Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint contamination, and satellite coverage limitations. Level 4 (L4) gap-free images are generally created by averaging over specific periods (e.g., weekly, monthly, seasonal) or re-gridding data with coarser resolution to overcome these limitations. These approaches, however, often fail to capture anomalous events or fine-scale resolution processes, calling for more advanced methods. The Data Interpolating Empirical Orthogonal Function (DINEOF) method has proved effective in reconstructing missing OC data and capturing smaller-scale features in noisy fields. To the best of authors knowledge, DINEOF is here used for the first time to interpolate multispectral Remote Sensing Reflectance (Rrs) to produce a consistent and gap-free L4 Rrs dataset, minimizing errors in inferred ocean products, such as Chlorophyll-a (Chl), the most widely used proxy for phytoplankton biomass. Specifically, using a multivariate approach, we assessed the DINEOF technique’s capability to reconstruct Rrs, focusing on six bands (412, 443, 490, 510, 555, and 670 nm) and validating the results using extensive in situ datasets. Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. This study highlights the effectiveness of using Rrs as a standalone dataset for DINEOF interpolation. Operationally, it can derivate various gap-free and consistent biogeochemical parameters with reduced uncertainty, thus providing a more reliable and versatile method.

中文翻译：

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实现精确的 L4 海洋色彩产品：通过 DINEOF 插值遥感反射率


海洋色彩 （OC） 遥感通过提供与可持续海洋资源开发相关的连续生物和生态参数来造福社会。它增强了我们对气候变化的理解，并使我们能够监测各种变率尺度的海洋现象。然而，由于云层覆盖、大气校正失败、太阳光污染和卫星覆盖范围限制，每天都会出现重大数据缺口。4 级 （L4） 无间隙图像通常是通过对特定时间段（例如，每周、每月、季节性）进行平均或以较粗糙的分辨率重新网格化数据来创建的，以克服这些限制。然而，这些方法通常无法捕获异常事件或精细的解析过程，因此需要更先进的方法。数据插值经验正交函数 （DINEOF） 方法已被证明可以有效地重建缺失的 OC 数据和在噪声字段中捕获较小尺度的特征。据作者所知，DINEOF 在这里首次用于插值多光谱遥感反射率 （Rrs），以生成一致且无间隙的 L4 Rrs 数据集，从而最大限度地减少推断海洋产品中的误差，例如叶绿素-a （Chl），这是浮游植物生物量使用最广泛的代理。具体来说，使用多变量方法，我们评估了 DINEOF 技术重建 Rrs 的能力，重点关注六个波段（412、443、490、510、555 和 670 nm），并使用广泛的原位数据集验证结果。我们的结果表明，这种 “上游插值 ”方法可以生成一致的 Rrs 数据集，从而在用作远程 Chl 估计算法的输入时提高 L4 Chl 预测的准确性。 我们预计，使用来自即将到来的高光谱卫星任务的更丰富的光谱信息，L4 Rrs 精度将进一步提高。本研究强调了使用 Rrs 作为 DINEOF 插值的独立数据集的有效性。在操作上，它可以得出各种无间隙且一致的生物地球化学参数，同时降低不确定性，从而提供更可靠和通用的方法。