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Building height information is essential for determining urban morphology, urban planning studies, and manage sustainable growth. This study aims to use machine learning algorithms to estimate building heights from airborne LiDAR and spaceborne ICESat-2/ATLAS data. The performance of different machine learning algorithms was investigated when analyzing ICESat-2/ATLAS and airborne LiDAR data. The accuracy of building height information was compared with field measurements. Machine learning algorithms such as K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANNs), and Random Sample and Consensus (RANSAC) were used to classify spaceborne and airborne LiDAR data. Among all the algorithms applied to ICESat-2/ATLAS, the RF algorithm provided the best results for the strong and weak beams with 0.9683 and 0.9614, respectively. The K-NN yielded the best result for the airborne LiDAR dataset with 0.9999. Statistical analyzes were applied to both LiDAR datasets. The results of statistical analyzes for the pair of field measurement and ICESat-2 were R2 = 0.9894, RMSE = 0.4131, MSE = 0.1706, MAE = 0.3184, and ME = 0.0003; for the pair of field measurement and airborne LiDAR: R2 = 0.8368, RMSE = 1.9646, MSE = 3.8597, MAE = 1.0586, and ME = -0.3450; and for the pair of airborne LiDAR and ICESat-2: R2 = 0.8275, RMSE = 1.6664, MSE = 2.7770, MAE = 0.9040, and ME = 0.4598. As a result of the analysis, it was seen that the data obtained from the ICESat-2 system was successful in estimating building height and provided reliable data. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required. [ABSTRACT FROM AUTHOR]

The temperature for cloud glaciation importantly determines the initialization of precipitation and lifetime of clouds. The role of anthropogenic pollutants as ice nucleating particles (INPs) to determine the cloud glaciation remains uncertain. In this study, based on satellite radar and lidar observations, the clouds either in pure liquid or mixed‐phase with liquid top were statistically analyzed over China during 2006–2019, to obtain the transition freezing temperature (T*) of cloud top where mixed‐phase becomes more frequent than pure water, with further validation by the aircraft in situ measurements. Anthropogenic pollution was observed to raise T* up to −9°C, significantly increasing it by approximately 5°C per unit of aerosol optical depth. The results provide regional‐scale evidence that anthropogenic pollutants act as efficient INPs, increasing the freezing temperature of mixed‐phase clouds. Plain Language Summary: Cloud phase transition (from liquid water to ice) is of great importance to Earth's energy and water cycle. Forming ice crystals in mixed‐phase clouds requires ice nucleating particles (INPs). However, whether the anthropogenic pollution can be efficient INPs remains unclear. In this study, based on satellite and aircraft observations in China, the temperature when clouds start to freeze is estimated by analyzing the frequency of liquid water and glaciated cloud tops, under which the occurrence of glaciated cloud tops started to overtake that of pure liquid water cloud tops. We found that the clouds over the anthropogenically polluted region over China presented higher freezing temperature than other cleaner regions. The results suggest anthropogenic pollution can efficiently control cloud glaciation in regional scale by serving as INPs. Key Points: Transition freezing temperature is estimated by analyzing the frequency of mixed‐phase and liquid clouds over China using satellite dataAnthropogenic pollution can cause freezing temperature of clouds up to −9°CAnthropogenic pollutants can be efficient ice nuclei and increase the freezing temperature of mixed‐phase clouds [ABSTRACT FROM AUTHOR]

Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization. [ABSTRACT FROM AUTHOR]

Axial estimation is an important task for detecting non-cooperative space targets in orbit, with inverse synthetic aperture radar (ISAR) imaging serving as a fundamental approach to facilitate this process. However, most of the existing axial estimation methods usually rely on manually extracting and matching features of key corner points or linear structures in the images, which may result in a degradation in estimation accuracy. To address these issues, this paper proposes an axial estimation method for spaceborne targets via ISAR image sequences based on a regression network. Firstly, taking the ALOS satellite as an example, its Computer-Aided Design (CAD) model is constructed through a prior analysis of its structural features. Subsequently, target echoes are generated using electromagnetic simulation software, followed by imaging processing, analysis of imaging characteristics, and the determination of axial labels. Finally, in contrast to traditional classification approaches, this study introduces a straightforward yet effective regression network specifically designed for ISAR image sequences. This network transforms the classification loss into a loss function constrained by the minimum mean square error, which can be utilized to adaptively perform the feature extraction and estimation of axial parameters. The effectiveness of the proposed method is validated through both electromagnetic simulations and experimental data. [ABSTRACT FROM AUTHOR]

The continuous miniaturization of semiconductor devices has increased the demand for advanced process control technologies. This process requires real-time measurement systems to monitor manufacturing parameters to ensure efficiency and high quality. This study introduces a novel optical module that uses a spatial light modulator to extract key-point intensity distributions from diffraction images in scatterometry. The efficacy of this method is demonstrated on a grating target with a pitch of 855 nm using a feature extraction algorithm that identifies key point locations based on calculated diffraction images. A particularly designed off-axis extraction pattern facilitates the acquisition of key-point intensity distributions. Moreover, incorporating a cylindrical lens into the optical setup reduces the image feature dimensionality, thereby decreasing the data storage space and enabling the output in a streamlined vector format conducive to further analysis. Experimental data on the development of this scatterometry-based optical module and the subsequent validation of the key-point extraction method indicate a maximum mean absolute error of 0.0080 and a cosine similarity consistently above 0.9999. This study integrates image analysis and measurement techniques by optics, providing a more efficient pathway for key-point extraction in diffraction images, offering the potential for improving real-time process monitoring in the semiconductor manufacturing industry. [ABSTRACT FROM AUTHOR]

