Search Results (14,834)

Methane emissions from dairy farms are a significant driver of climate change, yet their relationship with farm-specific practices remains poorly understood. This study employs Sentinel-5P satellite-derived methane column concentrations as a proxy to examine emission dynamics across 11 dairy farms in Eastern Canada, using data collected between January 2020 and December 2022. By integrating advanced analytics, we identified key drivers of methane concentrations, including herd genetics, feeding practices, and management strategies. Statistical tools such as Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) addressed multicollinearity, stabilizing predictive models. Machine learning approaches—Random Forest and Neural Networks—revealed a strong negative correlation between methane concentrations and the Estimated Breeding Value (EBV) for protein percentage, demonstrating the potential of genetic selection for emissions mitigation. Our approach refined concentration estimates by integrating satellite data with localized atmospheric modeling, enhancing accuracy and spatial resolution. These findings highlight the transformative potential of combining satellite observations, machine learning, and farm-level characteristics to advance sustainable dairy farming. This research underscores the importance of targeted breeding programs and management strategies to optimize environmental and economic outcomes. Future work should expand datasets and apply inversion modeling for finer-scale emission quantification, advancing scalable solutions that balance productivity with ecological sustainability. Full article

In the optical satellite on-orbit imaging quality estimation system, the calculation of Modulation Transfer Function (MTF) is not fully standardized, and the influence of atmosphere is often simplified, making it difficult to obtain completely consistent on-orbit MTF measurements and comparisons. This study investigates the effects of various factors—such as edge angle, edge detection methods, oversampling rate, and interpolation techniques—on the accuracy of MTF calculations in the commonly used slanted-edge method for on-orbit MTF assessment, informed by simulation experiments. A relatively optimal MTF calculation process is proposed, which employs the Gaussian fitting method for edge detection, the adaptive oversampling rate, and the Lanczos (a = 3) interpolation method, minimizing the absolute deviation in the MTF results. A method to quantitatively analyze the atmospheric scattering and absorption MTF is proposed that employs a radiative transfer model. Based on the edge images of GF-2 satellite, images with various atmospheric conditions and imaging parameters are simulated, and their atmospheric scattering and absorption MTF is obtained through comparing the MTFs of the ground and top atmosphere radiance. The findings reveal that aerosol optical depth (AOD), viewing zenith angle (VZA), and altitude (ALT) are the primary factors influencing the accuracy of GF-2 satellite on-orbit MTF measurements in complex scenarios. The on-orbit MTF decreases with the increase in AOD and VZA and increases with the increase in ALT. Furthermore, a collaborative analysis of the main influencing factors of atmospheric scattering and absorption MTF indicates that, taking the PAN band of the GF-2 satellite as an example, the atmospheric MTF values are consistently below 0.7905. Among these, 90% of the data are less than 0.7520, with corresponding AOD conditions ranging from 0 to 0.08, a VZA ranging from 0 to 50°, and an ALT ranging from 0 to 5 km. The results can provide directional guidance for the selection of meteorological conditions, satellite attitude, and geographical location during satellite on-orbit testing, thereby enhancing the ability to accurately measure satellite MTF. Full article

Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based methods and manual review processes are often insufficient for detecting these complex anomalies, especially considering the distinct characteristics of different satellite types. To address these limitations, this study proposes an automated anomaly detection method using the IF-TEA-LSTM model. By transforming broadcast ephemeris data into multivariate time series and integrating anomaly score sequences, the model enhances detection robustness through data integrity assessments and stationarity tests. Evaluation results show that the IF-TEA-LSTM model reduces the RMSE by up to 20.80% for orbital parameters and improves clock deviation prediction accuracy for MEO satellites by 68.37% in short-term forecasts, outperforming baseline models. This method significantly enhances anomaly detection accuracy across GEO, IGSO, and MEO satellite orbits, demonstrating its superiority in long-term data processing and its capacity to improve the reliability of satellite operations within the BDS. Full article

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities. Full article

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies. Full article

Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro–nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter’s adaptability to dynamic environments. A segmented adaptive filtering strategy is introduced, allowing for flexible parameter adjustment in different dynamic scenarios. A micro–nano satellite equipped with a miniaturized dual-frequency GPS receiver is employed to demonstrate precise orbit determination capabilities. On-orbit GPS data from the satellite, collected in two specific scenarios—slow rotation and Earth-pointing stabilization—are analyzed to evaluate the proposed algorithm’s ability to cope with weak GPS signals and satellite attitude instability as well as to assess the achievable orbit determination accuracy. The results show that, compared to traditional Extended Kalman Filters (EKF) and other improved filtering algorithms, the IARKF performs better in reducing post-fit residuals and improving orbit prediction accuracy, demonstrating its superior robustness. The three-axes orbit determination internal consistency precision can reach the millimeter level. This work explores a feasible approach for achieving high-performance orbit determination in micro–nano satellites. Full article

