermal test data showed that upon heating to 440 K, the intensities of R-FAU/Ag (R = Li, Na, K) LEDs decreased to 81%, 79%, and 75% of their initial intensities measured at 280 K, respectively. This research proposes a method for regulating the luminescent properties of silver nanoclusters in FAU zeolite by modifying the extra-framework cations and demonstrates excellent performance in LED products. Full article

Multi-angle remote sensing observations play an important role in the remote sensing of solar radiation absorbed by land surfaces. Currently, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) teams have successively applied the Ross–Li kernel-driven bidirectional reflectance distribution function (BRDF) model to integrate multi-angle observations to produce long time series BRDF model parameter products (MCD43 and VNP43), which can be used for the inversion of various surface parameters and the angle correction of remote sensing data. Even though the MODIS and VIIRS BRDF products originate from sensors and algorithms with similar designs, the consistency between BRDF parameters for different sensors is still unknown, and this likely affects the consistency and accuracy of various downstream parameter inversions. In this study, we applied BRDF model parameter time-series data from the overlapping period of the MODIS and VIIRS services to systematically analyze the temporal and spatial differences between the BRDF parameters and derived indices of the two sensors from the site scale to the region scale in the red band and NIR band, respectively. Then, we analyzed the sensitivity of the BRDF parameters to variations in Normalized Difference Hotspot–Darkspot (NDHD) and examined the spatiotemporal distribution of zero-valued pixels in the BRDF parameter products generated by the constraint method in the Ross–Li model from both sensors, assessing their potential impact on NDHD derivation. The results confirm that among the three BRDF parameters, the isotropic scattering parameters of MODIS and VIIRS are more consistent, whereas the volumetric and geometric-optical scattering parameters are more sensitive and variable; this performance is more pronounced in the red band. The indices derived from the MODIS and VIIRS BRDF parameters were compared, revealing increasing discrepancies between the albedo and typical directional reflectance and the NDHD. The isotropic scattering parameter and the volumetric scattering parameter show responses that are very sensitive to increases in the equal interval of the NDHD, indicating that the differences between the MODIS and VIIRS products may strongly influence the consistency of NDHD estimation. In addition, both MODIS and VIIRS have a large proportion of zero-valued pixels (volumetric and geometric-optical parameter layers), whereas the spatiotemporal distribution of zero-valued pixels in VIIRS is more widespread. While the zero-valued pixels have a minor influence on reflectance and albedo estimation, such pixels should be considered with attention to the estimation accuracy of the vegetation angular index, which relies heavily on anisotropic characteristics, e.g., the NDHD. This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. The method used in this study also helps improve the theoretical framework for cross-sensor product consistency assessment and clarify the uncertainty in high-precision ecological monitoring and various remote sensing applications. Full article

Grassland fires are one of the main disasters in the temperate grasslands of the Mongolian Plateau, posing a serious threat to the lives and property of residents. The occurrence of grassland fires is affected by a variety of factors, including the biomass and humidity of fuels, the air temperature and humidity, the precipitation and evaporation, snow cover, wind, the elevation and topographic relief, and human activities. In this paper, MCD12Q1, MCD64A1, ERA5, and ETOPO 2022 remote sensing data products and other products were used to obtain the relevant data of these factors to predict the occurrence of grassland fires. In order to achieve a better prediction, this paper proposes a generalized geographically weighted boosted regression (GGWBR) method that combines spatial heterogeneity and complex nonlinear relationships, and further attempts the generalized spatiotemporally weighted boosting regression (GSTWBR) method that reflects spatiotemporal heterogeneity. The models were trained with the data of grassland fires from 2019 to 2022 in the Mongolian Plateau to predict the occurrence of grassland fires in 2023. The results showed that the accuracy of GGWBR was 0.8320, which was higher than generalized boosted regression models’ (GBM) 0.7690. Its sensitivity was 0.7754, which is higher than random forests’ (RF) 0.5662 and GBM’s 0.6927. The accuracy of GSTWBR was 0.8854, which was higher than that of RF, GBM and GGWBR. Its sensitivity was 0.7459, which is higher than that of RF and GBM. This study provides a new technical approach and theoretical support for the disaster prevention and mitigation of grassland fires in the Mongolian Plateau. Full article

