Long-time coherent integration works to significantly increase the detection probability for maneuvering targets. However, during the observation time, the problems that often tend to occur are range cell migration (RCM) and Doppler frequency cell migration (DFCM), due to the high velocity and acceleration of the maneuvering target, which can reduce the detection of the maneuvering targets. In this regard, we propose a new coherent integration approach, based on the product scale zoom discrete chirp Fourier transform (PSZDCFT). Specifically, by introducing the zoom operation into the modified discrete chirp Fourier transform (MDCFT), the zoom discrete chirp Fourier transform (ZDCFT) can correctly estimate the centroid frequency and chirp rate of the linear frequency-modulated signal (LFM), regardless of whether the parameters of the LFM signal are outside the estimation scopes. Then, the scale operation, combined with ZDCFT, is performed on the radar echo signal in the range frequency slow time domain, to remove the coupling. Thereafter, a product operation is executed along the range frequency to inhibit spurious peaks and reinforce the true peak. Finally, the velocity and acceleration of the target estimated from the true peak position, are used to construct a phase compensation function to eliminate the RCM and DFCM, thus achieving coherent integration. The method is a linear transform without energy loss, and is suitable for low signal-to-noise (SNR) environments. Moreover, the method can be effectively fulfilled based on the chirp-z transform (CZT), which prevents the brute-force search. Thus, the method reaches a favorable tradeoff between anti-noise performance and computational load. Intensive simulations demonstrate the effectiveness of the proposed method. Full article

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points. Full article

Recent works have made strong efforts to produce standardised photometry in RGB bands. For this purpose, we carefully defined the transmissivity curves of RGB bands and defined a set of standard sources using the photometric information present in Gaia EDR3. This work aims not only to significantly increase the number and accuracy of RGB standards but also to provide, for the first time, reliable uncertainty estimates using the BP and RP spectrophotometry published in Gaia DR3 instead of their integrated photometry to predict RGB photometry. Furthermore, this method allows including calibrated sources regardless of how they are affected by extinction, which was a major shortcoming of previous work. The RGB photometry is synthesised from the Gaia BP and RP low-resolution spectra by directly using their set of coefficients multiplied with some basis functions provided in the Gaia catalogue for all sources published in Gaia DR3. The output synthetic magnitudes are compared with the previous catalogue of RGB standards available. Full article

The Oostvaardersplassen nature reserve in the Netherlands is grazed by large herbivores. Due to their increasing numbers, the area became dominated by short grazed grasslands and biodiversity decreased. From 2018, the numbers are controlled to create a diverse landscape. Fine-scale mapping and monitoring of the aboveground biomass is a tool to evaluate management efforts to restore a heterogeneous and biodiverse area. We developed a random forest model that describes the correlation between field-based samples of aboveground biomass and fifteen height-related vegetation metrics that were calculated from high-density point clouds collected with a handheld LiDAR. We found that two height-related metrics (maximum and 75th percentile of all height points) produced the best correlation with an R² of 0.79 and a root-mean-square error of 0.073 kg/m². Grassland segments were mapped by applying a segmentation routine on the normalized grassland’s digital surface model. For each grassland segment, the aboveground biomass was mapped using the point cloud and the random forest AGB model. Visual inspection of video recordings of the scanned trajectories and field observations of grassland patterns suggest that drift and stretch effects of the point cloud influence the map. We recommend optimizing data collection using looped trajectories during scanning to avoid point cloud drift and stretch, test horizontal vegetation metrics in the model development and include seasonal influence of the vegetation status. We conclude that handheld LiDAR is a promising technique to retrieve detailed height-related metrics in grasslands that can be used as input for semi-automated spatio-temporal modelling of grassland aboveground biomass for supporting management decisions in nature reserves. Full article

