Radar Data Sets Research Articles

Sea State Characterization Using Experimental Synthetic Aperture Radar Raw Data in Two-Dimensions and the Modified Fractal Signature Method

This paper presents a novel method for the characterization of the sea state using a set of raw experimental Synthetic Aperture Radar (SAR) data in two dimensions, i.e., in the “fast time” and “slow time” directions (as explained in the text) and the so-called “Modified Fractal Signature” (MFS) method. That is, experimental SAR radar signatures in the above two dimensions (i.e., “raw data” in the time domain) were provided to our research group by the Norwegian Institute of Defence (FFI Institute), Norway, which we processed and analyzed using the MFS method in a novel way, as presented in detail in this paper. The numerical results obtained here show an easy categorization of the sea surface as “calm sea” or “turbulent sea”, thus establishing a very promising technique for the characterization of sea state in real time, as described in detail in the text.

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Land cover is the most critical information required for land management and planning because human interference on land can be easily detected through it. However, mapping land cover utilizing optical remote sensing is not easy due to the acute shortage of cloud-free images. Google Earth Engine (GEE) is an efficient and effective tool for huge land cover analysis by providing access to large volumes of imagery available within a few days after acquisition in one consolidated system. This article demonstrates the use of Sentinel-1 datasets to create a land cover map of Pusad, Maharashtra using the GEE platform. Sentinel-1 provides Synthetic Aperture Radar (SAR) datasets that have a temporally dense and high spatial resolution, which is renowned for its cloud penetration characteristics and round-the-year observations irrespective of the weather. VV and VH polarization sentinel-1 time series data were automatically classified using a support vector machine (SVM) and Random Forest (RF) machine learning algorithms. Overall accuracies (OA), ranging from 82.3% to 90%, were obtained depending on polarization and methodology used. RF algorithm with VV polarization dataset stands better in comparison to SVM achieving OA of 90% and Kappa coefficient of 0.86. The highest user accuracy was obtained for the water class with both classifiers.

Sub-Hourly to Daily Rainfall Intensity-Duration-Frequency Estimation Using Stochastic Storm Transposition and Discontinuous Radar Data

Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial resolution. Still, the observation periods are short relative to established rain gauge networks. A stochastic framework, “stochastic storm transposition” shows great promise in recreating rainfall statistics from radar rainfall products, similar to rain gauge-derived statistics. This study estimates intensity–duration–frequency (IDF) relationships at both point and urban catchment scales. We use the stochastic storm transposition framework and a single high-resolution, 17-year long (however, discontinuous), radar rainfall dataset. The IDF relations are directly compared to rain gauge statistics with more than 40 years of observation, and rainfall extremes derived from the original, and untransposed, radar dataset. An overall agreement is discovered, however, with some discrepancies in short-duration storms due to scaling errors between gauge and radar.

1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset.

Quantification of NSSL Warn-on-Forecast System Accuracy by Storm Age Using Object-Based Verification

Abstract The National Severe Storm Laboratory’s Warn-on-Forecast System (WoFS) is a convection-allowing ensemble with rapidly cycled data assimilation (DA) of various satellite and radar datasets designed for prediction at 0–6-h lead time of hazardous weather. With the focus on short lead times, WoFS predictive accuracy is strongly dependent on its ability to accurately initialize and depict the evolution of ongoing storms. Since it takes multiple DA cycles to fully “spin up” ongoing storms, predictive skill is likely a function of storm age at the time of model initialization, meaning that older storms that have been through several DA cycles will be forecast with greater accuracy than newer storms that initiate just before model initialization or at any point after. To quantify this relationship, we apply an object-based spatial tracking and verification approach to map differences in the probability of detection (POD), in space–time, of predicted storm objects from WoFS with respect to Multi-Radar Multi-Sensor (MRMS) reflectivity objects. Object-tracking/matching statistics are computed for all suitable and available WoFS cases from 2017 to 2021. Our results indicate sharply increasing POD with increasing storm age for lead times within 3 h. PODs were about 0.3 for storm objects that emerge 2–3 h after model initialization, while for storm objects that were at least an hour old at the time of model initialization by DA, PODs ranged from around 0.7 to 0.9 depending on the lead time. These results should aid in forecaster interpretation of WoFS, as well as guide WoFS developers on improving the model and DA system. Significance Statement The Warn-on-Forecast System (WoFS) is a collection of weather models designed to predict individual thunderstorms. Before the models can predict storms, they must ingest radar and satellite observations to put existing storms into the models. Because storms develop at different times, more observations will exist for some storms in the model domain than others, which results in WoFS forecasts with different accuracy for different storms. This paper estimates the differences in accuracy for storms that have existed for a long time and those that have not by tracking observed and predicted storms. We find that the likelihood of WoFS accurately predicting a thunderstorm nearly doubles if the storm has existed for over an hour prior to the forecast. Understanding this relationship between storm age and forecast accuracy will help forecasters better use WoFS predictions and guide future research to improve WoFS forecasts.

