Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (R_w), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m³). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

卫星产品不确定性估计对于遥感算法的进一步开发和评估以及用户群体（例如建模者、气候科学家和决策者）至关重要。水质的光学遥感受到大气效应校正产生的重大不确定性的影响，以及缺乏可普遍应用于跨越几个数量级的非共变物质浓度的水体的算法。我们开发了一种方法，可以在光学水类型 (OWT) 分类方案中逐个像素地估算叶绿素 a (Chla) 卫星产品的不确定性。该方案有助于为每个卫星像素动态选择最合适的算法，而相关的不确定性通知下游数据的使用（例如 g.，用于趋势检测或建模）以及算法研究的未来方向。对 Chla 的观察与 13 个先前建立的 OWT 类别相关，这些类别基于它们相应的出水反射率（R _w )，每个类别对应于特定的生物光学特性。对应于特定算法的不确定性模型 - Chla 的 OWT 组合然后表示为 OWT 类成员分数的函数。将这些不确定性模型嵌入到卫星图像的模糊 OWT 分类方法中，可以在没有水体生物地球化学特征的先验知识的情况下估计 Chla 和相关产品的不确定性。根据每像素模糊 OWT 隶属度对 Chla 算法结果进行混合后，Chla 检索在广泛的类隶属关系上显示出普遍稳健的响应，表明应用范围很广（从 0.01 到 362.5 mg/m ³）。低 OWT 成员分数和高产品不确定性确定了需要进一步探索光学水类型的条件，以及需要进一步改进生物地球化学卫星检索算法的条件。此处为中分辨率成像光谱仪 (MERIS) 演示了该过程，但可以针对其他传感器、大气校正方法和光学水质变量重复此过程。