The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated ( r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN.

中文翻译：

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使用CMEMS开源原位数据评估地中海地区Sentinel-3卫星图像中的叶绿素a浓度

这项研究的目的是评估用于从Sentinel-3计算叶绿素a（Chl-a）的两种著名算法（MERIS的洋色4 [OC4Me]和神经网络[NN]）的效率。海洋和陆地颜色仪器（OLCI）与覆盖地中海的现场测量结果进行了比较。从哥白尼海洋环境监测局（CMEMS），更具体地说从标题为INSITU_MED_NRT_OBSERVATIONS_013_035的数据集获得的原位数据集，在不同深度处提取了Chl-a值。计算在渗透深度的Chl-a的浓度。然后，将水分类为Case-1和Case-2两类。对于Case-2水域，OC4Me在0–2 h的时间范围内与原位数据呈现中等程度的相关性。与NN算法相比，在计算非常弱的相关性的情况下，对于OC4Me算法，计算了Case-1水域的偏倚统计指标的较低值。与NN相比，OC4Me算法的Pearson相关性更高（r> 0.5）。OC4Me的性能优于NN。