title: Development of an explicit algorithm for remote sensing estimation of chlorophyll a using symbolic regression.
abstract: The primary mission of ocean color remote sensing is to provide accurate marine bio-optical properties from satellite data. We propose a new algorithm that uses symbolic regression to estimate chlorophyll a (chl a) concentrations from remote sensing reflectance. We compared the accuracy and computational efficiency of the new algorithm to that of the explicit empirical algorithms (OC4v4 and OC4v6), and implicit algorithms based on neural networks or support vector machines (SVM). Results show that the accuracy of the symbolic regression algorithm is higher than that of the OC4 algorithms and comparable to that of implicit algorithms. The improvement is particularly important for high biomass areas (chl a ≥ 3 mg m(-3)) that are often found in optically complex waters. The computational efficiency of the explicit algorithm developed by symbolic regression is comparable to that of the two versions of OC4 algorithms and better than that of implicit algorithms based on SVM. With its good precision and fast processing, the symbolic regression algorithm is a powerful tool for remote sensing of chl a that could be used advantageously in the reprocessing of large datasets.
authors.affiliation: Fisheries and Oceans Canada, Freshwater Institute, Winnipeg, Canada. sltang@scsio.ac.cn
authors.forename: Shilin
authors.initials: S
authors.lastname: Tang
authors.forename: Christine
authors.initials: C
authors.lastname: Michel
authors.forename: Pierre
authors.initials: P
authors.lastname: Larouche
mesh_terms: D000465:Algorithms; D002734:Chlorophyll / Q000032:analysis*; D000077194:Chlorophyll A; D012044:Regression Analysis; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D002734:Chlorophyll; D000077194:Chlorophyll A