Search Results (45)

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

To unravel hydrological controls on dissolved organic matter (DOM)–microbe interactions in river ecosystems, this study integrated 3D excitation–emission matrix spectroscopy (3D-EEMs), parallel factor analysis (PARAFAC), and 16S rRNA sequencing to characterize seasonal DOM dynamics and microbial assembly in China’s Rushan River Basin. PARAFAC resolved contrasting DOM signatures between dry (four protein-like, two humic-like components) and wet seasons (three protein-like, three humic-like components). Dry-season DOM was dominated by tyrosine-like substances (58.03%), reflecting microbial degradation and phytoplankton activity, while wet-season DOM showed elevated tryptophan-like components (34.38%) and terrestrial fulvic acids (17.14%), which may be related to rain-driven external inputs. The α -diversity of the microbiota is relatively high in the wet season, mainly consisting of Proteobacteria (34.06–68.10%) and Actinobacteriota (9.15–20.76%). In the dry season community, there are Bacteroidota (14.71–38.45%) and Verrucomicrobiota (6.13–14.32%). The structural equation model (SEM) semi-quantified the comprehensive pathways by which microorganisms inhibit unstable proteins and enhance humification. These results reveal the synergistic regulatory role of hydrological seasonality on DOM and microorganisms, and provide a basis for adaptive water quality management. Full article

The Cochin Backwater region in Southern India is one of the most dynamic estuaries, strongly influenced by seasonal river runoff and seawater intrusion. This study explores the relationship between monsoonal rains, salinity, and stable isotopic composition (δ¹⁸O and δ¹³C) to estimate the contribution of freshwater fluxes at different seasonal intervals for the Cochin Backwater (CBW) estuary. Seasonal variations in oxygen isotopes and salinity revealed distinct trends indicative of freshwater–seawater mixing dynamics. The comparison of Local and Global Meteoric Water Lines highlighted the occurrence of enriched isotope values during the Premonsoon season, showing significant evaporation effects. Carbon (C) isotopic analysis in dissolved inorganic matter (δ¹³C_DIC) at 17 stations during the Premonsoon season revealed spatially distinct carbon dynamics zones, influenced by various sources. These characteristic zones were categorized as Zone 1, dominated by seawater, exhibiting heavier δ¹³C_DIC values; Zone 2, showing significant contributions of lighter terrestrial δ¹³C; and Zone 3, reflecting inputs from regional and local paddy fields with a distinct C₃ isotopic signature (−25‰), modified by estuarine productivity. In addition, different advanced machine learning techniques were tested to improve analysis and prediction of seasonal variations in isotopic composition and salinity. Although the data were sufficiently robust for demonstrating the feasibility and advantages of ML in isotopic hydrology, further expansion of the dataset would be essential for improving the accuracy of models, especially for δ¹³C. The combination of these advanced machine learning models not only improved the predictive accuracy of seasonal freshwater fluxes but also provided a robust framework for understanding the estuarine ecosystem and could pave the way for better management and conservation strategies of the CBW estuarine system. Full article

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

This study investigates the linkage between the radar reflectivity slope and raindrop size distribution (DSD) in precipitation with bright bands through coordinated C-band/Ka-band radar and disdrometer observations in southern China. Precipitation is classified into three types based on the reflectivity slope (K-value) below the freezing level, revealing distinct microphysical regimes: Type 1 (K = 0 to −0.9) shows coalescence-dominated growth; Type 2 (|K| > 0.9) shows the balance between coalescence and evaporation/size sorting; and Type 3 (K = 0.9 to 0) demonstrates evaporation/size-sorting effects. Surface DSD analysis demonstrates distinct precipitation characteristics across classification types. Type 3 has the highest frequency of occurrence. A gradual decrease in the mean rain rates is observed from Type 1 to Type 3, with Type 3 exhibiting significantly lower rainfall intensities compared to Type 1. At equivalent rainfall rates, Type 2 exhibits unique microphysical signatures with larger mass-weighted mean diameters (D_m) compared to other types. These differences are due to Type 2 maintaining a high relative humidity above the freezing level (influencing initial D_m at bottom of melting layer) but experiencing limited D_m growth due to a dry warm rain layer and downdrafts. Type 1 shows opposite characteristics—a low initial D_m from the dry upper layers but maximum growth through the moist warm rain layer and updrafts. Type 3 features intermediate humidity throughout the column with updrafts and downdrafts coexisting in the warm rain layer, producing moderate growth. Full article