Accurate and reliable land cover data are essential for environmental monitoring, climate research, and sustainable land management. However, the proliferation of multi-source global land cover datasets with long time series poses challenges for selecting the best products for specific applications. Existing assessments often lack systematic comparisons of classification accuracy and time consistency across geographic areas. This study addresses the critical gap in cross-product comparability by systematically comparing five recent global time-series land cover products (GLC_FCS30D, Esri Land Cover, MCD12Q1, ESA CCI, and Dynamic World) against a reference dataset (CGLS-LC100). Through a unified classification system, resolution resampling, and random sampling validation, we assessed their classification accuracy and time-series change accuracy across three transitional regions representing diverse environmental contexts: rapidly urbanizing regions, agriculturally intensive zones, and high-latitude forested areas. The results indicate that while datasets exhibit spatial consistency, significant discrepancies exist in land cover classification, with each dataset demonstrating varying levels of accuracy depending on the environmental context and land cover type. High-resolution products (e.g., GLC_FCS30D, Dynamic World) are optimal for monitoring fragmented landscapes and urban expansion, whereas long-term datasets (e.g., ESA CCI, MCD12Q1) suit climate trend analysis in stable ecosystems. Based on the evaluation, we provide generalized guidance for dataset selection aligned with land cover types and monitoring objectives, emphasizing the need for region-specific and application-oriented choices. This study highlights challenges in dynamic datasets, including classification system discrepancies, resolution effects, and reference data limitations, and suggests that future advancements should focus on improving classification algorithms, refining sampling methods, and developing assessment systems that incorporate high-precision, real-time validation data. Full article

The leaf area index (LAI) is a crucial parameter for climate change research, agricultural management, and ecosystem monitoring. Despite extensive use of remote sensing data to estimate the LAI, comprehensive evaluations of product consistency and uncertainty remain limited. This study evaluated the uncertainties of four LAI products—GLASS, MCD15A2H, VNP15A2H, and CLMS—across diverse land cover types in the Heihe River Basin through two triple collocation approaches, innovatively. Each approach, respectively, focused on achieving more precise temporal characteristics and spatial characteristics of product uncertainties. The results indicate that all products generally met the Global Climate Observing System’s precision requirement (±0.5) for most biomes during the growing season. When comparing monthly uncertainties within grid cells, GLASS demonstrates superior performance, particularly in grasslands and croplands, whereas CLMS exhibits a slightly weaker ability to represent the spatial distribution of the LAI, especially in regions with high LAI values. When time series data are used to analyze the seasonal uncertainties of the products, MCD15A2H and VNP15A2H show more pronounced distortions, indicating their limited capability in capturing the temporal dynamics of the LAI. Correlation analyses revealed strong product agreement in regions with a low LAI, but discrepancies increased during the growing season and in heterogeneous land covers like croplands. These findings provide critical insights into the reliability of LAI products, offering a robust reference for validating their performance and ensuring their alignment with user requirements across diverse applications. The study highlights the importance of addressing spatial and temporal variability in uncertainties to improve the practical utility of LAI datasets in ecological and climate-related research. Full article

Satellite data provide the spatial distributions of burned areas worldwide; assessing their accuracy and comparing burned area estimates from different products is relevant to gain insights into their reliability and sources of error. We compared BA maps derived from multispectral satellite data with different spatial resolutions, ranging from Planet (3 m) to Sentinel-2 (S2, 10–20 m), Sentinel-3 (S3, 300 m), and MODIS (250–500 m), over selected African sites for the year 2019. Planet and S2 images were processed to derive BA maps with a supervised Random Forest algorithm and used to assess the spatial agreement of the FireCCISFD20, FireCCI51, FireCCIS311, and MCD64A1 products by computing omission and commission errors, Dice Coefficient, and Relative bias. The products based on S2 images showed the greatest agreement with the very high-resolution Planet BA maps (overall Dice Coefficient was found to be greater than 80%). The coarse-resolution products showed a lower spatial agreement with reference perimeters. Among the coarse spatial resolution products, FireCCIS311 was found to outperform the others. The spatial resolution of satellite data was found to be influential on accuracy, with the omission error greater than the commission (RelB < 0) for coarser resolution BA products. The spatial patterns of burns and the vegetation type were found to be significant in the mapping accuracy, and BA detection in Sahelian savannas was found to be more accurate. This study provides insights into the variability of the spatial accuracy of different burned area products derived from very high- to coarse-resolution satellite imagery. Full article

