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.

This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of 1.82 m · s-1], followed by subswath 2 (RMSE of 1.92 m · s-1), while subswath 1 showed the lowest retrieval accuracy (RMSE of 2.37 m · s-1). The average RMSE of wind speeds retrieved from all the three subswaths is 2.08 m · s-1under low-to-high wind speed regimes (wind speeds-1). We further observed that the dependence of VH-polarized normalized radar cross section (NRCS) on incidence angle is attributable to the high and changing noise equivalent sigma zero (NESZ) with incidence angle under low-to-moderate wind speed regimes. And that strong VH-polarized radar signals could overcome the NESZ effect, thereby eliminating the dependence of VH-polarized NRCS on incidence angle under strong wind conditions. For Sentinel-1 IW mode VH-polarized data, the effect of NESZ could be ignored when wind speeds are greater than 15 m · s-1, as a better wind speed retrieval performance of these data has been recorded in this paper at wind speeds greater than 10 m · s-1, owing to an RMSE below 1.6 m · s-1and biases ranging from -0.5 to 0.5 m · s-1.

High-spatial-resolution wind fields derived from synthetic aperture radar (SAR) instruments are crucial to a wide range of applications. However, the spatial scale effect on wind speed retrieval accuracy has seldom been reported. For the purpose of understanding this issue, this letter makes a quality assessment of wind speed retrieval accuracy based on four commonly used C-band geophysical model functions (CMOD4, CMOD-IFR2, CMOD5, and CMOD5.N) at spatial resolutions ranging from 50 m to 50 km using Sentinel-1 interferometric wide (IW) swath mode images. Our results show that the CMOD5 function is the most effective among these functions, owing to a low root-mean-square error (RMSE) of 1.17 m/s and a bias of -0.28 m/s for wind speed retrieval at a spatial resolution of 500 m. It is further observed that the variance of wind speeds retrieved from copolarized SAR images decreases exponentially with the decrease of spatial resolutions. For Sentinel-1 IW mode images, the variance of wind speeds retrieved with CMOD5 decreases rapidly from 50 to 500 m, with a drop in RMSE of 40%, and thereafter levels off. Thus, a spatial resolution of 500 m, with the CMOD5 function, is recommended optimal in this letter, for wind speed retrieval using Sentinnel-1 IW mode data.

This paper presents a comparison strategy for investigating the influence of spatial resolutions on sea surface wind speed retrieval accuracy with cross-polarized synthetic aperture radar images. First, for wind speeds retrieved from vertical transmitting-vertical receiving (VV)-polarized images, the optimal geophysical C-band model (CMOD) function was selected among four CMOD functions. Second, the most suitable C-band cross-polarized ocean (C-2PO) model was selected between two C-2POs for the VH-polarized image data set. Then, the VH-wind speeds retrieved by the selected C-2PO were compared with the VV-polarized sea surface wind speeds retrieved using the optimal CMOD, which served as a reference, at different spatial resolutions. Results show that the VH-polarized wind speed retrieval accuracy increases rapidly with the decrease in spatial resolutions from 100 to 1000 m, with a drop in root-mean-square error of 42%. However, the improvement in wind speed retrieval accuracy levels off with spatial resolutions decreasing from 1000 to 5000 m. This demonstrates that the pixel spacing of 1 km may be the compromising choice for the tradeoff between the spatial resolution and wind speed retrieval accuracy with cross-polarized images obtained from RADASAT-2 fine quad-polarization mode.

The rapid accumulation of microarray data translates into an urgent need for tools to perform integrative microarray analysis. Integrative Array Analyzer is a comprehensive analysis and visualization software toolkit, which aims to facilitate the reuse of the large amount of cross-platform and cross-species microarray data. It is composed of the data preprocess module, the co-expression analysis module, the differential expression analysis module, the functional and transcriptional annotation module and the graph visualization module.