A flexible approach for geometric modelling of point clouds obtained from Terrestrial Laser Scanning (TLS) is by means of B-splines. These functions have gained some popularity in the engineering geodesy as they provide a suitable basis for a spatially continuous and parametric deformation analysis. In the predominant studies on geometric modelling of point clouds by B-splines, uncorrelated and equally weighted measurements are assumed. Trying to overcome this, the elementary errors theory is applied for establishing fully populated covariance matrices of TLS observations that consider correlations in the observed point clouds. In this article, a systematic approach for establishing realistic synthetic variance–covariance matrices (SVCMs) is presented and afterward used to model TLS point clouds by B-splines. Additionally, three criteria are selected to analyze the impact of different SVCMs on the functional and stochastic components of the estimation results. Plausible levels for variances and covariances are obtained using a test specimen of several dm—dimension. It is used to identify the most dominant elementary errors under laboratory conditions. Starting values for the variance level are obtained from a TLS calibration. The impact of SVCMs with different structures and different numeric values are comparatively investigated. Main findings of the paper are that for the analyzed object size and distances, the structure of the covariance matrix does not significantly affect the location of the estimated surface control points, but their precision in terms of the corresponding standard deviations. Regarding the latter, properly setting the main diagonal terms of the SVCM is of superordinate importance compared to setting the off-diagonal ones. The investigation of some individual errors revealed that the influence of their standard deviation on the precision of the estimated parameters is primarily dependent on the scanning distance. When the distance stays the same, one-sided influences on the precision of the estimated control points can be observed with an increase in the standard deviations. Full article

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere). Full article

With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method. Full article

We develop a linear inversion technique for measuring the modal composition and directionality of ambient seismic noise. The technique draws from similar techniques used in astrophysics and gravitational-wave physics, and relies on measuring cross-correlations between different seismometer channels in a seismometer array. We characterize the sensitivity and the angular resolution of this technique using a series of simulations and real-world tests. We then apply the technique to data acquired by the three-dimensional seismometer array at the Homestake mine in Lead, SD, to estimate the composition and directionality of the seismic noise at microseism frequencies. We show that, at times of low-microseism amplitudes, noise is dominated by body waves (P and S), while at high-microseism times, the noise is dominated by surface Rayleigh waves. Full article

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions. Full article

Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation. Full article

Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF₆₈₇: R² = 0.53, p < 0.001; SIF₇₆₀: R² = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies. Full article

In order to deal with the problem that some existing semantic segmentation networks for 3D point clouds generally have poor performance on small objects, a Spatial Eight-Quadrant Kernel Convolution (SEQKC) algorithm is proposed to enhance the ability of the network for extracting fine-grained features from 3D point clouds. As a result, the semantic segmentation accuracy of small objects in indoor scenes can be improved. To be specific, in the spherical space of the point cloud neighborhoods, a kernel point with attached weights is constructed in each octant, the distances between the kernel point and the points in its neighborhood are calculated, and the distance and the kernel points’ weights are used together to weight the point cloud features in the neighborhood space. In this case, the relationship between points are modeled, so that the local fine-grained features of the point clouds can be extracted by the SEQKC. Based on the SEQKC, we design a downsampling module for point clouds, and embed it into classical semantic segmentation networks (PointNet++, PointSIFT and PointConv) for semantic segmentation. Experimental results on benchmark dataset ScanNet V2 show that SEQKC-based PointNet++, PointSIFT and PointConv outperform the original networks about 1.35–2.12% in terms of MIoU, and they effectively improve the semantic segmentation performance of the networks for small objects of indoor scenes, e.g., the segmentation accuracy of small object “picture” is improved from 0.70% of PointNet++ to 10.37% of SEQKC-PointNet++. Full article