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change. Full article

Vegetation phenology has attracted considerable attention as one of the most sensitive indicators of global climate change. Remote sensing has significantly expanded our understanding of the spatial divergences of vegetation phenology. However, the current understanding of the reasons behind spatial divergences of vegetation phenology is not yet complete, and there is an urgent need to unravel the landscape processes driving spatial divergences of vegetation phenology. In light of this, the present study focused on montane forests of the cold temperate zone as its study area, collecting datasets such as the MCD12Q2 land surface phenology product, climate, topography, and stand height and adopting regression analysis and geo-detector model to investigate the individual and interactive effects of variables such as temperature, precipitation, elevation, slope, aspect, and forest height on forest phenology. The results indicated that because of the complexity of topography, the impacts of temperature on forest phenology were nonlinear. With fluctuation of elevation, the development of forest occurred later at the base and ridges of mountain and earlier in the valley bottom lands and mid-upper slopes. Temperature and precipitation exhibited a bilaterally strong interactive effect with slope on forest greenup. Both forest greenup and dormancy occurred earlier on shady slopes and later on sunny slopes. There may also exist an interactive effect between forest height and topographic factors on the spatial divergences of forest phenology. Future research may need to focus on whether there is a trade-off or synergy between the macroclimatic regulatory function of topography and the microclimatic regulatory function of canopy structure. Full article

Exploring the phenological divergences in vegetation caused by global climate change is of great significance for gaining a deeper understanding of the carbon cycling process in natural ecosystems. However, in many existing studies, the response of the start of the growing season (SOS) and the end of the growing season (EOS) to temperature exhibited multi-scale inconsistencies. In view of this, we took 259 Chinese urban agglomerations and their rural regions as the study areas, using MODIS phenological products (MCD12Q2), land surface temperature (LST) datasets, altitude, and latitude as data, and explored the phenological divergences in vegetation with LST changes in different geographical zones through box plots, linear regression models, and Spearman’s correlation analysis. The mean SOS and EOS in urban areas were both the earliest on approximately the 100.06th day and 307.39th day, respectively, and were then gradually delayed and advanced separately along an urban–rural gradient of 0–25 km. The divergences in vegetation phenology were no longer significant in rural areas 10 km away from urban boundaries, with change amplitudes of less than 0.4 days. In high latitude (40–50° N) regions, the correlation coefficients between the SOS and EOS of various urban agglomerations and LST were −0.627 and 0.588, respectively, whereas in low latitude (18–25° N) regions, the correlation coefficients appeared to be the opposite, being 0.424 and −0.426, respectively. In mid- to high-altitude (150–400 m) areas, LST had a strong advanced effect on SOS, while in high-altitude (above 1200 m) areas, LST had a strong delayed effect on EOS, with the R² values all being above 0.7. In summary, our study has revealed that within the context of varying geographical zones, the effects of LST on phenology exhibited significant spatial heterogeneity. This may provide strong evidence for the inconsistencies in the trends of phenology observed across previous studies and more relevant constraints for improving vegetation phenology prediction models. Full article

Spring phenology (start of growing season, SOS) in boreal forests plays a crucial role in the global carbon cycle. At present, more and more researchers are using solar-induced chlorophyll fluorescence (SIF) to evaluate the land surface phenology of boreal forests, but few studies have utilized the primary SIF directly detected by satellites (e.g., GOME-2 SIF) to estimate phenology, and most SIF datasets used are high-resolution products (e.g., GOSIF and CSIF) constructed by models with vegetation indices (VIs) and meteorological data. Thus, the difference and consistency between them in detecting the seasonal dynamics of boreal forests remain unclear. In this study, a comparison of spring phenology from GOME-2 SIF, GOSIF, EVI2 (MCD12Q2), and FLUX tower sites, PEP725 phenology observation sites, was conducted. Compared with GOSIF and EVI2, the primary GOME-2 SIF indicated a slightly earlier spring phenology onset date (about 5 days earlier on average) in boreal forests, at a regional scale; however, SOSs and SOS-climate relationships from GOME-2 SIF, GOSIF, and EVI2 showed significant correlations with the ground observations at a site scale. Regarding the absolute values of spring phenology onset date, GOME-2 SIF and FLUX-GPP had an average difference of 8 days, while GOSIF and EVI2 differed from FLUX-GPP by 16 days and 12 days, respectively. GOME-2 SIF and PEP725 had an average difference of 38 days, while GOSIF and EVI2 differed from PEP725 by 24 days and 23 days, respectively. This demonstrated the complementary roles of the three remote sensing datasets when studying spring phenology and its relationship with climate in boreal forests, enriching the available remote sensing data sources for phenological research. Full article

