Abstract The accurate estimate of surface chlorophyll a concentrations (Chla) by remote sensing presents a number of challenges where inherent and apparent optical properties have significant spatial or temporal variability. Indeed, Chla algorithms for Case 2 waters are often lake or region specific, and they are usually highly sensitive to changes in the dominant chromophoric constituents. This study develops and validates an absorption-specific approach to estimating Chla across an optically heterogeneous dataset. The approach is based on the classification of the optically dominant constituent. We tested this approach with in situ data from Taihu Lake, Poyang Lake, Chaohu Lake, Shitoukoumen Reservoir, Pearl River Estuary and Daya Bay as well as using HydroLight simulated data. The results show an improved performance when compared to most single Chla algorithms. We validated the approach with data from the Visible Infrared Imager Radiometer Suite (VIIRS). Results showed that this absorption-specific approach provided good Chla estimates over clear to very turbid waters with a wide range of optical conditions (R2 = 0.76, rRMSE = 35%, n = 230, p

中文翻译：

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基于来自 VIIRS 观测的水分类，在各种光学条件下远程估计叶绿素 a 浓度

摘要 通过遥感对表面叶绿素 a 浓度 (Chla) 的准确估计提出了许多挑战，其中固有和明显的光学特性具有显着的空间或时间可变性。事实上，案例 2 水域的 Chla 算法通常是特定于湖泊或区域的，并且它们通常对主要发色团成分的变化高度敏感。这项研究开发并验证了一种吸收特定的方法来估计光学异质数据集的 Chla。该方法基于光学优势成分的分类。我们使用来自太湖、鄱阳湖、巢湖、石头口门水库、珠江口和大亚湾的原位数据以及使用 HydroLight 模拟数据测试了这种方法。结果表明，与大多数单一 Chla 算法相比，性能有所提高。我们使用来自可见光红外成像仪辐射计套件 (VIIRS) 的数据验证了该方法。结果表明，这种特定于吸收的方法在各种光学条件下（R2 = 0.76，rRMSE = 35%，n = 230，p