To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods. Full article

The precise orbit determination (POD) accuracy of the Chinese BeiDou Navigation Satellite System (BDS) is still not comparable to that of the Global Positioning System because of the unfavorable geometry of the BDS and the uneven distribution of BDS ground monitoring stations. Fortunately, low Earth orbit (LEO) satellites, serving as fast moving stations, can efficiently improve BDS geometry. Nearly all studies on Global Navigation Satellite System POD enhancement using large LEO constellations are based on simulations and their results are usually overly optimistic. The receivers mounted on a spacecraft or an LEO satellite are usually different from geodetic receivers and the observation conditions in space are more challenging than those on the ground. The noise level of spaceborne observations needs to be carefully calibrated. Moreover, spaceborne observational errors caused by space weather events, i.e., solar geomagnetic storms, are usually ignored. Accordingly, in this study, the actual spaceborne observation noises are first analyzed and then used in subsequent observation simulations. Then, the observation residuals from the actual-processed LEO POD during a solar storm on 8 September 2017 are extracted and added to the simulated spaceborne observations. The effect of the observational errors on the BDS POD augmented with different LEO constellation configurations is analyzed. The results indicate that the noise levels from the Swarm-A, GRACE-A, and Sentinel-3A satellites are different and that the carrier-phase measurement noise ranges from 2 mm to 6 mm. Such different noise levels for LEO spaceborne observations cause considerable differences in the BDS POD solutions. Experiments calculating the augmented BDS POD for different LEO constellations considering spaceborne observational errors extracted from the solar storm indicate that these errors have a significant influence on the accuracy of the BDS POD. The 3D root mean squares of the BDS GEO, IGSO, and MEO satellite orbits are 1.30 m, 1.16 m, and 1.02 m, respectively, with a Walker 2/1/0 LEO constellation, and increase to 1.57 m, 1.72 m, and 1.32 m, respectively, with a Walker 12/3/1 constellation. When the number of LEO satellites increases to 60, the precision of the BDS POD improves significantly to 0.89 m, 0.77 m, and 0.69 m for the GEO, IGSO, and MEO satellites, respectively. While 12 satellites are sufficient to enhance the BDS POD to the sub-decimeter level, up to 60 satellites can effectively reduce the influence of large spaceborne observational errors, i.e., from solar storms. Full article

The vegetation coverage on the Loess Plateau (LP) of China has clearly increased since the implementation of the Grain for Green Project in 1999, but there is a debate about whether the improved greenness was achieved at the expense of the balance between the supply and demand of water resources. Therefore, developing reliable indicators to evaluate the water availability is a prerequisite for maintaining ecological sustainability and ensuring the persistence of vegetation restoration. This study was designed to evaluate water availability on the LP during 2000–2015, using the evaporative stress index (ESI) derived from a remote sensing dataset. The relative dependences of the ESI on climatic and biological factors (including temperature, precipitation and land cover change) were also analyzed. The results showed that the leaf area index (LAI) in most regions of the LP showed a significant increasing trend (p < 0.05), and larger gradients of increase were mainly detected in the central and eastern parts of the LP. The evapotranspiration also exhibited an increasing trend in the central and eastern parts of the LP, with a gradient greater than 10 mm/year. However, almost the whole LP exhibited a decreased ESI from 2000 to 2015, and the largest decrease occurred on the central and eastern LP, indicating a wetting trend. The soil moisture storage in the 0–289-cm soil profiles showed an increasing trend in the central and eastern LP, and the area with an upward trend enlarged with the soil depth. Further analysis revealed that the decreased ESI on the central and eastern LP mainly depended on the increase in the LAI compared with climatic influences. This work not only demonstrated that the ESI was a useful indicator for understanding the water availability in natural and managed ecosystems under climate change but also indicated that vegetation restoration might have a positive effect on water conservation on the central LP. Full article

Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views. Full article

Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support. Full article

The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio at 355 nm are 0.13 ± 0.05 and 52 ± 9 sr, respectively. It is the typical value of a mixture of dust and urban haze. Here we quantify the contributions of cross-regional transported natural dust and urban haze mass concentrations to Beijing’s air quality. There is a significant correlation between urban haze mass concentrations and surface PM₂.₅ (R = 0.74, p < 0.01). The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. Moreover, the trans-regional transmission of natural dust dominated the air pollution during the 2019 National Day parade, with a relative contribution to particulate matter mass concentrations exceeding 74% below 4 km. Our results highlight that controlling anthropogenic emissions over regional scales and focusing on the effects of natural dust is crucial and effective to improve Beijing’s air quality. Full article

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently. Full article