Marine oil spills pose significant threats to ecosystems and human health, emphasizing the importance of synthetic aperture radar (SAR) images for reliable and all-weather monitoring. However, current methods face two major challenges. The first is data limitations, including insufficient data quantity and noise, such as speckle noise and distortions introduced during preprocessing. The second is look-alike confusions, which pose challenges in distinguishing oil spills from visually similar phenomena. This article introduces TransOilSeg, a novel method designed to address these challenges and enhance oil spill detection performance. TransOilSeg employs a transfer learning component (TLC) to integrate data from diverse geographical regions and varying quality, learning general features from multisource datasets. By leveraging a gradient aggregation algorithm, the model combines features from limited and noisy SAR oil spill (SOS) datasets, transferring data deficiencies. In addition, the adaptive attention hybrid encoder (AAHE) analyzes contextual features and adapts to varying datasets, enabling the model to effectively distinguish oil spills from look-alike phenomena. Comprehensive evaluations across multiple datasets demonstrate the robust generalization capability of TransOilSeg. On the M4D dataset, which includes 1002 training samples, the model achieved a mean intersection over union (mIoU) of 61.38% for oil spill detection and 62.41% for look-alike detection. Furthermore, TransOilSeg maintained strong performance when transferred between datasets with varying levels of noise and distortions, demonstrating its adaptability to challenging conditions. These results highlight its potential as a reliable tool for marine oil spill detection and monitoring.

This study proposes a deep learning (DL) approach for retrieving high wind speeds during tropical cyclones using a random forest algorithm applied to RADARSAT and Sentinel-1A/B Synthetic Aperture radar (SAR) images. The effectiveness of the proposed DL-based model is then demonstrated through a comprehensive validation of the results. Statistical analysis of the results showed that the proposed method performs well, with a low root-mean-square error, mean bias, and high correlation coefficient when compared to SFMR (Stepped-Frequency Microwave Radiometer) winds. The reconstructed wind speeds and inner-core structures were found to be in good agreement with surface wind measurements from SFMR. These findings could have significant implications for improving our understanding and prediction of tropical cyclone dynamics, as well as for operational forecasting and disaster management.

Ship target search tasks aim to match specific ships across two or more satellite images. Like pedestrian and vehicle re-identification tasks in computer vision, accurate ship re-identification encounters challenges, including subtle differences between ships of the same type and substantial intra-instance variations due to satellite angle of view and spectral differences. To tackle these challenges, this paper introduces a deep learning-based two-branch framework for ship target search, integrating ship detection and re-identification tasks. One branch extracts the target ship features while the other captures the search region features. These features are then fused through a dedicated layer, and the final output is derived from the keypoint detection header. A new dataset was curated using Sentinel-2 and Gaofen-1 satellite data. Experimental results validate the robustness of the proposed method, achieving an accuracy of 94.37% on the new dataset. Our method’s scalability has been validated through experiments using CBERS-04 and Gaofen-6 satellite data.

This article introduces an innovative deep-learning approach for retrieving tropical cyclone (TC) rainfall information from C-band Sentinel-1 synthetic aperture radar (SAR) imagery. We collected 17 SAR images under TC conditions from 2016 to 2021 and matched them with synchronous observational Next Generation Weather Radar (NEXRAD) Level-III data, forming a dataset of 302689 data pairs for model development. The model inputs include SAR-measured physical parameters in normalized radar cross section (NRCS), texture features represented by the gray-level co-occurrence matrix (GLCM), and statistical parameters of VV-polarized NRCS. A deep-learning-based TC rain rate retrieval (TC3R) model, combining a convolutional network and a fully connected (FC) network, was developed to retrieve quantitative TC rainfall information effectively. The test results demonstrate that the TC3R model can offer reasonable and stable quantitative rainfall estimation, particularly effectively detecting areas with medium-to-heavy rainfall events (2.5–40 mm/h) in SAR images where the NRCS is significantly affected by rain. Furthermore, to offer valuable insights into the performance of the TC3R model, we analyzed results across TC events of different intensities as case studies. Our results show high structural similarity (SSIM) in rainfall patterns between SAR and NEXRAD across all cases, consistently achieving SSIM values above 0.67. Moreover, in areas where SAR signals are notably affected by rainfall, the SSIM index even exceeds 0.80. Finally, our model’s performance was evaluated by comparing its results with the independent global precipitation measurement (GPM) data, demonstrating effective rainfall prediction, particularly for the primary spiral rain band, in the two cases analyzed.

