Semi-Analytical Retrieval of the Diffuse Attenuation Coefficient in Large and Shallow Lakes from GOCI, a High Temporal-Resolution Satellite

Abstract

Abstract:Monitoring the dynamic characteristics of the diffuse attenuation coefficient (K d (490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ K d (490) and the remote-sensing reflectance, a semi-analytical algorithm for K d (490) was developed to determine the short-term variation of K d (490). From 2006 to 2014, the data about 412 samples (among which 60 were used as match-up points, 282 for calibrating dataset and the remaining 70 for validating dataset) were gathered from nine expeditions to calibrate and validate the aforesaid semi-analytical algorithm. The root mean square percentage error (RMSP) and the mean absolute relative error (MAPE) of validation datasets were respectively 27.44% and 22.60 ± 15.57%, while that of the match-up datasets were respectively 34.29% and 27.57 ± 20.56%. These percentages indicate that the semi-analytical algorithm and Geostationary Ocean Color Imager (GOCI) data are applicable to obtain the short-term variation of K d (490) in the turbid shallow inland waters. The short-term GOCI-observed K d (490) shows a significant seasonal and spatial variation and a similar distribution to the matching Moderate Resolution Imaging Spectroradiometer (MODIS) which derived K d (490). A comparative analysis on wind (observed by buoys) and GOCI-derived K d (490) suggests that wind is a primary driving factor of K d (490) variation, but the lacustrine morphometry affects the wind force that is contributing to K d (490) variation.

Inherent Optical Properties Attenuation Coefficient Modelling for Optical Shallow Water in Kepulauan Seribu of Jakarta, Indonesia

“…This method has limitations because measuring in situ data and lacking theoretical understanding may affect the attenuation prediction results for interpreting ocean colur data (Padial and Thomaz, 2008;Gomes et al, 2018). This condition causes dependence on obtaining Kd (490) data from the marine colour satellite sensor using semianalytical algorithms (Jamet et al, 2010;Huang and Yao, 2017). Although this method is less accurate in predicting attenuation in cloudy, shallow waters due to absorption by CDOM and phytoplankton and backscattering by suspended sediments, it tends to increase attenuation in the water column (Kvale and Meissner, 2017).…”

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

“…However, Wu et al [25] declared that either the GDPS algorithm or SeaDAS algorithm-based Rrs could not be retrieved successfully in the highly turbid coastal waters. Moreover, many studies have used the GDPS algorithm or SeaDAS algorithm, as well as improved algorithms, to retrieve other products, such as colored dissolved organic matter (CDOM) absorption coefficient, the diffuse attenuation coefficient, and chlorophyll-a concentration [6,16,21,22,26,27].…”