Soil salinity is challenging to measure accurately because soil is highly heterogeneous. This study first explores the feasibility of retrieving near-surface soil salinity using the GNSS transmissive signals received by an antenna shallowly buried underground. Soil salinity is measured by calculating the power attenuation of the signal in the soil received by the underground antenna with respect to the reference antenna mounted above the ground using GNSS Carrier-to-Noise Ratio (CNR) observations. Three-day observations in the Yellow River Delta, a typical saline-alkali land, are used to verify the approach. The results show that GNSS-derived soil salinity across different bands follows the overall trend measured by the laboratory. The GPS L1 and BDS B3 bands exhibit the highest retrieval accuracy, with the Root Mean Square Error (RMSE) being 0.15% and 0.25%, respectively, and achieved an average RMSE of 0.20%. The findings of this study initially show the potential of the proposed GNSS transmission mode to retrieve soil salinity and provide supportive information for the future development of new instruments.

Wetland is one of the three ecosystems in the world, and collaborative monitoring using hyperspectral images (HSIs) and light detection and ranging (LiDAR) has been important for wetland ecological protection. However, because of the domain shift of different images, cross-scene wetland classification of HSIs and LiDAR is a practical challenge, necessitating the development of models trained solely on the source domain (SD) and directly transferred to the target domain (TD) without retraining. To address this issue, an instance-wise domain generalization network (IDGnet) is proposed for HSI and LiDAR cross-scene wetland classification. An instance-wise random domain expansion module (IWR-DEM) is developed to simulate the domain shift, establishing the extended domain (ED). Specifically, the original HSI and LiDAR data are separated as semantic and background information in the frequency domain, a random background shift is applied to the HSI, and a semantic random shift is deployed to LiDAR. The HSI and LiDAR fusion features are extracted from the SD and ED by a weight-shared network. Multiple condition constraints are proposed for domain and class alignment, learning the domain-invariant and class-specific information and improving model generalization. Experiments conducted on two wetland datasets demonstrate the superiority of the proposed IDGnet for cross-scene wetland classification with HSI and LiDAR data. The codes will be available from the website:https://github.com/bigshot-g/IEEE_TGRS_IDGnet.

Global Navigation Satellite System Reflectometry (GNSS-R) has long been explored for retrieving sea ice properties, but in-situ validation in the central Arctic during the freezing season is rare, limiting its application. The primary objective of this study is to advance the current understanding of multi-polarization GNSS-R remote sensing for sea ice application. This paper presents observations from the full-polarization GNSS-R(FpolGNSSR) prototype during the MOSAiC expedition. The FpolGNSSR, with four polarization channels and high antenna gain (11.3 dB), aims to assess the impact of sea-ice thickness and permittivity on GNSS-R data, with observations from October 2019 to January 2020, the onset period of ice growth. First, the reflectivity is simulated by a four-layer model, and the sensitivity of multi-polarization GNSS-R to sea ice is qualitatively analyzed. Subsequently, a simplified model reveals a linear relationship between reflectivity and ice thickness, with regression showing a correlation of 0.74 (P<0.01). The retrieval error (RMSE) of sea ice thickness retrieval is 0.13 m for first-year ice (0.3–1.0 m thick). Additionally, the imaginary component of sea ice permittivity is estimated, between 0.018 and 0.039. This study provides valuable insights for the GNSS-R community, which could be summarized as: (1) the “H-polarization advantage”, which is first proposed in sea ice GNSS-R, (2) the validity of a simplified model, reinforcing the feasibility of satellite-based sea ice thickness estimations using Left-hand-circular, V, and H polarizations, and (3) a lower apparent permittivity of GNSS-R than previously reported, corresponding to a deeper penetration depth.

Crop field edge is the key characteristic of agricultural crop management. Edge detection with dual-polarization SAR time series have been widely studied, with the data advantages of sensitivity to crop growth. Existing methods rarely consider time-varying dynamic polarimetric characteristics, making it difficult to detect complete crop field edges. Based on this, this proposes a joint edge strength, which combines two kinds of polarimetric distances with a novel spatial-temporal homogeneity measure. This measure applies spatial- and temporal-varying contexts to pre-identify edge and homogenous area, and adaptatively allocate various distances in one pixel. There are 14 Sentinel-1 SAR time series images are utilized to evaluate the proposed method. By the comparison of the visual differences, our method has lower missing rate and lower false alarm rate than conventional methods.

With the development of the polarimetric statistical measures, change detection algorithms have been widely applied in the flood disaster detection. The representative methods utilize the Wishart distance to generate the difference map between the pre- and the post-disaster data. Furthermore, the difference map is applied into the threshold segmentation to generate the binary result. However, the existing methods rarely consider the impact of rainfall on the polarimetric distance, which causes abnormal polarimetric differences and incorrect detection results. Based on the fact of different sensitivities of the co- and the cross-polarization intensity for the rainfall, this paper uses the cross ratio of two polarization intensities to eliminate the impacts of the rainfall events on the polarimetric distances. Then the Markov Random Field (MRF) model is utilized to detect the flood disaster with the initialization of OTSU threshold segmentation. Two Sentinel-1 SAR data in the Poyang Lake Basin is used to assess the effectiveness of our method. The results have demonstrated the superiority of our method over the conventional methods, especially in the rainy areas.

Accurate information on ground features is essential for the ecological protection and efficient management of this wetland. The resolution of satellite remote sensing images often falls short of the requirements for fine-scale classification. The Yellow River Estuary wetland is primarily vegetated, characterized by similar spectral curves. The use of images with limited spectral information additionally restricts the precision of classification. In response to the above issues, this study proposes a multi-branch fusion Transformer (MB Transformer) method based on high spatial resolution unmanned aerial vehicle (UAV) hyperspectral data. By constructing vegetation index features, dimensionality-reduced spectral features, and original spectral features, a multi-branch fusion Transformer model is used to classify in the Yellow River Estuary wetland at a fine scale. The results indicate that this method has obvious advantages in fine-scale classification of wetland in the Yellow River Estuary.

