# Diurnal dynamics and coastal challenges in chlorophyll-a monitoring using Himawari-8 satellite data

Quang-Tu Bui, Stephane Saux Picart, Jerome Vidot

► To cite this version:

Quang-Tu Bui, Stephane Saux Picart, Jerome Vidot. Diurnal dynamics and coastal challenges in chlorophyll-a monitoring using Himawari-8 satellite data. Science of Remote Sensing, 2025, 12 (De-cembre 2025), pp.100320. 10.1016/j.srs.2025.100320. hal-05364975

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Full length article

# Diurnal dynamics and coastal challenges in chlorophyll-a monitoring using Himawari-8 satellite data

Quang-Tu Bui ID *, Stéphane Saux Picart ID, Jerome Vidot ID

Météo-France, CNRS, Univ. Toulouse, CNRM, Centre d'Études en Météorologie Satellitaire, Lannion, France

## ARTICLE INFO

Dataset link: https://data.eumetsat.int/, https://www.eorc.jaxa.jp/ptree/

**Keywords:**

Chlorophyll-a

Himawari-8

Sentinel-3

Ocean color

Remote sensing

Geostationary satellite

Diurnal variation

## ABSTRACT

Chlorophyll-a concentration estimated from satellite measurements is useful to monitor phytoplankton biomass. However, traditional methods of using polar orbit satellites only provide low frequency observation, inadequate to detect hourly changes that often trigger ecological events. With high temporal frequency data, geostationary sensors are expected to solve this problem. This research investigates whether high frequency data from Himawari-8, when applied rigorous quality control, can be reliable for oceanographic applications. A large dataset of Himawari-8 pixels was collocated with Sentinel-3 during four different three-day periods, corresponding to solstices and equinox in 2019. After applying a homogeneous filter (CV <0.2), full-disk analysis has exhibited high correlations between two datasets with *r* ≈ 0.82 in June, September, and December. However, the correlation decreases in March with *r*=0.685 due to misestimation of chlorophyll-a concentration at high viewing zenith angle. In low viewing zenith angle areas, Himawari-8, overestimates chlorophyll-a concentration compared to Sentinel-3, while in areas with higher viewing zenith angle of above 35°, chlorophyll-a concentration is underestimated by up to 12%–19% compared to Sentinel-3. These misestimations are mostly located in the viewing zenith angle range of 45° to 55°. For temporal analysis, one-hour timeseries analysis shows a very low day-to-day variation in offshore oligotrophic gyres (<0.05 mg/m³). In the coastal area, the noon variation can peak up to 0.2 mg/m³. Although these patterns are similar to the known diurnal variation, when phytoplankton absorbs available sunlight and nutrients to grow, there is no direct evidence to prove that observed chlorophyll-a variations are caused by the growth of phytoplankton.

## 1. Introduction

Chlorophyll-a (Chl-a), a main pigment found in photosynthetic species, is essential in the marine ecosystem, where it uses solar energy to initiate the photosynthesis process that converts inorganic carbon into organic matter. As the richest pigment in marine photosynthetic species, it has an important role in ocean biological pumps with a fixation rate of 50–60 Gt C each year, which represents half of the global fixed amount (De La Rocha and Passow, 2014). Besides, phytoplankton contributes to about half of the world's oxygen production through photosynthesis, making Chl-a an important component in the ecological balance of the Earth. Almost all of this oxygen is consumed back by marine organisms, making the net contribution of the ocean to atmospheric oxygen close to zero (Field et al., 1998; Friend et al., 2009; Lewis, 2011; Grégoire et al., 2023). In addition, the latest Global Carbon Budget (2024) estimates an oceanic CO₂ sink of ≈2.9± 0.4 Gt C yr⁻¹ over 2014–2023, underscoring the value of reliable, high-cadence ocean-color observations for biogeochemical monitoring (Friedlingstein et al., 2025). Therefore, changes in Chl-a concentration can indicate

variations in phytoplankton biomass, which has direct implications for the structure of marine food webs, the efficiency of biological carbon sequestration, and the biodiversity and resilience of marine ecosystems (Behrenfeld et al., 2007).

