Rainfall over the ocean strongly influences microwave remote sensing by altering electromagnetic scattering mechanisms, complicating radar backscatter interpretation and its relationship to surface wind. However, in-depth understanding of these effects under different surface and frequency conditions remains limited. In this study, for the first time, coordinated multi-frequency radar observations at C-, X-, and Ku-bands were carried out to investigate scattering from (i) airborne raindrops, (ii) rain perturbed windless water surface, and (iii) wind-driven sea surface agitated by rain. Field measurements combined radar backscatter intensity with Doppler spectral signatures to elucidate the underlying scattering mechanisms. Synchronized radar and optical imaging enabled joint characterization of scattering signatures and geometric features of wind waves and rain-induced ring waves. Results reveal pronounced variations in both airborne raindrop scattering and surface backscattering under different rainfall rates, frequency bands and polarizations. Under low sea-state conditions, Doppler spectra exhibit a distinct dual-peak structure attributable to ring wave backscattering, whereas at higher sea states this feature is masked by the dominant spectral energy of wind waves. Using rain-induced ring wave theory and the small-slope approximation (SSA), Doppler spectra due to ring waves were simulated and validated against experimental measurements. This study clarifies the mechanisms of rain-induced scattering and demonstrate the dominant role of ring waves in shaping the observed Doppler spectral signatures.

It is of great theoretical and practical significance to study the multidimensional statistical characteristics of sea clutter at different range resolutions, such as optimizing clutter modeling and suppression, improving radar system design and remote sensing data interpretation ability. The analysis of the effect of range resolution based on different sea clutter datasets poses a significant challenge in the drawing of reliable conclusions, owing to the variability in radar parameters and sea state conditions. In this paper, a new method is proposed to obtain sea clutter data with different range resolutions based on high resolution radar data by reducing the sampling time of the original intermediate frequency (IF) signal. Base on this method, the statistical characteristics of sea clutter at different range resolutions are analyzed for the first time, including the amplitude distribution, coherence length of sea texture, phase difference distribution, Doppler spectrum, and wave radial velocity of sea clutter at three range resolutions: high (0.75m), moderate (7.5m), and low (25m), while all other environmental parameters and radar parameters remain constant. The results clearly show the effect of range resolution on sea clutter properties and can be used to guide development of clutter models.

Wind speed and wind fetch are two critical factors influencing the development of wind waves. However, due to the challenges in acquiring accurate wind fetch, most existing empirical models for retrieving significant height of wind waves (SHww) primarily consider wind speed while neglecting the influence of wind fetch, resulting in relatively low inversion accuracy. Given the strong correlation between wind wave mean period and wind fetch, this study indirectly accounted for the impact of wind fetch on wind wave development by incorporating wind wave mean period, and developed twoSHwwinversion models based on the Elfouhaily spectrum and theSHww, wind speeds and wind wave mean periods provided by ECMWF, which are defined as Model 1 (M1) and Model 2 (M2), respectively. The two developedSHwwinversion models were evaluated using multiple datasets, including sea surface data provided by ECMWF and NDBC, GNSS buoy measurements, wind speeds provided by meteorological station, and airborne SAR imagery. The results demonstrate that both models achieve high inversion accuracy forSHwwin most scenarios, with M2 exhibiting particularly robust stability, and the correlation coefficients between theSHwwretrieved by M2 and the referenceSHwware greater than 0.95, theRMSEandMAEare approximately 0.19m and 0.13m, respectively. Furthermore, when developing theSHwwinversion model, this study enhanced the inversion accuracy by incorporating the wind wave mean period to indirectly account for wind fetch effects on wind wave development, thereby providing a novel research direction for obtaining high-precisionSHww.

Systematic attitude errors (the roll, pitch, and yaw errors) are important sources of altimetric error in interferometric radar altimeters (IRA). So far, the effect of systematic attitude errors on the measurement of IRA has been investigated based on the interferometric geometry, in which the impact of the attitude errors on the IRA image quality and the coupling between each attitude error are not considered. In this work, the influence of attitude errors on the measurement accuracy of IRA is reanalyzed according to the IRA imaging principle and the interferometry theory. The theoretical formulas derived in this paper demonstrate that the roll is the most important factor affecting the measurement accuracy of IRA. Although the altimetric error directly introduced by yaw and pitch is smaller, the yaw and pitch would cause the altimetric error to be offset along the azimuth direction, and the offset grows approximately linearly across the range direction in the IRA images. Meanwhile, the attitude jitter would also introduce additional high-frequency altimetric errors, although these high-frequency altimetric errors can be removed through a Gaussian low-pass filter with the cut-off frequency determined by wavelet analysis. To verify the validity of the theoretical formulas and exhibit the impact of diverse attitude errors, a full-link simulation method is proposed, which encompasses the entire process from IRA observation to imaging and interference processing. Furthermore, the theoretical results are compared with the results of the full-link simulation and the experimental data acquired by an airborne IRA.