Phase linking is the crucial step in distributed scatterer InSAR processing, which determines the quality of time-series interferometric phases. The weight matrix is the key measure of phase linking method, which controls the participation of each interferometric pair for the single-master phase linking. Existing methods don’t consider the impacts of temporal-changed polarimetric scattering characteristics, leading to large phase closure errors. Based on the polarimetric stationarity, we propose a novel scattering consistency weight measure. Combined with the coherence weight, a joint weight is generated to improve three general phase linking methods. The methods are validated with time-series Radarsat-2 PolSAR data over Kilauea Volcano, Hawaii. The results show that the proposed methods obtain higher temporal posterior coherence and better equivalent single-master (ESM) interferometric phase than traditional methods.

The EN regression models are implemented for estimating significant wave height (SWH) from quad-polarization Gaofen-3 SAR wave mode data based on the collocated data set of ~11200 Gaofen-3 imagettes matched with SWH from ERA5 reanalysis. The importance of SAR features for SWH estimation from EN is analyzed. The model performance is evaluated through a comparison with observations from buoys and altimeters. The results show that the 20 EOF spectral parameters, NRCS, cvar, and θ are significant for EN to estimate SWH from Gaofen-3 SAR. The EN models achieve good performance with RMSEs smaller than 0.5 m. The co-polarization models show better performance at low sea states but worse performance at high sea states compared to the cross-polarization models.

Satellite altimeters require correction for tropospheric delay to precisely measure sea surface heights (SSHs). Wide-swath altimeters (WSAs), operating near nadir, experience a slowdown in microwave signals and slight path bending due to changes in the atmospheric refractive index. The continued fraction-based global navigation satellite system (GNSS) mapping function, originally designed for low-elevation-angle GNSS observations, inadequately addresses the challenges faced by WSAs at very high-elevation angles. In addition, it necessitates time-and-location-specific calculations based on numerical weather models (NWMs), limiting its effectiveness in offline applications. This letter introduces a novel atmospheric delay mapping function specifically developed for WSAs with incident angles below 10°. Utilizing the European Centre for Medium-Range Weather Forecasts (ECMWF)’s stratified atmospheric pressure data and the ray-tracing method, this function improves accuracy by supplementing a first-order trigonometric function with remainders. This approach markedly reduces model errors from the millimeter-to-centimeter scale down to the sub-millimeter-to-millimeter scale. This new mapping function, dependent on elevation angle, allows for direct projection in the slant-range direction once the zenith delay is determined, thereby significantly simplifying its application.

The Surface Wave Investigation and Monitoring (SWIM) aboard the China-France Oceanic Satellite (CFOSAT) provides the ocean wave spectrum (70–50 m wavelength range). However, the accuracy of this data is affected by speckle noise, low-frequency parasitic peaks, and missing information in the short wavelength range. To improve the accuracy of the SWIM wave spectrum, this letter introduces a vision transformer (ViT) deep learning (DL) model combined with a deconvolution block, which leverages buoy wave spectrum and full wavenumber wind wave spectrum to improve the SWIM wave spectrum with high precision and wide wavelength range. The results show that the linear correlation coefficient of the improved wave spectrum has increased from 0.510 to 0.833. Furthermore, the accuracy of spectrum parameters is enhanced. Particularly, compared with the original SWIM spectrum, the root mean square error (RMSE) for the mean wave period (MWP) and peak wave period (PWP) decreased by 70.19% and 71.68%, respectively.

Precision agriculture management relies on the delineation of crop field edges. Multi-polarization SAR technology has the ability to penetrate clouds and capture morphological structures or moistures, suited for extracting crop field edges. Due to the time-dependent characteristics and phenological evolutions of crops, the methods with single-date data are difficult to detect complete edges. Moreover, the existing methods fail to extract the dynamic time-varying patterns, limiting the improvement of edge detection accuracy. Based on this, this paper proposes a novel crop field edge detection method based on the time-varying polarimetric characteristics. First, a spatial-temporal homogeneity measure is proposed to pre-identify the edge and homogenous area, for guiding the adaptive calculation of edge strength. Based on the time-series polarimetric stationarity and the trace moment estimation theory, the proposed measure enlarges the separating degree of various crop parcels. Second, a joint edge strength is proposed to enlarge strength contrast between edge and homogenous area. With the spatial-temporal homogeneity measure, it combines the similarity with the root mean square and the similarity with time-series average covariance matrix. Based on the advantages of two kinds of similarities, it highlights the field edges and reduces the impact of speckle noises. Evaluated by 8 quad-polarization and 14 dual-polarization SAR images, the proposed edge detection method achieves better visual presentations and detection accuracies than traditional methods. With the statistics of the signal-noise ratio (SNR), the joint edge strength also has higher strength contrast than conventional strengths. The relevant codes can be found in https://github.com/DawnHanGeo/TSPolEdge.git.

With the development of earth observation technology, hyperspectral image (HSI) and light detection and ranging (LiDAR) data collaborative monitoring has shown great potential in the ecological protection and restoration of coastal wetlands. However, due to the different working principle adopted by the HSI sensor and LiDAR sensor, the data obtained by them has different distribution characteristics. The distribution difference limits the fusion of HSI and LiDAR data, bringing a great challenge for coastal wetland classification. To tackle this problem, a multi-source feature embedding and interaction fusion network is proposed for coastal wetland classification, named MsFE-IFN. First, the HSI and LiDAR data are embedded in the same feature space, where the feature distribution of multi-source remote sensing are aligned to alleviate data distribution differences. Second, the aligned HSI and LiDAR features interact information in channels and pixels, which is able to establish the relationship of spectral, elevation and geospatial. Third, the HSI and LiDAR feature are sent into the feature fusion network, in which the low-frequency residual is retained to enrich intra-class features. Finally, the fused feature is applied for final class prediction. Experiments conducted on three coastal wetland HSI-LiDAR datasets created by ourselves demonstrate the superiority of the proposed MsFE-IFN for coastal wetland classification. The codes will be available from the website:https://github.com/bigshot-g/IEEE_TGRS_MsFE-IFN.