The concentration of Chl-a is monitored traditionally through in-situ measurements and satellite observations (Gohin et al., 2019; Arabi et al., 2020). In-situ measurements, such as water sampling and laboratory analysis, provide high accuracy but are limited in space and consume a lot of resources, usually only providing a snapshot of the conditions at the time and specific location (Harmel et al., 2023; Coraggio et al., 2022). These approaches cannot capture the dynamics of Chl-a concentration on a large ocean scale or in difficult-to-reach areas. In contrast, the satellite-based remote sensing allows observation of the surface Chl-a over vast ocean areas (Dai et al., 2023). The polar-orbiting standard satellites, such as Sea-viewing Wide Field-of-view Sensor (SeaWIFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), Ocean and Land Color Instrument (OLCI), provide general coverage

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: quang-tu.bui@meteo.fr (Q.-T. Bui).</footnote>
by taking water leaving reflectance from the ocean surface. However, these satellites are limited by their relatively low time resolution, with most areas that are only revisited once daily at best. This limitation is significant, as it can lead to missing observations of fast changes, such as diurnal variation of Chl-a concentration (Kwiatkowska and McClain, 2009).

To overcome this limitation, geostationary satellites provide advantages with respect to the high frequency of observation. Unlike polar-orbiting satellites, geostationary satellites are positioned at a fixed location on Earth's equator with an altitude of approximately 35,786 km, allowing them to constantly observe the same area. This ability to provide high frequency data in near real-time should allow geostationary satellites to capture diurnal variations in Chl-a concentration (IOCCG, 2012). By providing continuous monitoring, geostationary satellites can provide a deep understanding of short-term fluctuations controlled by sunlight cycles, temperatures, tidal cycles, and nutrients, which are essential to understand phytoplankton productivity (Li et al., 2025; Marra, 1992; Köhler et al., 2017). These fluctuations can occur within hours, and processes such as blooming algae can grow and spread rapidly, emphasizing the need to collect high frequency data throughout the day to track their beginning and progress (Dobricic et al., 2016).

Two geostationary satellites equipped with ocean color sensors have been developed to monitor Chl-a concentrations. The Geostationary Ocean Color Imager (GOCI), launched in 2010 aboard South Korea's Communication, Ocean, and Meteorological Satellite (COMS), was the world's first geostationary ocean color observation satellite (Choi et al., 2012). It acquires data in eight spectral bands (six in the visible, two in the near-infrared) with a spatial resolution of about 500 m, focusing on the waters around the Korean Peninsula. These spectral bands are necessary for discriminating Chl-a from other components in water, such as colored dissolved organic matter (CDOM). Building upon GOCI's success, South Korea launched the Geostationary Ocean Color Imager-II (GOCI-II) in February 2020 aboard the GEO-KOMPSAT-2B satellite. GOCI-II has twelve spectral bands, including UV, VIS, and NIR bands that range from 380 to 865 nm, providing advanced capabilities to monitor ocean color and Chl-a concentration (Lee et al., 2023). In contrast, there are some other geostationary satellites such as Himawari-8, operated by the Japan Aerospace Exploration Agency (JAXA), and the Flexible Combined Imager (FCI) aboard the Meteosat Third Generation (MTG) series operated by EUMETSAT, are not dedicated ocean color missions but still provide, or are being prepared to provide valuable ocean color products. Himawari-8 obtained multispectral data at a spatial resolution of up to 500 m in the visible bands and provided observations every ten minutes, thus capturing daily changes in ocean biology and rapid changes in coastal dynamics (Murakami, 2016). Similarly, efforts are underway to develop ocean color demonstration products from the FCI aboard the MTG, responding to a broad user interest in exploiting geostationary satellites for ocean color applications (Durand et al., 2015; Lavigne and Ruddick, 2018).

In this study, we first verify how consistent the Himawari-8 Chl-a concentration data are relative to the dedicated ocean-color mission Sentinel-3, and then examine the capabilities of Himawari-8 satellites in monitoring diurnal variation of Chl-a concentration. This helps solve the current gap caused by the limited temporal resolution of polar orbiting missions, which usually provide only one observation every day. This study focuses on two main goals: (i) Large scale comparison of observations from the geostationary satellite (Himawari-8) with data from the polar orbiting satellite (Sentinel-3) through an appropriate quality control criteria to assess the consistency and reliability of data for latter diurnal variability assessment; (ii) Based on the results of the reliability assessment, assess the potential of monitoring diurnal variation of Chl-a concentration through temporal analysis of Himawari-8 data. Through this study, we aim to investigate whether high frequency data from Himawari-8, when applied with rigorous quality control, can be reliable for oceanographic applications.

The Sentinel-3 and Himawari-8 datasets, Chl-a algorithm, and the match-up method are fully detailed in Section 2. The intercomparison between Sentinel-3 and Himawari-8 data and angular sensitivity analysis are described in Section 3, which also showcases the result on the diurnal changing of Chl-a concentration through case studies with timeseries data and spatial assessment. Then the results are discussed in Section 4. Finally, the conclusion of intercomparison between Himawari-8 data, temporal analysis, and recommendations for future research are provided in Section 5.

