MDPI Machine Learning Algorithm User Guide

Keywords: Himawari-8, ocean colour, artificial
neural networks, Great Barrier Reef, coastal waters, total
suspended solids, machine learning, water quality

Product Usage Instructions

1. Introduction

The Remote Sensing Article provides insights into the use of
machine learning algorithms for retrieving total suspended solids
in the Great Barrier Reef using data from Himawari-8. The article
discusses the challenges and benefits of utilizing geostationary
Earth orbit satellites for continuous observation of coastal
areas.

2. Retrieval Process

The article highlights the importance of geostationary
satellites like Himawari-8 in capturing near real-time data on
coastal processes. It emphasizes the limitations of low Earth orbit
satellites for resolving short-term variability compared to
geostationary satellites.

3. Ocean Colour Sensors

The article mentions the significance of ocean colour sensors on
satellites for acquiring spatial information related to water
quality. It discusses the temporal dynamics observed by
geostationary satellites and their impact on monitoring coastal
phenomena.

Frequently Asked Questions (FAQ)

Q: What is the main focus of the Remote Sensing Article?

A: The main focus is on using a machine learning algorithm with
Himawari-8 data to retrieve total suspended solids in the Great
Barrier Reef.

Q: Why are geostationary satellites preferred for coastal
monitoring?

A: Geostationary satellites offer near continuous observation of
large areas with higher frequency, allowing for better monitoring
of rapidly changing coastal processes.

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remote sensing

Article
A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef
Larissa Patricio-Valerio 1,2,* , Thomas Schroeder 2, Michelle J. Devlin 3 , Yi Qin 4 and Scott Smithers 1

1 College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia; scott.smithers@jcu.edu.au
2 Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, GPO Box 2583, Brisbane, QLD 4001, Australia; thomas.schroeder@csiro.au
3 Centre for Environment Fisheries and Aquaculture Science, Parkfield Road, Lowestoft, Suffolk NR33 0HT, UK; michelle.devlin@cefas.co.uk
4 Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, GPO Box 1700, Canberra, ACT 2601, Australia; yi.qin@csiro.au
* Correspondence: larissa.patriciovalerio@my.jcu.edu.au

Citation: Patricio-Valerio, L.; Schroeder, T.; Devlin, M.J.; Qin, Y.; Smithers, S. A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef. Remote Sens. 2022, 14, 3503. https://doi.org/ 10.3390/rs14143503
Academic Editor: Chris Roelfsema
Received: 15 May 2022 Accepted: 19 July 2022 Published: 21 July 2022
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Abstract: Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km2 spatial resolution. Coupled ocean atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L-1 within the validation range of 0.14 to 24 mg L-1, with a detection limit of 0.25 mg L-1. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR.
Keywords: Himawari-8; ocean colour; artificial neural networks; Great Barrier Reef; coastal waters; total suspended solids; machine learning; water quality
1. Introduction Ocean colour sensors onboard low Earth orbit (LEO) satellites, such as MODIS/Aqua,
VIIRS/Suomi-NPP, and OLCI/Sentinel-3, have provided long-term records of valuable and cost-effective observations to examine daily to inter-annual dynamics of water quality in the Great Barrier Reef (GBR) [15]. The LEO satellites scan the same geographic area within one or two days at best; however, the time-lag between two consecutive and identical orbits (i.e., revisit periodicity) commonly varies between one and up to four weeks. In addition, the ocean colour imagery may be largely affected by the presence of clouds and sun glint, limiting the retrieval of high quality observations [6]. This can require a weeklyto-monthly set of daily images from the same area to develop a composite cloudless view of the ocean. Consequently, the temporal capability of LEO satellites is insufficient to develop a comprehensive observational system and to effectively monitor short-term dynamic coastal processes, such as phytoplankton diel cycles, daily progression of flood plumes, and

Remote Sens. 2022, 14, 3503. https://doi.org/10.3390/rs14143503

https://www.mdpi.com/journal/remotesensing

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tidal and wind-driven resuspension [79]. Researchers and environmental managers still

rely on LEO ocean colour produ-cts for acquiring cost-effective spatial information- in the

coastal GBR [10,11], but recognise the limitations of these techniques to- resolve shor-t-term

variability.

–

Satellites on a g-eostationary Earth orbit (GEO), otherwise, allow near continuous

observation of large areas of the globe at higher frequency (minutes to hours) com- pared

to the near daily revisit frequency of LEO platforms, particularly over the tropics [9]. The

world’s first Geostationary Ocean Colour Imager (GOCI-I), launched in 2010, has revealed

the temporal dynamics of rapidly changing coastal- processes in Northeast Asia, such as

of turbidity plumes and harmful algal blooms [12,13]. Its success provided a useful case

for the future development of global GEO ocean colour missions [14]; however, none of

the missions proposed for launching within the next decade were designed for observing

Australian waters. Nevertheless, GEO satellites are globally operated for meteorological ob-

servations and recent technological advances have leveraged their capabilities for collecting data over the oceans, allowing more dynamic processes to be observed from space [-1517].

Tofhbe annedxst-ignentheera-vtiiosinblGe EspOemctreutemor(o2loogri3cailnssetenasdorosfaorenleyq1uibpapnedd)

with an increased number combined with improved

ragreadendovtisaoltyTnmahctpieeeortsornAivaacdlirsldvyoeiawnnpnsgoeciitdednivd,itui-ftHooryfnri-(mavsthliiagemewwnfieaaatlrr-etsi-totouIrm-tno-nimplaoorgiegsee,eci-rceara(daA-lnteioeHnobat)Ires)a-edtnorrdrnvueabeovtoniicaosboirntoldosafr-uHroedrvqiemecudarealvAni-wbicusraiauesrtstiair-ol[8ai1ns/l8ia9ac]ta.,iGopinnEacbOloiulfsidtaEiietnaesrglt[lih9tth]e.–feriTosGhmcBeusRrae-.

Himawa-ri-8 is positioned at 140.7E above the equator and with a 10 min scan rate, it captures at least 48 fu-ll-disk observations within a day (8 am to 4 pm local time). While the AHI instrument was designed for meteorological applications, its visible and near-in-frared

(VNIR) bands (Figure 1 and Table 1) enable the detection of marine features with strong

optical signals, such as those from highly turbid waters [1921]. In addition, Himawari-8

ultra-high- tempo- ral resolution observations allow the monitoring of ocean properties from

sub-hourly to inte-r-annual time s-cales for the entire GBR lagoon and the adjacent oceanic

basin without inter-orbital data g-aps.

wFiigthurtehe1.trHainmsmawisas-riio-n8

spectral response functions of the visible and infrared bands (solid white lines) of the atmospheric gases (grey filled line) and the transmission by ozone (red

solid line) between 400 and 1000 nm.

