Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Relationship between Low Latitude Scintillation Onset and Sunset Terminator over Africa

The solar terminator is a moving boundary between day-side and night-side regions on the Earth, which is a substantial source of perturbations in the ionosphere. In the vicinity of the solar terminator, essential parameters like S4 index measurements are widely analyzed in order to monitor and predict perturbations in the ionosphere. The utilization of the scintillation index S4 is a well-accepted approach to describe the amplitude/intensity fluctuation of a received signal, predominantly caused by small-scale irregularities of the ionospheric plasma. We report on the longitudinal daily and seasonal occurrence of GNSS signal scintillations, using the data derived from the GNSS stations in Bahir Dar, Ethiopia, Lomé, Togo and Dakar, Senegal. The observed seasonal climatology of GNSS signal scintillations in equatorial Africa is adequately explained by the alignment of the solar terminator and local geomagnetic declination line. It should be pointed out that the strongest scintillations are most frequently observed during the time when the solar terminator is best aligned with the geomagnetic declination line. At all three stations, the comparison of computational and observational results indicated that the scintillation activity culminated around equinoxes in the years 2014, 2015 and 2016. Comparatively, the western equatorial Africa sector has the most intense, longest-lasting, and highest scintillation occurrence rate in equinoctial seasons in all three years. For the first time, we show that the seasonal variation of the scintillation peaks changes systematically from west to east at equatorial GNSS stations over Africa. A detailed analysis of the solar day–night terminator azimuth at ionospheric heights including the time equation shows that the scintillation intensity has a maximum if the azimuth of the terminator coincides with the declination line of the geomagnetic field. Due to the remarkable change of the declination by about 10° at the considered GNSS stations, the distance between scintillation peaks increases by 46 days when moving westward from the Bahir Dar to the Dakar GNSS station. The observations agree quite well with the computational results, thus confirming Tsunoda’s theory.
Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.
Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.
Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (<30 m) satellites together with modern machine learning algorithms provide new prospects for spaceborne large area forest inventory. In this study, we investigated the capability of Sentinel-2 (S2) image and metadata, topography data, and canopy height model (CHM), as well as their combinations, to predict growing stock volume with deep neural networks (DNN) in four forestry districts in Central Finland. We focused on investigating the relevance of different input features, the effect of DNN depth, the amount of training data, and the size of image data sampling window to model prediction performance. We also studied model transfer between different silvicultural districts in Finland, with the objective to minimize the amount of new field data needed. We used forest inventory data provided by the Finnish Forest Centre for model training and performance evaluation. Leaving out CHM features, the model using RGB and NIR bands, the imaging and sun angles, and topography features as additional predictive variables obtained the best plot level accuracy (RMSE% = 42.6%, |BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept.
Robust Fault Estimation of Vehicular Yaw Rate Sensor Using a Type-2 Fuzzy Approach

In this article, a proportional integral (PI) fault observer is introduced due to its accuracy and expeditiousness. Considering the time-varying vehicle speed and other uncertain parameters in vehicle dynamics system, a type-2 fuzzy model is proposed to describe the system nonlinearity. Moreover, it could also address the problem of uncertainty in the membership function, which resulted from the parameter uncertainty. In addition, the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> technique is studied to attenuate the effect of disturbance on the estimation performance and a set of linear matrices inequalities are obtained. The particle swarm optimization (PSO) approach is then adopted to find solutions to the PI fault observer. Finally, a simulation test is carried out based on the experimental data, which are collected from an electric vehicle. The simulation results demonstrate the effectiveness of the proposed <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }/PSO$</tex-math></inline-formula> method.
Climate Variability May Delay Post-Fire Recovery of Boreal Forest in Southern Siberia, Russia

Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive shifts in post-fire plant successional trajectories. The response of post-fire vegetation communities to climate variability is under-studied, despite being a critical phase determining the ultimate successional conclusion. This study investigated the responses of post-fire recruited species to climate change and inter-annual variability at 16 study sites that experienced high-severity fire events, mostly in early 2000, within the Scots pine forest-steppe zone of southeastern Siberia, Russia. These sites were originally dominated by Scots pine, and by 2018, they were recruited by different successional species. Additionally, three mature Scots pine stands were included for comparison. A Bayesian Additive Regression Trees (BART) approach was used to model the relationship between Landsat-derived Normalized Difference Vegetation Index (NDVI) time series, temperature and precipitation in the 15 years after a stand-replacing fire. Using the resulting BART models, together with six projected climate scenarios with increased temperature and enhanced inner-annual precipitation variability, we simulated NDVI at 5-year intervals for 15 years post-fire. Our results show that the BART models performed well, with in-sample Pseudo-R2 varying from 0.49 to 0.95 for fire-disturbed sites. Increased temperature enhanced greenness across all sites and across all three time periods since fires, exhibiting a positive feedback in a warming environment. Repeatedly dry spring periods reduced NDVI at all the sites and wetter summer periods following such dry springs could not compensate for this, indicating that a prolonged dry spring has a strong impact consistently over the entire early developmental stages from the initial 5 years to 15 years post-fire. Further, young forests showed higher climate sensitivity compared to the mature forest, irrespective of species and projected climatic conditions. Our findings suggest that a dry spring not only increases fire risk, but also delays recovery of boreal forests in southern Siberia. It also highlights the importance of changing rainfall seasonality as well as total rainfall in a changing climate for post-fire recovery of forest.
Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.
Deciphering a Timeline of Demise at Medieval Angkor, Cambodia Using Remote Sensing