Marine oil spills pose a significant risk to the ecological balance and human health. It is crucial to promptly and accurately identify the type of oil spill to facilitate emergency response and inform scientific decisions. Remote sensing technology is at the forefront of current research on oil type identification. This article presented comprehensive research on the systematic identification of oil types. The optical and thermal infrared data were gathered for various typical oils to elucidate their optical and thermal characteristics (OTC). On this basis, we developed the oil-type OTC dual-driven identification model (OTC-DDIM). This model incorporates a sample expansion module [OTC-conditional generative adversarial network (CGAN)] to increase sample diversity, a characteristic extraction module (OTC-EM) to extract OTC, and an adaptive identification module to fuse and enhance OTC for identifying oil-spill types. Further research revealed the critical role of optical characteristic screening in eliminating redundant information interference and improving the identification accuracy and efficiency. Temperature, a dominant environmental factor (EF), played a key constraint on the generation of high-quality thermal infrared extension samples by OTC-CGAN. Under ideal oil-spill scenarios, the model demonstrated excellent identification capabilities, achieving an overall accuracy (OA) of 96.15%, with both Kappa and average$F_{1}$-score reaching 0.96. The method verification and application were conducted under simulated oil-spill scenarios. The experimental results demonstrated that OTC-DDIM could accurately and reliably identify oil-spill types using OTC, achieving accuracies of 91.71%, 0.92, and 0.90, respectively. In summary, this study could provide essential technical support for emergency responses to marine oil-spill accidents.

Marine oil spills and emulsions are highly detrimental to the ecological environment and human health. Optical and thermal infrared remote sensing technologies provide important approaches for marine oil-spill detection, but accurate cross-spatiotemporal remote sensing detection in complex scenarios is still challenging. In this article, optical and thermal infrared observation experiments on oil spills and emulsions were designed and conducted to analyze the optical and thermal response characteristics and the technical feasibility of remote sensing detection. Then, a detection model coupling the optical and thermal response characteristics is proposed to break through the bottleneck of oil-spill detection across time and space in complex scenes, which can enhance the diversity of samples by exploiting the 3-D spatial-spectral feature (SSF) generation adversarial expansion module constrained by the sun glint intensity index (SGII). Meanwhile, based on the thermal infrared super-resolution enhancement module guided by texture features, the thermal infrared spatial geometric features of oil spills are improved, and the model’s ability to resist cloud and fog interference is enhanced. Besides, under the guidance of the optical feature guidance (OFG) module, the SSF information can be extracted based on the SSF convolution deep belief network (SSF-CDBN) to realize the oil-spill remote sensing migration detection both temporally and spatially. The results show that the model can accurately detect oil spills and emulsion type, the overall accuracy (OA) and Kappa are larger than 84.46% and 0.758 in ideal scenarios, 80.11% and 0.745 in complex scenarios, which is expected to provide new technical support for oil-spill accidents.

Altimeters, spectrometers, scatterometers, and synthetic aperture radars (SARs) are widely used for detecting marine dynamic elements. The development of multimode integrated microwave remote sensors has become a trending research topic in the field of microwave remote sensing; a single load on a satellite presents relatively few functions, and multiple loads on a satellite create large volume and mass and high operational risks. The system parameters of a multimode integrated microwave remote sensor are designed, and then the minimum energy of the transmitted signal is taken as the optimization objective function. The constraints are defined in many aspects, and a time sequence optimization model of the signal for the multimode integrated microwave remote sensor is established. The genetic algorithm is used to solve the established optimization model. The simulation results show that the proposed optimization method can design a signal timing that meets a variety of constraints and requires minimal energy.

Crude oil and its emulsions seriously threaten marine ecological environment and human health. Thermal infrared remote sensing technology is an important means of optical remote sensing marine oil-spill monitoring. In this paper, based on the UAV thermal infrared radiometer, a 24-hour continuous oil-spill brightness temperature image acquisition experiment was carried out, and oil-spill BTD polar coordinate thermal model was constructed to analyze the thermal infrared response characteristics of crude oil and emulsified oil. The results show that the thermal infrared response characteristics of oil-in-water (OW) emulsified oil and seawater are difficult to distinguish, which of crude oil and water-in-oil (WO) emulsion are similar. During 11:00~14:00, there is strong thermal infrared separability between oil and seawater, and the BTD values of crude oil and WO emulsified oil are both greater than 15. During 10:30~15:00, there is a strong positive correlation between BTD and oil film thickness, R2is greater than 0.9, so it is determined that the optimal time window for oil-spill thermal infrared monitoring is 11:00~14:00. The thermal infrared remote sensing monitoring based on oil-spill BTD model and optimal time window is expected to provide new technical and method support for marine oil-spill emergencies.

Internal solitary wave is a common mesoscale dynamic process in the global ocean. In this study, SAR and MODIS satellite remote sensing images are effectively combined to make up for each other’s shortcomings and carry out remote sensing detection of internal solitary wave distribution in the global ocean. Using 1231 ENVISAT ASAR, Sentinel-1A/B and MODIS satellite remote sensing images, the position distribution of internal solitary wave in the global ocean was mapped by extracting the internal solitary wave crest lines in each remote sensing image. The internal solitary waves in the global ocean are mainly distributed between 60 degrees south and north latitude, which is consistent with the research results of Jackson (2007) using MODIS. No internal solitary waves have been detected above 60 degrees south latitude. MODIS remote sensing image can only detect the internal solitary waves in the middle and low latitudes, while SAR can detect the internal solitary waves in the higher latitudes.

Through the oil spill simulation experiment of designing outdoor small scenes, this paper finds that: (1) the thermal infrared image obtained by the two sensors shows that the diurnal variation trend of the thermal infrared brightness temperature (BT) value of different OFT is the same, but the values will be different depending on the sensitivity of the sensor and the shooting angle. (2) the larger the oil film thickness (OFT), the larger the brightness temperature difference (BTD) between oil and water in the daytime, while the relationship between OFT and BTD is not monotonous in the nighttime. When the OFT is less than 317μm, the larger the OFT, the smaller the BTD, and when OFT is greater than 317μm, the conclusion is reversed. (3) there is a strong correlation between OFT and BTD. The correlation is greatest around noon, which is most conducive to the detection of OFT.