The goal of this research is to assess and guide the development of next-generation synthetic aperture radar (SAR) satellites, optimize their performance, and present the requirements related to the design parameters. In the current era, characterized by the rapid advancement of SAR technologies, the challenge of designing compact and efficient satellites is more relevant than ever. The present research provides a comprehensive analysis of design parameters for microsatellite applications, including altitude, incidence angle, operating frequency, antenna sizing, and transmitting power. The complex relationships between these parameters and their combined impact on SAR system performance and satellite dimensions are demonstrated through various scenarios. Special attention is given to balancing the SAR antenna area and the transmitting power needs, which are primary constraints in SAR microsatellite design. A detailed comparative study is presented, highlighting how each design decision affects the overall functionality and performance. Modern SAR satellites with masses under 150 kg can operate with approximately 1 kW of transmitting power and a 10 m2 SAR antenna area. The present results analyze and validate the key parameters related to these satellites, coping with the challenging trade-offs through optimization. Furthermore, this study aims to guide future innovative spaceborne SAR system design, highlighting the potential of optimization techniques in advancing spaceborne SAR technology. [ABSTRACT FROM AUTHOR]

This study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each 1°×1° $1{}^{\circ}\times 1{}^{\circ}$ DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the 50th $5{0}^{th}$ percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation. Plain Language Summary: We compare the accuracy of three global maps of Earth's surface topography that were derived from spaceborne radar missions, namely, SRTM, NASADEM, and GLO30. The accuracy is evaluated using spaceborne Lidar data sets from three missions: ICESAT, ICESAT‐2, and GEDI. Lidars are typically more accurate in estimating elevation of the ground, but their measurements are like sparse point measurements. We evaluated the radar‐derived DEMs separately over bare ground regions and forests. We found that NASADEM is a significant improvement over the SRTM DEM with an error of 1.5 m in bare ground areas. But the GLO30 DEM is more accurate with error of only 0.55 m. The presence of forests causes radar‐derived elevation to be higher than the ground, somewhere mid‐canopy. Key Points: NASA's Digital Elevation Model is better than the Shuttle Radar Topography Mission Digital Elevation Model, but GLO‐30 is the most accurateAll radar‐derived elevations exhibit a strong correlation with forest canopy height with bias similar to the Lidar's relative height 50The Ice, Cloud and land Elevation Satellite‐2 Lidar measurements provide a better Lidar alternative for detecting the ground below forest [ABSTRACT FROM AUTHOR]

In recent years, Synthetic Aperture Radar Interferometry (InSAR) has become widely utilized for slow‐moving landslide monitoring due to its high resolution, accuracy, and extensive coverage. By integrating data from various orbits/platforms and monitoring sources, one‐dimensional (1‐D) line‐of‐sight (LOS) InSAR measurements can be explored to infer three‐dimensional (3‐D) movements. However, inconsistencies in observation times among different orbits and monitoring sources pose challenges in accurately capturing dynamic 3‐D movements over time (referred to as 4‐D). In this study, we propose a novel method, termed KFI‐4D that incorporates spatiotemporal constraints into the traditional Kalman filter. This enhancement transforms the underdetermined problem of 4‐D movement acquisition into a dynamic parameter estimation problem, enabling precise monitoring of landslide movements. The KFI‐4D method was evaluated using both synthetic data sets and real data from the Hooskanaden landslide, demonstrating an improvement exceeding 50% in root mean square errors (RMSEs) compared to conventional methods. Additionally, the high‐resolution characteristics of InSAR‐derived 4‐D movements allow for the analysis of strain invariants, providing insights into block interactions and landslide dynamics. Our findings reveal that strain invariants effectively indicate the distribution and activity of landslide blocks and slip surfaces as well as their response to triggers. Notably, abnormal signals identified in strain invariants prior to the catastrophic event at Hooskanaden suggest potential for early warning of landslides. The future integration of data from advanced satellites, such as NISAR, ALOS4 PALSAR3, and Sentinel‐1C, is expected to further enhance the KFI‐4D method's capabilities, improving temporal resolution and early warning potential for landslide monitoring. Plain Language Summary: InSAR, a radar technology, is a powerful tool for monitoring landslides, providing detailed and accurate displacement data. Combining data from multiple sources offers a more complete picture of 3‐D displacements. However, satellites observe at different times, making it challenging to get an accurate picture of slow‐moving landslide movements over time (4‐D monitoring). To address this, we developed KFI‐4D, a new method that enhances the traditional Kalman filter by adding space and time constraints and improving the tracking of landslide movements in 4‐D. Testing with both simulated data and real data from the Hooskanaden landslide showed that KFI‐4D improved accuracy by over 50% compared to the traditional method. Our method also helps to study how different parts of a landslide interact. We found that analyzing these interactions can provide important insights into the behavior and triggers of landslides. Notably, unusual signals detected before the major event of the Hooskanaden landslide suggest that our method could be useful for early warning systems. Overall, KFI‐4D could make landslide monitoring more accurate and reliable, allowing for more data from different satellites and enhancing the monitoring process. Future satellites are expected to further improve this method, making it even more effective for early warning of landslides. Key Points: A novel method is proposed for estimating 4‐D landslide movements using spaceborne synthetic aperture radar interferometric observations4‐D movements of the Hooskanaden landslide are derived to infer landslide's strain invariants, block count, and slip surface distributionPrior to the catastrophic event in February 2019, an anomalous strain invariant signal of the Hooskanaden landslide was detected [ABSTRACT FROM AUTHOR]

The accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a ∼ ${\sim} $900 km2 $\mathrm{k}{\mathrm{m}}^{\mathrm{2}}$ region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship. Plain Language Summary: Surface deformation estimates derived from spaceborne imaging radar data are often impacted by weather conditions and surface vegetation changes. While it is common to select high‐quality data based on the time separation between two image passes, we designed a new data selection strategy based on measurable phase artifacts. We applied the new method to two vegetated regions that experienced land subsidence due to agricultural groundwater pumping (Tulare Basin, California) or oil and gas production (Eagle Ford region, Texas). In both cases, we show that surface deformation estimates can vary substantially depending on the choice of data subsets. Our strategy better mitigates different InSAR measurement noise terms at the same time. We showed that the observed subsidence in the Eagle Ford region is linearly proportional to the volume of produced oil and water from unconventional oil and gas resources exploitation. Furthermore, ongoing production activities have led to an increase in human‐induced earthquakes. Based on these findings, accurate surface deformation derived from InSAR data is now achievable in densely vegetated regions and can play an important role in future induced seismicity studies. Key Points: InSAR phase coherence does not always decrease with temporal baselinesChoosing interferograms based on phase unwrapping errors rather than temporal baselines better mitigates InSAR measurement noiseThe improved InSAR analysis strategy reveals up to 11.9 cm of LOS deformation signals related to oil and gas operations over the Eagle Ford [ABSTRACT FROM AUTHOR]

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The current emphasis in the advancement of space-based synthetic-aperture radar (SAR) is on lightweight payloads under 100 kg with resolutions surpassing 1 m. This focus is directed toward meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) satellite, launched on 23 January 2024 and accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80 kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system's performance. The SAR payload provides various operating modes like slide-spot, strip, Scan 1, Scan 2, and others, with a maximum achievable resolution exceeding 1 m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more. [ABSTRACT FROM AUTHOR]

This paper presents a simple and streamlined compensation technique for improving the quality of synthetic aperture radar (SAR) images based on the Range Doppler Algorithm (RDA). Incorrect Doppler estimation in the space orbit, caused by unexpected radar motion errors, orbit mismatches, and other factors, can significantly degrade SAR image quality. These inaccuracies result in mismatches between the azimuth-matched filter and the received Doppler chirp signal. To address this issue, we propose a Doppler estimation method that leverages the Fractional Fourier Transform (FrFT) and cross-correlation techniques. The received signals are compared with the azimuth-matched filter based on the rotation angle in the FrFT domain, and the Doppler centroid is adjusted to achieve the optimal alignment. This process ensures high correlation values and enhanced resolution in the final SAR image. The efficacy of the proposed technique is validated through experiments using real spaceborne SAR data from the practical satellite. The results demonstrate significant improvements in image quality and resolution compared to conventional algorithms, highlighting the advantages of our approach for various remote sensing applications. [ABSTRACT FROM AUTHOR]

This article presents a new neural network-based model called SARCoorP-RBFNet for localizing synthetic aperture radar (SAR) images. The model addresses the limitations of traditional models in complex scenarios and has been tested on SAR images in China. It utilizes pairs of geodetic and image space coordinate points as input and output, respectively, and incorporates Gaussian functions as radial basis functions (RBFs). The model is trained using the generalized inverse matrix method and evaluated using the root mean square error function. The study found that the model performs well on single-scene imagery but has reduced accuracy with different-resolution images of the same area. However, it still achieves high fitting and generalization capabilities when trained with mixed samples from different regions. The study suggests that the proposed model can be a viable alternative to traditional geometric processing models for SAR images. [Extracted from the article]

A new technology for remote measurements of marine surface pressure has been proposed, employing a V‐band differential absorption radar and a radiometric temperature sounder to calculate the total column atmospheric mass. Observing System Simulation Experiments (OSSEs) are performed to evaluate the potential impact of Spaceborne Marine Surface Pressure (SMSP) on Numerical Weather Prediction. These experiments build on prior efforts (Privé, McLinden, et al., 2023, https://doi.org/10.16993/tellusa.3254), but with an updated version of the OSSE framework and with more sophisticated simulation of the SMSP observations and a longer experiment period. Several different instrument configurations are compared, including both scanning and non‐scanning orbits. SMSP impacts are calculated for analysis quality and forecast skill, and a forecast sensitivity observation impact tool is employed to place SMSP observations in context with the global observing network. The effects of rain contamination on observation quality are explored. Different magnitudes of simulated SMSP observation error are tested in the context of data assimilation to show the range of potential behaviors. Overall, SMSP observations are found to be most beneficial in the southern hemisphere extratropics, with statistically significant forecast improvements for the first 72 hr of the forecast. A constellation of four non‐scanning SMSP satellites is found to outperform a single scanning instrument with a 250 km wide swath. Plain Language Summary: Measurements of surface pressure are very useful for weather prediction because they tell us about phenomena such as winter storms, as well as large scale atmospheric circulation patterns. Over the ocean, the only surface pressure measurements currently come from buoys and some ships, but these measurements are very far apart. Recently, some new radar technology is under development that would allow ocean surface pressures to be remotely retrieved from aircraft or satellites. In this work, these proposed new radar surface pressure measurements are simulated and tested in a weather forecast model. Several different configurations of an orbiting spaceborne radar are simulated and compared to find out how much the radar data would improve weather forecasts. The results show that a constellation of four smaller radar satellites has the most improvement on weather prediction. The effect of instrument noise was also tested, and it was found that the radar was helpful for weather forecasts for a wide range of instrument performance. Key Points: The greatest observation impacts are found in the southern hemisphere extratropics, with neutral impacts in the tropicsThe most beneficial observation impacts occur with a four‐satellite platform of non‐scanning instrumentsBeneficial impacts are found for a wide range of observation error magnitudes [ABSTRACT FROM AUTHOR]