An extensive range of applications for monitoring and management of oceanic areas have the potential to be derived from Him-awari-8, including for ocean colour -[22,23]. Recent studies have demonstrated the feasibility of Hima-wari-8 observations for detection of total suspended solids (TSS) in coastal waters [17,24] and for chloroph-yll-a concen-tration (CHL) in the open ocean [22]. These results suggest an exciting opportunity for mon-itoring high-frequ-ent and dynamic processes in the coastal GBR. However, although s-everal ocean colour algorithms may be available for satellite retrieval of coastal water quality parameters, they may be unsuitable for the optical complexity of the GBR or not applicable to Himawari-8 observations.

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– –
Tleanbglteh1s.anHdimbaanwdawrii-d8-thA, dasvsaoncciaetdedHsipmaatiwalarreisIomluatgioenr.vSiisgibnlael-aton-dn- oni-esearr–aintiforsar(SeNd Rb)anfrdosmcpenertrfoarlmwaanvce–etest results [25].

Band # (Name) #1 (blue) #2 (green) #3 (red) #4 (NIR)

Band Centre (Width) 470.64 (45.37) nm 510.00 (37.41) nm 639.15 (90.02) nm 856.69 (42.40) nm

Spatial Resolution 1 km 1 km 0.5 km 1 km

SNR @100% Albedo 585 (641.5) 645 (601.9) 459 (519.3) 420 (309.3)

Model-b-ased ocean colour algorithms that utilise radiative transfer simulations have shown superior performance for application in multi–temporal remote sensing studies of coastal waters compared to empirical algorithms [26]. Specifically, neural networks are a computationally efficient inversion method for remote sensing applications in optically complex coastal waters due to their capability to approximate non–linear functional rela-tionships [2735]. This paper describes the development of a model–based neural network ocean colour algorithm (Figure 2) for Himawari-8-and parameterised for the coastal water-s of the GBR. The one-step- inversion algorithm was developed to estimate TSS directly from Himawari–8 top–of–atmosphere (TOA) observations with a multilayer perceptron, a class of artificial neural networks (ANN). First, the spectral angular distribution of the TOA reflectances RTOA() sr-1 was simulated at the VNIR Himawari–8 bands with an existing coupled oceanatmosphere radiative transfer (RT) model (forward model). The RT simulations included realistic variations in water quality parameters, and atmospheric and illumination conditions. Several ANN experiments (inverse models) were then de-signed, trained, and tested to retrieve TSS at the Himawari–8 bands based on the simulated TOA radiances. Finally, the Himawari–8 retrieved TSS outputs were statistically assessed against concurrent in situ water quality data in the GBR and the limitations of the selected algorithm were investigated.

Figure 2. Flow diagram of the model–based ocean colour algorithm developed for Himawari–8.
2. Methods The parameterisation of the radiative transfer simulations and the design of the
ANN inverse model are specified in the following subsections. The forward and inverse model parameterisations follow an approach previously developed for European coastal waters [3638] but were adapted in this study f-or the in-water optical conditions of the GBR [39]. Additionally, the H-imawari-8 acquisition, processing and masking procedures, and the ocean colour processor are described for th-e model-based algorithm developed here. The validation protocol and methods for the assessment of the algorithm limitation-s are presented, as well as first results for TSS monitoring in the GBR.

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2.1. The Forward Model
In this work, a scalar version of the Matrix-Operator MOdel (MOMO) [40,41] was employed for the coupled oceanatmosphere radiative transfer simulations of the Himawari-8 VNIR bands (Table 1). Neglecting atmospheric polarisation may lead to errors of 12% at TOA, which is acceptable for coastal water applications [42]. The Himawari-8 RTOA() were simulated for a realistic range of in-water and atmospheric optical properties of the GBR.
The simulated oceanatmosphere system is stratified in several horizontally homogeneous plane-parallel layers where the defined types and concentrations of aquatic and atmospheric optical constituents are considered. The height of the simulated atmosphere (TOA) is 50 km thick and divided into 11 layers where the vertical profiles of pressure, temperature, and humidity follow a U.S. Standard Atmosphere [43]. The attenuation by Rayleigh scattering is accounted for with two barometric surface pressures of 980 hPa and 1040 hPa. The atmosphere is split into a boundary layer (02 km), a free troposphere (212 km), and a stratosphere (1250 km). In each layer, the simulations were performed for eight distinct aerosol assemblages with varying concentrations of the aerosol optical thickness (a) at 550 nm between 0.015 and 1.0. Each aerosol assemblage is composed of the three main aerosol models, a maritime model in the boundary layer, a continental model in the free troposphere, and a sulphuric acid model in the stratosphere, at a relative humidity between 70% and 99%. The a range was determined from multi-annual Level 2 sun-photometer observations of the AERONET [44,45] station at the Lucinda Jetty Coastal Observatory (LJCO) located in the central GBR [18.52S, 146.39E]. Analysis of the corresponding Ångström coefficients [46] between 550 and 870 nm at the LJCO AERONET station confirm a mixture of maritime and continental aerosol types corresponding to those used in the RT simulations.
The transmission of atmospheric gases (except for O3) were derived from the HighResolution Transmission Molecular Absorption (HITRAN) database [47] and implemented in the radiative transfer simulations via the modified k-distribution model of Bennartz and Fischer [48]. The radiative transfer simulations were performed assuming a constant ozone loading of 344 Dobson Units (DU) [43]. The Himawari-8 bands were simulated for 17 solar and observation angles and 25 equally spaced relative azimuth angles. The simulations were conducted for realistic water quality fluctuations, represented by randomly selected unique concentrations of CHL, TSS, and yellow substances (YEL), hereafter referred to as concentration triplets. The ranges of the simulated concentration triplets were defined based on the dispersion of in situ correlated concentrations found in the GBR, following the approach by Zhang et al. [49]. The simulated concentration triplets were equally distributed in logarithmic space, so each order of magnitude was similarly represented while avoiding duplicated simulations.
The total spectral absorption of the sea water a() was modelled by a four-component bio-optical model accounting for the pure water absorption (aw), the absorption of phytoplankton and all dead organic material (i.e., detritus) ap1 as a function of CHL [0.01, 15], the absorption of non-algal particles ap2 as a function of TSS [0.01, 100.0], and the absorption of yellow substances ay at 443 nm [0.002, 2.5]. The absorption coefficient of pure water (aw) was modelled according to Pope and Fry [50] for the Himawari-8 visible bands 13 and by Hale and Querry [51] for band 4. The spectral absorption of phytoplankton and detritus ap1 followed a parameterisation of Bricaud et al. [52], while the absorption of non-algal particles ap2 was parameterised according to Babin et al. [53], with a mean slope Sp2 of 0.012 that was derived from in situ bio-optical data sampled in the GBR between 2002 and 2013. The spectral absorption coefficient of yellow substances ay was modelled according to Babin et al. [53], with a mean slope Sy of 0.015 that was also derived from in situ observations from the GBR [39].
The total spectral scattering of the sea water (b()) was modelled by a two-component bio-optical model [53] accounting for the scattering of pure water (bw) and scattering or organic and inorganic particles bp as a function of TSS. The pure seawater scattering