The Greater Angkor Region was the center of the Khmer Empire from the 9th until the 13th to the 14th centuries CE, when it entered a period of decline. Many studies have suggested that the decline of Angkor was precipitated by several factors, including severe monsoons, geopolitical shifts, and invasions. In this paper, we use light detection and ranging and ground penetrating radar to investigate the possible intersection of two of these existential threats in one feature: the North Bank Wall. Our results indicate that this feature was designed with dual functionality of extending the urban area’s defenses to the east of Angkor Thom while maintaining the existing infrastructure for the distribution and disposal of water. These findings suggest that the North Bank Wall was built before the severe droughts in the mid-13th century. The timing of the construction indicates that the perceived need for additional security—whether from internal factional disputes or external adversaries—predated the final adaptations to the hydraulic network during the unprecedented monsoon variability of the 14th century. These results indicate that perceived political unrest may have played a more important role in the decline of the site than previously known.
Performance Improvement of Spaceborne Carbon Dioxide Detection IPDA LIDAR Using Linearty Optimized Amplifier of Photo-Detector

The spaceborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system was found to be helpful in observing atmospheric CO2 and understanding the carbon cycle. The airborne experiments of a scale prototype of China’s planned spaceborne IPDA LIDAR was implemented in 2019. A problem with data inversion caused by the detector module nonlinearity was found. Through many experiments, the amplifier circuit board (ACB) of the detector module was proved to be the main factor causing the nonlinearity. Through amplifier circuit optimization, the original bandwidth of the ACB was changed to 1 MHz by using a fifth-order active filter. Compared with the original version, the linearity of optimized ACB is improved from 42.6% to 0.0747%. The optimized ACB was produced and its linearity was verified by experiments. In addition, the output waveform of the optimized ACB changes significantly, which will affect the random error (RE) of the optimized IPDA LIDAR system. Through the performance simulation, the RE of more than 90% of the global area is less than 0.728 ppm. Finally, the transfer model of the detector module was given, which will be helpful for the further optimization of the CO2 column-averaged dry-air mixing ratio (XCO2) inversion algorithm.
The Precipitation Imaging Package: Phase Partitioning Capabilities

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.
Assessment of the Homogeneity of Long-Term Multi-Mission RO-Based Temperature Climatologies

Atmospheric data obtained from the radio occultation (RO) technique are a well-recognized source of information for weather and climate studies. From the Challenging Minisatellite Payload (CHAMP) mission launched in July 2000 to the most recent Constellation Observing System for Meteorology, Ionosphere, and Climate follow-on (COSMIC-2) program, a continuous RO dataset of about 20 years has been collected, and a new opportunity for long-term climate analyses using multi-mission RO observations has subsequently arisen. Therefore, assessments of the long-term homogeneities of multi-mission RO data have become a necessary research task. For this purpose, in this study, we identified systematic discrepancies between the RO temperature profiles from the CHAMP, COSMIC, and Meteorological Operational Polar Satellite (METOP) missions. The results show that the temperature profiles from all three RO missions agree well in the upper troposphere and lower stratosphere (UTLS, 9–20 km altitude) regions, while some systematic discrepancies are found in the lower troposphere (2–8 km) and the high-altitude region (21–30 km). The homogeneities of long-term RO temperature climatologies were assessed by comparing them with radiosonde temperature records. The results of this comparison show obvious temporal inhomogeneities in the lower troposphere. The reasons for these temporal inhomogeneities include the systematic discrepancies between multi-mission RO profiles, the different monthly numbers of RO profiles, and the residual sampling error. The results of this study suggest that the systematic discrepancies between different RO missions should be thoroughly considered in the development of long-term multi-mission RO-based climatologies.
Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS.
Ka-Band Radar Cross-Section of Breaking Wind Waves

The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.