## 2. Chlorophyll-a data and method

### 2.1. Data

To evaluate the Chl-a concentration estimated from geostationary satellite imagery, this study employs Chl-a concentration data acquired from two sensors: the OLCI sensor onboard the polar-orbiting satellite Sentinel-3, as a reference, and the Advanced Himawari Imager (AHI) sensor on the geostationary satellite Himawari-8. For purposes of intercomparison, four datasets of collocation between Sentinel-3 and Himawari-8 have been collected over four distinct periods, March 19–21, June 19–21, September 16–22, and December 20–22, corresponding to the solstices and equinoxes of 2019. These datasets cover full disk of Himawari-8 with the central longitude at 140.7°E. For temporal analysis, four locations were selected and grouped into two study areas based on their viewing zenith angles: one area with viewing zenith angles between 0–20° and the other between 20–40°. Each area included one coastal site and one open-ocean site. Collocated data between Sentinel-3 and Himawari-8 were collected for these locations over six days, from September 17–22, 2019.

#### 2.1.1. OLCI/Sentinel-3

The OLCI level 2 marine netCDF files were directly downloaded from the EUMETSAT Data Store (https://data.eumetsat.int/). This product includes geophysical properties such as Chl-a concentrations (EUMETSAT for Copernicus, 2022). These products are available at full spatial resolution, with a nominal resolution of 300 m. The OLCI sensor carried by the polar-orbiting satellites series Sentinel-3 began operating in 2016, with a sun-synchronous orbit scan system with a field of view of 68.5° and swath width of 1270 km. Therefore, OLCI's revisit time is less than two days. This is one of the disadvantages of polar orbiting when users need to continuously monitor time-dependent phenomena (Kwiatkowska and McClain, 2009; Vanhellemont et al., 2014). The OLCI sensor has a total of seven different bands in the visible spectrum, ranging from 412 nm to 665 nm, as shown in Table 1.

For processing OLCI data to isolate the marine signal, termed water leaving reflectance ($\rho_w$), from the measured top-of-atmosphere (TOA) reflectance, atmospheric correction is a crucial component that needs to be taken into account (EUMETSAT, 2018). This atmospheric correction algorithm for OLCI was originally developed for the MERIS instrument and adapted for Sentinel-3 OLCI. It involves decomposing TOA reflectance ($\rho_t(\lambda)$) into atmospheric path reflectance ($\rho_{path}(\lambda)$), with explicit treatment of gaseous absorption, Rayleigh and aerosol scattering, bidirectional effects, and ocean-surface contributions including sun glint (Antoine and Morel, 1999).

$$ \rho_t(\lambda) = \rho_{path}(\lambda) + t_d(\lambda) \times \rho_w(\lambda) \quad (1) $$

The atmospheric correction algorithm identifies aerosol types by comparing reflectance at two near-infrared wavelengths where marine signal is negligible. This method relies on a “mixing ratio” between aerosol scattering and Rayleigh scattering, assumed constant across visible wavelengths (Antoine and Morel, 1998). Lookup tables (LUTs), generated using radiative transfer simulations, enable this characterization and correction. The diffuse transmittance factor $t_d(\lambda)$ accounts for scattering losses and absorption along the sensor's viewing path,
<table><caption>**Table 1**
Comparison of the sensor characteristics of the two ocean color satellites, AHI/Himawari-8 and OLCI/Sentinel-3.</caption><thead><tr><th>Characteristics</th><th>AHI/Himawari-8</th><th>OLCI/Sentinel-3</th></tr></thead><tbody><tr><td>Mission life</td><td>2014 – present</td><td>2016 – present</td></tr><tr><td>Scanning mechanism</td><td>Full disk centered at 140.7° longitude</td><td>Scanning in the along track direction with 68.5° field of view, nadir pointing, covers a swath width of 1270 km</td></tr><tr><td>Temporal frequency</td><td>Every 10 min for full disk</td><td>&lt;2 days</td></tr><tr><td>Spatial resolution</td><td>1-km at nadir</td><td>300-m at nadir</td></tr><tr><td>Spectral bands (nm)</td><td>470 (1); 510 (2); 639 (3)</td><td>412 (1); 442.5 (2); 490 (3); 510 (4); 560 (5); 620 (6); 665 (7)</td></tr></tbody></table>

calculated using radiative transfer based lookup tables (Gordon and Wang, 1994; Antoine and Morel, 1999).

