Glacial lake outburst flood (GLOF) is one of the major natural disasters in the Qinghai-Tibetan Plateau (QTP). On 25 June 2020, the outburst of the Jiwenco Glacial Lake (JGL) in the upper reaches of Nidu river in Jiari County of the QTP reached the downstream Niwu Township on 26 June, causing damage to many bridges, roads, houses, and other infrastructure, and disrupting telecommunications for several days. Based on radar and optical image data, the evolution of the JGL before and after the outburst was analyzed. The results showed that the area and storage capacity of the JGL were 0.58 square kilometers and 0.071 cubic kilometers, respectively, before the outburst (29 May), and only 0.26 square kilometers and 0.017 cubic kilometers remained after the outburst (27 July). The outburst reservoir capacity was as high as 5.4 million cubic meters. The main cause of the JGL outburst was the heavy precipitation process before outburst and the ice/snow/landslides entering the lake was the direct inducement. The outburst flood/debris flow disaster also led to many sections of the river and buildings in Niwu Township at high risk. Therefore, it is urgent to pay more attention to glacial lake outburst floods and other low-probability disasters, and early real-time engineering measures should be taken to minimize their potential impacts. Full article

High-resolution DEMs are important spatial data, and are used in a wide range of analyses and applications. However, the high cost to obtain high-resolution DEM data over a large area through sensors with higher precision poses a challenge for many geographic analysis applications. Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs to generate high-resolution DEMs. An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic high-resolution DEM. The results were compared with those generated with the bicubic, bilinear, and EDSR methods. The numerical accuracy and terrain feature preserving effects of the EDEM-SR method can generate reconstructed DEMs that better match the original DEMs, show lower MAE and RMSE, and improve the accuracy of the terrain parameters. MAE is reduced by about 30 to 50% compared with traditional interpolation methods. The results show how the EDEM-SR method can generate high-resolution DEMs using low-resolution DEMs. Full article

Impact craters are the most prominent features on the surface of the Moon, Mars, and Mercury. They play an essential role in constructing lunar bases, the dating of Mars and Mercury, and the surface exploration of other celestial bodies. The traditional crater detection algorithms (CDA) are mainly based on manual interpretation which is combined with classical image processing techniques. The traditional CDAs are, however, inefficient for detecting smaller or overlapped impact craters. In this paper, we propose a Split-Attention Networks with Self-Calibrated Convolution (SCNeSt) architecture, in which the channel-wise attention with multi-path representation and self-calibrated convolutions can generate more prosperous and more discriminative feature representations. The algorithm first extracts the crater feature model under the well-known target detection R-FCN network framework. The trained models are then applied to detecting the impact craters on Mercury and Mars using the transfer learning method. In the lunar impact crater detection experiment, we managed to extract a total of 157,389 impact craters with diameters between 0.6 and 860 km. Our proposed model outperforms the ResNet, ResNeXt, ScNet, and ResNeSt models in terms of recall rate and accuracy is more efficient than that other residual network models. Without training for Mars and Mercury remote sensing data, our model can also identify craters of different scales and demonstrates outstanding robustness and transferability. Full article

Defining the origin of ground deformation, which can be a very challenging task, may be approached through several investigative techniques. Ground deformation can originate in response to both natural (e.g., tectonics) and anthropic (e.g., groundwater pumping) contributions. These may either act simultaneously or be somewhat correlated in space and time. For example, the location of structurally controlled basins may be the locus of enhanced human-induced subsidence. In this paper, we investigate the natural and anthropic contributions to ground deformation in the urbanized area of the inner Sarno plain, in the Southern Apennines. We used a multidisciplinary approach based on the collection and analysis of a combination of geomorphological, stratigraphical, structural, hydrogeological, GPS, and DInSAR datasets. Geomorphological, stratigraphical, and structural data suggested the occurrence of a graben-like depocenter, the Sarno basin, bounded by faults with evidence of activity in the last 39 ka. Geodetic data indicated that the Sarno basin also experienced ground deformation (mostly subsidence) in the last 30 years, with a possible anthropogenic contribution due to groundwater pumping. Hydrogeological data suggested that a significant portion of the subsidence detected by geodetic data can be ascribed to groundwater pumping from the alluvial plain aquifer, rather than to a re-activation of faults in the last 30 years. Our interpretation suggested that a positive feedback exists between fault activity and the location of area affected by human-induced subsidence. In fact, fault activity caused the accumulation of poorly consolidated deposits within the Sarno basin, which enhanced groundwater-induced subsidence. The multidisciplinary approach used here was proven to be successful within the study area and could therefore be an effective tool for investigating ground deformation in other urbanized areas worldwide. Full article