Change detection (CD) aims to identify differences in the same scene at different times. With the increasing amount of hyperspectral images (HSIs), more and more change detection techniques use HSIs as the raw data. HSIs often contain redundant bands, where only a few are crucial for CD while others may be detrimental. However, most existing HSI-CD methods extract features directly from full-dimensional HSIs, leading to a degradation of feature discrimination. To tackle this issue, in this paper, we propose an end-to-end hyperspectral image change detection network based on band selection (ECDBS), unlocking the potential synergy between band selection and CD. The network compromises a deep learning based band selection module and cascaded band-specific spatial attention (BSA) blocks. The band selection module selectively retains bands favourable to CD according to the importance of the bands measured based on band correlation. The BSA block tailors the feature extraction strategy for each band based on its feature distribution, allowing extracting sufficient features from each band. Experimental evaluations were conducted on three widely used HSI-CD datasets, demonstrating the effectiveness and superiority of our proposed method over other state-of-the-art techniques.

The acquisition of wide geophysical data of vast oceans by satellites can be impeded by clouds, which may result in gaps in the data acquired in the infrared and visual bands, such as sea surface temperature (SST) data, leading to limited data usage. To address this issue, we proposed a general and robust method, named the empirical function (EF) method, which involves expanding an ocean field with space-time-separation functions and determining the functions by minimizing the expansion’s residual on the observation data while considering the prior-knowledge constraint that an ocean field varies smoothly in space and time. To test the effectiveness of the EF method, we applied it to reconstruct the 14-year cloud-free SST data in the Gulf Stream region spanning from 24.5°N to 44°N and 82.5°W to 54.5°W. The original data consists of the 0.025°×0.025° gridded daily composite daytime SST products of the Moderate-Resolution Imaging Spectroradiometer on the Aqua sun-synchronous satellite, with an annual data-missing rate fluctuating around 78% in the region. In addition, we validated the reconstructed data against in-situ buoy measurements. The reconstructed SST data’s accuracies are -0.11 ± 0.91°C (bias ± standard deviation of error) and -0.12 ± 0.67°C in the areas without and with satellite observations, respectively, which are slightly lower and higher than the gappy satellite SST products’ accuracy of -0.14±0.77°C.

Waterline extraction from synthetic aperture radar (SAR) images can be transformed into a sea-land segmentation task. However, two aspects of deep learning sea-land segmentation have been ignored in the literature: 1) deep learning models are commonly built using whole images rather than focusing on their sea-land transition parts and 2) a higher resolution input may not render a better segmentation result under the constraint of a fixed-size receptive field. Our investigation on the aspects indicates that focusing the modeling process on the sea-land transition parts can benefit the waterline extraction, and the highest resolution may not be the best choice for all pixels. We proposed masked soft intersection over union (MSIoU) loss and the difference-focusing fusion decision (DFFD) ensemble learning method. MSIoU loss incorporates the mask of the transition parts to focus the modeling process on the transition parts. The DFFD ensemble learning method imitates manual labeling and can avoid selecting the resolution of input images. The DFFD ensemble model’s member models segment an image’s sea and land areas at different resolutions. Then, its fusion model further recognizes the pixels where the member models inconsistently predict sea-land types. We applied the DFFD ensemble model to 10-m-resolution SAR images of the test set. Compared to the traditional single-resolution model, the DFFD ensemble model with MSIoU loss achieved a 10.32–12.58-m accuracy in waterline extraction with a 2.08–2.78-m error reduction in the study area. Moreover, the DFFD framework is independent of data and model choice and can be readily modified for other segmentation tasks.

