Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs
Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs
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DOI：
10.1016/j.rse.2021.112710
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发表时间：
2021-12
影响因子：
13.5
通讯作者：
Xiaohan Liu;C. Steele;S. Simis;M. Warren;A. Tyler;E. Spyrakos;Nick Selmes;P. Hunter
中科院分区：
文献类型：
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作者：
Xiaohan Liu;C. Steele;S. Simis;M. Warren;A. Tyler;E. Spyrakos;Nick Selmes;P. Hunter
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。由于对大气效应进行校正，以及缺乏可普遍适用于非共变物质浓度跨越几个数量级的水体的算法，水质的光学遥感受到重大不确定性的影响。我们开发了一种在光学水类型（OWT）分类方案中逐像元估算叶绿素-a （Chla）卫星产品不确定性的方法。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，趋势检测或建模）以及算法研究的未来方向。Chla的观测结果与先前建立的13个OWT类别相关，每个类别对应特定的生物光学特性。然后将Chla的特定算法- OWT组合对应的不确定性模型表示为OWT类隶属度评分的函数。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检索在较宽的类隶属度范围内表现出普遍的鲁棒性响应，表明其应用范围较广（0.01 ~ 362.5 mg/m3）。低OWT成员分数和高产品不确定度确定了光学水类型需要进一步探索的条件，以及生物地球化学卫星检索算法需要进一步改进的条件。这里演示了中分辨率成像光谱仪（MERIS）的过程，但可以重复用于其他传感器，大气校正方法和光学水质变量。
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 (Rw), 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.