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality. Full article

Regular automatic recordings of the time series of the magnetic field, together with routine manual absolute measurements for establishing dynamic baselines at Lampedusa Island—south of Sicily—Italy (geographic coordinates 35°31′N; 12°32′E, altitude 33 m a.s.l.), show a signature of very low electromagnetic noise. The observatory (provisional IAGA code: LMP) lays inside a restricted and remote wildlife reserve, far away from the built-up and active areas of the island, which at present is the southernmost location of the European territory for such observations. The availability of high-quality data from such site, whose survey started in 2005, is valuable for filling the spatial gap due to the lack of observatories in the whole south Mediterranean and North African sectors. We compare observations at Lampedusa, in both time and frequency domains, with those at the other Italian observatories (Castello Tesino and Duronia-L’Aquila), operating since the 1960s of last century, allowing us to report even the secular variation. Using data recorded in the last few years, we investigate higher frequency variations (from diurnal to Pc3-4 pulsations) in order to magnetically characterize the Italian territory and the local response to external forcing. In particular, we present a characterization in terms of diurnal variation and its seasonal dependence for the three observatories. This latter feature is in good agreement with a geomagnetic Sq-model, leading us to speculate about the position of the north Sq-current system vortex and its seasonal displacement with respect to the geographic positions of the observatories. We also study the geomagnetic individual response to intense space weather events by performing Superposed Epoch Analysis (SEA), with an ad-hoc significance test. Magnetic responses in the Ultra Low Frequency range (ULF) from spectral, local Signal-to-Noise Ratio (SNR) analyses under different local time, and polarization rates are computed. These latter studies lead us to search for possible signatures of magnetic field line resonances during intense space weather events, using cross-phase multi-observatory analysis, revealing the promising detection capability of such technique even at low latitudes. The geomagnetic observatories prove to be important points of observation for space weather events occurring at different spatial and time scales, originating in both upstream and ionospheric regions, here analyzed by several well-established methodologies and techniques. The quiet environmental site of LMP, providing high-quality geomagnetic data, allows us such investigations even at inner Earth’s magnetospheric shell. Full article

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event. Full article

Urban vegetation can be highly dynamic due to the complexity of different anthropogenic drivers. Quantifying such dynamics is crucially important as it is a prerequisite to understanding its social and ecological consequences. Previous studies have mostly focused on the urban vegetation dynamics through monotonic trends analysis in certain intervals, but not considered the process which provides important insights for urban vegetation management. Here, we developed an approach that integrates trends with dynamic analysis to measure the vegetation dynamics from the process perspective based on the time-series Landsat imagery and applied it in Shenzhen, a coastal megacity in southern China, as an example. Our results indicated that Shenzhen was turning green from 2000–2020, even though a large-scale urban expansion occurred during this period. Approximately half of the city (49.5%) showed consistent trends in greening, most of which were located in the areas within the ecological protection baseline. We also found that 35.3% of the Shenzhen city experienced at least a one-time change in urban greenness that was mostly caused by changes in land cover types (e.g., vegetation to developed land). Interestingly, 61.5% of these lands showed trends in greening in the recent change period and most of them were distributed in build-up areas. Our approach that integrates trends analysis and dynamic process reveals information that cannot be discovered by monotonic trends analysis alone, and such information can provide insights for urban vegetation planning and management. Full article

