热带地理 ›› 2019, Vol. 39 ›› Issue (3): 357-364.doi: 10.13284/j.cnki.rddl.003137

• 论文 • 上一篇    下一篇

基于Sentinel-1A数据在广东省近海海面风场反演应用

吴萍昊1,2,3,钟凯文2,胡泓达2,赵 怡1,2,3,许剑辉2,王云鹏1   

  1. (1. 中国科学院 广州地球化学研究所,广州 510640;2. 广州地理研究所,广州 510070;3. 中国科学院大学,北京 100049)
  • 收稿日期:2018-11-19 修回日期:2019-05-01 出版日期:2019-05-05 发布日期:2019-05-05
  • 通讯作者: 钟凯文(1972—),男,研究员,主要从事数字城市及其应用研究、水色遥感、遥感与地理信息系统集成等研究,(E-mail)zkw@gdas.ac.cn。
  • 作者简介:吴萍昊(1995—),女,江西省抚州市人,硕士研究生,主要从事遥感信息分析与应用研究,(E-mail)wupinghao@gdas.ac.cn;
  • 基金资助:

    广东省科学院院属骨干科研机构创新能力建设专项项目(2017GDASCX-0101);广东省科技计划项目(2019B020208013);广东省科学院2018年实施创新驱动发展能力建设专项资金项目-引进全职博士学位人才资助专项(2018GDASCX-0902)

Application of Sentinel-1A Data in Offshore Wind Field Retrieval within Guangdong Province

Wu Pinghao1,2,3, Zhong Kaiwen2, Hu Hongda2, Zhao Yi1,2,3, Xu Jianhui2 and Wang Yunpeng1   

  1. (1. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; 2. Guangzhou Institute of Geography, Guangzhou 510070, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China)
  • Received:2018-11-19 Revised:2019-05-01 Online:2019-05-05 Published:2019-05-05

摘要:

采用基于风条纹提取风向的方式,利用地球物理模式函数,基于Sentinel-1A数据,通过CMOD5模型反演2017年3、5、7、12月份广东省近海海域风场。将反演结果与实测数据对比,风速普遍比实测风速大,风速反演的平均绝对误差为1.98 m/s,均方根误差为2.74 m/s,相关系数为0.8。其中3、5、7月的风速较为接近,且平均绝对误差和均方根误差都<2 m/s,而12月份平均风速>8 m/s,实测数据与卫星过境时间不完全匹配,导致平均绝对误差和均方根误差都偏大。哨兵一(Sentinel-1A)影像反演结果整体上与实测数据相一致,验证了COMD5反演模型适用于广东省近海高分辨率海洋风场反演,可为下一步估算广东省风能资源储量提供可能。

关键词: 海面风场, 哨兵一, 风条纹, 合成孔径雷达, 遥感反演, 广东省近海

Abstract:

Offshore wind energy resources are a common clean energy source. They have rich reserves and are widely distributed, inexpensive, and safe with few environmental constraints and large selection space. They exhibit considerable development and utilization prospects. In this paper, wind direction data were extracted based on wind stripes from Sentinel-1A data, and the CMOD5 retrieval model was used to invert the wind field in the offshore waters of Guangdong Province in March, May, July, and December 2017. Eight sites were selected, and 56 samples were compared with measured data; the sample wind speed was generally higher than measured wind speed. The mean absolute deviation (MAE) of wind speed retrieval was approximately 1.98 m/s, the root mean square error (RMAE) was approximately 2.74 m/s, and the correlation coefficient was 0.8. The wind speeds in March, May, and July were relatively close, and the MAE and RMSE were all under 2 m/s, while the average wind speed in December was above 8 m/s with a higher deviation in MAE and RMSE as the measured data did not exactly reflect satellite transit times. A comparison of the December CCMP data with the measured site data revealed a mean absolute deviation of approximately 2.96 m/s, root mean square error of approximately 4.02 m/s, and correlation coefficient of approximately 0.59. Differences were observed between the CCMP data and the measured data in December, which was similar to the retrieved wind speed data. G3358, G7427, G3704, and 59490 station errors were calculated by the four stations with the largest errors in December. The G3358 station exhibited the most significant errors: the mean absolute deviation was approximately 3.43 m/s and the root mean square error was approximately 5.01 m/s. The data period in which the G3358 station data exhibited the highest error was December 2017, and CCMP wind field data was interpolated to the study area at this period. It was found that the G3358 station is located at the wind speed stage of 12-15 m/s, which is nearest to the large wind speed area in all stations. The reason for the large deviation between the measured site data and the retrieval wind field and CCMP wind field may be related to the higher wind speed. The results of Sentinel-1A image retrieval were consistent with the measured data overall, which verifies that the COMD5 model is applicable to offshore high-resolution marine wind field data retrieval in Guangdong Province, and it provides a possibility for future research on wind energy resource and reserves estimation in Guangdong Province.

Key words: ocean wind field, Sentinel-1A, wind stripe, synthetic aperture radar, remote sensing retrieval, offshore Guangdong Province