Air pollution is a vital concern for developing countries, and the growing concentration of air pollutants in Kabul—the most polluted city in Afghanistan—has raised concerns about the health of its citizens. This study examines Kabul’s ambient air quality from a socio-economic and environmental perspective, primarily focusing on some crucial parameters, such as the Air Quality Index (AQI), nitrogen dioxide (NO₂), particulate matter (PM2.5), and carbon monoxide (CO). Using multiple regression analysis modeling in R and data from satellite imagery, air quality monitoring stations, and Geographic Information Systems (GIS), this study demonstrates a strong relationship between air quality and urban green spaces, population growth, vehicle count, temperature, and electricity availability. Key results indicate that increasing urban green areas improves air quality, but that population growth and heat make air pollution worse. This study suggests that airborne pollutants could be reduced through efficient emissions management, green energy sources, and urban planning. These observations provide policymakers and urban planners with practical recommendations to enhance Kabul’s air quality and general public health. Full article

In recent years, precipitation extremes in China have increased due to global warming, posing a significant threat to human life and property. It is thus crucial to understand the changes in population exposure to precipitation extremes and the causes of these changes, since complex terrain areas are not accurately simulated by rain gauge interpolation data. Thus, we first used three satellite-based precipitation products—TRMM 3B42, CHIRPS, and CMORPH—combined with population data to analyze the spatiotemporal changes of precipitation extremes and population exposure from 1998 to 2019 in the Beijing–Tianjin–Hebei (BTH) region. In addition, the contributions of population, climate, and composite factors were quantified. The results showed that TRMM 3B42 outperformed the other two datasets in the BTH region. Over the past 22 years, the precipitation extremes in the central and northeastern regions, especially in Beijing, reached 2.5 days per decade, while the northern and southern regions showed a downward trend. The highest population exposure was mainly concentrated in central Beijing, most areas of Tianjin, and the urban centers of cities in southeastern Hebei province. Compared to the 2000s, a significant increase in exposure was observed in Beijing, Tianjin, and Zhangjiakou in the 2010s, whereas other regions showed negligible changes during this period. Climatic factors had the greatest influence on population exposure in most cities such as Qinhuangdao and Hengshui, where their climatic contribution exceeded 70%. While population change was more responsible for the increase in population exposure in the densely populated cities such as Tianjin, Handan, and Langfang, these cities contributed over 60% of the population. The interaction effect in Beijing and Tianjin was relatively obvious. The results of this study can provide a scientific basis for formulating targeted disaster risk management measures against climate change in the BTH region. Full article

Accurate crop yield estimation is critical to successful agricultural operations. Current crop growth models often overlook the spatial and geographic components of the lands, leading to suboptimal yield estimates. To address this issue, assimilation of satellite vegetation products into these models can account for spatial variations in the land and improve estimation accuracy. In this paper, the AquaCrop model, a water-driven crop growth model, was selected for recalibration and assimilation of satellite-derived biophysical products due to its simplicity and lack of computational complexity. To this end, field samples of soil (sampled before cultivation) and crop features were collected during the growing season of silage maize. Digital hemisphere photography (DHP) and destructive sampling methods were used for measuring fraction vegetation cover (fCover) and biomass in Qaleh-Now County, southern Tehran, in 2019. Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. Here, we propose the use of an optimization water cycle algorithm (WCA) instead of a PSO algorithm as an assimilation method for the parameter calibration of AquaCrop. This study also focused on using both fCover and biomass state variables simultaneously in the model, as opposed to only the fCover, and found that using both variables led to significantly higher calibration accuracy. The WCA method outperformed the PSO method in AquaCrop’s calibration, leading to more accurate results on maize yield estimates. It has enhanced results, decreasing RMSE values by 3.8 and 4.7 ton/ha, RRMSE by 6.4% and 10%, and increasing R² by 0.17 and 0.35 for model calibration and validation, respectively. These results suggest that assimilating satellite-derived data and optimizing the calibration process through WCA can significantly improve the accuracy of crop yield estimations in water-driven crop growth models, highlighting the potential of this approach for precision agriculture. 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