Chlorophyll-a concentrations are then estimated using the Ocean Color for MERIS (OC4ME) empirical algorithm, which employs a fourth order polynomial relationship based on green-to-blue band water leaving reflectance ratios (EUMETSAT for Copernicus, 2022; O'Reilly et al., 1998; EUMETSAT, 2018; Morel and Antoine, 2011), following equations (2), (3):

$$ \log_{10}(\text{Chl} - a_{OLCI}) = a_0 + a_1 \times X + a_2 \times X^2 + a_3 \times X^3 + a_4 \times X^4 \quad (2) $$

$$ X = \log_{10}(\text{Max}(\rho_{w_{442.5}}, \rho_{w_{490}}, \rho_{w_{510}})/\rho_{w_{560}}) \quad (3) $$

where the coefficients $a_0$ to $a_4$ are retrieved from in-situ fit and have values of 0.4502748, -3.259491, 3.522731, -3.359422, and 0.949586, respectively.

Based on recommendations for Sentinel-3 OLCI Ocean Color product, a set of common flags has been applied to remove cloudy or unreliable data (EUMETSAT, 2022), as shown in Table S1 of Supplementary Materials.

### 2.1.2. AHI/Himawari-8

Regarding data from Himawari-8, the level-3 full disk hourly products from Himawari-8 on Chl-a concentration have been used and downloaded through HimawariCloud called JAXA Himawari MonITOR (https://www.eorc.jaxa.jp/ptree/). The AHI sensor onboard the Himawari-8 satellite was launched in 2014 and has been in operation since then (Murakami, 2016). Different from Sentinel-3 with a synchronous orbit scan system, the AHI sensor of Himawari-8 performs full-disk scans of the Earth with its center at longitude of 140.7 degrees every ten minutes (Table 1). This helps mitigate limitations that polar orbiting satellites encounter, such as cloudiness, orbital gaps, and sun glint (Kwiatkowska and McClain, 2009). Himawari-8 level-1 product has original spatial resolutions ranging from 0.5 km (for the 0.64 µm visible band) to 2 km for other reflective bands at nadir (Bessho et al., 2016). To reduce radiometric noise, the Himawari-8 TOA reflectance data were resampled onto a coarser 5 km grid by JAXA. First, the native 0.5–1 km resolution TOA reflectances were averaged to an intermediate 2 km pixel size (this corresponds to 4 × 4 pixel averaging for 0.5 km bands and 2 × 2 for 1 km bands), consistent with the Level-1 gridded data provided by the Japan Meteorological Agency. Then, the resulting 2 km pixels were resampled (nearest neighbor, without additional averaging) onto a regular 0.05° latitude–longitude grid (approximately 5 km at the equator). This two step resampling yields a final Himawari-8 dataset at 5 km spatial resolution, matching the grid used by the operational Himawari-8 Level-2 products. Besides the full disk product, satellite products from the AHI sensor also include three separate rapid scans for the surrounding area of Japan with a repetition frequency of 2.5 min. The AHI sensor contains three different bands in visible range: blue (470 nm), green (510 nm), and red (639 nm). These primary color bands allow calculations of Chl-a concentrations to be performed from satellite data (Murakami, 2016).

To retrieve water leaving reflectances from Himawari-8 data, the atmospheric correction algorithm described by Fukushima et al. (2002) has been used (Fukushima, 2002). This algorithm was adapted from a previous approach developed for the Global Imager (GLI) sensor

and shares a common foundation with the SeaWiFS two band ratio method (Gao and Li, 2020). It relies on precomputed look-up tables (LUTs) generated via the Pstar-3 radiative transfer code (Murakami, 2016). These LUTs provide key atmospheric parameters, including reflectance contributions from molecular (Rayleigh) scattering, aerosol scattering, gas absorption, and ocean surface interactions (including sunglint and whitecaps) (Stamnes et al., 1988; Nakajima and Tanaka, 1988; Ota et al., 2010; Murakami et al., 2005). Using this information, the algorithm corrects TOA radiances for isolating genuine water leaving signals as $ρ_w$. The Chl-a concentration is then estimated by combining blue/green bands ratio of remote sensing reflectance ($R_{rs}$), which is calculated from water leaving reflectance by dividing by $π$ to normalize the directional radiance to the hemispherical downwelling irradiance, and Color Index (CI) based algorithm (Murakami, 2016). The coefficients are derived by NASA bio-Optical Marine Algorithm dataset (Werdell and Bailey, 2005) using the following equations:

$$ \text{Chl} - a_{\text{AHI}} = \text{Chl} - a_{\text{CI}} \times p_{\text{CI}} + \text{Chl} - a_{123} \times (1 - p_{\text{CI}}) \quad (4) $$