Remote Sens. 2022, 14, 3503

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coefficient was expressed as a wavelength dependent power law based in Morel [54],

defined for a global salinity average of 35 PSU. The scattering contribution of organic and

inorganic particles was combined to derive the total particulate scattering coefficient bp following the parameterisation of Babin et al. [55]. The mass specific scattering coefficient

of TSS particles bp of 0.31 m2 g-1 was calculated for the GBR waters, following Babin et al. [55]. A backscattering probability model for Case 2 waters was applied [49,56] to

calculate and select the in-water scattering phase functions (, ) based on the ratio of TSS and YEL. The simulations were performed for a large number of random concentration

triplets and atmospheric conditions, as previously outlined, to build a comprehensive

database of azimuthally resolved Himawari-8 RTOA(). From this database, statistically

representative training and test subsets were randomly extracted to develop the inverse

model. The training and test subsets each comprised 100,000 input vectors

containing

the: simulated RTOA at 470, 510, 640, and 856 nm bands, sea level atmospheric pressure between 980 and 1040 hPa, solar zenith angle (s), observing zenith (v), and relative azimuth ().

2.2. The Inverse Model

In this study, a multilayer perceptron (MLP), a class of feed-forward artificial neural network (ANN) [57], has been implemented as inverse model based on the Neural Network Simulator C-program developed by Malthouse [58], to approximate the functional relationship between the Himawari-8 RTOA() and the TSS concentration. The present MLP comprises an input layer, a hidden layer, and an output layer of neurons. Each neuron is connected with each neuron of the next layer by a weight. The supervised machine learning or training procedure can be described as follows:

The input neurons (ni) receive the input vector

, containing simulated reflectances

and the ancillary data described above, and propagates it to the hidden layer neurons

(nh).

· In the hidden layer, the artificial neurons sum up the weighted input signals and pass these through a non-linear transfer function and subsequently forward their outputs

to the output layer neurons (no).

· The cost function (i.e., mean squared errors, MSE–Equation (1)) between the sim-

ulated target outputs y t and the ANN computed outputs y c is calculated for the entire training dataset (N = 100,000), and the internal weights (W1, W2) of the network are adjusted.

· The training of the ANN is repeated until the cost function between output and target value is minimised.

MSE = y c – y t /N

(1)

The cost function is minimised by adapting the weight matrices (W1, W2) iteratively using a Limited Memory BroydenFletcherGoldfarbShanno optimisation algorithm [59]. For a three-layer MLP architecture, the complete analytic function is given by Equation (2):

W2 × S1

W1 × x

(2)

where S1 and S2 are the non-linear (Equation (3)) and linear transfer functions employed in the output and hidden layer, respectively.

S(x) = 1 + e-x -1

(3)

The number of neurons in the input and output layers were determined by the number of input and output parameters of the problem, whereas several experimental attempts

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were required to determine the optimal number of neurons in the hidden layer. The

experiments were designed by varying the number of hidden layer neurons from 10 to 100,

in increments of 10. A random but for all experiments fixed seed was used to initialise the –

weight configuration of the networks. The experiments included a principal component

analysis (PCA) as a pre-processing ste- p to decorrelate the RTOA() inputs. In addition, the experiments were designed with 0.8% spectrally uncorrelated signal-dependent rando-m – noise added to the RTOA inputs in each band. The ANN experiments were trained and tested with a subset of 100,000 input vectors randomly extracted from the radiative transfer

simulated dataset. Each input vector was associated with a logarithmic TSS concentration, – which was selected as the target output to be approximated by the supervised learning

procedure. All experiments were trained for 1000 iterations and the minimisation of the cost

function (Equation (1)) was computed over the entire training dataset at each iteration. An

independent test dataset of N = 100,000 vectors was used to monitor the network training

performance and to avoid over-fitting.

–

2.3.

TBhaesHicipmraowceasrsi-in8- gOscteeapns

Colour Processing for Himawari-8 raw

data

into

TSS

products

are

shown

Figure

Level 1 (L1) full disk Himawari-8 VNIR ban- ds were acquired, extracted over the GBR area –

(10 S, 29 S, 140 E, 157 E), geolocated, a-nd navigation corrected. The geolocated raw data

were transformed into Level 1b (L1b) TOA radiances (LTOA() W m-2sr-1µm-1 ) through –

tghreidawppalsicraetsiaomnpolfedpofrsot-mlau0.n5ckhmuptoda1tkedmctaolimbraattcihonthceoreefsfiocliuetn-itosn[o60f ]t.heTahseso6c4i0atnemd VbNanIRd

bands. The L1b calibrated LTOA() were normalised by the extra-terrestrial solar irradiance F() W -m-2 for each band. F() wascalculated as a function ofthe day of the year

and using the mean extra-terrestrial solar irradiance F values b-ased on Kurucz [61] and adapted to the Himawari-8 bands [62]. The resultant TOA reflect-ances RTOA() sr-1 at the VNIR Himawari-8 bandsserved as inputs to the inver-sion method. In addition, the

s, v, and values were calculated for each pixel of the satellite image as a function of latitude, longitude, and local time, following existing procedures [63], and converted into

cartesian coordinates (x, y, z).

Figure 3. Himawari-8-Ocean Colour Processing flowchart. HSD refers to Himawari-8 Stan-dard Data, GBR refers to Great Barrier Reef, VNIR refers to the Himawari-8 visible an-d near infrared bands (470, 510, 640, and 856 nm), and ANN refers to Artificial Neural Network.

the

ACulsoturadlimanasckoinntginoenf tHainmdaswuarrroi–u8nodbisnegrvwaatitoenrss.

was The

developed by Qin et al. [64] for 2 km resolution cloud mask was

resampled to the dust and smoke

1plkummHesimfraowmabrii-o8mg-raisds

and includes masking of pixels contaminated with burning. Likewise, pixels identified as emerged

surfaces, such as continental areas, islands, and shoals, were masked based on shapefiles

available from the Great Barrier Reef Marine Park Authority [65] database. A sun-glint

