Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing

Abstract

Abstract:Remote sensing reflectance (R rs ) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, 2003; summer and winter, 2006/2007; and spring and autumn, 2007). Specifically, we analyzed classification features of R rs spectra and obtained spatio-temporal characteristics of reflectance and bio-optical properties in the coastal waters. Yellow Sea waters were classified into the following four typical regions based on their spatial distribution characteristics: middle of the Yellow Sea (MYS), north Yellow Sea (NYS), coastal Shandong (CS), and Jiangsu shoal (JS), and five water type categories consisting of Classes A-E were used to represent water colors from clear to very turbid. Application of this classification scheme to Medium Resolution Imaging Spectrometer (MERIS) imagery revealed seasonal variations in the data, which suggests that the water types have both significant temporal and spatial distributions. In particular, the area of Class E waters in the Jiangsu shoal tended to gradually shrink in summer and expand in winter. The spatio-temporal variability was due to the influence of various environmental factors such as currents, tidal activity, fresh water discharges, monsoon winds, and typhoons.

Evaluation of Five Atmospheric Correction Algorithms over French Optically-Complex Waters for the Sentinel-3A OLCI Ocean Color Sensor

“…The 37 in situ spectra are representative of a large range of bio-optical water types from weakly-turbid to very turbid waters, with a large range of magnitudes at 400 nm and a peak magnitude ranging around 490, 555, or 700 nm (or around 820 nm in French Guiana) [106,107]. The use of the QAS algorithm allowed us to get 10 out of 23 optical water type clusters following the clustering method developed by Wei et al [92].…”

Optical types of inland and coastal waters

“…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 ).…”

“…7), 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 2001(Moore et al , 2009(Moore et al , 2014Reinart et al 2003;Lubac and Loisel 2007;Le et al 2011;M elin et al 2011;Spyrakos et al 2011;Vantrepotte et al 2012;Tilstone et al 2012 Ye et al 2016) 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 (2012Sun et al ( , 2014 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.…”

Preface: Remote Sensing in Coastal Environments

“…Ye et al [11] characterized the bio-optical properties of the Yellow Sea by using in situ R rs data acquired in six cruises from 2003 to 2007 from 618 stations. The mixed classification method was able to separate the Yellow Sea in to four regions and five water types based on the spatial distribution of the water color from clear to very turbid consisting of classes A to E. They applied the classification scheme to MERIS data which showed that the water types have significant seasonal variations.…”