Dust in sand dunes is an effective and important source of dust emission. The Taklimakan Desert (TD) is one of the main sources of global dust: the sand dunes account for approximately 85% of the total area of the TD. However, the dust concentration, emission characteristics, and physical factors of different parts of the sand dunes in the TD during the day and night, as well as dust and non-dust days, remain unclear. In this study, dust observations were collected over a 3 month period in the TD to investigate the physical processes by which dust moves across a surface and generates PM₁₀ and PM_2.5 from the top and bottom of sand dunes. The results showed that the daily average maximum concentrations of particulate matter with diameters below 2.5 and 10 µm (PM_2.5 and PM₁₀) in the dune during the observation period reach ~90 and ~190 µg·m⁻³, respectively. Dust emission generated in the saltation process (maximum emission of PM₁₀ was 3–5 mg·m⁻²·s⁻¹) in the TD dunes was larger than that in other areas and had a good correlation with u_* (friction velocity), where u_* = 0.4 m·s⁻¹ was the threshold of sand dune dust emission. The wind speed at the top of dunes was larger than that at the bottom, which was conducive to the accumulation of PM₁₀ at the top of the dune. Furthermore, the MLH (mixed layer height) decreased after sunset and the turbulence weakens, which was conducive to the retention of dust in the air. Moreover, the dust made the solar radiation at the top of the dune −15 W·m⁻² (average) lower than at the bottom. These results provided a new understanding of dune emissions in the TD and could be used for dust modeling in regional and global models. Full article

The area-to-point kriging method (ATPK) is an important technology of downscaling without auxiliary information in remote sensing. However, it uses a constant semivariogram to downscale geospatial variables, which ignores the spatial heterogeneity between the geospatial objects. To deal with this kind of heterogeneity, this study proposes a fuzzy geospatial object-based ATPK method, which mainly consists of three steps: the extraction of fuzzy geospatial objects, the estimation of semivariograms for each object, and the downscaling of each object by ATPK with the corresponding semivariogram. Two groups of membership functions acquired from Worldview-2 and Sentinel-2 are used to test the proposed approach. Six classic downscaling algorithms are compared, and the results of two experiments show a better performance than the classical methods. Full article

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high threshold for ordinary remote sensing technicians. To solve this problem, we propose an RTC method based on the rational polynomial coefficient (RPC) model, which is widely used in optical remote sensing and is simpler and more practical than the RD model. China’s GF-3 polarimetric SAR data were used to verify the proposed method. The experimental results showed that the RTC method based on RPC is effective and can achieve better correction effects on the premise of reducing the complexity of the algorithm. The correction effect based on the RPC model can be similar to that based on the RD model. The proposed approach can realize the correction of 4~5 dB terrain radiation distortion to a 0.5 dB level. Full article

The application of the automated analysis of remote sensing data processed into high-resolution digital terrain models (DTMs) using geographic information systems (GIS) tools provides a geomorphometric characterization of the diversity of the relief of loess patches over large areas. Herein, a quantitative classification of 79 loess patches with a total area of 3361 km², distributed within the eastern part of the Polish Uplands belt, is carried out. A high-resolution 1 × 1 m DTM was generated from airborne laser scanning (ALS) data with densities ranging from 4 pts/m² to 12 pts/m², which was resampled to a resolution of 5 × 5 m for the study. This model was used to classify landform surfaces using the r.geomorphon (geomorphon algorithm) function in GRASS GIS software. By comparing the values in the neighborhood of each cell, a map of geomorphometric features (geomorphon) was obtained. The classification and typology of the relief of the studied loess patches was performed using GeoPAT2 (Geospatial Pattern Analysis Toolbox) software. Pattern signatures with a resolution of 100 × 100 m were extracted from the source data grid, and the similarity of geomorphological maps within the signatures was calculated and saved as a signature file and segment map using the spatial coincidence method. The distance matrix between each pair of segments was calculated, and the heterogeneity and isolation of the maps were generated. R system was used to classify the segments, which generated a dendrogram and a heat map based on the distance matrix. This made it possible to distinguish three main types and eight subtypes of relief. The morphometric approach used will contribute to a better understanding of the spatial variation in the relief of loess patches. Full article