Rain backscatter corrupts Ku-band scatterometer wind retrieval by mixing with the signatures of the ${\sigma }^{\circ }$ (backscatter measurements) on the sea surface. The measurements are sensitive to rain clouds due to the short wavelength, and the rain-contaminated measurements in a wind vector cell (WVC) deviate from the simulated measurements using the wind geophysical model function (GMF). Therefore, quality control (QC) is essential to guarantee the retrieved winds’ quality and consistency. The normalized maximum likelihood estimator (MLE) residual ($R_n$) is a QC indicator representing the distance between the ${\sigma }^{\circ }$ measurements and the wind GMF; it works locally for one WVC. $J_{OSS}$ is another QC indicator. It is the speed component of the observation cost function, which is sensitive to spatial inconsistencies in the wind field. $R_{n}J$ is a combined indicator, and it takes both local information ($R_n$) and spatial consistency ($J_{OSS}$) into account. This paper focuses on the QC for WindRAD, a dual-frequency (C and Ku band) rotating-fan-beam scatterometer. The $R_n$ and $R_{n}J$ have been established and thoroughly investigated for Ku-band-only and combined C–Ku wind retrieval. An additional 0.4% of WVCs are rejected with $R_{n}J$, as compared to $R_n$ for both Ku-band-only and combined C–Ku wind retrievals. The number of accepted WVCs with high rain rates (>7 mm/h) is reduced by half, and the wind verification with respect to ECMWF winds is generally improved. The C-band measurements are little influenced by rain, so the Ku-based $R_n$ is more effective for the combined C–Ku wind retrieval than the total $R_n$ from both the C and Ku bands. The rejection rate of the combined C–Ku retrievals reduces by about half compared to the Ku-band-only retrieval, with similar wind verification statistics. Therefore, adding the C band into the retrieval suppresses the rain effect, and acceptable QC capabilities can be achieved with fewer rejected winds. Full article

Mesoscale convective systems are effective sources of atmospheric disturbances that can reach ionospheric heights and significantly alter atmospheric and ionospheric conditions. Convective systems can affect the Earth’s atmosphere on a continental scale and up to F-layer heights. Extratropical cyclone “Zyprian” occurred at the beginning of July, 2021 and dominated weather over the whole of Europe. An extensive cold front associated with “Zyprian” moved from the western part to the eastern part of Europe, followed by ground-level convergence and the formation of organized convective thunderstorm systems. Torrential rains in the Czech Republic have caused a great deal of damage and casualties. Storm-related signatures were developed in ground microbarograph measurements of infrasound and gravity waves. Within the stratosphere, a shift of the polar jet stream and increase in specific humidity related to the storm system were observed. At the ionospheric heights, irregular stratification and radio wave reflection plane undulation were observed. An increase in wave-like activity was detected based on ionograms and narrowband very-low-frequency (VLF) data. On directograms and SKYmaps (both products of digisonde measurements), strong and rapid changes in the horizontal plasma motion were recorded. However, no prevailing plasma motion direction was identified within the F-layer. Increased variability within the ionosphere is attributed mainly to the “Zyprian” cyclone as it developed during low geomagnetic activity and stable solar forcing. Full article

In regions with limited precipitation information, like Peru, many studies rely on precipitation data derived from satellite products (SPP) and model reanalysis. These products provide near-real-time information and offer global spatial coverage, making them attractive for various applications. However, it is essential to consider their uncertainties when conducting hydrological simulations, especially in a key region like the Pacific drainage (Pd), where 56% of the Peruvian population resides (including the capital, Lima). This study, for the first time, assessed the performance of two bottom-up Satellite-based Precipitation Products (SPP), GPM + SM2RAIN and SM2RAIN-ASCAT, and one top-down approach SPP, ERA5-Land, for runoff simulation in the Pacific drainage of Peru. Hydrological modeling was conducted on 30 basins distributed across the Pd, which were grouped into 5 regions (I–V, ordered from south to north). The results showed that SM2RAIN-ASCAT performed well in regions I-III-IV, ERA5-Land in region II, and GPM + SM2RAIN in region V. The hydrological model GR4J was tested, and better efficiency criteria were obtained with SM2RAIN-ASCAT and GPM + SM2RAIN when comparing the simulated versus observed streamflows. The hydrological modeling using SM2RAIN-ASCAT and GPM + SM2RAIN demonstrated satisfactory efficiency metrics (KGE > 0.75; NSE > 0.65). Additionally, ten hydrological signatures were quantified to assess the variability of the simulated streamflows in each basin, with metrics such as Mean Flow (Q mean), 5th Quantile Flow (Q5), and 95th Quantile Flow (Q95) showing an overall better performance. Finally, the results of this study demonstrate the reliability of using bottom-up satellite products in Pd basins. Full article