This paper investigates a low-Earth orbit (LEO) satellite downlink for high-speed data communication in interference situations. A choke ring horn type antenna is used as the data transmitting antenna with an isoflux pattern in the LEO satellite, which has a beam coverage of ±51.6° and a bore-sight gain of 4.4 dBi at 8 GHz. The receiving antenna on the ground station is a parabolic type antenna with a diameter of 11.3 m, and it has a half-power beam width (HPBW) of 0.2° with a maximum gain of 59 dBi at 8 GHz. The jamming-to-signal ratio (J/S) is calculated assuming that the LEO satellite transmits signals to the ground station, and an elevation angle of the interference source varies from 0° to 90° at an altitude of 10 km. Applying antenna characteristics, such as HPBWs and side lobes, to the calculated space wave path loss makes it possible to predict the J/S results according to the location of the interference source and the satellite. The results show that it is necessary to consider the space environment to accurately analyze the LEO satellite downlink, especially at the low elevation angle of the satellite. Full article

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC. Full article

High-resolution remote-sensing imagery has proven useful for building extraction. Unfortunately, due to the high acquisition costs and infrequent availability of high-resolution imagery, low-resolution images are more practical for large-scale mapping or change tracking of buildings. However, extracting buildings from low-resolution images is a challenging task. Compared with high-resolution images, low-resolution images pose two critical challenges in terms of building segmentation: the effects of fuzzy boundary details on buildings and the lack of local textures. In this study, we propose a sparse geometric feature attention network (SGFANet) based on multi-level feature fusion to address the aforementioned issues. From the perspective of the fuzzy effect, SGFANet enhances the representative boundary features by calculating the point-wise affinity of the selected feature points in a top-down manner. From the perspective of lacking local textures, we convert the top-down propagation from local to non-local by introducing the grounding transformer harvesting the global attention of the input image. SGFANet outperforms competing baselines on remote-sensing images collected worldwide and multiple sensors at 4 and 10 m resolution, thereby, improving the IoU by at least 0.66%. Notably, our method is robust and generalizable, which makes it useful for extending the accessibility and scalability of building dynamic tracking across developing areas (e.g., the Xiong’an New Area in China) by using low-resolution images. Full article

A devasting Mw7.2 earthquake struck southern Haiti on 14 August 2021, leading to over 2000 casualties and severe structural failures. This earthquake, which ruptured ~70 km west of the 2010 Mw7.0 event, offers a rare opportunity to probe the mechanical properties of southern Haiti. This study investigates the kinematic multi-fault coseismic rupture process by jointly analyzing teleseismic and interferometric synthetic aperture radar (InSAR) datasets. We determined the optimal dip of different segment faults through finite-fault inversion, and the results show that the dips of the first, second and third faults are 62°, 76° and 76°, respectively, coinciding with the relocated aftershock distribution. The results estimated from our joint inversion revealed that the slip was dominated by reverse motion in the first segment and strike-slip motion in the second and third segments. Three slip patches were detected along the strike, with a peak slip of 3.0 m, and the rupture reached the surface at the second segment. The kinematic rupture process shows a unilateral rupture with a high centroid rupture velocity (5.5 km/s), and the rupture broke through the stepover and caused a cascade rupture. The rupture front experiences a directivity pulse of high ground motions with high amplitude and short duration, which may be an additional factor explaining the many landslides concentrated on the western end of the fault. The Coulomb failure stress change result indicates the increases in the probability of future events to the east and west of the 2021 main shock. Full article

In engineering practice, ground penetrating radar (GPR) records are often hindered by clutter resulting from uneven underground media distribution, affecting target signal characteristics and precise positioning. To address this issue, we propose a method combining deep learning preprocessing and reverse time migration (RTM) imaging. Our preprocessing approach introduces a novel deep learning framework for GPR clutter, enhancing the network’s feature-capture capability for target signals through the integration of a contextual feature fusion module (CFFM) and an enhanced spatial attention module (ESAM). The superiority and effectiveness of our algorithm are demonstrated by RTM imaging comparisons using synthetic and laboratory data. The processing of actual road data further confirms the algorithm’s significant potential for practical engineering applications. Full article

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications. Full article

Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, and so forth on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment-pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the downward SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2021, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, namely multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset in an attempt of improving the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations and known algorithms based on machine learning methods for estimating downward SW flux using remote sensing (MODIS) imagery. The estimates of downward SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site. Full article

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 Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model’s overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the “Analysis Ready Data (ARD) Technology Research for Domestic Satellite” project. 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