Search Results (155)

In April 2023, a major dust storm event in Lanzhou attracted widespread attention. This study provides a comprehensive analysis of the causes, progression, and dust sources of this event using multiple data sources and methods. Backward trajectory analysis using the HYSPLIT model was employed to trace the origins of the dust, while FY-2H satellite data provided high-resolution dust distribution patterns. Additionally, the MAIAC AOD product was used to analyze Aerosol Optical Depth, and concentration-weighted trajectory (CWT) analysis was used to identify key dust source regions. The study found that PM₁₀ played a dominant role in the storm, and the AOD values during the storm in Lanzhou were significantly higher than the annual average, highlighting the severe impact on regional air quality. Key meteorological conditions influencing the storm’s occurrence were analyzed, including the formation and eastward movement of a high-potential ridge, convection driven by diurnal temperature variations, and surface temperature increases coupled with decreased relative humidity, which together promoted the generation and development of dust. Backward trajectory and dust distribution analyses revealed that the dust primarily originated from Central Asia, western Mongolia, Xinjiang, and Gansu. From the 19th to the 21st, the dust distribution showed similarities between day and night, with a noticeable increase in dust concentration from night to day due to strong vertical atmospheric mixing. To mitigate the impacts of future dust storms, this study highlights both short-term and long-term strategies, including enhanced monitoring systems, public health advisories, and vegetation restoration in key source regions. Strengthening regional and international cooperation for transboundary dust management is also emphasized as critical for sustainable mitigation efforts. These findings are significant for understanding and predicting the causes, characteristics, and environmental impacts of dust storms in Lanzhou and the Northwestern region. Full article

Air quality is directly related to people’s health and quality of life and has a profound impact on the sustainable development of cities. Good air quality is the foundation of sustainable development. To solve the current problem of air quality for sustainable development, we used high-resolution (1 km) satellite-retrieved aerosol optical depth (AOD), meteorological, nighttime light and vegetation data to develop a spatiotemporal convolution feature random forest (SCRF) model to predict the PM_2.5 concentration in Shandong from 2016 to 2019. We evaluated the performance of the SCRF model and compared the results of other models, including neural network (BPNN), gradient boosting (GBDT), and random forest (RF) models. The results show that compared with the other models, the improved SCRF model performs best. The coefficient of determination (R²) and root mean square error (RMSE) are 0.83 and 9.87 µg/m³, respectively. Moreover, we discovered that the characteristic variables AOD and air temperature (TEM) data improved the accuracy of the model in Shandong Province. The annual average PM_2.5 concentrations in Shandong Province from 2016 to 2019 were 74.44 µg/m³, 65.01 µg/m³, 58.32 µg/m³, and 59 µg/m³, respectively. The spatial distribution of air pollution increases from northeastern and southeastern to western Shandong inland. In general, our research has significant implications for the sustainable development of various cities in Shandong Province. Full article

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product. Full article

Over the past half-century, environmental degradation and human disturbances have threatened the aboveground biomass carbon (AGC) in China’s karst ecosystems. However, recent ecological programs have led to environmental improvements, leaving it unclear whether China’s karst ecosystems act as an AGC sink or AGC source. In this study, we utilized L-band vegetation optical depth to quantify the dynamics of AGC across the karst regions of China from 2015 to 2021. We observed an increase in AGC density of 0.73 Mg C ha⁻¹ yr⁻¹, suggesting that karst ecosystems in China functioned as an AGC sink throughout the research period. The largest increase in AGC density, 1.29 Mg C ha⁻¹ yr⁻¹, was observed in Central China, indicating an AGC sink capacity stronger than that of other regions. Among the different land-use types, forests played a dominant role, exhibiting the largest net change in AGC density at 1.03 Mg C ha⁻¹ yr⁻¹. Furthermore, using the random forest model, temperature, soil clay content, and altitude were identified as the primary factors driving AGC changes. Our results enhance the understanding of the role of China’s karst terrestrial ecosystem in the global carbon cycle, emphasizing its contribution to the global carbon sink. Full article

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CI_{red edge}). The results revealed that the coefficient values (β₀ and β₁) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β₀ and β₁) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CI_{red edge} were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat. Full article