–

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mask was created by calculating the coordinates of the principal point of sun glint (PPS) as a function of the day of the year (solar inclination), local hour, latitude, and longitude [66], at 1 km spatial resolution. The contour of the sun disk was buffered for a circular radius of 1300 km from the coordinates of the PPS. The radius size was chosen after a series of visual tests were employed to ensure maximum coverage of the main sun disk area.
The Himawari-8 observations were normalised pixel-by-pixel and for each band with near-concurrent satellite data of total column ozone extracted from the Total Ozone from Analysis of Stratospheric and Tropospheric Satellite components (TOAST) product [67] prior to inversions. The TOAST product, with spatial resolution of 1.25 by 1 degrees and daily temporal resolution, was resampled to 1 km for compliance with the Himawari-8 grid. The Himawari-8 observations were normalised at each band by the ratio between the transmission of TOAST-derived ozone to the transmission of the simulated ozone column density of 344 DU. In addition, the mean sea level atmospheric pressure data from NCEP/NCAR `Reanalysis 2′ PaRt2m [6870] were utilised as inputs for the inversion of Himawari-8 observations. The `Reanalysis 2′ data are averaged every 6 h (0, 6, 12, 18 UTC) and sampled on a regular global grid of 2.5 degrees spatial resolution [71]. The closest concurrent PaRt2m data were acquired and resampled to the 1 km Himawari-8 grid. The retrieved TSS, associated masks, and metadata were saved in a NetCDF file, including pixel-wise associated flags for out-of-range inputs and outputs. The ranges of valid inputs and outputs were defined based on the RT simulated dataset. For instance, if a certain pixel input and/or output parameter exceeded the simulated ranges, the pixel was assigned a corresponding flag. The input and output flags were summed for each pixel of the Himawari-8 grid. The out-of-range flags were applied to the water quality products prior to the subsequent validation and application analyses.
2.4. Great Barrier Reef in Situ Data
In situ TSS measured between 2015 and 2018 by the Australian Institute of Marine Sciences (AIMS) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) were obtained from the IMOS Bio-optical Database [72] through the Australian Ocean Data Network (AODN) portal. Both CSIRO and AIMS use the gravimetric method to determine TSS concentration in seawater. The method consists of measuring the dry weight of suspended solids from a known volume of seawater sample after it has been vacuum filtered on a pre-weighted membrane filter. Further details on the methodology employed by AIMS and CSIRO are described in Great Barrier Reef Marine Park Authority [73] and Soja-Woz´niak et al. [74], respectively. Despite AIMS and CSIRO laboratories using slightly different methods to determine TSS (i.e., number of replicates, filter pads, rinsing, etc.), these datasets have been combined in this validation exercise. A total of 347 in situ data points with TSS ranging from 0.01 to 85 mg L-1 and a mean of 3.5 mg L-1 were considered. In situ data points within 1 km from coastline or reefs were excluded from the analysis to reduce uncertainties due to adjacency effects [75]. We included all in situ seawater samples taken at the surface (<0.5 m depth) of stations located at variable water depths (1.5 m to 40 m), with the shallowest data point presenting TSS > 10 mg L-1.
2.5. Validation Protocol
The validation protocol utilised in this study follows the experience of previous validation exercises for ocean colour remote sensing in Australia, including in the coastal GBR [27,76,77]. These studies described processing steps for extraction of satellite observations concurrent to in situ measurements in the coastal GBR, as well as useful statistical performance metrics.
Multiple Himawari-8 observations can be combined within a timeframe (i.e., hourly) to eliminate potential outliers and reduce sensor and environmental noise, likely improving estimates and validation performances [7,9,16]. Therefore, all available Himawari-8 observations scanned within ±30 min from the recorded in situ time were acquired for this validation exercise. Selected and processed 10 min Himawari-8 observations at the VNIR

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– —

bands with associated sun and observation geometry were subset to 3-by-3-pixe-l bo-x-es,

centred at the coordinates of each concurrent in situ data point. Likewise, 3-by-3-pixel subsets of concurrent masks (i.e., clouds, lan-d, reefs, and sun glint) and ancillary data (i.e., – ozone and pressure) were extracted. Near-true colour composites of selected Himawari-8 –

observations were visually inspected to eliminate matchups in waters with sharp horizontal

gradients in optical properties (i.e., turbidity fronts) or nearby clouds.

–

Hourly composites of valid subsets were computed by temporal average, disregarding –

masked pixels. The hourly aggregated- su-bsets were processed with the ANN inversion

algorithms and masked for out-of-range values. Finally, the median and standard deviation

of hourly TSS subsets were computed, excluding m- asked pixels. Only those subsets with two or less pixels masked per pixel-box were considered valid for matchup. The ANN

outputs were-computed in logarithmic scale (log10) and the concurrent in situ TSS was logtransformed for statistical analysis. An overview of the validation procedure is illustrated

in Figure 4. The performances were evaluated with regards to their root mean square error

(RMSE–or absolute error), bias, mean absolute percentage error (MAPE–or relative error), and the coefficient of determination (R2). Bias, R2, and RMSE were calculated in log10

–

space and MAPE was calculated in linear measurement and p the satellite-derived

psproadceu,cftowlloitwhi-nNgtEhqeunautimonbser(4o)f(v7a)l,iwd hmearetcmhuispsth. e

RMSE = 1/N (m -p)2

(4)

MAPE = 100/N |(m -p)|/p 2

(5)

R2 =

N(mp)- ( m)( p) m2 – ( m)2 N p2 – (

p)2

(6)

Bias = 1/N (m -p)

(7)

The ANN match-up- experiments were ranked based on the statistical metrics described – above. Preference was given to those experiments with the lowest RMSE because this statistical parameter is the cost function that is minimised during the ANN training. The best-perfo- rming experiment with the lowest number of neurons in the hidden layer was selected, to reduce the computational efforts for the inversion of Himawari-8 observa-tions – over the entire GBR.

Figure 4. A simplified overview of the algorithm validation procedure.

2.6. Assessment of Limitations

The signal-t-o-n- oise ratios (SNR) were computed for the visible and near-inf-rared

HEaimstearwnaSrti-a-8ndLTaOrdA

(Tim) oeb–seArvEaStTio)nast

scanned selected

between 08:00 to 16:00 local dates and cloud-fr-ee areas

time (Australian of the Coral Sea

–

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(16.25S, 151E and at 20.60S, 153.53E). Only post-July 2017 observations were considered for this analysis, given that their calibration coefficients were corrected for coherent and horizontal striping noise [63,78]. True colour snapshots available through the Himawari-8 Monitor P-Tree System [79] were browsed for target area selection and to ensure these were spatially uniform and unlikely to be influenced by clouds, sun glint, bio-optical features, and smoke plumes from terrestrial burning [80,81]. The selected Himawari-8 observations were converted from raw counts to physical units by applying calibration coefficients [60], with subsets of 51-by-51-pixels extracted and centred at the coordinates of the regions of interest. In addition, the subsets, associated masks, and geometric parameters were hourly aggregated. The 10 min and hourly aggregated subsets were masked for clouds, land, reefs, and sun glint, and their near-true colour composites were inspected for undetected features such as coral cays, reefs, cloud shadows, and sensor artefacts.
The SNR was calculated for each Himawari-8 band following Equation (8) [80]. Averaging LTOA() for all valid pixels within the target area gives Ltypical(), and taking the standard deviation () within the same area gives the noise equivalent radiance (Lnoise()). The SNR is calculated as the ratio between Ltypical and Lnoise at each band:

SNR() = Ltypical ()/Lnoise() = LTOA()/(LTOA())

(8)

The diurnal variability and magnitude differences between SNR computed with 10 min and hourly aggregated Himawari-8 observations (SNRSING() and SNRAGG(), respectively) were inspected at each band. In addition, their spectral characteristics were evaluated for ranges of s because noise levels are known to vary with solar elevation [80]. Finally, the associated percentage noise levels (%Noise) were computed for s = 45 ± 1 and utilised to evaluate the algorithm’s sensitivity to Himawari-8 typical noise levels.
The TSS algorithm developed in this study was trained with spectrally flat (uncorrelated) photon noise (0.8%) that was added to the training dataset, assuming limited knowledge of sensor performance characteristics over oceanic targets. To evaluate the inversion stability and to provide a baseline sensitivity analysis of the TSS algorithm, spectrally flat photon noise of 0.1, 1.0, and 10 and 50% were added to the testing dataset and inverted. In addition, the %Noise associated with the Himawari-8 bands were added to the testing dataset to quantify the effects of spectrally dependent noise levels on the accuracy of TSS retrievals. The retrieval stability was interpreted in terms of constant increments of RMSE across a wide range of TSS (0.01 to 100 mg L-1) equally spaced in logarithmic concentrations. In addition, longitudinal transects of TSS products taken in homogeneous and cloud-free waters of the coastal GBR and in the Coral Sea were evaluated at a pixel scale for a qualitative assessment of noise levels of Himawari-8.

3. Results
3.1. Algorithm Validation
Multiple networks were trained with varied architecture configurations and the bestperformance network with lowest possible RMSE and lowest number of neurons in the hidden layer was selected for inversions. The selected experiment, with 50 neurons in the hidden layer, retrieved TSS ranging from 0.14 to 24 mg L-1, with a positive R2 and bias of 0.014 mg L-1, MAPE of 75.5%, and 10RMSE of 2.08 mg L-1, as shown in Figure 5.

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Figure 5. In situ and Himawari-8-de- r-ived TSS with the b-est-performing ANN experiment, with in situ TSS values colour-code-d in logarithmic scale. Error bars represen-t the intra-pix-el standard deviation of TSS within a 3-by-3–pi-xe- l box. Different symbols indicate in situ data collected by AIMS
and by CSIRO at LJCO.
–
3.2. Himawari-8 Total Suspended S-olids for the Great Barrier Ree-f
Figure 6 shows a near-true colour composite of Himawari-8 (left panel) taken on 27 October 2017 ove-r the GBR area, and the corresponding TSS product a-t 10 min temporal resolution (right panel). The waters within the GBR lagoon have TSS generally at or above 1 mg L-1, whereas the waters offshore the GBR present values below 1 mg L-1. The TSS product revealed severe granulation and striping noise in the open ocean areas of the Coral Sea.

Figure 6. Near–true colour Himaw-ari-8 imagery of the GBR acquired on 27 October 2017 at 15:00 AEST (left panel) and the associated TSS p- roduct [mg L-1] (right panel). Pixels masked in black due to cloud-s a-nd out-of-range values.

Himawari–8 TSS fluctuations were investigated Burdekin River mouth and over the southern GBR

for the coastal waters reef matrix (Figure 7

saunrdroaunn-imdinatgiothnes

in link). The Burdekin flood event of 12 February 2019 generated a sediment plume that

rea- ched the outer reefs (50 km from the mouth) between 3 to 4 pm, with TSS > 20 mg L-1.

–

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Th- e Burdekin River sediment plume developed during the incoming tide with a range of 0.3 m between low and high tide. The coastal waters near the reefs experienced an order magnitude increase in TSS (3.6, 26.4 mg-L-1) within a s-emi-diurnal tidal cycle (cross mark in Figure 7 (left panel) and Figure 8a). The reefs covered by floodwaters were exposed to TSS ~40 times higher than the guideline threshold of 0.7- mg L-1 [82]. The areas wher-e TSS exceeded 100 mg- L-1, near the mouth, were masked (black areas) as- ou- t-of-range values (ANN flags). An animation of the TSS fluctuations following the main discharge event is available in Figure S1.

Figure 7. Flood plume discharging from the Burdekin River, February 2019 (left panel). TSS tidal jets within the GBR reef matrix in November 2016 (right panel). Note the different ranges in each plot. Pixels masked in black are due to out-of–ra-nge TSS values.
While major flood events display clear TSS features in the coastal GBR, sub-meso- scale tidal jets are observed surrounding the matrix of shallow and submerged reefs in the southern GBR (Figure 7 (right panel)), demonstrating how these different conditions both influence short-term- TSS variability. The animation provided in Figure S2 illustrates the dynamics of tidally induced TSS fluctuations, where the high (4 m) and low (0.2 m) tides took place at 10 a.m. and 6 p.m., respectively (Figure 8b). The TSS concentrations near Heralds Reef (cross marked) fluctuated about one order in magnitude within a day (0.3, 2.0 mg L-1), w- ith values exceeding the water quality guideline thresholds recomme-nded for the open coastal GBR (0.7 mg L-1). –

Figure 8. Time series of 10 min Himawa-ri–8-derived TSS at the mouth of the Burdekin River during the floods of February 2019 (a) and in the southern GBR reef matrix in November 2016 (b), as shown in Figure 7. Error bars represent intr-a-pixel standard deviations. Guideline thresholds for inshore (2.0 mg L– 1) and mid- -shelf (0.7 mg-L-1) waters are marked in red. Note the different time ranges in each figure.
– –
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3.3. Detection Limits The SNR computed from two sets of Himawari-8 observations are shown in the
graphics of Figure 9. A few single observations were missed due to intensive cloud coverage, particularly on 06 September 2017, and resulted in data gaps in the time series. SNRSING and SNRAGG presented clear diurnal fluctuations, with the highest SNR occurring at the lowest s (<30), between 11 a.m. and 12 p.m. The magnitude and diurnal variability were higher for SNRAGG and at the blue and green bands (470 and 510 nm), when compared to values computed for SNRSING. The SNR calculated for the 640 nm and 856 nm bands were at least three times lower than the SNR computed for the blue and green bands, with subtle diurnal variations. The diurnal fluctuations of SNR between days and locations were varied, especially for the blue band and from SNRAGG. On 06 September 2017 (mean v~22), the SNRAGG in the blue and green bands were similar in magnitude (Figure 9b). On 25 September 2017 (at a different location with mean v~28), the blue band presented SNRSING nearly twice as high as the green band (Figure 9d).

Figure 9. Time series of sig-na-l-to-noise ratios (SNR, right axis) computed for single (SNRSING) (a,c) and for aggregated (SNRAGG) observations (b,d) with associated s (left axis). The S-NR is
colour-coded by band.