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading. Full article

Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method’s performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability. Full article

Electron density measured at high latitudes by the Swarm satellites was compared with measurements by the incoherent scatter radars at Resolute Bay and Poker Flat. Overall, the ratio of Swarm-based electron density to that measured by the radars was about 0.5–0.6. Smaller ratios were observed at larger electron densities, usually during the daytime. At low electron densities less than 3 × 10¹⁰ m⁻³, the ratios were typically above 1, indicating an overestimation effect. The overestimation effect was stronger at night and for Swarm B. It was more evident at lower solar activity when the electron densities in the topside ionosphere were lower. Full article

GNSS real-time applications greatly benefit from the International GNSS Service’s (IGS) real-time service (RTS). This service does more than provide for terrestrial precise point positioning (PPP); it also brings more possibilities for space-borne technology. With this service, the State-Space Representation (SSR) product, which includes orbit corrections and clock corrections, is finally available to users. In this paper, the GPS real-time orbit and clock corrections provided by 11 analysis centers (ACs) from the day of the year (DOY) 144 to 153 of 2022 are discussed from 3 perspectives: integrity, continuity, and accuracy. Moreover, actual observation data from the GRACE-C satellite are processed, along with SSR corrections from different ACs. The following can be concluded: (1) In terms of integrity and continuity, the products provided by CNE, ESA, and GMV perform better. (2) CNE, ESA, and WHU are the most accurate, with values of about 5 cm for the satellite orbit and 20 ps for the satellite clock. Additionally, the clock accuracy is related to the Block. Block IIR and Block IIR-M are slightly worse than Block IIF and Block IIIA. (3) The accuracy of post-processing reduced-dynamic precise orbit determination (POD) and kinematic POD are at the centimeter level in radius, and the reduced-dynamic POD is more accurate and robust than the kinematic POD. Full article

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images. Full article

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. Full article

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality. Full article

In this study, we investigated the quality of Interferometric Synthetic Aperture Radar (InSAR) data to measure surface displacements in upstate New York, an area with dense vegetation, snowy winters, and strong seasonal signals. We used data from the German Space Agency’s TerraSAR-X and TanDEM-X satellites (X-band, 3.1 cm radar wavelength) as well as the European Space Agency’s Sentinel-1 satellite (C-band, 5.6 cm radar wavelength); both datasets covered a ~3-year time period from 2018 to 2021. Using persistent scatterer interferometry (PSI), we were able to observe several deforming features in the region with sub-centimeter/year deformation rates. We also examined a version of the X-band data that we spatially averaged to the same pixel size as the Sentinel-1 imagery in order to separate out the effects of wavelength and pixel size on PSI accuracy and coverage. Overall, the largest number of stable PS points was found in the full-resolution X-band data, which was followed by the C-band data and then by the downsampled X-band data. Our analysis also included a subset of snow-free imagery so that we could assess the effect that snow-covered images had on the distribution and accuracy of PS points and the resulting time series. This analysis revealed that PS populations increased by 50–60% for the snow-free data when compared with analyses using the full datasets. The average deformation rates inferred from the time series generated using only snow-free images were nearly identical to those estimated from the full time series. We assessed the accuracy of the inferred rates through comparisons between the results of different datasets and with limited ground survey data. We found that all of the inferred deformation rates from each of the datasets agreed with in situ measurements in an area of known ground subsidence above an underground salt mine in Lansing, NY. The S1 datasets, however, had higher levels of noise. Full article

