Remote sensing product uncertainties for phytoplankton chlorophyll-a (chla) concentration in oligotrophic and mesotrophic lakes and reservoirs were characterised across 13 existing algorithms using an in situ dataset of water constituent concentrations, inherent optical properties (IOPs) and remote-sensing reflectance spectra $\left(R_{rs}\left(\lambda \right)\right)$ collected from 53 lakes and reservoirs (346 observations; chla concentration < 10 mg m^-3, dataset median 2.5 mg m^-3). Substantial shortcomings in retrieval accuracy were evident with median absolute percentage differences (MAPD) > 37% and mean absolute differences (MAD) > 1.82 mg m^-3. Using the Hyperspectral Imager for the Coastal Ocean (HICO) band configuration improved the accuracies by 10–20% compared to the Ocean and Land Colour Instrument (OLCI) configuration. Retrieval uncertainties were attributed to optical and biogeochemical properties using machine learning models through SHapley Additive exPlanations (SHAP). The chla retrieval uncertainty of most semi-analytical algorithms was primarily determined by phytoplankton absorption and composition. Machine learning chla algorithms showed relatively high sensitivity to light absorption by coloured dissolved organic matter (CDOM) and non-algal pigment particulates (NAP). In contrast, the uncertainties of red/near-infrared algorithms, which aim for lower uncertainty in the presence of CDOM and NAP, were primarily explained through the total absorption by phytoplankton at 673 nm $\left(a_{\varphi }\left(673\right)\right)$ and variables related to backscatter. Based on these uncertainty characterisations we discuss the suitability of the evaluated algorithm formulations, and we make recommendations for chla estimation improvements in oligo- and mesotrophic lakes and reservoirs.

使用水成分浓度、固有光学特性 (IOP) 和遥感反射光谱的原位数据集，通过 13 种现有算法对贫营养和中营养湖泊和水库中浮游植物叶绿素a (chla) 浓度的遥感产品不确定性进行了表征$\left(R_{rs}\left(\lambda \right)\right)$从 53 个湖泊和水库收集（346 个观测值；chla 浓度 < 10 mg m ^-3，数据集中位数 2.5 mg m ^-3）。中位绝对百分比差 (MAPD)​​ > 37% 和平均绝对差 (MAD) > 1.82 mg m ^-3. 与海洋和陆地颜色仪器 (OLCI) 配置相比，使用用于沿海海洋 (HICO) 波段配置的高光谱成像仪将准确度提高了 10-20%。检索不确定性归因于通过 SHapley Additive exPlanations (SHAP) 使用机器学习模型的光学和生物地球化学特性。大多数半解析算法的叶绿素检索不确定性主要取决于浮游植物的吸收和组成。机器学习 chla 算法对有色溶解有机物 (CDOM) 和非藻类色素颗粒 (NAP) 的光吸收表现出相对较高的敏感性。相比之下，红色/近红外算法的不确定性旨在降低存在 CDOM 和 NAP 的不确定性，$\left({\mathrm{一个}}_{\phi }\left(673\right)\right)$以及与反向散射相关的变量。基于这些不确定性特征，我们讨论了评估算法公式的适用性，并为改善贫营养和中营养湖泊和水库的 chla 估计提出了建议。