首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Estimating advective near-surface currents from ocean color satellite images
【24h】

Estimating advective near-surface currents from ocean color satellite images

机译:从海洋彩色卫星图像估计平流近地流

获取原文
获取原文并翻译 | 示例

摘要

Improved maximum cross correlation (MCC) techniques are used to retrieve ocean surface currents from the sequential ocean color imagery provided by multiple newer generations of satellite sensors on hourly scales in the Yellow Sea and the U.S. East and Gulf coasts. The MCC calculation is validated in a series of Bio-Optical Forecasting (BioCast) experiments with predetermined synthetic velocities, and its products are evaluated by examining the errors and biases with respect to the High Frequency Radar (HFRadar) measurements. The root-mean-square (RMS) errors in our best current products derived from the overlap of satellite sensor swath between the VIIRS sequential orbits are less than 0.17 m s(-1) in the evaluation area outside of the Chesapeake Bay. The most accurate current products are those derived from the imagery data of R-rs(551), B-b(551) and C(551), while the image sequences of B-b(551) and Z(eu)_lee are identified as the most suited products for the retrieval of currents because of their best production capacities of valid velocity vectors. Mechanisms between the advective processes and the dynamic changes of bio-optical properties are discussed regarding the performances of various color products on the retrieval of currents. Similarities of velocity distribution in the retrieved vector arrays are collected across different MCC products derived from ocean color datasets that are of different types and derived from different spectral channels of satellite overpasses. The inter-product similarities themselves can be used to characterize the near-surface advection as well and usually have smaller errors than each of the individual MCC currents. Moreover, efforts are also under way to improve the ocean color derived currents by merging several of the MCC products with similarities to increase the total spatial coverage. This study not only seeks the image-derived products best representing the sea surface current structures in coastal areas, but also exploits how these currents can be improved or optimized to support the ocean forecasts. (C) 2014 Elsevier Inc All rights reserved.
机译:改进的最大互相关(MCC)技术用于从黄海,美国东部和墨西哥湾沿岸的每小时尺度的多个新一代卫星传感器提供的连续海洋彩色图像中检索海面洋流。 MCC计算在一系列具有预定合成速度的生物光学预测(BioCast)实验中得到了验证,并且其产品通过检查相对于高频雷达(HFRadar)测量的误差和偏差进行了评估。在切萨皮克湾以外的评估区域中,由VIIRS连续轨道之间的卫星传感器测绘带重叠得出的我们最好的当前产品的均方根(RMS)误差小于0.17 m s(-1)。当前最准确的乘积是从R-rs(551),Bb(551)和C(551)的图像数据得出的那些,而Bb(551)和Z(eu)_lee的图像序列被认为是最准确的。由于其有效速度矢量的最佳生产能力,因此适合用于电流检索的产品。讨论了对流过程与生物光学特性动态变化之间的机制,探讨了各种颜色产物在电流检索中的性能。跨不同的MCC产品收集了所检索的向量阵列中速度分布的相似性,这些产品来自不同类型的海洋颜色数据集,并且来自卫星立交桥的不同光谱通道。产品间的相似性本身也可用于表征近表面对流,并且通常比每个单独的MCC电流具有较小的误差。此外,通过合并几种具有相似性的MCC产品以增加总的空间覆盖范围,人们也在努力改善洋色产生的洋流。这项研究不仅寻求最能代表沿海地区海表电流结构的图像衍生产品,而且还探索了如何改善或优化这些海流以支持海洋预报。 (C)2014 Elsevier Inc保留所有权利。

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