The present study contributes to the scientific effort for a better understanding of the potential of the Australian biomass burning events to influence tropospheric trace gas abundances at the regional scale. In order to exclude the influence of the long-range transport of ozone precursors from biomass burning plumes originating from Southern America and Africa, the analysis of the Australian smoke plume has been driven over the period December 2019 to January 2020. This study uses satellite (IASI, MLS, MODIS, CALIOP) and ground-based (sun-photometer, FTIR, ozone radiosondes) observations. The highest values of aerosol optical depth (AOD) and carbon monoxide total columns are observed over Southern and Central Australia. Transport is responsible for the spatial and temporal distributions of aerosols and carbon monoxide over Australia, and also the transport of the smoke plume outside the continent. The dispersion of the tropospheric smoke plume over Oceania and Southern Pacific extends from tropical to extratropical latitudes. Ozone radiosonde measurements performed at Samoa (14.4°S, 170.6°W) and Lauder (45.0°S, 169.4°E) indicate an increase in mid-tropospheric ozone (6–9 km) (from 10% to 43%) linked to the Australian biomass burning plume. This increase in mid-tropospheric ozone induced by the transport of the smoke plume was found to be consistent with MLS observations over the tropical and extratropical latitudes. The smoke plume over the Southern Pacific was organized as a stretchable anticyclonic rolling which impacted the ozone variability in the tropical and subtropical upper-troposphere over Oceania. This is corroborated by the ozone profile measurements at Samoa which exhibit an enhanced ozone layer (29%) in the upper-troposphere. Our results suggest that the transport of Australian biomass burning plumes have significantly impacted the vertical distribution of ozone in the mid-troposphere southern tropical to extratropical latitudes during the 2019–20 extreme Australian bushfires. Full article

Precipitation estimation by weather radars in Alaska is challenging. In this study, we investigate National Weather Service (NWS) precipitation products that are produced from the seven NEXRAD radar sites in Alaska. The NWS precipitation processing subsystem generates stages of data at each NEXRAD site which are then input to the weather forecast office to generate a regionwide precipitation product. Data from the NEXRAD sites and the operational rain gauges in the weather forecast region are used to produce this regionwide product that is then sent to the National Centers for Environmental Prediction (NCEP) to be included in the NCEP Stage IV distribution. The NCEP Stage IV product for Alaska has been available since 2017. We use the United States Climate Reference Network (USCRN) data from Alaska to compare to the NCEP Stage IV data. Given that the USCRN can be used in the production of the NCEP Stage IV data for Alaska, we also used the NEXRAD Digital Precipitation Array (DPA) that is generated at the site for comparison of the radar-only products. Comparing the NEXRAD-based data from Alaska to the USCRN gauge estimates using the USCRN site information on air temperature, we are able to condition the analysis based on the hourly or 6-hourly average air temperature. The estimates in the frozen phase of precipitation largely underestimate as compared to the gauge, and the correlation is low with larger errors as compared to other phases of precipitation. In the mixed phase the underestimation of precipitation improves, but the correlation is still low with relatively large errors as compared to the rain phases of precipitation. The difficulties in precipitation estimation in cold temperatures are well known and we show the evaluation for the NCEP Stage IV regional data for Alaska and the NEXRAD site specific Digital Precipitation Array (DPA) data. Results show the challenges of estimating mixed-phase and frozen precipitation. However, the DPA data shows somewhat better performance in the mixed precipitation phase, which suggests that the NWS Precipitation Processing Subsystem (PPS) is tuned to the climatology as it relates to precipitation in Alaska. Full article