Human Motion Recognition With Spatial-Temporal-ConvLSTM Network Using Dynamic Range-Doppler Frames Based on Portable FMCW Radar

As a noncontact and noninvasive sensor, radar offers attractive modality for human motion recognition in many fields, such as surveillance, smart homes, and assisted living. As a dynamic process, a comprehensive multidomain analysis in stream is crucial for human motion recognition. In this article, a novel spatial–temporal convolution long short-term memory (ST-ConvLSTM) network using dynamic range-Doppler frames (DRDF) based on portable frequency-modulated continuous-wave (FMCW) radar is proposed to address this problem. First, DRDF is introduced to characterize different human motions in time, range, Doppler, and radar cross section (RCS) domains with a time-sequenced range-Doppler maps. Then, ConvLSTM cell is investigated to extract continuous 2-D image features by combining both convolution operation and LSTM cell. Furthermore, a spatial attention module is utilized to emphasize the spatial distribution of human motion in the range-Doppler domain, while a temporal attention module is designed to obtain optimal temporal weights considering behavior continuity. Finally, an extensive radar dataset from 16 volunteers demonstrated its feasibility and superiority in the classification of six typical daily human motions. The contributions of attention modules and its robustness facing individual diversity are also investigated and discussed.

A Method for Correcting Staggered Pulse Repetition Time (PRT) and Dual Pulse Repetition Frequency (PRF) Processor Errors in Research Radar Datasets

Abstract Mobile weather radars at high frequencies (C, X, K, and W bands) often collect data using staggered pulse repetition time (PRT) or dual pulse repetition frequency (PRF) modes to extend the effective Nyquist velocity and mitigate velocity aliasing while maintaining a useful maximum unambiguous range. These processing modes produce widely dispersed “processor” dealiasing errors in radial velocity estimates. The errors can also occur in clusters in high shear areas. Removing these errors prior to quantitative analysis requires tedious manual editing and often produces “holes” or regions of missing data in high signal-to-noise areas. Here, data from three mobile weather radars were used to show that the staggered PRT errors are related to a summation of the two Nyquist velocities associated with each of the PRTs. Using observations taken during a mature mesoscale convective system, a landfalling tropical cyclone, and a tornadic supercell storm, an algorithm to automatically identify and correct staggered PRT processor errors has been developed and tested. The algorithm creates a smooth profile of Doppler velocities using a Savitzky–Golay filter independently in radius and azimuth and then combined. Errors are easily identified by comparing the velocity at each range gate to its smoothed counterpart and corrected based on specific error characteristics. The method improves past dual PRF correction methods that were less successful at correcting “grouped” errors. Given the success of the technique across low, moderate, and high radial shear regimes, the new method should improve research radar analyses by affording the ability to retain as much data as possible rather than manually or objectively removing erroneous velocities.

Yarkovsky effect detection from ground-based astrometric data for near-Earth asteroid (469219) Kamo’oalewa

