Abstract
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.
Highlights
The health of the world's freshwater bodies is of vital importance to the biosphere but monitoring water quality in all estimated 117 million lakes is not possible with conventional methods (Downing et al, 2006; Dudgeon et al, 2006; Verpoorter et al, 2014)
This study aimed to introduce a framework for generating per-pixel product uncertainty for Chla and associated products from satellite ob servations in optically complex inland waters
Four Chla algorithms were included from previous round-robin comparison and algorithm coefficient optimization (Neil et al, 2019) and assignment to 13 Optical Water Type (OWT) (Spyrakos et al, 2018)
Summary
The health of the world's freshwater bodies is of vital importance to the biosphere but monitoring water quality in all estimated 117 million lakes is not possible with conventional methods (Downing et al, 2006; Dudgeon et al, 2006; Verpoorter et al, 2014). Chla products from Earth Observation (EO) can describe variations over time and space, providing critical insight into changing trophic status and environmental stressors including associated changes in phenology (Palmer et al, 2015) This makes Chla one of the most fundamental parameters in oceanic and limnologic research, climate change studies, and aquatic ecosystem management (Boyer et al, 2009; Karydis and Kitsiou, 2019; Kromkamp and Van Engeland, 2010). As with any EO measurand, product uncertainty forms an inherent element of aquatic remote sensing (IOCCG, 2019; Merchant et al, 2017) It is common practice in studies of ocean-colour to evaluate product uncertainty using concurrent in situ measurements at global or regional scales (Mueller and Fargion, 2002). The capability to accurately predict product uncertainty is crucial in such situations to inform downstream applications, further research directions and new remote sensing capabilities
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