$$ \text{Chl} - a_{\text{CI}} = 10^{-0.57242+290.13167 \times \text{CI}_{\text{AHI}}} \quad (5) $$

$$ \text{Chl} - a_{123} = 10^{0.03262-4.26726 \times r} \quad (6) $$

where $\text{Chl} - a_{\text{AHI}}$ is the Chl-a concentration calculated for AHI sensor. $\text{Chl} - a_{\text{CI}}$ and $\text{Chl} - a_{123}$ are Chl-a concentration estimated by using Color Index based Algorithm method and blue/green ratio method, respectively. The subscripts 1, 2, and 3 represent the band number of AHI used in the algorithms, which are 470, 510, and 639 nm, respectively. $\text{CI}_{\text{AHI}}$ is the Color Index value of AHI, which is defined by the difference between $R_{rs}$ in the green and a reference linearly interpolated between $R_{rs}$ in the blue and red. $\text{CI}_{\text{AHI}}$ acts as a sensitivity indicator for Chl-a. As the Chl-a concentration increases, the reflectance at blue band decreases, which lowers the baseline reference and results in negative value of $\text{CI}_{\text{AHI}}$ (Chuanmin et al., 2012). These values are calculated in detail as follows:

$$ \text{CI}_{\text{AHI}} = R_{rs}(510 \text{ nm}) - 0.76642 \times R_{rs}(470 \text{ nm}) \quad (7) $$

$$ p_{\text{CI}} = \begin{cases} \frac{0.0002+\text{CI}_{\text{AHI}}}{0.0002-0.0010}, & \text{if } -0.0010 \le \text{CI}_{\text{AHI}} \le -0.0002 \\ 0, & \text{if } \text{CI}_{\text{AHI}} > -0.0002 \\ 1, & \text{if } \text{CI}_{\text{AHI}} < -0.0010 \end{cases} \quad (8) $$

$$ r_r = \begin{cases} \frac{r_{12}+0.1}{0.1+0.1}, & \text{if } -0.1 \le r_{12} \le 0.1 \\ 0, & \text{if } r_{12} > 0.1 \\ 1, & \text{if } r_{12} < -0.1 \end{cases} \quad (9) $$

$$ r_{xy} = \log_{10}(R_{rs}(\lambda_x)/R_{rs}(\lambda_y)) \quad (10) $$

$$ r = r_{12} \times r_r + ((r_{23} - 0.8075)/3.5237) \times (1 - r_r) \quad (11) $$

### 2.2. Matching method

In the assessment of data consistency between Himawari-8 and Sentinel-3 regarding Chl-a concentrations, the first crucial step is to match the datasets through reprojection and resampling processes,
<figure><img src="image_3.png" /><figcaption>Fig. 1. Comparison of data distribution of Chl-a concentration (mg/m³) retrieved from Sentinel-3 (left) and Himawari-8 (right) on September 17, 2019, over the waters surrounding Japan. The color scale represents Chl-a concentration, with higher values in coastal regions.</figcaption></figure>

since the two satellite systems have inherent differences in the pro-
jection and spatial resolutions. In this study, regarding the Sentinel-3
data, we used the full 300 m resolution products. As for Himawari-8,
to ensure representativeness and uniformity in the Himawari-8 dataset,
this study only utilizes the full disk product with spatial resolution of
5 km. For aligning these datasets, the Sentinel-3 data were resampled
onto the Himawari-8 grid by using averaging method, which ensures
a similar spatial reference. Specifically, all native 300 m resolution
pixels of Sentinel falling within each 5 km Himawari-8 grid cell were
averaged.

In the process of selecting data, matchups between Sentinel-3 and Himawari-8 observations have been chosen on the same location with the same resolution of 15 km, which corresponds to a 3 × 3-pixel box. The size 3 × 3 has been chosen to minimize mixing of distinct water masses in dynamic coastal waters. Enlarging to 5 × 5 (25 km) more frequently mixes distinct water masses, which reduces the match-up precision and increases CV. This is also in line with established matchup guidance off OLCI, which recommends using default 5 × 5 but explicitly allows reducing to 3 × 3 in non-homogeneous conditions to preserve representativeness (Eumetsat, 2022). Several quality control criteria were then applied to ensure spatial and temporal consistency. Specifically, we required that: (1) the presence of a requisite number of valid pixels (ensuring 100% of pixels are valid, means nine valid pixels within the 9-pixel extraction), (2) the spatial consistency within the matchup subsets, assessed through the coefficient of variation (Section 2.3) (Tran et al., 2023), and (3) the temporal proximity between the Sentinel-3 and Himawari-8 observations (ensuring a time gap of less than 30-min). In total, 1,971,327 matchups were selected for intercomparison purposes, utilizing 290 granules from Sentinel-3A/B and 102 full-disk images from Himawari-8 across four distinct periods corresponding to solstices and equinoxes in 2019. These matchups are used differently for each section.