The Surface Waves Investigation and Monitoring (SWIM) omnidirectional wave spectra and its shape parameters are compared by matching the SWIM wave spectrum data with the buoy wave spectra of the National Data Buoy Center (NDBC). The results show that the omnidirectional wave spectrum of SWIM has significant overestimation at low frequencies and significant underestimation at high frequencies. And there are spurious peaks at low frequencies. The overestimation and underestimation of the SWIM 6° beam wave spectrum are the most obvious. We believe that the presence of spurious peaks is due to the amplification of the noise floor in the SWIM omnidirectional spectrum at low frequencies. This phenomenon is greatly relieved with the increase of Significant Wave Height (SWH) . The difference between the SWIM spectrum shape parameters frequency spread (σf) and the "peakedness" of the omnidirectional spectrum (Qp) and the buoy is large. The difference between them decreases significantly with increasing SWH.

The 200 dual-polarized Sentinel-1 SAR images covering typhoons from 2018 to 2022 are collocated with ERA5 reanalysis data. The SAR-ERA5 collocations are randomly divided into two subsections: one for model training (80%), and the other for independent testing (20%). Based on the selected training samples, three machine learning models, including the Back Propagation Neural Networks (BPNN), the Random Forest (RF), and the Gaussian Process Regression (GPR), are built for the estimation of typhoon sea surface wind speed (SSWS) from VV data. The results show that the three machine learning models achieve significantly better performance than the traditional method. BPNN has the best performance with the bias, root mean square error (RMSE) and scattering index (SI) respectively being -1.33 m/s, 3.25 m/s and 1.5%. The GPR model performs slightly better than BPNN at higher wind speeds. However, the performance of RF is relatively poor. The additional introduction of VH information improves the model performance.

Estimating the M (the proportion of oil in the oil-water mixture) is crucial for the emergency response to oil spill pollutions. Synthetic aperture radar (SAR) has been extensively utilized in monitoring oil spills. Previous studies predominantly focused on distinguishing oil spills from sea backgrounds. In contrast, the objective of this study is to evaluate the ability of a fully polarimetric SAR to quantitatively estimate the M of marine oil spills and develop a method to achieve this goal. This paper analyzes the correlation between different polarimetric features, such as polarization scattering entropy, mean scattering angle, and more, and their relationship with the M values using L-band fully polarimetric SAR images obtained during the 2010 Deepwater Horizon (DWH) oil spill accident. The results indicate that the mean scattering angle is the most suitable polarization feature for inverting the oil-water mixing ratio. Based on these findings, a novel method for estimating the M of marine oil spills using fully polarimetric SAR images is proposed. This study represents the initial endeavor to explore the benefits of employing a fully polarimetric SAR for the purpose of inverting oil-water mixing ratios.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult, and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this paper, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves a SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

Deep learning methods have been proved outperforming the traditional methods in the field of hyperspectral image classification (HSIC). However, in pursuit of higher accuracy, HSIC networks have become deeper and more complex, resulting in excessive parameters and computational cost. To deploy neural networks on small platforms such as mobile or embedded devices, many studies have focused on lightweight HSIC networks. Currently, these researches are dominated by patch-based networks, which suffer from the low accuracy caused by lightweight scale and slow inference speed derived from structural deficiencies of such networks. It is worth noting that full convolutional networks are able to achieve fast inference, but they tend to consume massive memory. To this end, this paper proposes a novel lightweight HSIC method, which consists of a successive spatial rectified network (SSRNet) and a non-central positional sampling (NCPS) strategy. SSRNet is composed of a local channel attention based spectral full convolutional network and several separable atrous spatial pyramid modules. These shallow sub-networks are concatenated together to progressively optimize their outputs by successive spatial rectified learning. For decreasing memory access cost, SSRNet makes little patches as input to perform patch-wise pixels-to-pixels learning. After training, SSRNet is able to adapt to any size of hyperspectral images and complete fast inference of the full image directly. In particular, the NCPS sampling strategy enables all labeled pixels to equally traverse all spatial positions of each training patch through the positional shift sampling, which effectively alleviates the sparse problem of hyperspectral semantic labels. Experiments upon three public benchmark datasets indicate that SSRNet is comparable to the state-of-the-art methods in classification accuracy with less than 0.15M parameters and only occupy less than 10MB memory for single forward computation. Moreover, SSRNet behaves significantly superior to the traditional patch-based networks in term of the inference speed. The source codes can be available from the website of https://github.com/Pancakerr/HSIC-platform.

Oil film thickness (OFT) is an important indicator for estimating the amount of oil spill, and accurately quantifying the OFT is of great significance for loss assessment. In this paper, hyperspectral images (HSIs) of different OFTs (0.01-3.04 mm) through a ground experiment were obtained, and the spectral characteristics were analyzed. To address the issue of poor spectral separability for different OFTs, the 1DConvolutional Neural Network_Gate Recurrent Unit (1DCNN_GRU) model was developed for the quantitative inversion of OFT. It was validated through experiments on airborne Cubert-S185 HSI. The experimental results indicated that: (1) The proposed 1DCNN_GRU model effectively addressed the issue of reduced quantitative inversion accuracy resulting from poor spectral separability. The inversion results of it outperformed those of the SVR, CNN, and GRU models. Moreover, the optimal time for hyperspectral sensor to monitor OFT was at noon. (2) The proposed model using airborne hyperspectral data exhibited excellent inversion performance for OFT greater than 0.07 mm, especially with the best performance in 0.60-0.90mm. (3) The accuracy of HSI based OFT inversion assisted by brightness temperature (BT) data was superior to that of OFT inversion using single-source data. In particular, the proposed model had advantages in the feature level and decision level inversion of OFT in the ranges of 0.01-0.30mm and 1.00-3.04mm, respectively. This research provides technical support for the detection of OFT.

Water clarity (ZSD) is a critical water quality parameter that requires remote sensing mapping. Although great progress has been made inZSDretrieval over clear waters during past decades, challenges remain over turbid waters. To address this issue, a new model was proposed to retrieveZSDin clear oceanic to extremely turbid coastal waters, by improving theZSDretrieval in turbid waters. Firstly, waters were optically classified into three classes (clear, moderately turbid and extremely turbid waters) with band ratio of remote-sensing reflectance (Rrs(λ))f=Rrs(670)/Rrs(490). Secondly, class-specific algorithms were adopted to retrieve the spectral diffuse attenuation coefficientKd(λ) fromRrs(λ). Finally,ZSDwas semi-analytically estimated from minimumKd(λ) in the visible domain. Data from oceanic and coastal waters (N=2260) were used for the model parameterization, test and validation. To demonstrate the model applicability to major satellite sensors, 1299 images from six spaceborne sensors were matched up with independentin situ ZSD(N=1464,ZSD=0.2-51 m) from global oceans. The results indicate that the new model has a good performance with mean absolute percentage error (MAPE) and Root Mean Square Difference (RMSD) of 21%-26% and 0.3-2.8 m. Even over extremely turbid waters, the model still performs robustly (MAPE=22%-25%) and significantly better than the existing ones. Finally, the model application indicates that theZSDderived from six sensors show good agreement in both spatial distribution and temporal consistency. The model shows the potential to construct high-accuracyZSDrecords from multiple sensors for global oceans and can support sustainable management of marine ecological environment.