Although cross-polarized synthetic aperture radar (SAR) images play a crucial role in wind speed retrieval under extreme weather conditions, the retrieved wind speeds are susceptible to thermal noise. However, there is still a lack of research on how thermal noise affects the accuracy of wind speed retrieval. To address this issue, this paper proposes a strategy to quantitatively examine the impact of thermal noise on wind speed retrieval, using 910 Sentinel-1 cross-polarized SAR images acquired in Extra-Wide Swath (EW) mode. The thermal noise and wind speeds of these images range from -35 dB to -22.5 dB and from 5 m/s to 70 m/s, respectively. By considering the wind speed retrieved from dual-polarized SAR as a reference, the study reveals that higher levels of thermal noise result in increased uncertainty in the retrieved wind speeds using cross-polarized SAR images. Additionally, the error of wind speed retrieval from cross-polarized SAR rapidly decreases as wind speed increases, eventually converging to a stable level. Notably, as thermal noise levels decrease to less than -30 dB for wind speeds between 5 m/s and 25 m/s, the root-mean-square error (RMSE) associated with wind speed retrieval through cross-polarized SAR imagery experiences a rapid decline, with a 94% reduction in RMSE, which then stabilizes. This implies that when the thermal noise level falls below -30 dB, wind speed retrieval can be directly conducted using cross-polarized imagery for wind speeds exceeding those typical of a tropical storm, yielding comparable results to dual-polarized imagery. The findings of this study provide valuable insights for advancing wind speed retrieval algorithms and hold significant reference value for the design of the next-generation radar instruments that will incorporate the cross-polarized channel.

Characterizing the uncertainties in buoy ocean wave records is critical not only for understanding the limitations ofin situwave measurements, but also for interpreting the implied accuracies of the remotely sensed products in which these buoy data are used as validation references. This letter preliminarily assesses the error of long-period swell heights (Hss) representing specific directional wave partition energy observed from deep-water buoys moored in the northeast Pacific. We propose a buoyHsserror estimation method by combining dual and triple collocation using data derived from buoys, two kinds of space-borne radars and numerical simulations. Compared to traditional methods, the proposed approach can reveal “absolute” errors (with respect to the underlying truth) from buoyHss, accepting and then confirming that swell heights from buoy, satellite and model are all uncertain. This study simultaneously employs ocean swell products derived from synthetic/real aperture radars (Sentinel-1A/B and CFOSAT/SWIM) and WaveWatch III® ocean wave model hindcasts to diagnose the accuracy of theHssvalues observed by buoys of National Data Buoy Center (NDBC) and Coastal Data Information Program (CDIP) during the period from July 2019 to October 2021. We quantify that the NDBC’s 3-m heave-pitch-roll buoy (CDIP’s Waverider buoy) recordedHsshave root-mean-square error of 0.17 m (0.12 m), or have about 10.65% (7.06%) uncertainty relative to the meanHssvalue (approximately 1.6 m). Our findings imply that the reference value uncertainties should be taken into account when understanding direct satelliteHssvalidation against buoyin situ.

Satellite-based synthetic aperture radar (SAR) can provide a low-cost, frequent environment monitoring for dynamic intertidal zones. The critical problem is to realize pixel-level classification of SAR images of the intertidal zones with excellent and robust performance. Recently, deep learning, in particular deep convolutional neural networks, has provided us with promising solutions to this problem. Based on a sophisticated deep learning-based pixel-level classification model U2-Net, we propose an MB-U2-ACNet model suitable for intertidal zone land cover classification using dual-polarimetric SAR data integrated with environmental information, such as wind speed and tide level information. The MB-U2-ACNet model has a multi-branch nested U-shaped encoding-decoding structure. We extract and fuse features from multiple data sources, including satellite remote sensing and environmental information, by establishing the multi-branch structure. Furthermore, we propose an asymmetric convolution residual U-block for each encoding-decoding stage to improve the model’s feature extraction ability. Moreover, the model with attention mechanisms better distinguishes the importance of features from the channel’s perspectives and spatial dimensions. We construct a dataset with 106 Sentinel-1 SAR images from 2016 to 2020 for environment monitoring in the intertidal zone of Shanghai Nanhui. On the dataset, the proposed model reaches the overall classification accuracy of 96.40% and the mean intersection over union score of 0.8307. The experiments show the advantages of the proposed model compared with the benchmarking models due to better feature extraction and multi-source information fusion. In addition, the contributions of every added sub-structure are analyzed systematically.

