## 6 Conclusions  
In this study, we aimed to assess the performance of miniaturized SAR images in automatic TC-center estimation and the robustness of a previously proposed automatic estimation algorithm. Specifically, three wide-swath miniaturized SAR images were utilized to estimate the centers of TCs Noru, Doksuri, and Koinu. The algorithm's process is methodically divided into three stages: Stage 1 (coarse estimation), Stage 2 (precise estimation), and an optional stage (NRCS-adjusted estimation). To validate the accuracy of the estimated TC center positions, the algorithm's outputs were compared with visually located data. For TC Noru, the accuracy improved from 21.42 km (Stage 1) and 14.39 km (Stage 2) to 8.19 km (optional stage). For TC Doksuri, the accuracy varied, with distances from 14.36 km (Stage 1) and 20.48 km (Stage 2) to 17.10 km (optional stage). For TC Koinu, the accuracy improved from 47.82 km (Stage 1) and 31.59 km (Stage 2) to 5.42 km (optional stage). These results underscore the potential of miniaturized SAR satellites in monitoring TCs.  
However, a significant limitation of the algorithm is its dependence on the accuracy of wind direction retrieval. Inaccuracies in wind direction can lead to less precise TC-center estimations. Consequently, future research could focus on refining wind direction retrieval methods to enhance TC-center estimation accuracy. Additional studies will compare TC center positions derived from SAR images, satellite cloud images, and best-track data, examining discrepancies related to TC intensity, geographic regions, or other factors. Overall, the integration of miniaturized SAR technology into meteorological practices offers promising assistance in mitigating the effects of these powerful natural phenomena.  
Conceptualization, X.G. and S.S.; Methodology, Y.W. and H.F.; Datacuration, L.H., Z.H., Y.X. and G.W.; Writing--original draft, Y.W.; Writing--review & editing, X.G. and S.S.; Visualization, H.F. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Classification**} & \multicolumn{6}{c}{**Visually Located**} \\ \cline{3-6}  & & **Lon** & **Lat** & **(\({}^{\circ}\)**E**) & **Lat** & **\(D\)1 (km)** & **\(D\)2 (km)** & **\(D\)3 (km)** \\ \hline Noru & Tropical Storm & 129.90 & 17.10 & 21.42 & 14.39 & 8.19 \\ Doksuri & Tropical Storm & 129.13 & 14.56 & 14.36 & 20.48 & 17.10 \\ Koinu & Severe Typhoon & 123.20 & 22.05 & 47.82 & 31.59 & 5.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TC center positions estimated from SAR images and those from visually located data.  
**Funding:** This work was funded by the National Key Research and Development Project of China. (Grant 2017 YFC1404800).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data utilized in this article will be made available by the authors on request.  
**Acknowledgments:** The authors would like to thank the HiSeq-1 C-band SAR satellite project and all collaborators, especially Fujian Satellite Date Development Company Ltd., and Fujian Hisea Digital Technology Company Ltd., for their cooperation in SAR applications.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.