We explore the problem of efficiently mutually localizing panchromatic and multispectral images. We pose the problem as that of cross modal remote sensing image retrieval between panchromatic and multispectral images, and explore the employment of hash code co-construction strategy to achieve efficient retrieval. We design two special discriminative feature extractors for panchromatic and multispectral images according to the characteristics of them, and co-construct two discriminative hash code books for them. The two discriminative hash code books generate hash codes for panchromatic and multispectral images separately. Sorting the Hamming distance between the panchromatic and the multispectral image hash codes achieves efficient cross modal remote sensing image retrieval between panchromatic and multispectral images. Extensive experiments on the public data set validate the effectiveness of our method.

Taking advantage of AIS data to mine the dynamic characteristics of fishery resource exploitation helps to carry out scientific management of fishery and realize the sustainable development of marine resources. The paper selected 210 million records of AIS data of approximately 115,000 fishing vessels in the six Chinese fishing grounds. After processing the AIS dataset for fishing activities and fishing vessel types identification, we conducted a thorough mining and analysis of the characteristics of fishing vessel activities in winter and spring of 2019. The results showed that the number of fishing vessels was gradually increasing as the latitude decreased in winter, and that were quite different between winter and spring in the northern fishing grounds. Gillnetters were the most numerous fishing vessel type operating in the inshore fishing grounds with increased in spring, while seiners had an absolute advantage in the Xisha-Zhongsha fishing ground.

With the increasing demand of observation, multi-source remote sensing data has been widely used. Hyperspectral Images (HSI) and Light Detection and Ranging (LiDAR) data have shown the great potential in land cover classification. However, the redundant information of multi-source data influences the effectiveness of heterogeneous data features, which reduces the accuracy of joint classification. To tackle this problem, a dual graph convolution joint dense networks is proposed for HSI and LiDAR classification. In this method, a dual graph convolution network (GCN)is extracted the spectral feature from euclidean graph and cosine graph, which contains the spectrum absolute and relative differences. A dense network is employed to acquire spatial feature from LiDAR data. Finally, a fully connected network fuses the spectral and spatial feature for classification. Experiments conducted on the Huston dataset demonstrate the effectiveness of the proposed method on joint classification.

Compared with traditional underwater topography measurement methods such as multi-beam or sonar, remote sensing satellites provide a new method for underwater topography detection, but it's difficult to retrieve high-resolution and high-precision underwater topography only using a single SAR image. This paper aims to perform the complementary and synergetic effect of Multi-source SAR data and proposes a shallow sea topography detection model based on Multi-source SAR. We also verified the model with four SAR images of GF-3, Sentinel-1, ALOS PALSAR and ENVISAT ASAR satellite. The mean relative error of the detected topography is 12.50%, and the correlation coefficient is 0.91. The results show that the model proposed in this paper can effectively retrieve high-precision and high-resolution underwater topography maps.

Marine oil-spill accidents seriously threaten both the marine ecological environment and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses. Remote sensing is an important method for marine oil-spill detection and identification. In this study, thermal infrared remote sensing images of oil spills were obtained using thermal infrared imaging camera and UA V, and a marine oil-spill thermal infrared detection SVC model was proposed to conduct oil-spill detection research. The results of the land-based experiment show that there is a strong correlation between the thick oil film with different thicknesses and the surface temperature, and the R2 is larger than 0.92. The results of the UA V detection experiment show that the OA is larger than 76.84%, and the Kappa coefficient is larger than 0.740, which show the potential of UA V thermal infrared remote sensing for oil-spill detection.

Aiming at the needs and difficulties of shallow bathymetry in sea areas lacking in-situ depth information, taking Dongdao Island of Xisha Islands as an example, the active-passive fusion bathymetry using ICESat-2 photon counting lidar and WorldView-2 optical remote sensing image based on GRU deep learning model is studied. First, tidal corrections are carried out separately for multi-track ICESat-2 data, and the depth data are obtained through signal point extraction and refraction correction. Then, taking the depth data as control points, the GRU deep learning model is applied to carry out water depth inversion in the study area. The experiments show that the MAE and MRE of the bathymetric inversion for the whole area are 1.1m and 17.3% respectively. Furthermore, the MAE and MRE are 1.3m and 19.3% in a local area without Lidar data. Active-passive fusion bathymetry based on GRU model can provide a new means for shallow water depth detection.

Due to the influence of clouds and other factors on radiometers operating in the optical and infrared bands, satellite data of sea surface temperature (SST) usually has large areas missing. This will have a severe impact on its application. In this study, Data Interpolation Empirical Orthogonal Function (DINEOF) method was applied to reconstruct the missing values of SST in a region in Southeast Asia. The DINEOF was used to the 2019 Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua L3 SST data, and its accuracy was assessed by comparing Argo data, satellite data, and reconstructed data. And Argo data was used to correct the reconstructed data. The corrected Std and RMSE were 0.3992 and 0.3951, respectively.

The Sentinel-3 synthetic aperture radar altimeter provides two kinds of rain flag by using the changes of the three backscatter coefficients, which provides a foundation for the comprehensive use of all the backscatter coefficients to determine rain conditions. This paper analyzes the statistical relationship between the two backscatter coefficients of Ku band SAR mode and PLRM mode and the C band backscatter coefficient when it is not rain, then the deviation generated by the backscatter coefficients of the two bands in the rainfall state is compared, provides a new method for determining rain flag. Finally, the measurement data of precipitation radar and altimeter data are matched and verified, and the result of the new method of updating the threshold judgment condition is more accurate.