In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the brightness temperature data observed by the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. We used 97 TC cases from 2015 to 2018 to train the CNN models. Several models with different inputs and parameters are designed. A comparative study showed that the selection of different infrared (IR) channels has a significant impact on the performance of the TC intensity estimate from the CNN models. Compared with the ground truth Best Track data of the maximum sustained wind speed, with a combination of four channels of data as input, the best multicategory CNN classification model has generated a fairly good accuracy (84.8%) and low root mean square error (RMSE, 5.24 m/s) and mean bias (−2.15 m/s) in TC intensity estimation. Adding attention layers after the input layer in the CNN helps to improve the model accuracy. The model is quite stable even with the influence of image noise. To reduce the side-effect of the very unbalanced distribution of TC category samples, we introduced a focal_loss function into the CNN model. After we transformed the multiclassification problem into a binary classification problem, the accuracy increased to 88.9%, and the RMSE and the mean bias are significantly reduced to 4.62 and −0.76 m/s, respectively. The results show that our CNN models are robust in estimating TC intensity from geostationary satellite images.

Synthetic aperture radar (SAR) can monitor the sea surface imprints of tropical cyclones (TCs) with high spatial resolution, day and night. Automatically locating TC center positions in SAR images is a challenging task. This article developed a two-stage, fully automatic TC-center estimation algorithm. First, the sea surface wind directions (SSWDs) at SSWD points are retrieved by the improved local gradient (ILG) method. We incrementally deflected the SSWD outward at a 0.5° angle from −50° to 10° (the negative angles represent clockwise deflection). The heat maps are generated for each of the 121 angles, and the values at each heat map are the cumulative numbers of the lines perpendicular to the compensated SSWDs. The site corresponding to the maximum cumulative number in all 121 heat maps is the coarsely estimated center position. This center search is the culmination if it falls outside the SAR image. Otherwise, the second stage is triggered, and the sub-SAR image (150 km$\times150$km) centered at the coarsely estimated center position is extracted. Then, the first-stage procedure is repeated with the sub-SAR image to precisely estimate the center position. Optionally, the precisely estimated center position can be further adjusted by considering that normalized radar cross section (NRCS) is normally minimal at the TC center. We applied the algorithm to 87 SAR images. Five of these images do not contain TC centers. The results are in good agreement with the visually located TC center positions and those in the best track (BT) datasets.

Wind-induced oriented textures (WIOTs) are commonly used to retrieve sea surface wind directions from synthetic aperture radar (SAR) images. In this study, we found that WIOTs are also related to sea surface wind speeds (SSWSs). The entropy values in the gray-level cooccurrence matrices (GLCMs) for SAR images containing WIOTs will become steady with increasing distance between pairs of pixels. Furthermore, these steady values of entropy (SVEs) show a clear linear relationship with SSWSs. As a result, an SSWS retrieval model was developed based on this relationship. We used 2222/2223 Sentinel-1 SAR images (wind speed ranges from 5 to 20 m/s) to fit/validate the algorithm. The retrieved SSWSs were compared with the European Centre for Medium-Range Weather Forecast (ECMWF) SSWSs, Cross-Calibrated Multi-Platform (CCMP) SSWSs, and Tropical Atmosphere/Ocean (TAO) buoy measurements, and the root-mean-square differences (RMSDs) were 1.78, 1.70, and 1.78 m/s, respectively. The new model was also tested for SAR images acquired under hurricane conditions. The wind comparisons against stepped-frequency microwave radiometer (SFMR) measurements show an RMSD of 1.28 m/s. Our model’s performance was also tested with the images at different spatial scales in the validation data set. Since the model is based on inherent image patterns, it still works well for SAR images without precise calibration.