We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis. Significance Statement: The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis. [ABSTRACT FROM AUTHOR]

Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment of actual forest structural complexity, we derive a near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications. The GEDI Waveform Structural Complexity Index data product, derived from our analyses, provides researchers and conservationists with a single, easily interpretable metric by combining various aspects of canopy structure. Forest structural complexity plays a crucial role in ecosystem functioning, influencing factors like light, nutrient cycling, and biodiversity. Here, a global map of 3D canopy complexity modeled from spaceborne lidar reveals that highly complex forests are concentrated in the tropics, with significant variations observed across biomes. [ABSTRACT FROM AUTHOR]

Azimuth resolution and swath width are two crucial parameters in spaceborne synthetic aperture radar (SAR) systems. However, it is difficult for conventional spaceborne SAR to simultaneously achieve high-resolution wide-swath (HRWS) due to the minimum antenna area constraint. To mitigate this limitation, some representative HRWS SAR imaging techniques have been investigated, e.g., the azimuth multichannel technique, digital beamforming (DBF) technique, and pulse repetition interval (PRI) variation technique. This paper focus on a comprehensive review of the three techniques with respect to their latest developments. First, some key parameters of HRWS SAR are presented and analyzed to help the reader establish the general concept of SAR. Second, three techniques are introduced in detail, roughly following a simple-to-complex approach, i.e., start with the basic concept, then move to the core principles and classic technical details, and finally report the technical challenges and corresponding solutions. Third, some in-depth insights on the comparison among the three techniques are given. The purpose of this paper is to provide a review and brief perspective on the development of HRWS SAR. [ABSTRACT FROM AUTHOR]

The spaceborne Inverse Synthetic Aperture Radar (ISAR) has garnered significant attention due to its extensive observation range and robust anti‐attack capabilities. Consequently, the ISAR imaging research of air targets based on a spaceborne platform has crucial application value. However, unlike the traditional ground‐based radar system, the spaceborne platform moves along its own orbit while observing the air target, and the received signal energy is weakened due to the extended observation distance. Therefore, it is important to optimise the existing ISAR imaging geometry models and motion compensation algorithms. The authors first construct a geometric model of spaceborne ISAR imaging for air targets. Aiming at the problem of low signal‐to‐noise ratio (SNR), a novel translational motion compensation algorithm based on motion parameter estimation is proposed. The algorithm compensates for both distance migration and Doppler migration caused by the first‐order and second‐order motion components of relative motion, respectively. Finally, simulation and semi‐physical simulation results validate the effectiveness and superiority of the proposed algorithm under different SNR and motion conditions. [ABSTRACT FROM AUTHOR]

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Bistatic and multistatic SAR technology, with its multi-dimensional, ultra-wide swath, and high-resolution advantages, is widely used in earth observation, military reconnaissance, deep space exploration, and other fields. The LuTan-1 (LT-1) mission employs two full-polarimetric L-band SAR satellites for the BiSAR system. The bistatic mode introduces phase errors in echo reception paths due to different transmission links, making echo compensation a key factor in ensuring BiSAR performance. This paper proposes a novel bistatic internal calibration strategy that combines ground temperature compensation, in-orbit internal calibration, and pulsed alternate synchronization to achieve echo compensation. Prior to launch, temperature compensation data for the internal calibration system are obtained via temperature experiments. During in-orbit operation, calibration data are acquired by executing the internal calibration pulse sequence and noninterrupted pulsed alternate synchronization. In ground processing, echo compensation is completed based on the above two parts of calibration data. A comprehensive analysis of the entire calibration chain reveals a temperature compensation accuracy of 0.10 dB/1.38°. Additionally, a ground verification system is established to conduct BiSAR experiments, achieving a phase synchronization accuracy of 0.16°. Furthermore, the in-orbit test obtained DSM products with an average error of 1.3 m. This strategy provides a valuable reference for future spaceborne bistatic and multistatic SAR systems. [ABSTRACT FROM AUTHOR]