The dependence of azimuth cutoff wavelength (λc) on significant wave height (SWH) and wind speed (U) at C-band VV, HH, VH, and HV polarizations is analyzed based on the collocations between the quad-polarization Gaofen-3 SAR wave mode images and the ERA5 wind and wave reanalysis. Then the influence of pixel spacing on the dependence is discussed. The results show the co-polarized (VV and HH) λchas an evident positive dependence with SWH (with U), while for the cross-polarization (VH and HV), it is relatively weaker. In addition, the cross-polarized λcis more related to$U$than to SWH. Moreover, the size of pixel spacing affects the estimated value of λc. The dependence of λcon SWH and$U$shows a decreasing trend with pixel spacing increasing in all four polarizations. But this trend is more significant for co-polarization, especially for VV.

The upwind-downwind asymmetry in radar return from the sea surface is well known. This paper develops a modified two-scale model to better describe the difference between upwind and downwind of the radar backscatter at moderate incidence angles caused by skewness of the non-Gaussian sea surface. The unknown parameter in the modified model is estimated by fitting the model to CMOD5.n at different incidence angles under various wind conditions. Then the modified model is compared with CMOD5.n and the advanced scatterometer (ASCAT) backscatter measurements. The results show that the model predictions are in rather good agreement with the reference data. The modified model can accurately describe the difference between the upwind and downwind normalized radar cross section (NRCS) with the root mean square difference being about 0.02 dB compared with that of CMOD5.n.

Recently, marine oil spill incidents occur frequently, causing serious pollution, which has seriously endangered marine ecological environment security. The type of oil spill pollution is related to the formulation of punishment and cleaning scheme, which is an important basis for the disposal of oil spill pollution. Hyperspectral remote sensing is an effective means to monitor marine oil spills. Different types of light oils are difficult to identify effectively, which can not meet the needs of accurate monitoring applications. In this paper, the outdoor oil spill experiment is implemented. The data of five typical oil products are obtained by unmanned airborne hyperspectral imager, and the feature extraction and analysis are carried out. The RPnet deep learning recognition model of oil types under multi feature fusion is constructed to realize the effective identification of different oil spill types. It can provide important technical support for offshore oil spill monitoring of relevant business departments.

The azimuth cutoff wavelength of SAR is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on H-V linear polarization bases (HH, HV/VH, and VV) without considering the effects of other polarization bases (e.g., linear rotated, circular, and elliptical polarization). Benefiting from the quad-polarization advantage of GaoFen-3 SAR wave mode data and the support of polarization basis transformation theory, this study used 4,648 SAR data to study the correlation between cutoff wavelength and wind and wave parameters (e.g., significant wave height, and wind speed) under different polarization bases, and analyzed the variation of correlation coefficient caused by polarization basis change. Finally, the results were applied to evaluating the performance of wind and wave parameters retrieval. The results of the study show that the azimuth cutoff is strongly dependent on the polarization state of electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has higher correlation with wind and wave than that under H-V linear, circular, and linear rotated polarization bases. Using the azimuth cutoff wavelength of the elliptical polarization bases can significantly improve the retrieval accuracy of wind and wave parameters. This study shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties. The result implies that polarization basis is an important factor that must be considered in future ocean SAR studies.

We investigate the problem of efficiently localizing sketch depicted scenes in a remote sensing image dataset. We pose the problem as that of remote sensing image retrieval with sketch queries and explore the use of hashing techniques to achieve efficient retrieval. Given two training datasets of sketches and remote sensing images that have a common set of class labels, we develop a hashing strategy that coconstructs two hash code books for the sketches and the remote sensing images separately. The hash code book coconstruction strategy encourages hash codes for the sketches and remote sensing images from different classes to be far away from one another and those from the same class to be close. This property is maintained by two cohesion intensive cues: 1) an interclass pairwise disperse cue (InterPDC) and 2) an intraclass pairwise balance cue (IntraPBC). We use the two coconstructed hash code books for training two linear mapping models that generate hash codes for sketches and remote sensing images separately. Sorting the Hamming distance between the sketch hash codes and the remote sensing image hash codes renders efficient remote sensing image retrieval with sketch queries. This enables localizing the sketch depicted scenes in the remote sensing image dataset. In addition, our method can also be used for fast localizing sketch depicted scenes in a remote sensing image of large size. Extensive experiments on public datasets validate the effectiveness and efficiency of our method.

Accurate and high temporal-spatial resolution snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has become a popular topic. The terrain is a significant issue affecting GNSS-IR snow depth retrieval. This study explores detailed information about the problems and new solutions to this issue. The main conclusions include but are not limited to the following aspects: 1) Reflections from Below the Antenna (RBA) is the primary geometrical relationship of the GNSS multipath signal for snow depth retrieval; 2) The proposed GSnow_TERR model considers the terrain effects by incorporating the surface tilt angle (γ) into the derivative of the multipath relative phase (φ) with respect to the satellite elevation angle (e). Unlike the previous region-by-region solutions, the new model has a definite physical meaning which considers the γ as an independent non-negligible variable; 3) The new model performs well for repeatable and non-repeatable GNSS tracks. The former agrees with the PBO H2O product with r2= 0.97 and a slightly 1 ~ 2 cm improvement in the RMSE. The latter agrees with the baseline two-step clustering method for GLONASS, Galileo, and BDS, with r2= 0.96 and RMSD = 1.11 cm. The rate of data utilization increases by a mean value of 38.21% for non-repeatable tracks. The performance of the model in practical applications is consistent with theoretical analysis. The findings of this study provide a valuable reference for future GNSS-IR snow depth research and applications.

Triple collocation analysis is an effective method for assessing the error of observation systems with independent random error. However, this error independence is often violated, and systems show clear error correlations. Whereas many efforts have been devoted, the cause and determination of this error correlation are still challenging. In this work, we reveal that the error correlation is actually a signal observed by some systems, and taking this signal as the error correlation between systems will bias the error estimation results. Therefore, we define it as the representativeness signal instead of the representativeness error. Based on the multiscale signal approach and extended collocation analysis, we discuss the cause of this representativeness signal and its effect on sea surface salinity (SSS) data validation by synthesized experiments, satellite, buoy, and climatology products. The results suggest that the representativeness signal may vary with the different behaviors of observed variables. For SSS, the representativeness signal lies in satellite data with medium resolutions and similar representativeness. However, thein situand climatology data, which have different resolutions compared with satellites, do not show apparent representativeness signals with satellite data. The data collocation procedure also affects the representativeness signal, which decreases with increasing collocation intervals. The extended collocation analysis can estimate the error of SSS products in most cases but may not provide robust estimation when the collocation data pairs are limited.