The groups of

spectral variability of s, where the standard

SNRSING and SNRAGG is shown deviations within each group were

in Figure plotted as

10 for capped

three error

bars. The single observations typically yielded lower SNR than the aggregated observa-tions

in all bands, and SNR was the highest for Figure 9. The standard deviations of SNR

s < 30, in agreement with the data computed for single and aggregated

presented in observa-tions

wfoerresm>o4r0epartotnhoeubnlcueedbfoanr dsp>re4s0enatendd

at theblue and green bands. The standard deviations of 27 and of

SNR calculated 51 for SNRSING

and SNRAGG deviations of

, respectively, while the 13 and 26, respectively.

SNR computed for the green band presented standard These deviations are likely associated with the var-iable

atmospheric conditions of each location, which are intensified at the blue and green bands

and at high atmospheric pathlengths.

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Figure 10. Spectral distribution of si-gn-al-to-noise ratios calculated for single (SNRSING) (-a) and

aggregated observations (SNRAGG) (b), and grouped as standard deviations of SNR within each group of

for s.

three

ranges

Error

bars

were

computed

TgcorhemegpaSTtNuehtdReedASoGNbfGsoRerrvAvaaGallGluti,seoitsnnhsgceolwLemtioytphpbiicslaeesldr,=vaiann4t5diToaLn±bnsole1iwse2iawtwhnederersaec=saosob4m5copiuaitlt±eetddw1ipinceweTrcaaeebsrnelhetiai2ngg.chelLunaikdsoeeitswdheeifs(oc%eor, NtrchrooeeimssSpepN)oaf–nRordiSrsIiNaongnGg-. SNRSING, except in the red band. Nevertheless, the large noise levels in the red (~3%) and in tshigenNalIRdebsapnidtest(h~e5%eff)oinrtdsiicnataevtohiadtinthgeeSnNviRroAnGmG emnataylbceonmdoistitolynsafifnecitmedagbeystehleecattimono.sTp- hhiesriics particularly evident in the NIR band, where the water leaving radiances are considered negligible in clear open ocean waters.

–

Table 2. Visible and near-infrared Himawari-8 Ltypical and Lnoise W m-2sr-1µm-1 and asso- ciated

percentage noise (%Noise) for SNRAGG at s = 45 ± 1. Calculated SNRSING at s = 45 ± 1 values

were added for comparison.

Band 470 510 640 865

Ltypical 59.5 38.3 13.8 3.4

Lnoise 0.26 0.29 0.41 0.18

%Noise
0.44 0.76 3.02 5.26
–

SNRAGG 223 130 33 19

SNRSING 100 74 28 8

dalegpoerTnithdhemenopturptechosoemntoetsns ronefaorsiesotenriaiesbvililenlurgestTtrrSiaeStve(ad0l.0pin1erttfhooer1m0g0raamnpcgheisLcfs-o1or)fTwFSiSigthautrsoepre1ac1tb.roaI-vnlleyb0ofl.t1ahtmasgcnedL-n-sap1r,-ieoe-csxtr,catehlplyet

when 50% of spectrally flat photon noise is added Meanwhile, large errors (>300%) were obtained

to the Himawari-8 for TSS retriev-als

bands below

(Figur-e 0.1 mg

11a). L-1,

irrespective of noise type and level. On a m-ore realistic scenario, when spectrally depe-ndent

photon noise (i.e., %Noise from Table 2)-is added to the Himawari-8 bands, the errors are

mostly below 100%- for TSS > ~0.25 mg L-1 (Figure 11 (right panel)). Therefore, for obtaining

reliable retrievals from Himawari-8 with the curr-ent TSS algorithm, a detection li-mit of 0.25 mg L-1 was chosen. For comparison, t-he detection limits of TSS retrievals computed

from atmospherically corrected Himawari-8, as in Dorji and Fearns [17], is represented as a

vertical dashed line at 0.15 mg L-1.

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Figure 11. Retrieval RMSE errors (in mg- L-1) for spectrally flat (left panel) and spectrally dependent (right panel) photon noise levels. Radia-tive transfer (RT) TSS and associated RMSE val-ues are presented in logarithmic scale. The vertical dashed line at 0.15 m- g L-1 is the detection limit adapte-d from Dorji and Fearns [17], 2018. The vertical dashed line at 0.25 m- -g L– 1 is the detection limit of the present method.
A visual inspection of noise levels revealed severe granulation and horizontal stripes iogttitnnruhbraeraHstnbhneTsiierumdeSvlcCSaaactAtostwoiiGroaooaaGnsnfrl-tiTsaSw-(-h8lSeTaoaSaTSswSrS(SIesmNSSeeaIGdNvaspgeGra(iroenT)nendcSdlatruySnaeTcrad>atSessrSdei~r(nAdouF1GwicomgmeGpsu-da,egtrsaFnei-knLki1goie-an2ucn1g)ger,)geba.parne1raIetno2rgwwt)uaiaeacantd-eeutrdeenddlarciis1T-rtllill5oS(oyu1TuSnswSdtpErtS-haorpaeot<eenrnderdd~usTd0iic1Susn.t5St1ce(t2iwinmTnmtSEgag-eSasigArLnsoereG-baatrG-htn1siae)).eu.isnTalcTaienhontdhdaeioFesflnntiroigaoennluagmtgGnreliiedngatB–u1siRsnbi3ditlon.yaienignsoial–denefl,

–

Figure 12. Location of transects (magenta arrows) extracted for TSSSING(a) and TSSAGG(b). Note the

cumulative cloud masking in TSSAGG.Himawari-8 ob- servations taken on 9 September 2017 betwee-n

10:00 and 10:50 local time (AEST).

–

The transect sampled between 19S and 20Sin the Coral Sea (Figure 13a) pre-sented

TSSSING and TSSAGG values mostly below the detection limits of the method (0.25 mg L–1), which may present retrieval erro-rs over 100%. TSSSING presented spikes or differ-ent o-rders of magnitude values occurring successively on a pixel scale (orw ithin 1 km). As

a result, differences of up to 0.3 m-g L-1 were observed between neighbouring pixel-s,

as indicated by sented smoother

pplioxtela-tnon-potixaetilovnasriiantiFonigsu(r~e0.1036am. gMLe-an1)w. ShuilbetltehdeifafsesroencicaetsedweTrSeSoAbGsGerpvered-

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—

–

between TSSSING and T-SSAGG in the transects taken in the coastal GBR (Figure 13b), particularly for TSS -> 1 mg L-1. However, with increasing distance from the coast, TSS dropped below 1 mg L-1 and differences between TSSSING and TSSAGG were enhance-d. Although- most TSSSING pixels of Figure 13b were abo-ve-detection limits (0.25 mg L-1), they presented- poor spatial coherency in the coast-to-ocean transition area (151.4 to 152-.0E). Because TSSSING and TSSAGG provide comparable results for TSS > ~1 mg L-1, both may be appropriate for monitoring the coastal GBR. However, TSSAGG presents overall better spatia-l coherency and may be preferred over TSSSING, depending on the area of application.