Context. The Yarkovsky effect is a weak non-gravitational force but may significantly affect sub-kilometre-sized near-Earth asteroids. Yarkovsky-related drift may be detected, in principle, from astrometric or radar datasets of sufficient duration. To date, the asteroid Kamo’oalewa, the most stable of Earth’s quasi-satellites, has an ~18 yr-long arc of ground-based optical astrometry. These data provide an opportunity to detect the Yarkovsky effect acting on the asteroid Kamo’oalewa. Aims. We determined the Yarkovsky-related drift of asteroid Kamo’oalewa from ~18 yr of ground-based optical astrometry. Furthermore, we investigated the influence of the Yarkovsky effect on the orbital evolution of asteroid Kamo’oalewa based on this estimated value, and evaluated the potential improvements in the detection of non-gravitational accelerations (Yarkovsky effect and solar radiation pressure) for the asteroid Kamo’oalewa that could be provided by the future Chinese small-body exploration mission, Tianwen-2. Methods. The Yarkovsky-related drift of asteroid Kamo’oalewa was detected from the orbital fitting of the astrometry measurements. We checked the Yarkovsky effect detection based on both the orbit fitting results and the physical mechanisms of the Yarkovsky effect. Results. We report for the first time the detection of the Yarkovsky effect acting on asteroid Kamo’oalewa based on ~18 yr of ground-based optical astrometry data. The estimated semi-major axis drift is (−6.155 ± 1.758) × 10−3 au Myr−1. In addition, our numerical simulation shows that the Yarkovsky effect has almost no influence on the short-term orbital evolution of the asteroid Kamo’oalewa, but does have a long-term influence, by delaying the entry of the object into the Earth co-orbital region and accelerating its exit from this region, with a more significant signature on the exit than on the entry. In the context of spacecraft tracking data, the Tianwen-2 mission will improve both non-gravitational accelerations (Yarkovsky effect and solar radiation pressure) and predictions of its future ephemeris.

Analysis of polarimetric coherence data and derived roll-variant parameters of Sentinel-1 data for discriminating mangrove types in Pichavaram wetlands

The major focus of the present study is the polarimetric coherence, which corresponds to the correlation between two polarized channels of Synthetic Aperture Radar (SAR) datasets. The capability of the polarimetric coherence to detect target scatters utilizing the entire rotation domain rather than the fixed radar line of sight is the fundamental concept applied in this study. The present study attempts to test the potential of these concepts in mangrove types discrimination using the Sentinel-1 dual polarimetric (VV and VH) dataset. The study targets to characterize the Rhizophora and Avicennia mangrove types of the Pichavaram region of the eastern coast of India. The roll variant parameters were estimated using the polarimetric coherence values derived in the rotation domain for non-redundant angles. The seasonal influence on these parameters was prominent and confirmed through wet and dry season analysis using the ombrothermic diagram, which depicts the temperature and rainfall data. The temporal analysis with a monthly frequency confined to the period of 2018 to 2020 and revealed the capability of discriminating the selected mangrove types better in the pre-monsoon period. The results also unveiled the sensitivity of the polarimetric coherence and associated parameters to the abrupt change in climatic factors, especially during the dry to wet season transition. Finally, the results are verified with a statistical analysis performed using Coefficient of Variation (CV) and Scale Factor. The scope of the present study can be extended to the utility of roll variant parameters along with polarimetric decomposition derived parameters for better characterization of target scatterers. KEY POLICY HIGHLIGHTS Polarimetric coherence at a fixed radar line of sight can be extended over a rotation domain to discriminate mangrove types. The seasonal influences can be captured through polarimetric coherence pattern analysis. The combined climate and polarimetric coherence pattern analysis identify the appropriate period for mangrove type discrimination.

Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis

Abstract. Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DA is based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, whereas dynamic approaches use ensemble forecasts. Associating the two methods is known as hybrid DA, and it has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6 h Canadian Precipitation Analysis (CaPA). Based on optimal interpolation (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between (i) an OI based on the Regional Deterministic Prediction System (RDPS) and (ii) an ensemble Kalman filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight to the EnKF (50 % and 70 % for summer and winter, respectively) resulted in better analysis skill and a reduction in false alarms compared with the OI method. A deterioration in the moderate- to high-intensity precipitation bias was, however, observed during summer. Reducing the weight attributed to the EnKF to 30 % alleviated the bias deterioration while improving skill compared with the OI-based CaPA.