The Metropolitan Area of Barcelona is one of the most densely populated European regions. The hydrological conditions are very extreme, as are those of the Mediterranean Basin, with long drought periods. The management of water systems is one of the priorities, implying the understanding of the whole life cycle of water. One of the worst-known steps in this cycle corresponds to the rainfall events occurring between warm and cold periods, with quasi-tropical precipitation but with little or no lightning activity. This manuscript relies on the analysis of this type of precipitation for 2015–2022 for characterization and modelling to provide the signatures that can help diagnose these events in real time. The comparison of cold convective and warm rain events thorough radar, lightning and numerical weather prediction data has allowed us to find the main differences between both types. Warm rain events are predominant in the region, with more than 70% cases exceeding 10 mm of daily precipitation. The maritime influence is crucial in most of the warm rain episodes. Full article

Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production management system of cropland. However, due to the monsoon climate in southern China, it is cloudy and rainy throughout the year, which makes it difficult to obtain accurate information on rice cultivation based on optical time series images. Conventional image synthesis is prone to omission or redundancy of spectral and temporal features that are potentially important for rice-growth identification, making it difficult to determine the optimal features for high-precision mapping of paddy rice. To address these issues, we develop a method to granulate the effective use interval of classification features by extracting phenological signatures of rice to obtain cost-effective and highly accurate mapping results. Two steps are involved in this method: (1) analyzing the information on various features (spectra, polarization, and seasonal regularity) to identify three key phenological periods throughout the lifespan of paddy rice; (2) identifying the features with the highest class separation between paddy rice, non-paddy crops, and wetlands under different phenological stages; and (3) removing redundant features to retain the optimal feature combinations. Subsequently, the obtained feature sets are used as input data for the random forest classifier. The results showed that the overall accuracy of the identified rice results was 95.44% with F1 scores above 93% for both single- and double-cropping rice. Meanwhile, the correlation coefficient of our mapped rice area compared with the official statistics of rice area at county and district levels was 0.86. In addition, we found that combining Sentinel-1 and Sentinel-2 images for rice recognition was better than using Sentinel-1 or Sentinel-2 alone, and the classification accuracy was improved by 5.82% and 2.39%, which confirms that the synergistic Sentinel-1 and Sentinel-2 data can effectively overcome the problem of missing optical images caused by clouds and rain. Our study demonstrates the potential of distinguishing mixed rice-cropping systems in subtropical regions with fragmented rice-field distribution in a cloudy and rainy environment, and also provides a basis for the rational layout of rice production and improvement of cultivation systems. Full article

The upland lakes (ULs) in Carajás, southeastern Amazonia, have been extensively studied with respect to their high-resolution structural geology, geomorphology, stratigraphy, multielement and isotope geochemistry, palynology and limnology. These studies have generated large multiproxy datasets, which were integrated in this review to explain the formation and evolution of the ULs. These ULs evolved during the Pliocene–Pleistocene periods through several episodes of a subsidence of the lateritic crust (canga) promoted by fault reactivation. The resulting ULs were filled under wet/dry and warm/cool paleoclimatic conditions during the Pleistocene period. The multielement geochemical signature indicates that the detrital sediments of these ULs were predominantly derived from weathered canga and ferruginous soils, while the sedimentary organic matter came from autochthonous (siliceous sponge spicules, algae, macrophytes) and allochthonous (C3/C4 canga and forest plants and freshwater dissolved organic carbon) sources. Modern pollen rain suggests that even small ULs can record both the influence of canga vegetation and forest signals; thus, they can serve as reliable sites to provide a record of vegetation history. The integrated data from the sedimentary cores indicate that the active ULs have never dried up during the last 50 ka cal BP. However, subaerial exposure occurred in filled ULs, such as the Tarzan mountain range during the Last Glacial Maximum (LGM) and the Bocaína and S11 mountain ranges in the mid-Holocene period, due to the drier conditions. Considering the organic proxies, the expansion of C4 plants has been observed in the S11 and Tarzan ULs during dry events. Extensive precipitation of siderite in UL deposits during the LGM indicated drier paleoenvironmental conditions, interrupting the predominantly wet conditions. However, there is no evidence of widespread forest replacement by savanna in the Carajás plateau of southeastern Amazonia during the late Pleistocene and Holocene. Full article