Figure 13. Transects of Himawari–8–derived TSS (mg L– 1) taken in the Coral Sea (a) and within the
coastal GBR waters (b) from TSSSING (blue dots) and TSSAGG (red dots). The data gaps represent pixels masked for clouds, land, sun glint, or ANN flags, where appropriate. The annotated TSS (in black arrows) indicate pixel-t-o-p- ixel values and the green horizontal line marks the detection limit of
the method.

4. Discussion
Synoptic monitoring of water quality in the extensive and optically complex GBR is a priority, presenting a challenge for environmental managers and researchers [2,83]-. Although ocean colour remote sensing has stringent radiometric and spectral requirements, Himawari–8 offers an unprecedented number of observations for the advanced water quality monitoring of the GBR. This paper presents the first advanced remote sensing- algorithm locally tuned and validated for the synoptic monitoring of water quality a-t diurnal scales in the GBR.

4.1. Algorithm Development and Validation

The coupled oceanatmosphere radiative transfer simulations provided a large and

trhoebuospttdicaatlavbaasrieaobfilRityTOoAf dthisetrGibBuRt.ioTnhienmthaechHinime alewaarrnii–n8gVANNIRNbaalngdosr,itphamramdeevteelroispeeddfoinr

A(ptart0hhenrN.flieo0saev1Nadcwittdtvmoraoeaenr1dontkcr0st-eic0paeasogvhm,lnleaeoifiglnrcwsidoLcwceem-ocndh1ompc)irtea,chprhrweieaencirdtttdehtihhodtieoroeneuwatcqt[crteu2acal7iudalnn,lri3wivteat6iexyico,tr3pynhos7laifi,otoc8latfin4hmrt]tgea.hoeetefttmtDrhaRofiooeiuTndnssOtppaespAlidhbut-eieatatnrsosliHvgecfeddroicrmoerosomirirtaniorhvwensmtecihamtisraeiso.iu-iwnsM8nluavpisobtdeprerjeoredeesccocritettoavedrtnnesaoutrgloi,renteltfh-ihg.moweeTfdiahaataTtaciletsgStcariSuops-olerrrneviateatssacsh,vyleamuitnnnheot’dgesss—–f

robustness to input meet the minimum

rnaodisioemweatsriecsrpeeqcuiairlelymaednvtsanotfaogceeoaunsccoolnosuirdseerninsgo-rHs iamnadweanrvii-r8odnomeesnntoatl

noise, particularly from the atmosphere, can largely impact the retrievals. These results

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encouraged further application of Himawari-8 observations for validation against in situ water quality data in the GBR.
The retrieved Himawari-8 TSS matchup errors compared well with the mission targets defined for other ocean colour sensors, such as Sentinel-3 in Case 2 waters [85], particularly for TSS above 0.1 mg L-1. The performance of the present algorithm compares well with those using atmospherically corrected Himawari-8 observations [17,24], indicating the suitability of deriving coastal TSS with model-based one-step inversions. Explicit atmospheric correction procedures may improve retrievals for the lower TSS range (<~1 mg L-1), which are likely affected by the dominating atmospheric path radiance and the low radiometric performance of Himawari-8.
Performance improvements would require a larger and more comprehensive database of in situ bio-optical measurements covering the relevant spatial and temporal scales of variability. Moreover, rigorous measurement protocols need to be followed for reducing uncertainties associated with algorithm parameterisation and validation in coastal waters. For instance, triplicate samples are recommended for the determination of TSS with the gravimetric method. In addition, validation samples should be taken in optically homogeneous waters [86], which is especially difficult in highly dynamic coastal settings. Nevertheless, in situ measurements have been made available by multiple research agencies with diverse scientific priorities employing distinctive sampling and analysis methods. In addition, physical and environmental processes, such as bottom reflectance, fluorescence, bidirectional reflectance, polarisation, and harmful algal blooms, were not accounted for but may also contribute to the matchup retrieval errors.
4.2. Himawari-8 Total Suspended Solids for the Great Barrier Reef
Himawari-8 allowed the near-real time monitoring of an episodical flood event in the GBR, revealing an order magnitude TSS increase within a day. This event was observed during a wet season where the Burdekin discharged between 0.5 and 1.5 million ML/day for 10 consecutive days (Burdekin River at Clare station [87]). TSS fluctuations from the Burdekin flood plume were well above the water quality guideline threshold value of 2 mg L-1 for open coastal and mid-shelf waters, as well as 0.7 mg L-1 for offshore waters of the GBR [82]. The flood plume extended 50 km into the outer reefs, and its diurnal development was followed step-by-step with 10 min Himawari-8-derived TSS. Therefore, Himawari-8 provided an unprecedented number of observations for a complete qualitative and quantitative monitoring of flood events in the GBR. The masked pixels in floodwaters indicate values beyond 100 mg L-1, implying that the simulation range should be expanded for values above this limit for retrievals during floods in the GBR.
The TSS features in the southern reef matrix are likely resultant from short-lived sub-mesoscale resuspension eddies (110 km diameter), often referred to as tidal jets. In the southern GBR, large tidal ranges (510 m) induce strong currents [88,89], pushing water through narrow and relatively shallow channels [90]. These complex hydrodynamics promote the resuspension and injection of TSS from the shelf break into the reef matrix, and TSS concentrations in these regions are likely independent of terrestrial sources [91]. The tidal jets have been associated with localised upwelling and nutrient exchange between the Coral Sea and the GBR lagoon [92,93], being an important mechanism of transport and mixing of sediments, nutrients, and phytoplankton production [94]. However, the location and occurrence of tidal jets are scarcely described due to lack of appropriate spatial and temporal resolution observations [95,96]. Himawari-8 allowed the identification and tracking of such features within the GBR, at the required temporal resolution for resolving short-lived coastal processes.
4.3. Limitations
Himawari-8 provides inferior SNR compared to past and currently operational ocean colour sensors [80], and its sensitivity is far below minimum requirements for ocean colour applications, particularly over open ocean waters [9,97]. However, Himawari-