Using spaceborne lidar and radar observations, this study identifies deep convection systems (DCSs), including deep convection cores (DCCs) and anvils, over the Tibetan Plateau (TP) and tropical Indian Ocean (TO) and finds that DCSs over the TP are less frequent, exhibiting narrower and thinner DCCs and anvils compared to those over the TO. The thinner DCCs over the TP exert weaker radiative cooling effects at the top of atmosphere (TOA) compared to the TO. But, the shortwave TOA cloud radiative effect (CRE) of TP anvils is stronger than that of the TO possibly due to more densely packed cloud tops over the TP. It results in a stronger TOA CRE of DCSs over the TP than that of TO. In particular, the longwave CRE of DCSs over the TP is notably greater at surface and low-level atmosphere due to the distinct lower temperature and less water vapour. The width of DCSs shows a positive correlation with wind shear and atmospheric instability, and the underlying mechanisms are discussed. We also find that the impact of aerosols on cloud top heights and precipitation displays significant discrepancies between the two regions. It is because that the aerosol invigoration effect is less efficient on the TP DCSs, mainly attributed to the significantly colder cloud base. Due to competition between invigoration and direct/semi-direct radiative effects of aerosols, the correlation between precipitation and aerosols over the TP is not obvious. However, precipitation in the TO experiences invigoration followed by suppression with increasing aerosols, due to the dominance of aerosol radiative effects and enhancement entrainment under polluted conditions. [ABSTRACT FROM AUTHOR]

Spaceborne radar remote sensing of the earth system is essential to study natural and man-made changes in the ecosystem, water and energy cycles, weather and air quality, sea level, and surface dynamics. A major challenge with current approaches is the lack of broad spectrum tunability due to narrow band microwave electronics, that limit systems to specific science variable retrievals. This results in a significant limitation in studying dynamic coupled earth system processes such as surface and subsurface hydrology from a single compact instrument, where co-located broad spectrum radar remote sensing is needed to sense multiple variables simultaneously or over a short duration. Rydberg atomic sensors are highly sensitive broad-spectrum quantum detectors that can be dynamically tuned to cover micro-to-millimeter waves with no requirement for RF band-specific electronics. Rydberg atomic sensors can use existing transmitted signals such as from navigation and communication satellites to enable remote sensing. We demonstrate remote sensing of soil moisture, an important earth system variable, via ground-based radar reflectometry with Rydberg atomic systems. To do this, we sensitize the atoms to XM satellite radio signals and use signal correlations to demonstrate use of these satellite signals for remote sensing of soil moisture. [ABSTRACT FROM AUTHOR]

Understanding the macro and micro characteristics of precipitation in landfall typhoons is crucial to predicting the path and intensity of tropical cyclones by using numerical models. In this study, we use observations from the Global Precipitation Measurement Mission to analyze the microphysics of convection and stratiform precipitation during the landfall of Typhoon Lekima. The statistical results show that the correlation coefficient of the reflectivity factors of the spaceborne and ground-based radars is 0.77 and that the water content detected by the 18.7 GHz low-frequency vertical channel is positively correlated with the intense-precipitation rate. The storm top height is generally consistent with the location of heavy precipitation. The average near-surface precipitation rate and liquid water content of convective precipitation are higher than those of stratiform precipitation. The average mass-weighted raindrop diameter and particle number concentration of convective (stratiform) precipitation at a distance of 2 km above ground level are 1.52 mm (1.29 mm) and 39.52 (36.44, in decibel scale), respectively. Below the melting layer, there is a significant increase in average particle diameter, indicating that the collision aggregation growth process of raindrops is dominant. These results are potentially helpful in validating and improving microphysics parameterization in numerical models. [ABSTRACT FROM AUTHOR]

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Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

An improved microwave gaseous absorption scheme based on statistical regression is proposed in this study. In the new scheme, Monochromatic Radiative Transfer Model (MonoRTM) replaces the Millimeter‐wave Propagation Model (MPM) to train the new scheme and the effect of real Spectral Response Functions is included in the training process. After the replacement, results of the new scheme are closer to observations from Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite in low level channels while MPM has some advantages in upper level channels. Introducing ozone absorption can cause a systematic bias but results in small standard deviations in channels with frequency 183 ± 1.8 GHz and 183 ± 1 GHz. In addition, the new scheme updates the vertical interpolation of water vapor and optimizes the vertical distribution of Planck function. These updates can reduce biases caused by vertical interpolation, especially for water vapor absorption channels. The bias associated to vertical interpolation can reach 0.25 K whereas the new scheme can decrease it to 0.003 K. To further validate the accuracy of the new scheme, we apply the new scheme to Advanced Radiative transfer Modeling System and compare simulated results to RTTOV 13.2 under 37 and 137 level (L) atmosphere (atm). Observations from ATMS onboard NOAA‐20 are used as true values. Results show that the new scheme agrees with RTTOV 13.2 well in accuracy and performs even better in upper level channels and water vapor absorption channels. Plain Language Summary: Fast radiative transfer (RT) models play a crucial role in satellite data assimilation as well as remote sensing. Gaseous absorption calculation is a fundamental aspect of fast RT models. With the capability of new‐generation spaceborne microwave instruments to observe spectra beyond 200 GHz, Monochromatic Radiative Transfer Model (MonoRTM) is utilized to train the microwave gaseous absorption scheme. The absorption of oxygen, ozone, water vapor and nitrogen is considered. In addition, the mismatch of vertical coordinates between fast RT models and input profiles requires fast RT models to do vertical interpolation during simulations. Vertical interpolation may introduce notable biases in microwave water vapor absorption channels. In this study, we enhance the vertical interpolation of water vapor and optimize the vertical distribution of the Planck function to mitigate these biases. The accuracy of the new scheme is assessed by comparing it with MonoRTM and satellite observations. Key Points: A new microwave gaseous absorption scheme, trained using Monochromatic Radiative Transfer Model (MonoRTM), is developed and the effect of ozone and nitrogen absorption is evaluatedThe accuracy of the new scheme is enhanced through updates to water vapor interpolation and the vertical distribution of Planck functionAfter application in Advanced Radiative transfer Modeling System (ARMS), the new scheme aligns with RTTOV and gets greater accuracy in upper‐level and water vapor absorption channels [ABSTRACT FROM AUTHOR]

