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With the development of altimeters’ retracking algorithms, the signal-to-noise ratio of altimeter data in coastal regions has been improved significantly in recent years. However, due to the complexity of coastal waves, the traditional satellite-buoy/satellite-satellite collocation method using a spatial-temporal window, which is widely used in the evaluation of significant wave height (SWH) measurements of altimeters in the open ocean, is not reliable because coastal waves can vary significantly even in a small window. This makes it difficult to quantitatively evaluate the errors of altimeter coastal SWH observations. This study proposes an automated dynamic collocation method to solve this problem. The method combines SWH from coastal buoy observations and numerical wave model (NWM) outputs to generate an SWH reference field. The coastal altimeter observations can then be compared with this reference dataset. To test the effectiveness of the method, the SWH data from Sentinel-3A (S3A) synthetic aperture radar (SAR) mode in the coastal region of southwest England were evaluated. The results indicate that the coastal SWHs from the S3A SAR mode is good quality with an overall root mean square error of about 0.3 m when the offshore distance is more than 10 km. The results also show that the presented method works better than previous methods for the evaluation of coastal altimeter SWH observations. This method can be applied in future studies to evaluate and improve the performance of other coastal remotely sensed SWH observations. [ABSTRACT FROM AUTHOR]

Nowadays, Sentinel-1 (S-1) synthetic aperture radars (SARs) operating in wave mode and the real aperture radar (RAR) called Surface Waves Investigation and Monitoring (SWIM) onboard the China-France Oceanography SATellite (CFOSAT) are the only two kinds of spaceborne radars providing directional ocean wave information globally. To quantify the absolute uncertainties in the swell wave heights of a specific wave system (Hss) observed from these two spaceborne sensors, a triple colocation error model is exploited via WaveWatch III (WW3) wave model hindcasts for the first time. After implementing spatiotemporal collocation, cross-assigning swell partitions, and rejecting suspicious data, a database of the optimal-matched Hss triplets (S-1, SWIM, and WW3) is determined over a one-year period (June 2020–June 2021). Qualitatively, traditional dual intercomparisons indicate the inconsistency between the Hss from both SAR and RAR radars in terms of systematic biases. Furthermore, the triple collocated error analysis quantitatively reveals that, at a global scale, SWIM onboard CFOSAT has the least uncertainty in Hss [~0.2-m root mean square error (RMSE) and ~11% scatter index (SI)] compared with S-1 SAR (0.35–0.50-m RMSE and 17%–26% SI depending on incidence modes) and WW3, under the assumption that the random errors of the three data sources are independent, indicating that the newly launched SWIM instrument is an invaluable resource of directional wave information for the scientific community. The results are discussed with respect to regional error characteristics along with a feasible explanation of error sources. The findings could be helpful for better understanding and synergistically exploiting the Hss datasets from these two spaceborne radars. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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) $0.25^{\circ }\,\,\times0.25^{\circ }$ 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]