A framework for comparing two rainfields based on spatial structure: A case of radar against selected satellite precipitation products over southeast Queensland, Australia

• Rain gauge networks are not adequate to capture the complex spatial structure. • Radar and satellite precipitation products capture the spatial structure of rainfall. • Fine spatial scale rainfall data can be derived from satellite precipitation products. • SSI and correlogram are good tools for comparing two rainfields. • Some freely available satellite precipitation products are as good as radar rainfall. Rain gauge networks are not adequate to capture the complex spatial structure of rainfall for meaningful urban flood studies. While radar and satellite precipitation products (SPPs) are being developed to alleviate the limitations of rain gauge networks, they need to be evaluated for regional applications. This study compared radar against 3 SPPs of IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement), MSWEP (Multi-Source Weighted-Ensemble Precipitation) and PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System) that have sub-daily records for southeast Queensland, Australia, based on the spatial structure identified through the structural similarity index (SSI) and two-dimensional correlogram at the daily timescale. The SPPs were downscaled to 1 km × 1 km grid resolution to conform with the radar dataset using inverse distance weighting and bilinear interpolation. All three SPPs, that are freely available, reflected very well the similarity (>90 %) in the spatial pattern with that of the radar, although IMERG was marginally better. However, MSWEP appeared to be the closest to the gauge dataset, better than even the expensive radar dataset, stemming from its much better similarity in the mean similarity statistic. Nevertheless, there could be some atmospheric climatic factors that may cause one to select one SPP over the others for a given wet day, and this needs to be explored for the possibility of selecting different SPPs for different wet days.

Delineated and analysis of morphotectonic features utilizing ASTER GDEM/DSM and Radar dataset: An example is Gabel El-Ekma Durba-Arab central Gulf of Suez, Egypt.

Delineated and analysis of morphotectonic features utilizing ASTER GDEM/DSM and Radar dataset: An example is Gabel El-Ekma Durba-Arab central Gulf of Suez, Egypt.

A new approach for crop type mapping in satellite images using hybrid deep capsule auto encoder

The crop type prediction in high resolution satellite images is challenging due to the lack of ground truth labels. This reduces the crop prediction accuracy in satellite images. This work presents a new approach using hybrid deep capsule auto encoder for enhanced crop prediction in remote sensing images. Initially, the input images are pre-processed using a Refined Lee Filtering technique (RLF) to remove the speckle noise present in the images. Afterwards, spectral–spatial features with an extended morphological profile (EMP), extended attribute profiles (EAP), and hybrid wavelet features are extracted. Furthermore, a feature selection using a modified binary equilibrium optimizer (MBE) is presented to reduce the dimensionality of features. Finally, a hybrid deep capsule auto encoder maps the different crops in satellite images. Here, the adaptive atoms search optimization approach updates the optimized weights in the network. The implementation platform used in this work is PYTHON. The performance of the presented technique is examined with the Sentinal-2 dataset and Optical Radar dataset images. The experimental results of the presented technique provide improved performance than other existing approaches in terms of accuracy (98.78%), sensitivity (97.42%), F-score (97.62%), computational time, precision (95.09%), False Positive Rate (FPR) (0.83%), False Negative Rate (FNR) (2.58%), kappa coefficient (91.85%), and ROC curve with AUC is (0.98) for Sentinal-2 dataset and accuracy (98.57%) and kappa coefficient (91.85%) for optical Radar dataset.

Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure.

Burial Depths of Extensive Shallow Cryptomaria in the Lunar Schiller–Schickard Region

Quantifying the volumes and geologic nature of lunar volcanic eruptions is important for constraining the thermal and geologic evolution of the Moon. Cryptomaria are effusive, basaltic lava flows on the Moon that were subsequently buried, and therefore hidden, by higher-albedo basin and crater ejecta. Radar offers the ability to probe the subsurface for geologic units not otherwise apparent at the surface. We use Arecibo/Green Bank Observatory and Lunar Reconnaissance Orbiter Mini-RF radar data sets to characterize maria and cryptomaria within the Schiller–Schickard region. We find significant variability in the radar backscatter across the region that does not correspond to previously mapped boundaries of maria and cryptomaria in the literature. We use the characteristic low backscatter (due to the attenuating nature in radio waves of some basaltic minerals) to analyze the distribution of cryptomaria. We use the reduction in radar backscatter to estimate burial depths of cryptomaria across the area. We present a new map of Schiller–Schickard cryptomaria and the local variability in the thicknesses of the light plains that bury the basalts. We find burial depths ranging from >100 m in the deepest areas to just a few to tens of meters in areas with shallow cryptomaria (particularly prominent in the southeast). These areas are generally contiguous with maria, allowing us to track mare lava flow units into the subsurface at mare/highland margins. We propose that ∼67% of the region contains surface or buried basaltic volcanism, which represents over twice (2.7× increase) the areal extent of cryptomaria reported in previous studies.

