Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Abstract

Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. OPEN ACCESSRemote Sens. 2015, 7 14732POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

Optical types of inland and coastal waters

“…), many of which contained data from inland and coastal systems that importantly demonstrates a continuum of OWTs that extends across system boundaries. Previous related research (Moore et al ; Reinart et al ; Lubac and Loisel ; Le et al ; Mélin et al ; Spyrakos et al ; Vantrepotte et al ; Tilstone et al ; Mélin and Vantrepotte ; Shen et al ; Ye et al ) has suggested a substantially smaller number of optical clusters but these studies were primarily conducted at regional scales where sample sizes and the global representativeness of waterbodies considered might have limited the resolution of OWTs. Sun et al () suggested a different approach for optical classification of aquatic systems based on the normalized trough depth at 675 nm and data from turbid and productive waterbodies.…”

“…More recent studies have moved toward the differentiation of water types in optically complex environments using in situ and/or satellite‐derived reflectance data. Most of these studies have considered the range of optical classes in marine systems (English Channel and North Sea: Lubac and Loisel ; Tilstone et al ; Vantrepotte et al , Iberian coastal waters: Spyrakos et al ; Adriatic Sea: Mélin et al , Yellow Sea: Ye et al ; Northwest Atlantic shelf: Moore et al , global ocean: Moore et al , global coastal waters: Mélin and Vantrepotte ) with only a few studies focussed on inland systems (lakes and reservoirs in China: Le et al ; Shen et al ; Estonian and Finnish lakes: Reinart et al ). Overall, these classification schemes can substantially improve the remote sensing products associated with individual optical water types (OWTs), and have demonstrated the need for a better understanding of the underlying variability especially in nearshore and inland waterbodies (Moore et al ).…”

Algoritmo ckMeans Aplicado ao Sensoriamento Remoto

“…Em trabalhos como [7], [8] e [9] os autores trabalham com o algoritmo Fuzzy C-Means aplicado ao sensoriamento remoto. Pelo algoritmo ckMeans ser mais rápido que o algoritmo Fuzzy C-Means decidiu-se aplicar também na área de sensoriamento remoto.…”

Preface: Remote Sensing of Water Resources

“…Shen et al [14] presented a fuzzy c-mean (FCM) based clustering method to classify several optically complex waters in China using in situ remote sensing reflectance (R rs (λ)) data. Initially the proposed classification scheme clusters the remote sensing reflectance spectra into four classes and then it establishes the relationship between R rs (λ) and bio-optical/environmental parameters in each class.…”