A surface water extent downscaling framework was developed in the past using a floodability index based on topography. We presented here a new downscaling approach including several improvements. (1) The use of a new Floodability Index (FI), including better integration of auxiliary permanent waters (i.e., presence of water during the whole time record). By using this updated FI, the new downscaling became a true data-fusion with permanent water databases originating mainly from visible observations. (2) Some discontinuities between low resolution cells have been reduced thanks to a new smoothing algorithm. (3) Finally, a coastal extrapolation scheme has been presented to deal with coarse resolution data contaminated by the ocean. This new and complex downscaling framework was tested here on the GIEMS (Global Inundation Extent from Multi-Satellite) database but the approach is generalizable and any surface water database could be used instead. It was shown that this new downscaling procedure (including several processing steps, algorithms and data sources) is a significant improvement compared to the previous version thanks to the new floodability index and the downscaling processing chain. Compared to the previous version, the downscaling results (GIEMS-D) were more coherent with the permanent water database and preserved better the original low-resolution information (e.g., mean scare error water fraction (0–1) of 0.0041 for the old version, and 0.0018 for the new version, over flooded areas in the Amazon). GIEMS-D has also been evaluated at the global scale and over the Amazon basin using independent datasets, showing an overall good performance of the downscaling. Full article

This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with a multilayer perceptron (BiLSTM-MHSA-MLP). We constructed an Argo buoy data compression system using the main buoy control board, Jetson nano development board, and the BeiDou-3 satellite transparent transmission module. By processing input sequences bidirectionally, BiLSTM enhances the understanding of the temporal relationships within profile data, whereas the MHSA processes the outputs of the BiLSTM layer in parallel to obtain richer representations. Building on this preliminary probability prediction model, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced to achieve block compression during training, dynamically updating the model and optimizing symbol probability distributions for more accurate predictions. Experiments conducted on multiple 4000 m single-batch profile datasets from both the PC and Jetson nano platforms demonstrate that this method achieves a lower compression ratio, shorter compression time, and greater specificity. This approach significantly reduces the communication time between Argo buoys and satellites, laying a foundation for the future integration of Jetson Nano into Argo buoys for real-time data compression. Full article

The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological modeling. A comparative analysis with Terrestrial Laser Scanning (TLS) measurements assessed the agreement between these datasets. Multiple linear regression models were utilized to evaluate the relationships between the datasets and provide detailed insights into their accuracy and biases. The results indicate significant correlations between satellite DEMs and TLS measurements, with adjusted R-square values of 0.8478 for ALOS and 0.955 for the SRTM. To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. Additionally, slope and aspect analyses were performed to highlight terrain characteristics across the DEMs. Slope analysis showed a statistically significant negative correlation between SRTM and TLS slopes (R² = 0.16, p < 4.47 × 10⁻¹⁰ indicating a weak relationship, while no significant correlation was observed between ALOS and TLS slopes. Aspect analysis showed significant positive correlations for both ALOS and the SRTM with TLS aspect, capturing 30.21% of the variance. These findings demonstrate the accuracy of satellite-derived elevation models in representing terrain features relative to high-resolution terrestrial data. Full article

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, and health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on a global scale. However, traditional physical and statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence methods are increasingly applied to aerosol parameter retrieval, yet most current approaches focus on end-to-end single-parameter retrieval without considering the inherent relationships among multiple aerosol properties. In this study, we propose a sequence-to-sequence aerosol parameter joint retrieval algorithm based on the transformer model S2STM. Unlike conventional end-to-end single-parameter retrieval methods, this algorithm leverages the encoding–decoding capabilities of the transformer model, coupling multi-source data such as polarized satellite, meteorological, model, and surface characteristics, and incorporates a physically coherent consistency loss function. This approach transforms traditional single-parameter numerical regression into a sequence-to-sequence relationship mapping. We applied this algorithm to global observations from the Chinese polarimetric satellite (the Particulate Observing Scanning Polarimeter, POSP) and simultaneously retrieved multiple key aerosol optical and microphysical parameters. Event analyses, including dust and pollution episodes, demonstrate the method’s responsiveness in hotspot regions and events. The retrieval results show good agreement with ground-based observation products. This method is also adaptable to satellite instruments with various configurations (e.g., multi-wavelength, multi-angle, and multi-dimensional polarization) and can further improve its spatiotemporal generalization performance by enhancing the spatial balance of ground station training datasets. Full article

Geological mapping provides vital information about the structure, evolution, natural resource potential, and geohazards of a specific area. The role of geological mapping is especially valuable for mountainous countries like Georgia. In this context, we present a geological map of the Mulkhura River Valley in the Georgian Caucasus (43°3′ N, 42°52′ E) with accompanying cross-sections at a scale of 1:30,000, covering approximately 220 km². The geological information in the map is based on a comprehensive review of previously published geological maps and literature, combined with original analysis of satellite imagery and hitherto unpublished field data. The extensive spatial coverage and accompanying cross-sections provide detailed insights into the structure of the region. This new map will serve as a foundation for future geological research, hazard management, and resource exploration in the area, as well as for geoconservation to develop the national geotourism industry in this region. Full article