Unsupervised Domain Adaptation for Disguised-Gait-Based Person Identification on Micro-Doppler Signatures

In recent years, gait-based person identification has gained significant interest for a variety of applications, including security systems and public security forensics. Meanwhile, this task is faced with the challenge of disguised gaits. When a human subject changes what he or she is wearing or carrying, it becomes challenging to reliably identify the subject’s identity using gait data. In this paper, we propose an unsupervised domain adaptation (UDA) model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guided Subspace Alignment under the Class-aware</i> condition (G-SAC), to recognize human subjects based on their disguised gait data by fully exploiting the intrinsic information in gait biometrics. To accomplish this, we employ neighbourhood component analysis (NCA) to create an intrinsic feature subspace from which we can obtain similarities between normal and disguised gaits. With the aid of a proposed constraint for adaptive class-aware alignment, the class-level discriminative feature representation can be learned guided by this subspace. Our experimental results on a measured micro-Doppler radar dataset demonstrate the effectiveness of our approach. The comparison results with several state-of-the-art methods indicate that our work provides a promising domain adaptation solution for the concerned problem, even in cases where the disguised pattern differs significantly from the normal gaits. Additionally, we extend our approach to more complex multi-target domain adaptation (MTDA) challenge and video-based gait recognition tasks, the superior results demonstrate that the proposed model has a great deal of potential for tackling increasingly difficult problems.

Application of Sentinel-1A Synthetic Aperture Radar Datasets for the Estimation of Stream Flow in Various Sections of the Sone River

Application of Sentinel-1A Synthetic Aperture Radar Datasets for the Estimation of Stream Flow in Various Sections of the Sone River

Gait Recognition Using Spatio-Temporal Information of 3D Point Cloud via Millimeter Wave Radar

Gait recognition is one of the crucial methods in identity recognition, which has a wide range of applications in many fields, such as smart home, smart office, and health monitoring. The camera is the most mainstream traditional solution. But the camera is difficult to maintain stable performance in the dark, low light, and bad weather conditions. In addition, privacy leakage is also one of the important issues that people worry about. In contrast, as the latest research progress in gait recognition, millimeter wave radar can not only protect people’s privacy, but also maintain normal perception performance in dark conditions. In this paper, we propose a system for gait recognition named MTPGait using spatio-temporal information via millimeter wave radar. We specially design a neural network that can extract multiscale spatio-temporal features along space and time dimensions of 3D point cloud concisely and efficiently. We use LSTM to design the context flow of local and global time and space, fusing local and global spatio-temporal features. In addition, we construct and release a millimeter wave radar 3D point cloud data set, which consists of 960-minute gait data of 40 volunteers. Using the data set, we evaluate the system and compare it with four state-of-the-art algorithms. The experimental results show that MTPGait is able to achieve 96.7% recognition accuracy in a single-person scene on fixed route and 90.2% recognition accuracy when two people coexist, while none of the existing methods is more than 90% recognition accuracy in either scenario.

Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped-random volume over ground, the terrain-compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L-Band Synthetic aperture Radar (PALSAR) dataset.

Favorable winds speed up bird migration in spring but not in autumn.

Wind has a significant yet complex effect on bird migration speed. With prevailing south wind, overall migration is generally faster in spring than in autumn. However, studies on the difference in airspeed between seasons have shown contrasting results so far, in part due to their limited geographical or temporal coverage. Using the first full‐year weather radar data set of nocturnal bird migration across western Europe together with wind speed from reanalysis data, we investigate variation of airspeed across season. We additionally expand our analysis of ground speed, airspeed, wind speed, and wind profit variation across time (seasonal and daily) and space (geographical and altitudinal). Our result confirms that wind plays a major role in explaining both temporal and spatial variabilities in ground speed. The resulting airspeed remains relatively constant at all scales (daily, seasonal, geographically and altitudinally). We found that spring airspeed is overall 5% faster in Spring than autumn, but we argue that this number is not significant compared to the biases and limitation of weather radar data. The results of the analysis can be used to further investigate birds' migratory strategies across space and time, as well as their energy use.