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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Validation of empirical SeaWiFS algorithms for chlorophyll-α retrieval in the Mediterranean sea a case study for oligotrophic seas
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Validation of empirical SeaWiFS algorithms for chlorophyll-α retrieval in the Mediterranean sea a case study for oligotrophic seas

机译:经验性SeaWiFS算法在地中海海域叶绿素-α提取中的验证-以贫营养海为例

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The major aim of this paper is the validation of SeaWiFS-derived chlorophyll-a concentration in the Mediterranean Sea. A data set containing in situ chlorophyll-a profiles and optical measurements of in-water and above-water radiances was used to evaluate the performances of several ocean color algorithms in the Mediterranean Sea. The analysis revealed a systematic overestimation of chlorophyll-a concentration by National Aeronautics and Space Administration (NASA) global algorithms (OC2v4 and OC4v4). The error appears to be correlated with chlorophyll-a concentration, by exhibiting marked differences at low values (C < 0.15 mg/m{sup}3). In particular at low concentration, the bias observed for OC2v4 is about twice that observed for OC4v4. The same analysis made using the Gitelson et al. [J. Man Syst. 9 (1996) 283.] Coastal Zone Color Scanner (CZCS) regional algorithm (GIT) revealed that this model underestimates the pigments concentration but it does not exhibit a correlation between the error and the measures. On the other hand, when the NASA standard algorithms are applied to remotely sensed data, the behavior appears reversed: the OC2v4 algorithm exhibits better estimates than OC4v4, which is probably more affected by atmospheric correction problems. When applied to satellite data, the GIT algorithm performs bener than the NASA global algorithms, although the estimates are very poor in the high chlorophyll-a range. Two new Mediterranean algorithms are then proposed by fining our Mediterranean bio-optical data set with linear and OC2-like functional forms. The new algorithms perform well when applied either to the bio-optical measurements or to satellite data. The different behavior of the same algorithm when applied to biooptical measurements or to remotely sensed data demonstrates that the atmospheric correction is still the main source of error in ocean color data. Due to the relatively small number of available in situ data, the algorithms that we generated have to be considered very preliminary. Discussion was carried out on the reasons of the global algorithm misfit, providing possible explanations and some preliminary result. The influence of coccolithophores and of the yellow substance on the optical response of the Mediterranean waters is investigated, showing that they can at least partially explain the systematic misfit. All the above shows that a region like the Mediterranean Sea requires an independent treatment of the atmospheric and of the in-water bio-optical term to obtain reliable estimates of phytoplankton activity.
机译:本文的主要目的是验证SeaWiFS衍生的叶绿素a在地中海的浓度。包含原位叶绿素-a分布以及水中和水上辐射亮度的光学测量的数据集用于评估地中海中几种海洋颜色算法的性能。分析显示,美国国家航空航天局(NASA)全局算法(OC2v4和OC4v4)对叶绿素a浓度进行了系统的高估。通过在低值(C <0.15 mg / m {sup} 3)上显示出显着差异,该误差似乎与叶绿素a浓度相关。特别是在低浓度下,对OC2v4观察到的偏差约为对OC4v4观察到的偏差的两倍。使用Gitelson等进行的相同分析。 [J.曼系统9(1996)283.]海岸带彩色扫描仪(CZCS)区域算法(GIT)显示,该模型低估了颜料的浓度,但在误差和测量值之间未显示相关性。另一方面,将NASA标准算法应用于遥感数据时,该行为似乎是相反的:OC2v4算法比OC4v4表现出更好的估计,这可能更受大气校正问题的影响。当应用于卫星数据时,尽管估计值在高叶绿素a范围内非常差,但GIT算法比NASA全局算法表现出更好的效果。然后通过使用线性和类似OC2的函数形式完善我们的地中海生物光学数据集,提出了两种新的地中海算法。当应用于生物光学测量或卫星数据时,新算法表现良好。当应用于生物光学测量或遥感数据时,同一算法的不同行为表明,大气校正仍然是海洋颜色数据中误差的主要来源。由于可用的现场数据相对较少,因此我们生成的算法必须被认为是非常初步的。对全局算法不匹配的原因进行了讨论,提供了可能的解释和一些初步结果。考察了椰菜丝和黄色物质对地中海水域光学响应的​​影响,表明它们至少可以部分解释系统失配。所有上述结果表明,像地中海这样的地区需要对大气和水中生物光学术语进行独立处理,才能获得可靠的浮游植物活动估计。

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