Assessing the Potential of Geostationary Himawari-8 for Mapping Surface Total Suspended Solids and Its Diurnal Changes

Abstract

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

Spatio‐Temporal Variability of Suspended Sediment Fronts (SSFs) on the Inner Shelf of the East China Sea: The Contribution of Multiple Factors

“…Finally, SSFs can emerge, evolve, migrate, decay, and disappear on a timescale of less than 1 month (Yin & Huang, 2016). At the same time, tidal advection can cause offshore‐shore oscillations in SSFs (Hafeez et al., 2021; Hu et al., 2016; Min et al., 2014; H. Yang et al., 2016; Yin & Huang, 2016; Y. Zhou, Xuan, et al., 2020), with oscillatory ranges that can exceed 10 km (Hu et al., 2016). Such variations cannot be expressed in terms of the bi‐monthly FP.…”

“…For satellite data, extracting ocean fronts is a key step in describing the frontal features in areas involving complex dynamic processes (Belkin, 2021). At present, several methods have been applied to extract SSFs from satellite images, including: (a) selecting a threshold SSC value as the front (Ge et al, 2020;Hafeez et al, 2021;Min et al, 2014;Wu et al, 2016), (b) histogram-based edge detection algorithms (Miller, 2004;H. Yang et al, 2016), (c) Sobel gradient-based edge extraction algorithms (Y.…”

“…For satellite data, extracting ocean fronts is a key step in describing the frontal features in areas involving complex dynamic processes (Belkin, 2021). At present, several methods have been applied to extract SSFs from satellite images, including: (a) selecting a threshold SSC value as the front (Ge et al., 2020; Hafeez et al., 2021; Min et al., 2014; Wu et al., 2016), (b) histogram‐based edge detection algorithms (Miller, 2004; H. Yang et al., 2016), (c) Sobel gradient‐based edge extraction algorithms (Y. Yang & Pang, 2012), (d) entropy‐based edge detection algorithms (Hu et al., 2016), (e) the GRIDHIST method (Kirches et al., 2016), and (f) quantitative and qualitative methods based on reflectance gradients and SSC gradients (Framinan & Brown, 1996; Jaureguizar et al., 2003; Nagy et al., 2008; Yin & Huang, 2016; Y. Zhou, Xuan, et al., 2020). These methods have been applied to a variety of satellite datasets including GOCI, Advanced Very High‐Resolution Radiometer (AVHRR), Medium‐Resolution Imaging Spectrometer (MERIS), and Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS).…”

“…Finally, SSFs can emerge, evolve, migrate, decay, and disappear on a timescale of less than 1 month (Yin & Huang, 2016). At the same time, tidal advection can cause offshore-shore oscillations in SSFs (Hafeez et al, 2021;Hu et al, 2016;Min et al, 2014;H. Yang et al, 2016;Yin & Huang, 2016;Y.…”

Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes

“…With the recent availability of imagery captured by Landsat-8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI), there is a growing trend in applications of this imagery in mapping constituents of inland bodies of water [30,31]. This is mainly due to the enhanced radiometric resolution (12-bit) and high spatial resolution (10−30 m) of the imagery provided by these sensors, because the spatial resolution of ocean color sensors that are either sun-synchronous or geostationary are too coarse (hundreds of meters at the best) for most of the inland waters [32][33][34]. The first attempts to derive lake constituents using Sentinel-2 imagery have been made in Estonian lakes based upon empirical methods [31] and in an oligotrophic German lake using a physics-based method [35], which demonstrated promising potential.…”