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation. Full article

This study focuses on Central China (CC), including Shanxi, Henan, Anhui, Hubei, Jiangxi, and Hunan provinces. The 2019 average annual precipitation (PRE), average annual temperature (TEM), average annual wind speed (WS), population density (POP), normalized difference vegetation index (NDVI), aerosol optical depth (AOD), gross domestic product (GDP), and elevation (DEM) data were used as explanatory variables to predict the average annual PM_2.5 concentrations (PM_2.5Cons) in CC. The average annual PM_2.5Cons were predicted using different models, including multiple linear regression (MLR), back propagation neural network (BPNN), and random forest (RF) models. The results showed higher prediction accuracy and stability of the RF algorithm (RFA) than those of the other models. Therefore, it was used to analyze the contributions of the explanatory factors to the PM_2.5 concentration (PM_2.5Con) prediction in CC. Subsequently, the spatiotemporal evolution of the PM_2.5Cons from 2010 to 2021 was systematically analyzed. The results indicated that (1) PRE and AOD had the most significant impacts on the PM_2.5Cons. Specifically, the PRE and AOD values exhibited negative and positive correlations with the PM_2.5Cons, respectively. The NDVI and WS were negatively correlated with the PM_2.5Cons; (2) the southern and northern parts of Shanxi and Henan provinces, respectively, experienced the highest PM_2.5Cons in the 2010–2013 period, indicating severe air pollution. However, the PM_2.5Cons in the 2014–2021 period showed spatial decreasing trends, demonstrating the effectiveness of the implemented air pollution control measures in reducing pollution and improving air quality in CC. The findings of this study provide scientific evidence for air pollution control and policy making in CC. To further advance atmospheric sustainability in CC, the study suggested that the government enhance air quality monitoring, manage pollution sources, raise public awareness about environmental protection, and promote green lifestyles. Full article

Particulate matter (PM) in the atmosphere poses significant risks to both human health and the environment. Specifically, PM2.5, particulate matter with a diameter less than 2.5 micrometers, has been linked to increased rates of cardiovascular and respiratory diseases. In South Korea, concerns about PM2.5 exposure have grown due to its potential for causing premature death. This study aims to estimate high-resolution exposure concentrations of PM2.5 across South Korea from 2015 to 2021. We integrated data from the Community Multiscale Air Quality (CMAQ) model with surface air quality measurements, the Weather Research Forecast (WRF) model, the Normalized Difference Vegetation Index (NDVI), and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) satellite data. These data, combined with multiple regression analyses, allowed for the correction of PM2.5 estimates, particularly in suburban areas where ground measurements are sparse. The simulated PM2.5 concentration showed strong correlations with observed values R (ranging from 0.88 to 0.94). Spatial distributions of annual PM2.5 showed a significant decrease in PM2.5 concentrations from 2015 to 2021, with some fluctuation due to the COVID-19 pandemic, such as in 2020. The study produced highly accurate daily average high-resolution PM2.5 exposure concentrations. Full article

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future. Full article

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and soil quality assessment. The Qinghai–Tibet Plateau is one of the largest plateaus in the world. Therefore, in this region, SOC density and the spatial distribution of SOC are highly sensitive to climate change and human intervention. Given the insufficient understanding of the spatial distribution of SOC density in the Qinghai–Tibet Plateau, this study utilized machine learning (ML) algorithms to estimate the density and distribution pattern of SOC density in the region. In this study, we first collected multisource data, such as optical remote sensing data, synthetic aperture radar) (SAR) data, and other environmental variables, including socioeconomic factors, topographic factors, climate factors, and soil properties. Then, we used ML algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to estimate the topsoil SOC density and spatial distribution patterns of SOC density. We also aimed to investigate any driving factors. The results are as follows: (1) The average SOC density is 5.30 kg/m². (2) Among the three ML algorithms used, LightGBM showed the highest validation accuracy (R² = 0.7537, RMSE = 2.4928 kgC/m², MAE = 1.7195). (3) The normalized difference vegetation index (NDVI), valley depth (VD), and temperature are crucial in predicting the spatial distribution of topsoil SOC density. Feature importance analyses conducted using the three ML models all showed these factors to be among the top three in importance, with contribution rates of 14.08%, 12.29%, and 14.06%; 17.32%, 20.73%, and 24.62%; and 16.72%, 11.96%, and 20.03%. (4) Spatially, the southeastern part of the Qinghai–Tibet Plateau has the highest topsoil SOC density, with recorded values ranging from 8.41 kg/m² to 13.2 kg/m², while the northwestern part has the lowest density, with recorded values ranging from 0.85 kg/m² to 2.88 kg/m². Different land cover types showed varying SOC density values, with forests and grasslands having higher SOC densities compared to urban and bare land areas. The findings of this study provide a scientific basis for future soil resource management and improved carbon sequestration accounting in the Qinghai–Tibet Plateau. Full article

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM_2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km². The increase in PM_2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. Full article

