A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types
A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types
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DOI：
10.1016/j.rse.2019.04.027
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发表时间：
2019-08-01
影响因子：
13.5
通讯作者：
Tyler, A. N.
中科院分区：
文献类型：
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作者：
Neil, C.;Spyrakos, E.;Tyler, A. N.
人们已经开发出多种算法来从光学复杂水域收集的地球观测 (EO) 数据中检索叶绿素-a (Chla) 浓度 (mg m(-3))。当 Chla 与其他光学活性成分共存时，检索精度变化很大，并且常常不能令人满意。此外，检索算法在空间和时间上不同光学复杂系统中的适用性和局限性往往未被考虑。首先，本文为不同架构设计的 48 种 Chla 检索算法提供了广泛的性能评估。这些算法在其原始参数设置中进行了测试，然后使用从 185 个全球内陆和沿海水生系统（涵盖 13 种不同光水类型 (OWT)）收集的原位遥感反射率 (R-rs(lambda)、sr(-1)) 数据 (n = 2807) 进行重新调整。然后，本文展示了整个观测数据集和单个 OWT 内的检索性能，以确定在不同光学特性的水域中检索 Chla 的测试模型的最有效模型。结果表明，当将模型输出与完整原位数据集的原位测量 Chla 以及 13 个不同 OWT 内的原位测量 Chla 进行比较时，检索性能存在显着差异。重要的是，与使用完整的原位数据集（即一种算法，一种参数化）简单地校准相同算法相比，重新调整算法以优化每个 OWT 的参数化（即一种算法，多种参数化）可以改善 Chla 的整体检索。根据性能最佳 Chla 算法的相对百分比差异误差，这使得检索精度提高了 25%。通过允许模型类型和特定参数化在 OWT 之间变化（即多个算法、多个参数化），进一步实现了性能的改进。这种用于动态选择水下算法的自适应框架被证明可以在连续的生物地理光学条件下全面改进 Chla 检索。最终的动态集成算法生成（log(10) 转换的）Chla 估计值，相关系数为 0.89，平均绝对误差为 0.18 mg m(-3)。本研究中提出的 OWT 框架展示了一种统一的方法，通过汇集一系列算法来从空间监测全球范围内的内陆水域。
Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations (mg m(-3)) from Earth observation (EO) data collected over optically complex waters. Retrieval accuracy is highly variable and often unsatisfactory where Chla co-occurs with other optically active constituents. Furthermore, the applicability and limitations of retrieval algorithms across different optical complex systems in space and time are often not considered. In the first instance, this paper provides an extensive performance assessment for 48 Chla retrieval algorithms of varying architectural design. The algorithms are tested in their original parametrisations and are then retuned using in-situ remote sensing reflectance (R-rs(lambda), sr(-1)) data (n = 2807) collected from 185 global inland and coastal aquatic systems encompassing 13 different optical water types (OWTs). The paper then demonstrates retrieval performance across the full dataset of observations and within individual OWTs to determine the most effective model(s) of those tested for retrieving Chla in waters with varying optical properties. The results revealed significant variability in retrieval performance when comparing model outputs to in-situ measured Chla for the full in-situ dataset in its entirety and within the 13 distinct OWTs. Importantly, retuning an algorithm to optimise its parameterisation for each individual OWT (i.e. one algorithm, multiple parameterisations) is found to improve the retrieval of Chla overall compared to simply calibrating the same algorithm using the complete in-situ dataset (i.e. one algorithm, one parameterisation). This resulted in a 25% improvement in retrieval accuracy based on relative percentage difference errors for the best performing Chla algorithm. Improved performance is further achieved by allowing model type and specific parameterisation to vary across OWTs (i.e. multiple algorithms, multiple parameterisations). This adaptive framework for the dynamic selection of in-water algorithms is shown to provide overall improvement in Chla retrieval across a continuum of bio-geo-optical conditions. The final dynamic ensemble algorithm produces estimates of (log(10)-transformed) Chla with a correlation coefficient of 0.89 and a mean absolute error of 0.18 mg m(-3). The OWT framework presented in this study demonstrates a unified approach by bringing together an ensemble of algorithms for the monitoring of inland waters at a global scale from space.