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8’s moderate radiometric resolution of 11 bits is unlikely to saturate over bright targets, such as clouds [80], and over extremely turbid coastal waters (TSS ~100 mg L-1), while yielding enough sensitivity to provide a reasonable level of discretisation over clear waters (>0.25 mg L-1). Noise levels calculated from aggregated observations were generally lower than those from single observations in all bands, confirming the suitability of degrading the temporal resolution to improve image quality [7,16]. Despite diurnal SNR fluctuations being largely modulated by solar elevation angles, the spectral dependence implies that a considerable source of input noise (35% in the red and NIR bands) in open ocean waters may originate from the atmosphere [80]. Nevertheless, the detection limit of the present method (0.25 mg L-1) is comparable to those employing explicit atmospheric correction to the inversion of meteorological data [17,98].
The detection limit of 0.25 mg L-1 is close to the detection limit of in situ TSS measured with the gravimetric method of ~0.4 mg L-1, for AIMS and CSIRO. Relative uncertainties of the gravimetric method are associated with the measurement protocol employed by different laboratories, which include differences in filter types, operator bias, salt rinsing, etc. [99,100]. For instance, salt crystals trapped in glass fibre filters largely affect TSS measurements and salt should be removed by rinsing the filtration apparatus [101,102]. Yet, errors as large as 30% have been obtained employing different salt-rinsing techniques, hindering the accurate determination of TSS lower than 1 mg [101]. Therefore, the detection limits and relative uncertainties of in situ measurements and Himawari-8-derived TSS are comparable for the present study. This result suggests that Himawari-8 offers an opportunity to accurately monitor diurnal variability of water quality in the coastal GBR, for TSS between 0.25 and 100 mg L-1.
Himawari-8-derived TSS products presented a systematic horizontal striping, with size generally corresponding to individual horizontal scans (500 km), as previously identified by Murakami [22]. The striping resulted from differences in detector-to-detector calibration slopes from solar diffuser observations of the visible bands [103,104]. Although the calibration coefficients were applied for the post-July 2017 observations, the horizontal striping patterns were still present in offshore waters and with TSS < 1 mg L-1. Additionally, severe granulation was observed in TSS products derived every 10 min, potentially associated with the low radiometric performance of the Himawari-8 sensor over water targets [17,22]. However, the visual noise was largely reduced by temporal aggregation of several individual observations into hourly-derived TSS products [16]. Fortunately, granulated noise was negligible in coastal and moderately turbid waters (TSS > 1 mg L-1), either from 10 min or from hourly TSS products. This result may be associated with the increased backscattering of suspended particles, which increases the water-leaving radiance and overwhelms the photon noise [105]. Consequently, Himawari-8-derived TSS is more likely to be accurately retrieved over moderately turbid coastal waters than over the open ocean, corroborating the detection limits analysis.
Pixel-to-pixel variations in open ocean areas (TSS < 0.25 mg L-1) were likely related to the granulated patterns observed with visual inspection, due to the low sensitivity of the Himawari-8 sensor at 10 min resolution. The radiometric noise for TSS below 0.25 mg L-1 were largely reduced in aggregated TSS, corroborating the sensitivity and visual inspection analyses. Conversely, improved spatial coherency was observed in the coastal GBR transect for TSS > 1 mg L-1. As a result, Himawari-8 10 min-derived TSS can be utilised with as much confidence as TSS derived from hourly aggregated observations in coastal areas. Obtaining TSS at every 10 min in the coastal GBR improves the discrimination of rapidchanging water quality fluctuations within an hour. However, this near-real time temporal frequency requires large processing and storing capabilities that may be unfeasible for the entire GBR. Producing hourly TSS, otherwise, not only improves processing rates and storage capabilities but also helps eliminate outliers and increase the accuracy of TSS products.

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5. Conclusions and Future Perspectives
In-situ monitoring and LEO satellite data have provided much of our knowledge on flood plumes entering the GBR [4,106108]. However, infrequent and spatially scant observations hindered the full understanding of plume development and evolution over short time scales. This study demonstrated the suitability of Himawari-8 for reliable TSS retrievals in the coastal GBR and for flood plumes mapping, tracking, and monitoring. For the first time, coastal TSS features were reliably quantified for the entire GBR, at rates only possible with biogeochemical and hydrodynamic models [109]. Himawari-8 TSS products brings forth the ability to characterise and resolve periodical and short-lived phenomena at unprecedented spatiotemporal resolutions. These products will be useful for researchers, modellers, and stakeholders assessing the impact of water quality in GBR ecosystems currently only using LEO orbit ocean colour products [109]. Diurnal changes and drivers of water quality fluctuations should be further investigated in the GBR using Himawari-8 TSS products and data of coastal processes such as tides, winds, and freshwater discharge. Additionally, the algorithm presented in this study can be directly employed to the identical Himawari-9 AHI sensor, which is planned to succeed Himawari-8 by 2029. The next-generation Himawari mission (Himawari-10) is in the planning phase and additional channels in the visible range, as well as improved sensitivity and spatial resolution, are a possibility. These characteristics would largely advance the capabilities of ocean colour algorithms for geostationary sensors, allowing more accurate retrievals in coastal waters at diurnal scales. Likewise, the Advanced Meteorological Imager (AMI) on board the GEOKOMPSAT-2A, as well as the GOCI-II (GEOKOMPSAT-2B), are currently observing Australia and East Asia, and a similar machine learning algorithm could be developed for harnessing these large and abundant datasets in near-real time. In this context, the present study provides an advanced algorithm and a prospect of potential applications to be developed when ocean colour sensors onboard geostationary platforms become a reality for Australia.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/rs14143503/s1, Figure S1: Diurnal variability of Total Suspended Solids over the Burdekin River mouth in February 2019 from 10 min Himawari-8 observations, Figure S2: Diurnal variability of Total Suspended Solids over the Southern Great Barrier Reef near Heralds Reef in November 2016 from 10 min Himawari-8 observations.
Author Contributions: Conceptualization, L.P.-V. and T.S.; methodology, L.P.-V. and T.S.; software, L.P.-V., T.S. and Y.Q.; validation, L.P.-V.; formal analysis, L.P.-V.; data curation, L.P.-V., T.S. and Y.Q.; writing–original draft preparation, L.P.-V.; writing–review and editing, T.S., M.J.D., S.S. and Y.Q.; supervision, T.S., M.J.D. and S.S.; funding acquisition, L.P.-V. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Council for Scientific and Technological Development (CNPq) Foundation of the Brazilian Federal Government through the Sciences without Borders Program, grant number 206339/2014-3.
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
Acknowledgments: We acknowledge Juergen Fischer and Michael Schaale (Institute of Space Sciences, Department of Earth Sciences, Freie Universität Berlin) for providing access to the MOMO radiative transfer code and for the inverse modelling tool. Britta Schaffelke, Michele Skuza, and Renee Gruber (AIMS) are acknowledged for providing valuable in situ data collected as part of the Marine Monitoring Program for Inshore Water Quality, a collaboration between the Great Barrier Reef Marine Park Authority, the Australian Institute of Marine Science, James Cook University, and the Cape York Water Monitoring Partnership. The Japan Meteorological Agency is acknowledged for the operation of Himawari-8 and data distribution through the Australian Bureau of Meteorology. The Australian Bureau of Meteorology is acknowledged for providing tidal prediction data. In situ data were sourced from Australia’s Integrated Marine Observing System (IMOS)–IMOS is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS). NCRIS (IMOS) and CSIRO

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are acknowledged for funding the Lucinda Jetty Coastal Observatory. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS-enabled capability supported by the Australian Government.
Conflicts of Interest: The authors declare no conflict of interest.
References
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