Over recent decades, human activities have essentially depended on fossil fuels. The last Intergovernmental Panel on Climate Change reports recommend a shift to renewables and a more energy-efficient economy. To fulfill the potential of bioenergy, tools are required to overcome the complexities of the decision-making processes for viable projects. This work presents a decision-making tool to select the most feasible biomass residues and a case study of the state of Minas Gerais, in Brazil. Among the 13 evaluated criteria, eucalyptus residues demonstrated the highest potential for electricity production, followed by sugarcane bagasse and coffee husks. The choice of Minas Gerais as a case study is important due to its diverse agricultural landscape and the potential for biomass residue generation. The presented methodology uses the Analytical Hierarchy Process (AHP), a multi-criteria decision-making method (MCDM). Thirteen criteria were required to enable the best choice of biomass residue alternatives for electricity generation, which experts in the bioenergy field evaluated. The technical criterion was shown to be the one with the highest degree of importance. The results of the study identified that CO_2eq emissions (11.46%) and electricity demand (ED) were the most relevant sub-criteria for prioritizing the viability of agricultural waste. Eucalyptus was ranked as the most promising biomass, followed by sugarcane bagasse and coffee husks. In addition, the use of GIS tools made it possible to map the regions with the greatest potential in Minas Gerais, providing a robust approach to identifying strategic sites for bioenergy. Full article

The Great Salt Lake (GSL) is the largest saline lake in the Western Hemisphere. It supports billion-dollar industries and recreational activities, and is a vital stopping point for migratory birds. However, little is known about the spatiotemporal variation of phytoplankton biomass in the lake that supports these resources. Spectral reflectance provided by three remote sensing products was compared relative to their relationship with field measurements of chlorophyll a (Chl a). The MODIS product MCD43A4 with a 500 m spatial resolution provided the best overall ability to map the daily distribution of Chl a. The imagery indicated significant spatial variation in Chl a, with low concentrations in littoral areas and high concentrations in a nutrient-rich plume coming out of polluted embayment. Seasonal differences in Chl a showed higher concentrations in winter but lower in summer due to heavy brine shrimp (Artemia franciscana) grazing pressure. Twenty years of imagery revealed a 68% increase in Chl a, coinciding with a period of declining lake levels and increasing local human populations, with potentially major implications for the food web and biogeochemical cycling dynamics in the lake. The MCD43A4 daily cloud-free images produced by 16-day temporal composites of MODIS imagery provide a cost-effective and temporally dense means to monitor phytoplankton in the southern (47% surface area) portion of the GSL, but its remaining bays could not be effectively monitored due to shallow depths, and/or plankton with different pigments given extreme hypersaline conditions. Full article

Kaposi sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus and the etiological agent of several diseases. These include the malignancies Kaposi sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman disease (MCD), as well as the inflammatory disorder KSHV inflammatory cytokine syndrome (KICS). The KSHV lifecycle is characterized by two phases: a default latent phase and a lytic replication cycle. During latency, the virus persists as an episome within host cells, expressing a limited subset of viral genes to evade immune surveillance while promoting cellular transformation. The lytic phase, triggered by various stimuli, results in the expression of the full viral genome, production of infectious virions, and modulation of the tumor microenvironment. Both phases of the KSHV lifecycle play crucial roles in driving viral pathogenesis, influencing oncogenesis and immune evasion. This review dives into the intricate world of the KSHV lifecycle, focusing on the molecular mechanisms that drive its latent and lytic phases, their roles in disease progression, and current therapeutic strategies. Full article

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI_LOS is smaller than the CI_LFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. Full article