Against the backdrop of global warming and vegetation restoration, research on the evapotranspiration mechanism of the Yellow River basin has become a hot topic. The Budyko-Fu model is widely used to estimate basin-scale evapotranspiration, and its crucial parameter $\mathsf{\omega }$ is used to characterize the underlying surface and climate characteristics of different basins. However, most studies only use factors such as the normalized difference vegetation index (NDVI), which represents the greenness of vegetation, to quantify the relationship between $\mathsf{\omega }$ and the underlying surface, thereby neglecting richer vegetation information. In this study, we used long time-series multi-source remote sensing data from 1988 to 2015 and stepwise regression to establish dynamic estimation models of parameter $\mathsf{\omega }$ for three subwatersheds of the upper Yellow River and quantify the contribution of underlying surface factors and climate factors to this parameter. In particular, vegetation optical depth (VOD) was introduced to represent plant biomass to improve the applicability of the model. The results showed that the dynamic estimation models of parameter $\mathsf{\omega }$ established for the three subwatersheds were reasonable (R² = 0.60, 0.80, and 0.40), and parameter $\mathsf{\omega }$ was significantly correlated with the VOD and standardized precipitation evapotranspiration index (SPEI) in all watersheds. The dominant factors affecting the parameter in the different subwatersheds also differed, with underlying surface factors mainly affecting the parameter in the watershed before Longyang Gorge (BLG) (contributing 64% to 76%) and the watershed from Lanzhou to Hekou Town (LHT) (contributing 63% to 83%) and climate factors mainly affecting the parameter in the watershed from Longyang Gorge to Lanzhou (LGL) (contributing 75% to 93%). The results of this study reveal the changing mechanism of evapotranspiration in the Yellow River watershed and provide an important scientific basis for regional water balance assessment, global change response, and sustainable development. Full article

The integrity and resilience of our environment are confronted with unprecedented challenges, stemming from the escalating pressures of urban expansion and the need for ecological preservation. This study proposes an Improved Remote Sensing Ecological Index (IRSEI), which employs humidity (WET), the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), a standardized Building–Bare Soil Index (NDBSI), aerosol optical depth (AOD), and the comprehensive salinity index (CSI). The IRSEI model was utilized to assess the ecological quality of Hangzhou over the period from 2003 to 2023. Additionally, the random forest model was employed to analyze the factors driving ecological quality. Furthermore, the gradient effect in the horizontal direction away from the urban center was examined using the buffer zone method. Our analysis reveals the following: (1) approximately 95% of the alterations in ecological quality observed from 2003 to 2023 exhibited marginal improvements, declines, or were negligible; (2) the transformations in IRSEI during this period, including variations in surface temperature and transportation networks, exhibited strong correlations (0.85) with human activities. Moreover, the influence of AOD and the comprehensive salinity index on IRSEI demonstrated distinct spatial disparities; (3) the IRSEI remained generally stable up to 30 km outside the city center, indicating a trend of agglomeration in the center and significant areas in the surroundings. The IRSEI serves as a robust framework for bolstering the assessment of regional ecological health, facilitating ecological preservation and rejuvenation efforts, and fostering coordinated sustainable regional development. Full article

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. Full article

Inhalable particulate matter (PM₁₀) is a major air pollutant that has significant impacts on environmental climate and human health. Land-cover change is also a key factor influencing changes in atmospheric pollution. Changes in land-cover types can lead to changes in the sources and sinks of air pollutants, thus affecting the spatial distribution of PM₁₀, which poses a threat to human health. Therefore, exploring the relationship between PM₁₀ concentration change and land-cover change is of great significance. In this study, we constructed an extreme randomized trees model (ET) based on ground PM₁₀ monitoring data, satellite-based aerosol optical depth (AOD) data, and auxiliary data including meteorological, vegetation, and population data to retrieve ground-level PM₁₀ concentrations across China. The coefficient of determination (R²), the mean absolute error (MAE), and the root mean square error (RMSE) of the model were 0.878, 5.742 μg/m³, and 8.826 μg/m³, respectively. Based on this, we analyzed the spatio-temporal distribution of PM₁₀ concentrations in China from 2015 to 2021. High PM₁₀ values were mainly observed in the desert areas of northwestern China and the Beijing–Tianjin–Hebei urban agglomeration. The majority of China showed a significant decrease in PM₁₀ concentrations. Additionally, we also analyzed the nonlinear response mechanism of the PM₁₀ concentration change to land-cover change. The PM₁₀ concentration is sensitive to forest and barren land change. Therefore, strengthening the protection of forests and desertification control can significantly reduce air pollution. Attention should also be paid to emission management in agricultural activities and urbanization processes. Full article