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

In 2017, large-scale macroalgae blooms with different dominant species ofUlva proliferaandSargassumoccurred concurrently in the Yellow and East China Seas, which poses a challenge to the cognition and control of macroalgae disaster. Therefore, it is necessary to develop an algorithm to distinguishU. proliferaandSargassumfrom satellite images. In this study, the spectral difference betweenU. proliferaandSargassumand the capability of several multispectral satellite missions to distinguish them is first analyzed. The results show that the reflectance peak in visible wavelength is always in ~550 nm forU. proliferawhether it is floating in clear open water or turbid nearshore water. However, the reflectance ofSargassumfloating in clear and turbid water shows totally different characteristics, because most ofSargassumbody is submerged in the water and the observedSargassumreflectance is seriously affected by water reflectance. Compared with Landsat 8 Operational Land Imager (OLI), HuanJing-1, Charge-Coupled Devices (HJ-1 CCD), Aqua Moderate-resolution Imaging Spectroradiometer (MODIS), and Sentinel 2 Multi-Spectral Instrument (MSI), GaoFen-1, Wide Field of View (GF-1 WFV) can preferably capture the spectral difference betweenU. proliferaandSargassum. Based on the spectral difference analysis, we propose a random forest-based algorithm to distinguishU. proliferaandSargassumfrom GF-1 WFV images with an overall accuracy of 97.6% except whenU. proliferaandSargassummix together. The algorithm is more robust than the existing ones as it allowed moreSargassumsamples from different ocean regions to be used in the training; in addition, it avoids negative effects caused by the selection of a threshold. The proposed algorithm is proved effective in distinguishingU. proliferaandSargassumin the Yellow and East China Seas in May and June 2017 and in detectingSargassumin the Atlantic Ocean. Thus, this method can be used in researches including floating macroalgae traceability and competition and succession between different macroalgae species in different regions of the ocean with similar environments.

The tilt modulation is one of the key factors affecting sea wave inversion accuracy based on some microwave sensors such as SAR, microwave spectrometer, and so on. Up to now, the traditional tilt modulation-transfer functions (MTFs) based on the Bragg scattering model and Phillips spectrum have been used in the sea wave spectrum retrieval algorithm for microwave sensors operating at medium incident angles. The traditional tilt MTFs are only related to radar polarization and incident angle, but not affected by wind speed and wind direction. However, the values of the tilt MTFs evaluated on the basis of the C-band empirical geophysical model function (GMF) CMOD5.na demonstrate that wind speed and wind direction also have significant effects on the tilt MTFs. In addition, the comparisons show that the values of the traditional tilt MTFs for both HH (horizontal) and VV (vertical) polarization cases are obviously smaller, which indicates that the traditional tilt MTFs would underestimate the influence of the local incident angle change on the scattering fields. On the other hand, the difference between the horizontal and the vertical polarization tilt MTFs, however, is overestimated by the traditional tilt MTFs. Finally, by fitting the data evaluated based on the C-band empirical GMF, we propose new tilt MTFs, which can overcome the shortcomings of the traditional tilt MTFs.

The successful launch of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new and advanced tool for sea ice thickness (SIT) estimations in the Arctic. However, the performance of ICESat-2 for SIT estimations still remains unknown. In the present study, SIT estimates derived from ICESat-2 are examined using three retrieval methods, namely, two buoyancy methods with the merged snow depth and empirical snow depth (BMA and BME, respectively) and one empirical estimation method (EEM), and these estimates are compared to near-simultaneous airborne measurements from the IceBird mission in April 2019. Overall, the ICESat-2 total freeboard registers quite well with that from the near-concurrent IceBird mission with a mean bias of 2.5 cm, which demonstrates the high reliability of ICESat-2 data for SIT estimation. However, the much more evident difference between SIT estimations than total freeboard from ICESat-2 and IceBird indicates that other parameters (e.g., snow depth and snow/ice densities) may bring increased uncertainties to the SIT estimation. Overall, BMA is the best method for SIT estimation and has the closest thickness distribution to that of IceBird data with a mean bias of 0.11 m, followed by the BME and EEM methods. The dominate error sources for SIT estimation using the buoyancy method are ice density and snow depth that require further investigation in future studies.

This article is devoted to investigating the microwave scattering mechanisms of oil-free and oil-covered sea surfaces for an incidence angle smaller than 30° in a backscattering configuration. The Elfouhaily spectrum is used to simulate an oil-free sea surface, whereas the Elfouhaily spectrum combined with the Jenkins damping model is applied to the simulation of an oil-covered sea surface. Then, the Kirchhoff approximation-stationary phase approximation (KA-SP) and the first order of small-slope approximation (SSA-1) are employed to simulate the scattering coefficients induced by specular scattering and total scattering, respectively. Importantly, a new parameter defined as specular scattering to total scattering ratio (STR) is proposed in this article, which can be used to measure the ratio of specular backscattered power to total backscattered power. The dependencies of the scattering coefficient and the STR on incidence angles, wind speeds, wind directions, oil thicknesses, and so on, are investigated. This article provides new insights for a better understanding of the evolution of microwave scattering mechanisms from oil-free and oil-covered sea surfaces in the transition region of incidence angles (from about 15° to 30°).

Compared with transmit/receive (T/R) monostatic High-frequency surface wave radar (HFSWR), the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment and large coverage because of the separation between the radar transmitter and receiver locations. In this paper, a target monitoring method with bistatic compact HFSWR was proposed. The results of a target detection experiment using T-R bistatic compact HFSWR conducted in 2015 were presented, and the validity of the method and the tracing results were verified by using synchronous automatic identification system (AIS) data.