Forests play an essential role in maintaining the Earth’s overall energy balance. The variability in forest canopy structure, topography, and underneath vegetation background conditions create uncertainty in modeling solar radiation at the Earth’s surface, particularly for boreal regions in high latitude. The purpose of this study is to analyze seasonal variation in visible, near-infrared, and shortwave infrared reflectance with respect to land cover classes, canopy structures, and topography in a boreal region of Alaska. We accomplished this investigation by fusing Landsat 8 images and LiDAR-derived canopy structural data and multivariate statistical analysis. Our study shows that canopy structure and topography interplay and influence reflectance spectra in a complex way, particularly during the snow season. We observed that deciduous trees, also tall with greater rugosity, are more dominant on the southern slope than on the northern slope. Taller trees are typically seen in higher elevations regardless of vegetation types. Surface reflectance in all studied wavelengths shows similar relationships with canopy cover, height, and rugosity, mainly due to close connections between these parameters. Visible and near-infrared reflectance decreases with canopy cover, tree height, and rugosity, especially for the evergreen forest. Deciduous forest shows more considerable variability of surface reflectance in all studied wavelengths, particularly in March, mainly due to the mixing effect of snow and vegetation. The multivariate statistical analysis demonstrates a significant tree shadow effect on surface reflectance for evergreen forests. However, the topographic shadow effect is prominent for deciduous forests during the winter season. These results provide great insight into understanding the role of vegetation structure and topography in surface radiation budget in the boreal region. Full article

A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90° N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn. Full article

In challenging environments such as forests, valleys and higher latitude areas, there are usually fewer than four visible satellites. For cases with only two visible satellites, we propose a dual-satellite alternate switching ranging integrated navigation algorithm based on the broadband low earth orbit (LEO) constellation, which integrates communication and navigation (ICN) technology. It is different from the traditional dual-satellite integrated navigation algorithm: the difference is that it can complete precise real-time navigation and positioning without an altimeter and continuous observation. First, we give the principle of our algorithm. Second, with the help of an unscented Kalman filter (UKF), we give the observation equation and state equation of our algorithm, and establish the mathematical model of multipath/non-line of sight (NLOS) and noise interference. Finally, based on the SpaceX constellation, for various scenarios, we analyze the performance of our algorithm through simulation. The results show that: our algorithm can effectively suppress the divergence of the inertial navigation system (INS), in the face of different multipath/NLOS interference and various noise environments it still keeps good robustness, and also has great advantages in various indicators compared with the traditional dual-satellite positioning algorithms and some existing 3-satellite advanced positioning algorithms. These results show that our algorithm can meet the real-time location service requirements in harsh and challenging environments, and provides a new navigation and positioning method when there are only two visible satellites. Full article

Commonly used mass-concentration (mascon) solutions estimated from Level-1B Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On data, provided by processing centers such as the Jet Propulsion Laboratory (JPL) or the Goddard Space Flight Center (GSFC), do not give users control over the placement of mascons or inversion assumptions, such as regularization. While a few studies have focused on regional or global mascon optimization from spherical harmonics data, a global optimization based on the geometry of geophysical signal as a standardized product with user-defined points has not been addressed. Finding the optimal configuration with enough coverage to account for far-field leakage is not a trivial task and is often approached in an ad-hoc manner, if at all. Here, we present an automated approach to defining non-uniform, global mascon solutions that focus on a region of interest specified by the user, while maintaining few global degrees of freedom to minimize noise and leakage. We showcase our approach in High Mountain Asia (HMA) and Alaska, and compare the results with global uniform mascon solutions from range-rate data. We show that the custom mascon solutions can lead to improved regional trends due to a more careful sampling of geophysically distinct regions. In addition, the custom mascon solutions exhibit different seasonal variation compared to the regularized solutions. Our open-source pipeline will allow the community to quickly and efficiently develop optimized global mascon solutions for an arbitrary point or polygon anywhere on the surface of the Earth. Full article

The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm²), whereas the systematic error of the P940 dataset (−0.02 g/cm²) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within $\pm \left(0.05+0.10\ast PW{V}_{AERONET}\right)$ (PER10) of the P940 PWV dataset are 0.24 g/cm², 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm², 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm²), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed. Full article