2.3. *Coefficient of variation determination*

To ensure no inhomogeneous pixels, such as islands, clouds, etc., are included in the collocated data, we incorporate a quality control as part of the matching method. In particular, we calculate the coefficient of variation (CV) for a local 3 × 3 pixel window centered on each candidate match pixel. The CV is defined as the ratio of the standard deviation to the mean of the pixel values in this 3 × 3 box. It serves as an indicator of local homogeneity: a low CV signifies that pixel values in the neighborhood are nearly homogeneous, whereas a high CV indicates significant variability within that area.

In our research, we apply a CV threshold to filter out pixels with
high local variability. If the CV of the 3 × 3 neighborhood exceeds
a chosen threshold (commonly on the order of 0.1–0.4 in similar
studies Barnes et al., 2019), the pixels are excluded from the matchup.
In practice, this means that if a small cloud fragment is present in
part of the 3 × 3 window, the resulting high variability (high CV) will
cause that pixel to be discarded. Consistent with ocean-color matchup
protocols, which recommend discarding matchups when CV (560 nm)
>20% to ensure spatial homogeneity, we adopted CV = 0.2 (Eumetsat,
2022; Tran et al., 2023). An a priori scene-to-scene check (detailed in
Section 3.1) showed that CV = 0.2 tightened Sentinel-3 and Himawari-
8 agreement (r increased from 0.62 to 0.79; RMSE decreased from
0.762 to 0.303 mg/m³) while retaining sufficient data. Detailed result
on r, bias, RMSE for other CV threshold can be found in table S11 of
Supplementary Materials.

3. Results

3.1. *Scene-to-scene analysis*

The spatial distribution of Chl-a concentration is a critical indica-
tor for monitoring oceanic and coastal ecosystems. To evaluate the
consistency of data availability and spatial characteristics of Chl-a
concentration between various satellite datasets, a scene-to-scene com-
parison between Sentinel-3 and Himawari-8 has been conducted. Fig.
1 presents the Chl-a concentration in ocean area around Japan on
September 17, 2019, according to the observation of the two satellite
platforms. The left map represents data which is derived from a sub-
scene of Sentinel-3 taken on the designated date. The right map is a
synthetic image from Himawari-8, created by averaging six consecutive
10-min satellite images acquired within the same hour.

Comparing details of Chl-a concentration between Sentinel-3 and Himawari-8 data shows different patterns in both coastal and offshore areas. High Chl-a concentration (exceeding 1 mg/m³) can be seen mainly along coastal areas and around the islands on both maps. These areas, especially coastal areas near Kyushu Island and Japan, show significantly higher Chl-a concentration, one of the reasons may be due to enhanced nutrients from human activities such as aquaculture (Gao et al., 2022), supporting dense phytoplankton growth. In contrast, the offshore areas often display lower Chl-a concentrations (below 0.5 mg/m³), due to limited nutrients and reduced productivity (Kim et al., 2024).

In addition, the maps show the difference in the impact of sun glint
on Chl-a data. In the Sentinel-3 map, sun glint significantly affects the
quality of data in the eastern regions of the scene, particularly at high
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Coefficient of variation of Himawari-8 data over the Japan Sea on September 17, 2019. The color scale represents the coefficient of variation values, with higher values indicating greater variability in the observed data.</figcaption></figure>

longitude that exceeds 141°E, as shown in Figure S2 of Supplementary Materials. This effect leads to large areas of data that cannot be used due to the excessive reflection of sunlight on the water (Mobley, 1999). For Himawari-8, the sun glint effects only appear at latitude of 0 to 20 degrees, and it changes during a day, owing to its geostationary orbit and favorable observational geometry, which minimize sunglint effects (Bessho et al., 2016). Thus, the Himawari-8 map shows no evidence of sunglint in the corresponding regions.

These maps also show the cloud coverage, which affects the South East regions and the coastal areas. For example, near the coast at latitude of about 36°N and longitude of 134°E, the Sentinel-3 map shows significant missing data due to thick cloud cover, as in Figure S2 of Supplementary Materials. Clouds hinder the ability to measure the ocean color of the satellite, resulting in large masked areas in which the Chl-a concentration cannot be estimated (Barth et al., 2024; Stock et al., 2020). In contrast, the Himawari-8 map shows only minimal data loss due to cloud and sunglint in the same area, with only 62% data loss compared to 73% of Sentinel-3. This difference is related to the Himawari-8 synthesis approach, which combines multiple observations within a short time frame, increasing the possibility of obtaining data without clouds (Miura et al., 2019). To detect these differences, Fig. 2 introduces the coefficient of variation as a tool to quantify variability.