The two-scale model (TSM) has been frequently used in the study of EM (electromagnetic) scattering from rough surface due to its simple and practical merit. However, for microwave scattering from sea surface, it cannot provide accurate predictions for hh (horizontal) polarization. To overcome this problem, an improved version of the TSM (ITSM) which can be better used for predicting microwave scattering from sea surface is proposed in this paper. In the ITSM, we propose to use two cutoff parameters to separate sea surface roughness. For kwcs, the KA-SP (Kirchhoff approximation-stationary phase approximation) rather than KA-GO (Kirchhoff approximation-geometric optics approximation) is employed to simulate the specular scattering component. For kw>kcb, the SPM modulated by tilts of large-scale waves is employed to simulate the Bragg scattering component. The values of kcsand kcbare chosen according to the validity conditions of the KA and the SPM. The numerical comparisons illustrate that the ITSM performs better than the TSM and the SSA-1, especially in the prediction of hh polarized scattering coefficient.

In this paper, the spectral and texture features of Sargassum first are analyzed through calculating four measures of GLCM and sampling spectrum from typical pixels of Sargassum blooms with high-resolution satellite data. The four-dimensional spectral bands of the image, the first principal component and NVDI are used as the spectral features of the image, and four measures of GLCM are used as the texture features of the image. And then the Sargassum is extracted using SVM by constructing spectral-texture eigenvectors. The experiment achieves superior results compared with the conventional NDVI threshold method.

A novel controller for finding the best communication point is proposed for collecting data from a seabed platform by a single unmanned surface vehicle (USV) using underwater acoustic communication (UAC). As far as we know, extremum seeking based on climbing control is usually implemented by multiple vehicles or agents because of the large range of measurement and easy acquisition of gradient estimation. A single vehicle cannot rapidly estimate the field because of the limited extent for measurement; therefore, it is difficult for a single vehicle to seek the extremum point in a field. In this study, an oscillation motion (OM) is designed for a single USV to acquire UAC’s link strength data between the seabed platform and the USV. The field for UAC’s link strength is updated using new measurement from an OM of the USV based on a multi-variable weight linear iteration method. A controller for seeking the best UAC’s point of the USV is designed using gradient climbing and artificial potential considering iterative estimation of an unknown field and an OM operation, and the stability is proved. The reliability and efficiency are shown in simulation results.

The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.

Chinese satellite HY-2A, carrying a Ku-band scatterometer (HSCAT), was launched in August 2011. This letter validates wind vectors retrieved by the HSCAT during October 2011–July 2017 by comparing with wind data recorded by global moored buoys, the Advanced Scatterometer (ASCAT), and ERA-Interim. HSCAT wind data are collocated with moored-buoy observations made by the National Data Buoy Center, tropical atmosphere ocean/triangle trans-ocean buoy network, pilot research moored array in the Tropical Atlantic, and research moored array for African–Asian–Australian monsoon analysis and prediction. Only buoys located offshore are selected. Their spatial and temporal differences are limited to 25 km and 30 min. The results show that the HSCAT wind speed has bias of 0.09–0.28 m/s and root-mean-square error (RMSE) of 1.24–1.52 m/s, which satisfies the mission specification of less than 2 m/s. The HSCAT wind direction has bias of 0.8°–0.9° and RMSE of 21.5°–25.8°, which is close to the mission specification of less than 20°. The HSCAT wind retrievals are overestimates at wind speeds lower than 5 m/s. The RMSEs of the HSCAT wind speed and direction decrease with increasing wind speed. Wind vectors from HSCAT and ASCAT are compared using spatial and temporal windows of 0.1° and 10 min. Results show the consistency of the wind speed; RMSEs of wind speed and direction are 1.50 m/s and 22.90°. The comparison of HSCAT and ERS-Interim wind data shows that the HSCAT provides global ocean wind data at the level required for data application.

The detection of vessel targets could be effectively resolved in a high-frequency surface wave radar (HFSWR). However, signals reflected from vessels are concealed by sea clutter in the Doppler spectrum, where such detections are performed. Consequently, differences between these features in the Doppler domain cannot be readily observed, which greatly increases the difficulty in detecting vessel targets. In this letter, in situ sea state information is utilized to facilitate the detection of targets within sea clutter. First, the sea clutter spectrum, which is absent of vessel, is constructed. Second, sensitive sea clutter features that are influenced by vessel targets are selected and analyzed. Third, anomalies in sensitive sea clutter features are detected by obtaining respective thresholds. Finally, vessel targets are identified by the synthesized anomaly detection. Experimental results demonstrate the effectiveness of the proposed method, and the vessels detected using the HFSWR are further verified using synchronous automatic identification system information.

Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier-feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, K nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.

The TanDEM-X constellation is served by two X-band SAR satellites, which fly in close orbit formation acting as a large and flexible single-pass radar interferometer. This paper investigates the potentials for monitoring sea ice in the Bohai Sea with the unique constellation. Our results show that the coherence and interferometric phase of TanDEM-X data can be used to detect sea ice, and the radial velocity of sea ice can be measured with the along-track phase.

We address the problem of sea surface distribution modeling in a synthetic aperture radar (SAR) image by developing an innovative nonparametric method to tackle the main weakness of the traditional Parzen window kernel method, i.e., relatively low computation speed. We derive an explicit analytical solution of modeling sea surface distribution by a composite cubic Bézier curve and propose an adaptive segmentation strategy to improve the modeling precision. A comparative study validates that the average computation time of the proposed method is only 1/60 of the Parzen window kernel method and about 1/6 of the k-root and G0 methods. More importantly, in terms of modeling performance, the proposed method can achieve more adaptability and stability to different SAR sensors, resolutions, and sea scenes. The average goodness of fit tested on eight sea scenes of the proposed method, measured by |R̂̅2̅| (the smaller the better), is only 0.0006 and outperforms that of the Parzen window kernel method (0.0059), k-root (0.0390), and G0 (0.0678).

Selecting discriminate features and constructing an appropriate classifier are two essential factors for ship classification in a synthetic aperture radar (SAR) image. Unfortunately, these two factors are rarely considered together by existing studies. We propose a joint feature and classifier selection method by integrating the classifier selection strategy into a wrapper feature selection framework. The sequential forward floating searching algorithm is improved to conduct efficient searching for an optimal triplet of feature-scaling-classifier. Comprehensive experiments on two data sets demonstrate that the proposed method can select the optimal combination of a nonredundant complementary feature subset, appropriate scaling, and classifier to improve the performance of ship classification in a SAR image.