首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Sea Surface Salinity and Wind Retrieval Using Combined Passive and Active L-Band Microwave Observations
【24h】

Sea Surface Salinity and Wind Retrieval Using Combined Passive and Active L-Band Microwave Observations

机译:利用被动和主动L波段微波组合观测海表盐度和风

获取原文
获取原文并翻译 | 示例

摘要

This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions.
机译:本文介绍了一种从被动/主动L波段微波组合海面观测中同时检索海面盐度和风的算法。该算法利用了亮度温度和雷达反向散射对盐度,风速和方向的不同响应。该算法将最小平方误差(LSE)度量最小化,表示度量与亮度温度和雷达反向散射的模型函数之间的差异。测试了具有不同测量组合的三种LSE度量。 LSE措施之一仅使用被动微波数据,其盐度误差为2 psu,风速误差为2 m / s。第二个LSE度量将无源和有源微波数据用于垂直和水平极化。有源微波数据的添加将检索精度显着提高了大约五倍。为了减轻法拉第旋转对卫星观测的影响,我们建议使用法拉第旋转下不变的测量组合来进行第三次LSE测量。对于Aquarius,低风时的期望均方根SSS误差将小于0.2 psu,对于暖水,在25-m / s的风速下,期望的均方根SSS误差将增加至0.3 psu,并且检索到的风速的精度将很高(大约1-2 m / s或更低)。我们的结果表明,将被动和主动微波观测相结合,将可以检索海表盐度以及风速和风向。特别是,法拉第轮转下LSE不变的度量将直接适用于太空飞行任务,例如NASA水瓶座和土壤水分主动被动任务。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号