Common aquatic remote sensing algorithms estimate the trophic state (TS) of inland and nearshore waters through the inversion of remote sensing reflectance (Rrs ($\lambda$)) into chlorophyll-a (chla) concentration. In this study we present a novel method that directly inverts Rrs ($\lambda$) into TS without prior chla retrieval. To successfully cope with the optical diversity of inland and nearshore waters the proposed method stacks supervised classification algorithms and combines them through meta-learning. We demonstrate the developed methodology using the waveband configuration of the Sentinel-3 Ocean and Land Colour Instrument on 49 globally distributed inland and nearshore waters (567 observations). To assess the performance of the developed approach, we compare the results with TS derived through optical water type (OWT) switching of chla retrieval algorithms. Meta-classification of TS was on average 6.75% more accurate than TS derived via OWT switching of chla algorithms. The presented method achieved $>$ 90% classification accuracies for eutrophic and hypereutrophic waters and was $>$ 12% more accurate for oligotrophic waters than derived through OWT chla retrieval. However, mesotrophic waters were estimated with lower accuracy from both our developed method and through OWT chla retrieval (52.17% and 46.34%, respectively), highlighting the need for improved base algorithms for low - moderate biomass waters. Misclassified observations were characterised by highly absorbing and/or scattering optical properties for which we propose adaptations to our classification strategy.

常见的水生遥感算法通过遥感反射率（Rrs（$\lambda$））转换成叶绿素一个（叶绿素a）的浓度。在这项研究中，我们提出了一种直接转换Rrs（$\lambda$）放入TS，无需事先进行chla检索。为了成功应对内陆和近岸水域的光学多样性，该方法堆叠了监督分类算法，并通过元学习对其进行了组合。我们在49个全球分布的内陆和近岸水域中使用Sentinel-3海洋和陆地颜色仪器的波段配置演示了开发的方法（567个观测值）。为了评估所开发方法的性能，我们将结果与通过chla检索算法的光学水类型（OWT）切换得出的TS进行了比较。与通过chla算法的OWT切换得出的TS相比，TS的元分类平均平均准确率高6.75％。提出的方法实现了$>$ 富营养化和富营养化水域的分类精度为90％， $>$寡营养水比通过OWT chla检索获得的准确度高12％。然而，从我们开发的方法和通过OWT chla检索估计的中营养水的准确性较低（分别为52.17％和46.34％），这突出表明需要针对中低度生物质水改进基础算法。错误分类的观测的特征是具有高度吸收和/或散射的光学特性，因此我们建议对分类策略进行调整。