Fig. 2 reveals that high CV values in the Himawari-8 dataset are strongly associated with areas affected by cloud cover as detected by Sentinel-3. In these regions, the CV values often exceed 0.6, with some locations reaching nearly 1.0, indicating substantial variability. For instance, along the coastal region near 36°N latitude and 134°E longitude, high CV values of approximately 0.8 to 1.0 are observed. These results show that the data in this area can be heterogeneous, creating high variability. Thus, the CV high value pattern can help to detect unreliable data, particularly in areas where observational limitations negatively affect data quality. To confirm the broader applicability of these findings and systematically evaluate the consistency of Himawari-8 observations relative to Sentinel-3, the analysis was extended and took into account the entire four datasets.

The assessment for all four datasets underscores the utility of using CV as a quality control metric. After systematically testing various thresholds, a value of 0.2 was selected due to its best balance: it removed inhomogeneous data without discarding too many useful pixels. By establishing this threshold, inhomogeneous Himawari-8 data can be filtered out. This is depicted in the 2D density scatter plots in Fig. 3. The plots on the left 3(a) and right 3(b) correspond to the datasets before and after applying a coefficient of variation threshold of 0.2. In the

plot without the CV threshold (Fig. 3(a)), there is a noticeable spread of data points. The root mean square error (RMSE) is 0.762 (mg/m³), indicating moderate disagreement between Sentinel-3 and Himawari-8 data. The bias value of -0.066 (mg/m³) suggests an underestimation of Himawari-8 compared to Sentinel-3. The correlation coefficient (r) of 0.62 further supports observation of moderate disagreements between datasets.

Meanwhile, in case the CV threshold is 0.2 applied (Fig. 3(b)), the scatter plot shows a high density of data that are much closer surrounding the linear regression line, indicating an improved agreement between Himawari-8 and Sentinel-3. The correlation coefficient increased to 0.79, showing a stronger relationship between the two sets of data after deleting inconsistent data, affected by cloud cover, for instance. The RMSE decreased to 0.303 (mg/m³), showing that the agreement between the two sets of data has been improved. The bias value also decreases -0.028, displaying Himawari-8 data, after filtering, is now more in line with Sentinel-3 measurements. Notably, this threshold removes 628,838 matches but greatly enhances the quality of datasets. Even though Himawari-8 still tends to overestimate Chl-a concentration with respect to Sentinel-3 at high Chl-a concentration level, these results confirm the use CV threshold (CV < 20%), mentioned in Section 2.3, in systematically filtering unreliable Chl-a observations affected by observational issues.

## 3.2. Full-disk of Himawari-8 analysis

This section analyzes the correlation between Chl-a concentration derived from the Himawari-8 and Sentinel-3 satellite datasets using the full disk data from Himawari-8 to match with the Sentinel-3 observations. Analysis includes data from four separate periods in 2019: 19–21 March, 19–21 June, 16–18 September, and December 20–22, to check the spatial and time relationship between these datasets. These datasets are created by matching swath of Sentinel-3 with full disk scanning data from Himawari-8, using a time window of ±30 min. This approach ensures that observations from both satellites are strictly aligned, minimizing the difference due to rapid changes in ocean conditions and atmosphere.

Fig. 4 presented here provides an example of Chl-a concentration distribution for the case of March, illustrating the agreement between the two satellite products (three other periods can be found in Figure S3, S4 and S5 of Supplementary Materials). The left map represents Sentinel-3 data, while the right map represents Himawari-8 data, illustrating the spatial patterns of Chl-a concentration in the
<figure><img src="image_5.png" /><figcaption>Fig. 3. 2D Density scatter plots comparing Chl-a concentrations from Sentinel-3 and Himawari-8 over full-disk scan of Himawari-8 for combination data of all four datasets correspond in four periods in March, June, September, and December. Panel (a) shows the data without applying a coefficient of variation threshold, while panel (b) displays the data after applying a CV threshold of 0.2 to both Himawari-8 and Sentinel-3 measurements. Each point on the scatter plot represents a pair of Chl-a concentration measurements from the two datasets. The density of points is indicated by a color gradient.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Spatial distribution of Chl-a concentration from Sentinel-3 (left) and Himawari-8 (right) over the asia pacific region on March 19–21, 2019. The matchup data were obtained by collocating swath of Sentinel-3 with the full-disk observations of Himawari-8 using a temporal window of ±30 min. The color scale represents the Chl-a concentration in mg/m³, ranging from 0.001 to 20. The comparison highlights the differences in spatial coverage and retrieval patterns between the two satellite sensors.</figcaption></figure>

