Assessment of Algorithms for Estimating Chlorophyll-a Concentration in Inland Waters: A Round-Robin Scoring Method Based on the Optically Fuzzy Clustering

Abstract

Recently, numerous bio-optical algorithms have been proposed to estimate chlorophyll-a (Chla) concentration in global surface waters. The surge of algorithms is mainly due to the heterogeneity of waters, especially in inland systems. To reduce redundant modeling work for inland waters and to enhance the usability of algorithms, it is urgent to evaluate the published algorithms for similar optical water types. However, approaches of the algorithm assessment in previous studies vary in error metrics usage or relative comparison, leading to the current lack of a standard or consensus in the community. Thus, we proposed a round-robin scoring method based on optically fuzzy clustering for algorithm assessment. An improved scoring approach (namely the sort-based method) was employed to aggregate the metrics of candidate algorithms. We generate a new set of optical water types using 1702 in situ samples, showing an improvement on our previous work [Bi et al. (2019)]. Then, an integrated Chla result was produced by blending the estimations of optimal algorithms weighted by the membership values, which showed better performance than that of any single algorithm. The blending framework presents more flexible and expandable than previous studies. We aim to illustrate the importance of assessment which considers key properties of algorithms (i.e., Accuracy, Precision, and Effectiveness) and to advocate more in-depth research on certain water types under the fuzzy clustering-based estimation framework (such as clean inland waters with low Chla). This article is accompanied by the open-source R package FCMm for implementation of the proposed method.