Recently, deep learning has already shown its availability in synthetic aperture radar (SAR) image classification. To improve the deep learning model's performance on multisource remote sensing information fusion, we propose a multi-branch deep convolutional neural network model specially tailored from the U2-Net framework and with asymmetric convolutions. We name it the MB-U2-ACNet model. Based on experiments on a constructed dataset dedicated for Shanghai Nanhui Dongtan intertidal zone environment monitoring, it is verified that the proposed MB-U2-ACNet model has better performance than the existing representative deep and traditional methods.

The potential mymargin for improving storm surge simulation is demonstrated by using winds derived from ground-based Global Navigation Satellite System Reflectometry (GNSS-R) that uses BeiDou geostationary Earth orbit (GEO) satellite signals. We reconstruct wind fields by blending GNSS-R coastal winds with the European Center for Median Weather Forecasts (ECMWF) reanalysis product. The reconstructed winds agree well with the weather station data collected at Yangjiang in Guangdong, China. The ECMWF winds and the reconstructed winds are used to force a storm surge model off the Chinese coast during typhoon Utor 2013, respectively. The model storm surges forced by the reconstructed winds agree substantially better with tide-gauge observations than those forced by the ECMWF winds. The average error has been reduced by 30.5% from 24.3 cm with the ECMWF winds to 16.9 cm with the reconstructed winds. This letter suggests that GNSS-R coastal winds can have a positive impact on the accuracy of storm surge hindcasting directly and forecasting indirectly by improving the initial conditions.

Researchers have already verified that the deep learning (DL) technology can realize accurate and robust mapping of tropical cyclone-induced coastal inundation in synthetic aperture radar imagery. In order to liberate the DL-based inundation mapping from human supervision, we propose to use the clustering of deep convolutional autoencoder-generated features. The mapping results of Lekima 2019-induced inundation demonstrate the advantages and availability of the proposed method.

In this study, a deep convolutional neural network was designed to estimate the intensity of tropical cyclones over the Northwestern Pacific Ocean from high-frequency Himawari-8 satellite images. Our model achieved good results by using the brightness temperature derived from one single infrared band data. The accuracy of the top (top-1) and the second best (top-2) tropical cyclone intensity classification reaches 81.4% and 93.3%, respectively.

In the past few years, artificial intelligent (AI) technology has been widely used in many research fields for big data information mining and shown great potential applications in computer vision, natural language processing, bioinformatics, among others. In the area of remote sensing oceanography, we categorize its applications in four major categories: image classification, data fusion, algorithm development and oceanic phenomenon forecast. In this paper, we present two examples to demonstrate such applications. In the first example we applied a well-studied AI framework, U-Net, to a NASA JPL’s UAVSAR Synthetic Aperture Radar (SAR) image to classify coastal zone types in the Gulf Coast of USA. In the second example, we trained the AI framework, LSTM model, using the time series of blended microwave Sea Surface Temperature (SST) data, and made the equatorial SST pattern forecast. Validation studies in both cases showed the robustness of AI-based technology for oceanography research.

Tropical cyclones (TCs) are weather systems with vast destructive power. A key element in issuing warnings for TCs approaching land is the accurate and timely knowledge of the location of the circulation center. Current procedures are usually performed with manual input from a human analyst. Since subjective elements are involved in this process, analysts could disagree on the results even when multiple factors are considered. In this paper, we propose a new method for the automatic determination of the center of TCs using HY-2 and Quick Scatterometer wind vector products. First, we calculate the high-wind speed zone from the wind speed map. Next, we extract the vortexlike zone using the wind direction map. Finally, we automatically determine the center from the vortexlike zone. Six representative TCs (Haikui, Saola, Usagi, Florence, Ioke, and Gordon) and seventeen TCs in the 2013-2016 seasons are used to validate the TC center automatic determination (TCCAD) method. The results show that 1) the accuracy of the TCCAD method is close to that of the human expert method for most TCs and 2) the standard deviation in the TCCAD method is much smaller than that in the human expert method, which indicates that the TCCAD method is more efficient and reliable. Although the TCCAD method has some limitations because of the quality of scatterometer products and problems with the structure of TC eyes, it can automatically provide the practical, independent, and objective identification of TC centers.