The latitude-altitude structure of the double intertropical convergence zone (DITCZ), its zonal variations, annual cycle and interannual variability over tropical oceans, which were least explored so far, are investigated using the long-term (2006–2018) joint analysis of spaceborne cloud radar and lidar observations that can profile all types of clouds. In contrast to satellite imager observations, this analysis can unambiguously discriminate the DITCZ core and the cirrus outflow emanating from them, which enables the characterization of DITCZ features, including their vertical structure, width and strength even when the equatorial region separating the DITCZ bands has overcast clouds or one of the bands is weak. The role of observed surface wind divergence (SWD) and sea surface temperature (SST) in the genesis and annual migration of the DITCZ bands as well as the ability of vertical wind obtained from reanalysis in capturing the observed DITCZ features are evaluated. The DITCZ occurs over the central Pacific throughout the year, while its occurrence is mainly limited to March-April over the eastern Pacific and the Atlantic and in December over the Indian Ocean. Annual variations of the occurrence, vertical extent, strength and latitudinal positions of the DITCZ bands are mainly driven by the combined effect of SWD and SST variations. The significant differential cloud radiative heating between the equator and the DITCZ bands, derived from the profiles of cloud water content, suggests that it would feedback in enhancing the middle and upper tropospheric wind convergence and downdraft over the equator and contribute to sustain the DITCZ. [ABSTRACT FROM AUTHOR]

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified. [ABSTRACT FROM AUTHOR]

The increase in greenhouse gas concentrations, particularly CO2 , has significant implications for global climate patterns and various aspects of human life. Spaceborne remote sensing satellites play a crucial role in high-resolution monitoring of atmospheric CO2. However, the next generation of greenhouse gas monitoring satellites is expected to face challenges, particularly in terms of computational efficiency in atmospheric CO2 retrieval and analysis. To address these challenges, this study focuses on improving the speed of retrieving the column-averaged dry-air mole fraction of carbon dioxide (XCO2) using spectral data from the Orbiting Carbon Observatory-2 (OCO-2) satellite while still maintaining retrieval accuracy. A novel approach based on neural network (NN) models is proposed to tackle the nonlinear inversion problems associated with XCO2 retrievals. The study employs a data-driven supervised learning method and explores two distinct training strategies. Firstly, training is conducted using experimental data obtained from the inversion of the operational optimization model, which is released as the OCO-2 satellite products. Secondly, training is performed using a simulated dataset generated by an accurate forward calculation model. The inversion performance and prediction performance of the machine learning model for XCO2 are compared, analyzed, and discussed for the observed region over east Asia. The results demonstrate that the model trained on simulated data accurately predicts XCO2 in the target area. Furthermore, when compared to OCO-2 satellite product data, the developed XCO2 retrieval model not only achieves rapid predictions (<1 ms) with good accuracy (1.8 ppm or approximately 0.45 %) but also effectively captures sudden increases in XCO2 plumes near industrial emission sources. The accuracy of the machine learning model retrieval results is validated against reliable data from Total Carbon Column Observing Network (TCCON) sites, demonstrating its ability to effectively capture CO2 seasonal variations and annual growth trends. [ABSTRACT FROM AUTHOR]

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

This paper proposes a point-by-point weighted fusion algorithm based on an improved random sample consensus (RANSAC) and inverse distance weighting to address the issue of low-resolution point cloud data obtained from light detection and ranging (LiDAR) sensors and single technologies. By fusing low-resolution point clouds with higher-resolution point clouds at the data level, the algorithm generates high-resolution point clouds, achieving the super-resolution reconstruction of lidar point clouds. This method effectively reduces noise in the higher-resolution point clouds while preserving the structure of the low-resolution point clouds, ensuring that the semantic information of the generated high-resolution point clouds remains consistent with that of the low-resolution point clouds. Specifically, the algorithm constructs a K-d tree using the low-resolution point cloud to perform a nearest neighbor search, establishing the correspondence between the low-resolution and higher-resolution point clouds. Next, the improved RANSAC algorithm is employed for point cloud alignment, and inverse distance weighting is used for point-by-point weighted fusion, ultimately yielding the high-resolution point cloud. The experimental results demonstrate that the proposed point cloud super-resolution reconstruction method outperforms other methods across various metrics. Notably, it reduces the Chamfer Distance (CD) metric by 0.49 and 0.29 and improves the Precision metric by 7.75% and 4.47%, respectively, compared to two other methods. [ABSTRACT FROM AUTHOR]