We performed a joined multitemporal and multiscale analysis of ground vertical movements around the main seismogenic source of Ischia island (Southern Italy) that, during historical and recent time, generated the most catastrophic earthquakes on the island, in its northern sector (Casamicciola fault). In particular, we considered InSAR (2015–2019) and ground-levelling data (1987–2010), attempting to better define the source that caused the recent 2017 earthquake and interpret its occurrence in the framework of a long-term behavior of the fault responsible for the major historical earthquakes in Casamicciola. Our results unambiguously constrain the location and the kinematics of the 2017 rupture and further confirm the presence of a relatively large sliding area west of the 2017 surface break. Overall, the studied seismogenic fault reveals a complex dynamic, moving differentially and aseismically in the pre- and post-seismic event, in response to the long-term subsidence of the central sector of the island, dominated by Mt. Epomeo. The fault segment that slipped coseismically also is evidence of post-seismic viscous relaxation. The long-term differential vertical movement on the apparently creeping eastern sector of the Casamicciola fault provides an estimate of the slip rate occurring on the fault (0.82 mm/y⁻¹). The analysis of the time of occurrence and the magnitude of the known historical earthquakes reveals that this rate is consistent with the recurrence of the earthquakes that occurred during at least the past three centuries and suggests that the time to the next seismic event at Casamicciola might be a few decades. More generally, our findings provide evidence of the link between subsidence and earthquakes in volcanic areas indicating, in this case, a high hazard for the island of Ischia. Results might be also useful for characterizing capable faulting in similar volcano-tectonic settings worldwide. Full article

The electromagnetic (EM) scattering mechanism of dynamic oil spill–covered sea surface area is studied in this manuscript. Utilizing the theory of oil film diffusion combined with oil spill volume, a three–dimensional (3D) geometric model of dynamic oil spill–covered sea surface area is established. The changes of the geometric structure and statistical characteristics of the sea surface area under the influence of the oil film are also analyzed. The thinner the oil spill thickness, the more sensitive it is to the sea surface slope and wave height. The facet-based hybrid model and the multilayer dielectric scattering method are combined to measure the EM scattering on the sea surface when covered by dynamic oil spills. In addition, the hydrodynamic and tilt effect are discussed. The EM scattering mechanism of the dynamic oil spill–covered sea surface area is revealed, and the tilt modulation is greater than the hydrodynamic effect in the dynamic process of an oil spill. It provides an important reference for the remote sensing monitoring of oil. Full article

Satellite radar altimetry has been widely utilized in hydrological research, particularly with the advent of Sentinel-3, a Synthetic Aperture Radar (SAR) altimeter operating globally and equipped with an innovative onboard tracking system referred to as the open-loop tracking command (OLTC). Utilizing a pseudo-DEM (Digital Elevation Model), controlled through the OLTC, holds significant promise for the reliable observation of inland water bodies. Nevertheless, the complex geographical conditions in high mountain and reservoir river basins pose challenges in defining an appropriate pseudo-DEM for hydrological targets, potentially leading to reduced performance of Sentinel-3. This study aims to comprehensively evaluate the performance of Sentinel-3 by selecting the Lancang and Nu River basins in southwest China as a case study. These two rivers have a similar natural environment, but cascade reservoirs distinguish the Lancang River basin. By analyzing waveform energy from echoes of virtual stations (VSs) in both river basins (27 VSs in the Lancang River basin and 39 VSs in the Nu River basin), the performance of Sentinel-3 in different tracking modes and OLTC versions were compared. The results indicated that the detection rate of Sentinel-3A increased when transitioning from the closed-loop mode to the open-loop mode and with the implementation of newer OLTC versions (36.8% increased to 47.4%, 60.5%, and 63.2% in OLTC V5.0, V6.0, and V6.1, respectively). Similarly, the detection rate of Sentinel-3B rose from 64.3% (OLTC V2.0) to 71.4% and 75.0% in OLTC V3.0 and V3.1, respectively. Additionally, the cascade reservoir causing river channel expansion results in a better performance of Sentinel-3A in the Lancang River compared to the Nu River in the closed-loop mode (13.0% and 35.7%, respectively). Nevertheless, the considerable fluctuations in water surface caused by reservoir impoundment led to a wrong pseudo-DEM, resulting in poor performance of Sentinel-3 in reservoir regions before OLTC V6.0 was updated. The detection rate of low altitude, broad water surfaces (>500 m) decreased from 100% in a closed-loop mode to 0% in an open-loop mode, but increased to 100% in OLTC V6.0 and V6.1, respectively. The detection rate of high altitude, narrow water surfaces (<500 m) increased from 0% in a closed-loop mode to 40.9% in OLTC V6.1. Although the detection ability of Sentinel-3 is improving with the implementation of newer OLTC versions, the seasonal variations (usually more than 60 m) of water levels in reservoirs exceeded the size of the range window (60 m), rendering a complete measurement impossible. Full article