Gaofen-4 (GF-4) is the first high-resolution geostationary satellite of China, launched on December 29, 2015. Its visible-near infrared optical sensor is capable of imaging the earth at 50 m resolution, covering a 512 km × 512 km area with a minimum imaging interval of 20 s. More than 300 GF-4 images, taken in four sequences with imaging rates of 36 and 69 s per scene, captured the development of Typhoon Megi-2016-from its peak in the afternoon of September 26, 2016 to its dispersion two days later on September 28. These consecutive images recorded the motion field of the typhoon's top clouds nearly continuously. By using advanced image matching technology, the motion has been estimated for every pixel, at subpixel accuracy from image pairs with 179 and 206 s intervals. The process has generated time series of atmospheric motion vector (AMV) fields at 50 m spatial resolution for the whole imaged area. It is the first time, to our knowledge, that such high-resolution and nearly continuous AMV data of a typhoon system have been produced. The data provide accurate measurements of the typhoon top cloud motion speed and direction, and reveal quantitative details of the motion field spatiotemporal evolution at high altitude. The finding that the high altitude top cloud motion speed is significantly lower than that recorded at low altitude below the planetary boundary layer is of scientific value and further exploitation of the motion data can lead to a better understanding of typhoon dynamics and thus improve cyclone modeling.

This paper assesses different retrieval schemes used for the Chinese Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) co-polarized data. The data consist of 4186 GF-3 data points and collocated wind information from sources including the ASCAT scatterometer, HY2A-SCAT scatterometer, and National Data Buoy Center (NDBC) buoy wind data set. The VV-polarized geophysical model function (GMF) is a CMOD7 model while the HH-polarized GMF is a hybrid of the CMOD7 and PR model. Assessments involve comparisons between SAR-derived and collocated winds in terms of the root-mean-square difference (RMSD) and bias. First, a comparison between the two retrieval schemes for the VV-polarized data clearly shows that the optimal scheme performs better than the classical scheme for wind speed retrieval. Comparisons for HH-polarized data show similar results. These experiments indicate that the wind speed RMSDs for the GF-3 co-polarized data are within 2 m/s when using the optimal scheme. Moreover, the wind direction RMSDs from the two schemes have no significant difference, with values near 20°. Overall, these assessments indicate that the GF-3 co-polarized data are sufficient for operational wind speed retrieval using the optimal scheme. However, wind direction retrieval requires further improvement.

This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies.

The brightness temperature data acquired during 2012-2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite- Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) 0.25° × 0.25° gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs.

A gray-level co-occurrence matrix (GLCM)-based method was developed for better texture orientation estimation in remote sensing imagery. A GLCM is essentially the joint probability distribution of gray levels at the position pairs satisfying a specific relative position within an image. We first found that when the relative position is aligned with texture orientation, larger elements of the corresponding GLCM are concentrated diagonally. Then, we developed a new texture orientation estimation method. The method uses the GLCMs of relative positions equally spaced in orientation and distance, and three schemes of these GLCMs are calculated. A GLCM-derived parameter is then defined to quantitatively measure the degree of diagonal concentration of the GLCM elements, and its integral over the variable of relative distance is selected as an indicator to find the dominant texture orientation(s). For testing, we applied the method to 44 selected images containing one or multiple aligned textures. The results show that the method is in good agreement with visual inspections from 45 randomly selected people, and is insensitive to large typical noises and illumination change. In addition, using (any) one GLCM calculation scheme over the others does not significantly affect the results. Finally, the method was applied to sea surface wind direction (SSWD) retrieval from 89 synthetic aperture radar images. In the application test, the developed method achieves better SSWD retrieval accuracy than do the commonly used Fourier transform- and gradient-based methods by 8.13° and 16.09° against the European Centre for Medium-Range Weather Forecast ERA-Interim reanalysis data and 10.21° and 17.31° against the cross-calibrated multiplatform data.