We searched the Milky Way Plane along a 6-deg swath for pulses of monochromatic light as faint as 15th mag (V band) using a wide-field telescope equipped with a prism. Pulses with duration less than 1 s that occur more often than once every 10 min would be detected, and pulses arriving less frequently would be detected with proportionally lower probability. A 'difference-image' algorithm revealed 36 monochromatic sources. Subsequent assessment showed all were simply astrophysical objects emitting known atomic emission lines. No unexplainable monochromatic emission, pulsed or continuous, was detected. The detection threshold corresponds to an ∼70 GW laser having a diffraction-limited 10-m aperture 1 kpc away (depending on wavelength). Past all-sky optical and radio-wave surveys revealed thousands of unexpected objects exhibiting extraordinary spectral emission, but none were technological. Hypotheses of our Milky Way Galaxy teeming with advanced life must be demoted. [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments. [ABSTRACT FROM AUTHOR]

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed. [ABSTRACT FROM AUTHOR]

The spaceborne high-resolution and wide-swath synthetic aperture radar (HRWS-SAR) system combined with the ground moving target indication (GMTI) mode provides a promising prospect in the realization of wide-area target surveying and high-resolution target imaging. In this paper, a system design method is proposed for an HRWS-SAR-GMTI system with ideal reconstruction configuration. In the proposed method, the whole azimuth receiving channels are uniformly divided into multiple groups, where HRWS-SAR imaging is implemented in each sub-group and then GMTI processing is performed based on the reconstructed SAR images. Then, an optimal candidate PRF is properly selected with respect to the optimal reconstruction configuration. After that, the digital beam forming scanning on receive (DBF-SCORE) technique is applied to further enlarge the range swath and improve the noise equivalent scattering coefficient (NESZ). Based on the predesigned system, HRWS-SAR image-based GMTI processing can finally be accomplished. The effectiveness of the proposed method is validated by simulated experiments. [ABSTRACT FROM AUTHOR]

Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combination of Sentinel-2 multispectral imagery, 1D convolutional neural network (CNN), artificial neural network (ANN), and random forest (RF) for upscaling estimated forest height in the Guangxi Gaofeng Forest Farm. First, various environmental (i.e., slope, solar elevation, etc.) and acquisition parameters (i.e., beam type, Solar elevation, etc.) were used to select and optimize the L2A footprint. Second, pseudo-waveforms were simulated from the airborne LiDAR point cloud and were combined with a 1D CNN model to calibrate the L1B waveform data. Third, the forest height extracted from the calibrated L1B waveforms and selected L2A footprints were compared and assessed, utilizing the CHM derived from the airborne LiDAR point cloud. Finally, the forest height data with higher accuracy were combined with Sentinel-2 multispectral imagery for an upscaling estimation of forest height. The results indicate that through optimization using environmental and acquisition parameters, the ground elevation and forest canopy height extracted from the L2A footprint are generally consistent with airborne LiDAR data (ground elevation: R2 = 0.99, RMSE = 4.99 m; canopy height: R2 = 0.42, RMSE = 5.16 m). Through optimizing, ground elevation extraction error was reduced by 45.5% (RMSE), and the canopy height extraction error was reduced by 30.3% (RMSE). After training a 1D CNN model to calibrate the forest height, the forest height information extracted using L1B has a high accuracy (R2 = 0.84, RMSE = 3.13 m). Compared to the optimized L2A data, the RMSE was reduced by 2.03 m. Combining the more accurate L1B forest height data with Sentinel-2 multispectral imagery and using RF and ANN for the upscaled estimation of the forest height, the RF model has the highest accuracy (R2 = 0.64, RMSE = 4.59 m). The results show that the extrapolation and inversion of GEDI, combined with multispectral remote sensing data, serve as effective tools for obtaining forest height distribution on a large scale. [ABSTRACT FROM AUTHOR]

This paper investigates the airborne synthetic aperture radar via outfield experiments based on carrier-free ultra-wideband signals with fifth-order Gaussian pulses with imaging using the backward projection algorithm. Typical blanket jamming is selected in the study, and NAM, NFM, and SFM are used as jamming signals to investigate the backward projection BP imaging effect of airborne synthetic aperture radar when subjected to blanket jamming in a real-world environment. Subsequently, a variational modal decomposition algorithm is proposed, aiming at the anti-jamming processing of airborne synthetic aperture radar. Further, the variational modal decomposition algorithms are compared with the empirical modal decomposition for the anti-jamming effect, which verified the effectiveness and high efficiency of the method. This study provides a new solution for the imaging problem of airborne synthetic aperture radar when subjected to blanket jamming, and a helpful reference is provided for the selection of anti-jamming processing methods. [ABSTRACT FROM AUTHOR]

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements. [ABSTRACT FROM AUTHOR]

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model's target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets. [ABSTRACT FROM AUTHOR]

SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition. [ABSTRACT FROM AUTHOR]

