首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay
【24h】

Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay

机译:案例2水域中成分检索的光谱优化II:切萨皮克湾的验证研究

获取原文
获取原文并翻译 | 示例
       

摘要

Coastal waters (Case 2) are generally more optically complex than oceanic waters and contain much higher quantities of colored detrital matter (CDM, a combination of dissolved organic matter and detrital particulates) as well as suspended sediment. Exclusion of CDM in the retrieval can lead to an overestimation of chlorophyll a concentration (C). We present a validation of a Case 2 version of the coupled spectral optimization algorithm (SOA) for simultaneous atmospheric correction and water parameter retrieval using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color data. Modeling of water constituents uses the Carver, Siegel and Maritorena (GSM) semi-analytic bio-optical model locally tuned for Chesapeake Bay. This includes a parameterization for CDM through its absorption spectrum. SOA-retrieved C and CDM are compared with in situ measurements in Chesapeake Bay. Results are also compared with output from two alternate models 1) the standard algorithm (Std) and 2) the standard atmospheric correction combined with the locally tuned GSM model (StdGSM). The comparisons indicate that the SOA is a viable alternative to both given models in Chesapeake Bay. In contrast, StdGSM appears to require improvement before it can be considered for operational use in these waters. Perhaps the most important result is the high-quality of CDM retrievals with the SOA, They suggest that there is value added using the SOA method in Chesapeake waters, as the Std method does not retrieve CDM. In a companion paper we describe in detail the model implementation, and its accuracy and limitations when applied to the Chesapeake Bay.
机译:沿海水(情况2)通常比海洋水在光学上更为复杂,并且包含大量的有色碎屑(CDM,溶解有机物和碎屑颗粒的混合物)以及悬浮的沉积物。在检索中排除CDM可能会导致高估叶绿素a浓度(C)。我们提出了一种使用耦合光谱优化算法(SOA)的案例2版本的验证,该算法可同时使用海洋宽视野传感器(SeaWiFS)卫星海洋颜色数据进行大气校正和水参数检索。水成分建模使用针对切萨皮克湾进行局部调整的Carver,Siegel和Maritorena(GSM)半解析生物光学模型。这包括通过其吸收光谱对CDM进行参数化。将SOA回收的C和CDM与切萨皮克湾的现场测量结果进行比较。还将结果与两个备用模型的输出进行比较:1)标准算法(Std)和2)标准大气校正与本地调谐的GSM模型(StdGSM)组合。比较表明,SOA是切萨皮克湾两种给定模型的可行替代方案。相比之下,StdGSM似乎需要改进,然后才能考虑将其用于这些水域。也许最重要的结果是使用SOA进行CDM检索的高质量。他们建议在切萨皮克水域使用SOA方法会增加附加值,因为Std方法无法检索CDM。在随附的论文中,我们详细描述了模型的实现,应用于切萨皮克湾的模型的准确性和局限性。

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