Surface Wave Investigation and Monitoring (SWIM) will be launched on board the Chinese French Ocean SATellite (CFOSAT) in 2018. This paper proposes a joint method to simultaneously retrieve wave spectra at different scales from spaceborne Synthetic Aperture Radar (SAR) and CFOSAT SWIM wave spectrometer data. The method combines the output from the two different sensors to overcome retrieval limitations that occur in some sea states. The wave spectrometer sensitivity coefficient is estimated using an effective significant wave height (SWH), which is an average of SAR-derived and wave spectrometer-derived SWH. This averaging extends the area of the sea surface sampled by the nadir beam of the wave spectrometer to improve the accuracy of the estimated sensitivity coefficient in inhomogeneous sea states. Wave spectra are then retrieved from SAR data using wave spectrometer-derived spectra as first guess spectra to complement the short waves lost in SAR data retrieval. In addition, the problem of 180° ambiguity in retrieved spectra is overcome using SAR imaginary cross spectra.

China launched two new spaceborne microwave sensors in August and September 2016. One is the C band multi-polarization high resolution synthetic aperture radar (SAR) on board satellite GF-3. The other is the Ku band wide swath Interferometric Imaging Radar Altimeter (InIRA) on board space laboratory TG-2. This paper gives some preliminary results for the quantitative remote sensing of ocean winds and waves from the GF-3 SAR and the TG-2 InIRA. Comparisons to the ECMWF ERA-Interim reanalysis data show good agreements but more valuable details.

The location of a typhoon center plays an important role in moving track monitoring and forecasting. More and more new satellites are used to monitor typhoons. Combining multiple sources of satellite data has become popular for tropical cyclone monitoring in recent years. In this letter, we demonstrate a robust method of locating the typhoon center based on meteorological satellite (Feng Yun-2 (FY-2) satellite) and microwave scatterometer data (Hai Yang-2 (HY-2) scatterometer). First, we locate the typhoon center using HY-2 and FY-2 data independently. Next, we combine the results of the HY-2 and FY-2 analysis to produce an improved method of locating typhoon centers. Finally, three representative typhoons (“ChanHom,” “Soudelor,” and “DuJuan”) are used to validate our methodology. The results show that: 1) compared to FY-2 satellite data, the proposed results have more information on wind speed and direction and 2) compared to HY-2 satellite data, the proposed results have higher temporal resolution. Overall, the proposed method can improve typhoon monitoring results.

The Interferometric Imaging Radar Altimeter (InIRA) is a new generation radar altimeter developed by China, which combines the function of interferometric radar altimeter and Synthetic Aperture Radar (SAR). In this paper, we explored the retrieval method of wind speed from simulated InIRA echoes. The combined significant wave height (Hs) was first statistically derived from sea surface height (SSH), which was retrieved using the similar method by conventional altimeter. The swell Hs was estimated using swell spectrum extracted from InIRA data. Then the wind wave Hs was estimated using combined and swell Hs. Finally, the 10-m height wind speed was derived using an empirical relation between wind speed and wind wave Hs. Results showed InIRA has a good potential of retrieving wind speed.

This paper compares the typhoon centers from the tropical cyclone best track (BT) data sets of three meteorological agencies and those from synthetic aperture radar (SAR) and infrared (IR) images. First, we carried out algorithm comparison using two newly developed algorithms and one existing wavelet-based algorithm, which were used to extract typhoon eyes in six SAR images and two IR images. These case studies showed that the extracted eyes by the three algorithms are consistent with each other. The differences among them are relatively small. However, there is a systematic difference between the extracted centers and the typhoon centers from the three BT data sets, which were interpolated to the imaging times first. We then compared the typhoon centers determined from 25 SAR and 43 IR images with those from the three BT data sets to investigate the performance of the latter at the sea surface and at the cloud top, respectively. We found that the typhoon centers from the three BT data sets are generally closer to the locations extracted from the SAR images showing sea-surface imprints of the typhoons than those from the IR images showing cloud-top structures of the typhoons.