Accurate precipitation characterization relies on the estimation of raindrop size distribution (RDSD) from observations. While various techniques using centimeter-wavelength radars have been proposed for RDSD retrieval, the potential of millimeter-wavelength polarimetric radars, offering enhanced spatial and temporal resolution while capturing light to moderate rain, remains unexplored. This study focuses on retrieving the mass-weighted mean diameter Dm using a dual-frequency cloud radar. Since the differential reflectivity Zdr is ineffective for Dm retrieval at 94 GHz, and simulations demonstrate a strong dependence of the differential backscatter phase δco on Dm, the estimation of δco takes precedence in this paper. Notably, δco remains unaffected by attenuation and polarimetric calibration. Addressing the initial requirement of disentangling backscattering and propagation effects at millimeter wavelength, an automatic algorithm is proposed to detect Rayleigh plateaus in the spectral domain. Subsequently, a methodology for estimating δco and its associated error is presented. Leveraging simulation results, confidence intervals for Dm that align with δco confidence intervals are retrieved. The assessment of Dm and its confidence interval at 35 and 94 GHz is conducted employing disdrometer-derived Dm. The results demonstrate a comprehensive concordance within a margin of 0.2 mm, underscoring the cloud radar's efficacy in delineating nuanced variations in the raindrop mean diameter versus altitude. The validation process encounters difficulties for Dm below 1 mm, as the disdrometer-derived Dm may exhibit an overestimation, while the cloud-radar-derived Dm may exhibit an underestimation. The combination of 35 and 94 GHz serves to diminish the confidence interval associated with the retrieved Dm. [ABSTRACT FROM AUTHOR]

This paper examines the response of the ionosphere to the impact of two moderate geomagnetic storms observed on January 17 and 26–27, 2013, under conditions of strong sudden stratospheric warming. The study uses data from ground-based ionosonde measurements at the Alma-Ata ionospheric station (43.25 N, 76.92 E) combined with optical observation data (The Spectral Airglow Temperature Imager (SATI)). Ionosonde data showed that the geomagnetic storms under consideration do not generate ionospheric storms but demonstrate some unusual types of diurnal foF2 variations with large (up to 60%) deviations in foF2 from median values observed during the night/morning periods on 13–15 and 20–23 January, which do not have any relation to solar or geomagnetic activity. Wave-like disturbances in ΔfoF2, Δh'F, and daily averaged foF2 values with a quasi-period of 5–8 days and peak-to-peak amplitude from about 1 MHz to 2 MHz (~from 20% to ~40%) and ~40 km are observed during the period 9–28 January, after registration of the occurrence of the major SSW event on 6–7 January. The observed variations in the OH emission rate are found to be quite similar to those observed in the ionospheric parameters that assume a community of processes in the stratosphere/mesosphere/ionosphere system. The study shows that the F region of the ionosphere is influenced by processes in the lower ionosphere, in this case by processes associated with sudden stratospheric warming SSW-2013, which led to modification of the structure of the ionosphere and compensation of processes associated with the development of the ionospheric storms. [ABSTRACT FROM AUTHOR]

Terrain feature points, such as peaks, pits, and saddles, represent the macro-structure of the landform. Conventional techniques for extracting these points from digital elevation models (DEMs) often grapple with issues of inaccuracy, omission and redundancy, largely due to the problematic necessity of setting threshold values. This paper proposes an innovative approach for the automatic detection of terrain feature points based on the topological relationships of contours and the inherent constraints of terrain shape characteristics. The study provides a robust mathematical model of terrain feature points and an effective algorithm for their extraction. Comparing with manually reference data, the accuracy metrics including completeness, correctness, and quality of our extracted results demonstrate a high level, significantly surpassing those obtained through existing algorithms. This proposed approach not only avoids the spurious feature points produced by the local window method, but also prevents the omission of valid points and the creation of redundant ones. Moreover, by utilizing the contour interval as its only variable, our approach eliminates the need for various threshold settings, streamlining the extraction process. [ABSTRACT FROM AUTHOR]

Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open‐source Python library featuring a collection of state‐of‐the‐art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy‐to‐use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom‐mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME's potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost‐effective alternatives like bus‐mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies. Plain Language Summary: Magnetometers, tools used to measure magnetic fields, are crucial for space exploration but often face interference from a spacecraft's own systems. This can distort measurements, making it hard to get accurate data. In order to address this, we introduce MAGPRIME, an open‐source software that helps clean up these magnetic field measurements. It is user‐friendly and integrates various techniques to reduce interference in the data. We tested MAGPRIME in two scenarios: one with magnetometers on a long mechanical arm or boom, and another without the boom. The results from these cases show that the use of MAGPRIME can make it easier and cheaper to get reliable magnetic field data, especially for smaller satellites like CubeSats. Key Points: MAGPRIME is an open‐source Python library with advanced interference removal algorithms for magnetic field measurements in space missionsThe MAGPRIME library is designed to be a community‐driven platform for magnetometer signal processing algorithmsMonte Carlo simulation results show UBSS achieves significant interference reduction in both boom and bus‐mounted magnetometer setups [ABSTRACT FROM AUTHOR]

• An innovative two-branch-two-step deep learning model was proposed for the retrieval of POC and PIC from spaceborne lidar, which incorporates physical parameters and measured waveform information, climatologies and anomalies information as input layer in a more seamless and effective manner. • The inclusion of physical parameters significantly enhances both the model's generalizability and its interpretability. • The proposed model demonstrates a commendable performance when benchmarked against in-situ data as well as other machine learning-based models. • The first interannually resolved PIC and POC standing stock estimation by lidar in polar regions are presented. • The implementation of CALIOP in polar regions is crucial in addressing the shortfall inherent in passive measurements. The lidar data estimates the total global PIC standing stock to be 8% higher than MODIS, while the POC standing stock is 17.2% higher, accounting for the conditions during polar winter. POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on b bp measurements obtained through the application of algorithms that presume an empirical linear correlation between b bp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model's generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it's still the "better" product to use, but where there isn't MODIS data, the CALIOP product is extremely useful, especially in the polar regions. [ABSTRACT FROM AUTHOR]