Turbid water atmospheric correction for GOCI: Modification of MUMM algorithm

Abstract

:The early Sea-viewing Wide Field-of-view Sensor(SeaWiFS) atmospheric correction algorithm which is the basis of the atmospheric correction algorithm for Geostationary Ocean Color Imager(GOCI) assumes that water-leaving radiances is negligible at near-infrared(NIR) wavelengths. For this reason, all of the satellite measured radiances at the NIR wavelengths are assigned to aerosol radiances. However that assumption would cause underestimation of water-leaving radiances if it were applied to turbid Case-2 waters. To overcome this problem, Management Unit of the North Sea Mathematical Models(MUMM) atmospheric correction algorithm has been developed for turbid waters. This MUMM algorithm introduces new parameter α, representing the ratio of water-leaving reflectance at the NIR wavelengths. α is calculated by statistical method and is assumed to be constant throughout the study area. Using this algorithm, we can obtain comparatively accurate water-leaving radiances in the moderately turbid waters where the NIR water-leaving reflectance is less than approximately 0.01. However, this algorithm still underestimates the water-leaving radiances at the extremely turbid water since the ratio of water-leaving radiance at two NIR wavelengths, α is changed with concentration of suspended particles. In this study, we modified the MUMM algorithm to calculate appropriate value for α using an iterative technique. As a result, the accuracy of water-leaving reflectance has been significantly improved. Specifically, the results show that the Root Mean Square Error(RMSE) of the modified MUMM algorithm was 0.002 while that of the MUMM algorithm was 0.0048.

Atmospheric Correction of GOCI Using Quasi-Synchronous VIIRS Data in Highly Turbid Coastal Waters

“…In the absence of in situ data, the results of the new algorithm (represented by QSV) are compared with those processed by the B2010 [33], A2012 [34], and L2013 [35] algorithms. VIIRS and GOCI Level-1 data were obtained online (https://oceancolor.gsfc.nasa.gov).…”

“…Numerous red/NIR modeling approaches have been investigated to deal with non-zero ρ w (red/NIR) values within the AC process. Three kinds of AC approaches are briefly reviewed herein-the B2010 AC algorithm proposed by Bailey et al [33] is currently adopted in SeaDAS, while the A2012 algorithm proposed by Ahn et al [34] and the L2013 algorithm proposed by Lee et al [35] are implemented in GDPS.…”

A Review of Satellite Remote Sensing Techniques of River Delta Morphology Change

“…Ryu et al (2002) and Shen et al (2008) showed that in tidal flat zones, thermal-infrared (TIR) band is the most sensitive to the location of waterline through density slicing. Work on Landsat has shown that mid-infrared bands (band 5 in the case of Landsat TM) is the most suitable for extracting the land water interface because it exhibits a strong contrast between land and water features due to the high degree of absorption of the mid-infrared wavelength by water (Manavalan et al, 1993;Kelly et al, 1998;Frazier and Page, 2000;Lee et al, 2001;Alesheikh et al, 2007).…”

Monitoring changes in suspended sediment concentration on the southwestern coast of Korea

“…To retrieve the SS concentration from GOCI TOA (top-of-atmosphere) data, atmospheric correction was initially performed for all GOCI images. The modified MUMM approach was used (Ruddick et al, 2000;Choi et al, 2012;Lee et al, 2013), in which contributions by aerosol and water to satellite reflectance are estimated on a per-pixel basis, with the assumption of spatially constant band-7:band-8 ratios for aerosol reflectance (ε) and water reflectance (α). Each GOCI image was converted to radiance on the sea surface (L w ), and L w was converted to remote sensing reflectance (R rs ) using the extraterrestrial solar irradiance (F o ) values for each GOCI band.…”