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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.

Amino acids are the material basis of almost all life activities. An improved understanding of the source, state, and cycle of amino acids is essential for determining the energy flow and material circulation of marine ecosystems. In the present study, an in situ rapid detection method of ultraviolet (UV, 266 nm) laser-induced fluorescence (LIF) technology was used to detect three natural, aromatic amino acids in the seawater. The laser-induced fluorescence peaks of aromatic amino acids tryptophan, tyrosine, and phenylalanine were located at 350 nm, 300 nm, and 280 nm, respectively. High, linear correlations between the concentrations of the aromatic amino acids and the fluorescence peak heights were observed, and the lowest detectable concentrations of tryptophan, tyrosine, and phenylalanine were 4.70 &times, 10&minus, 9 mol/L, 2.76 &times, 8 mol/L, and 6.05 &times, 7 mol/L, respectively, which allowed us to quantify their concentrations by using laser-induced fluorescence. This paper not only provides a practical method for the detection of aromatic amino acids in seawater, but a new means to further understand the biogeochemical processes of carbon cycles in the deep sea.

The accurate estimate of surface chlorophyll a concentrations (Chla) by remote sensing presents a number of challenges where inherent and apparent optical properties have significant spatial or temporal variability. Indeed, Chla algorithms for Case 2 waters are often lake or region specific, and they are usually highly sensitive to changes in the dominant chromophoric constituents. This study develops and validates an absorption-specific approach to estimating Chla across an optically heterogeneous dataset. The approach is based on the classification of the optically dominant constituent. We tested this approach with in situ data from Taihu Lake, Poyang Lake, Chaohu Lake, Shitoukoumen Reservoir, Pearl River Estuary and Daya Bay as well as using HydroLight simulated data. The results show an improved performance when compared to most single Chla algorithms. We validated the approach with data from the Visible Infrared Imager Radiometer Suite (VIIRS). Results showed that this absorption-specific approach provided good Chla estimates over clear to very turbid waters with a wide range of optical conditions (R2 = 0.76, rRMSE = 35%, n = 230, p

An absorption-based approach was used to determine surface particulate organic carbon (POC) concentrations in both inland and coastal waters. The improved performance of this approach was based on the specification of local POC absorption characteristics based on dominant POC sources; phytoplankton or detritus based. This specification was made using a new POC-Index (PI), developed and tested across a range of POC (300–10,000 mg/m3) conditions in temporally and spatially heterogeneous inland and coastal waterbodies. The POC model was based on remote sensing reflectance (Rrs, sr−1) in four wavebands: Rrs(751), Rrs(488) and Rrs(R/G), where R is the red band [Rrs(672)] for detritus dominated waters and G is the green band [Rrs(555)] in the phytoplankton dominated waters. The model provided a high R2 (0.74) and relatively low rRMSE (42.0%, N = 136, p