The azimuth cutoff of interferometric radar altimeter (IRA) image, which is acquired at small incidence angles, is mainly determined by the vertical component of the orbital velocity of ocean waves and is almost independent of the wave direction. Using this property, an inversion method for wind waves and wind speed has been proposed based on the azimuth cutoff of IRA image in combination with the Elfouhaily wind wave spectrum, which can effectively solve the problem of small-scale wind wave parameters loss caused by velocity bunching. In the present work, the wind speed and the significant height of wind waves (SH$_{\mathrm {ww}}$) have been retrieved from five pairs of airborne IRA images acquired in offshore areas. The results show that the differences between SHww retrieved from the five pairs of IRA images used in this article by the new method and the reference SHww are acceptable in ocean wave inversion. However, if the wind fetch is small and the wind direction is inconsistent with wave propagation direction, there is a significant difference between the retrieved wind speed and the reference wind speed when using the new method to retrieve wind speed. Moreover, the results also show that for the sea area with infinite wind fetch, the inversion accuracy of wind speed and SHww determined by the accuracy of the retrieved radar radial significant orbital velocity of wind waves (SV$_{\mathrm {ww}}$). However, for the sea area with finite wind fetch, the inversion accuracy would also be affected by wind fetch.

It is still a challenge to get relatively accurate wind speed under extreme wind conditions. In this study, thirteen RADARSAT-2 (RS-2) ScanSAR wide mode dual-polarization data were selected to establish a new hurricane wind speed model. Combined with European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and Stepped-Frequency Microwave Radiometer (SFMR) data, the wind speed retrieval model, which has been corrected by an incidence angle function, was proposed through strip segmentation. Compared with the ECWMF wind speed below 22 m/s, the bias of 2.056 m/s RMSE and 1.579 m/s were obtained. Compared with the SFMR measured wind speed above 22 m/s, the RMSE was 3.229 m/s and the bias was 2.489 m/s. Compared with Shen model and Horstmann model, the accuracy of wind velocity inversion is improved to a certain extent, especially under high wind speed conditions.

Interferometric radar altimeter (IRA) is a new ocean remote sensing sensor. It can be used to retrieve sea surface height (SSH) by means of cross-track interferometry. Compared with the traditional cross-track interferometric synthetic aperture radar (XT-InSAR), IRA works at very small incidence angles for higher altimetry sensitivity. In this case, multiple discontinuous surface scatterers at sea surface would be cut into a same range pixel which leads to severe layover. This layover induced by ocean waves will reduce the correlation between the master-slave images acquired by IRA and increase random interferometric phase noise. At present, how to quantitatively analyze the impact of the ocean wave layover on the decorrelation of IRA images is still a problem that needs in-depth discussion. In this letter, theoretical analysis of the effect of ocean waves on the decorrelation of IRA images has been carried out when the ocean waves layover is considered. And the theoretical results are also compared with the airborne IRA data. It is found that the layover of ocean waves has significant influence on the decorrelation between the master-slave IRA images, especially at very low incidence angles.

The interferometric radar altimeter (IRA) is one of the main payloads of the “Guanlan” ocean science satellite proposed by the National Laboratory for Marine Science and Technology of China. To evaluate the effectiveness and accuracy of the IRA in retrieving the ocean dynamic parameters, such as sea surface height (SSH), ocean wave spectrum, and wind speed, two airborne IRA experiments were carried out at Qingdao Xiaomaidao (XMD) sea area on March 31, 2019, and Rizhao sea area on November 16, 2020. In the present work, wave-induced sea surface elevation (SSE) and its spectrum have been retrieved based on the interferograms acquired by the airborne IRA. To suppress the random phase noise, a mean filtering algorithm has been used in the multilook process of calculating the complex IRA images. The results show that the size of the filter window has a significant effect on the retrieved SSE. If the size of the filter window along THE range direction is too large, the flat earth would cause the spectral density of the retrieved ocean wave to be higher. In addition, the comparisons of the retrieved spectra with the buoy measurements demonstrate that the swell can be well-retrieved by IRA images at low sea-state conditions with significant wave height (SWH) less than 0.7 m. However, for wind wave, because of the effect of the velocity bunching along the azimuth direction, the wind wave spectrum can be extracted only when it propagates approximately along the ground-range direction of the IRA images.