Asia-Pacific region, including coastal, open, tropical, and subtropical areas. Himawari-8, with its geostationary orbit and full disk scanning capability, provides a wider geographical coverage than Sentinel-3, which is limited to a narrower swath but provides a higher space resolution and more accurate measurements. From the map, it can be seen that despite the overall consistency in large scale samples, differences in coverage and resolution are clear between the Sentinel-3 and Himawari-8. Himawari-8's data often appear smoother, which may be due to its lower spatial resolution, while Sentinel-3 can detect finer gradients and localized variations in Chl-a concentrations, especially in areas with rapid changes, such as coastal and upstream areas. This difference is especially noticeable in coastal areas rich in nutrients, where Sentinel-3 captures more localized variations, while Himawari-8 provides a broader but slightly less detailed view.

To analyze the correlation between the Himawari-8 and Sentinel-3 datasets, 2D density scatter plots for four distinct periods: March 19–21, June 19–21, September 16–18, and December 20–22 have been generated (Fig. 5). These density plots, labeled 5(a), 5(b), 5(c), and 5(d), visualize the agreement between the Chl-a concentrations of the two sensors. Key statistical parameters, including the root p value, mean square error, bias, and correlation coefficient, provide a quantitative assessment of their relationship, summarized in Table 2.

Across all periods, from Table 2 and Fig. 5, the datasets exhibit a high degree of correlation, with correlation coefficients ranging from 0.685 to 0.829. The March period (Fig. 5(a)) shows slightly greater scatter and a lower correlation coefficient ($r$ = 0.685), with a bias of −0.036 mg/m³ and RMSE of 0.305 mg/m³. This indicates higher variability, especially at Chl-a concentrations higher than 0.1 mg/m³.
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** 2D Density Scatter Plots of Chl-a Concentration Comparing Sentinel-3 and Himawari-8 Data. The panels (a), (b), (c), and (d) represent density plots for four different periods in 2019: March 19–21, June 19–21, September 16–18, and December 20–22, respectively. Each plot compares Chl-a concentrations from Himawari-8 (y-axis) and Sentinel-3 (x-axis) based on full-scan matchup data. The color scale indicates the density of scatter points, with higher densities shown in warmer colors. Linear regression parameters, root mean square error, bias, and correlation coefficient are provided in each panel for statistical comparison. The diagonal line represents the 1:1 reference line for ideal agreement between the two datasets.</figcaption></figure>

<caption>**Table 2**</caption>

Value of RMSE, Bias, coefficient of correlation, mean, and p value of Chl-a concentration between Sentinel-3 and Himawari-8 for different periods: March 19–21, June 19–21, September 16–18, and December 20–22, 2019.

<table><thead><tr><th>Characteristics</th><th>March</th><th>June</th><th>September</th><th>December</th></tr></thead><tbody><tr><td>RMSE (mg m<sup>-3</sup>)</td><td>0.305</td><td>0.328</td><td>0.231</td><td>0.330</td></tr><tr><td>Bias (mg m<sup>-3</sup>)</td><td>-0.036</td><td>-0.02</td><td>-0.017</td><td>-0.006</td></tr><tr><td>Coefficient of correlation</td><td>0.685</td><td>0.820</td><td>0.812</td><td>0.829</td></tr><tr><td>Mean Chl-a Himawari-8 (mg m<sup>-3</sup>)</td><td>0.140</td><td>0.240</td><td>0.204</td><td>0.191</td></tr><tr><td>Mean Chl-a Sentinel-3 (mg m<sup>-3</sup>)</td><td>0.177</td><td>0.261</td><td>0.221</td><td>0.197</td></tr><tr><td>P value</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td></tr></tbody></table>

In contrast, December (Fig. 5(d)) demonstrates the strongest correlation in general with *r* = 0.829, lowest bias of −0.006 mg/m³, RMSE of 0.330 mg/m³, and *p* value is smaller than 0.01, suggesting better alignment between the datasets during this period. September (Fig. 5(c)) and June (Fig. 5(b)) both show strong correlations (*r* > 0.8), with biases of −0.017 mg/m³ and −0.02 mg/m³, respectively. The RMSE values for these periods are also relatively low (0.231 mg/m³ for September and 0.328 mg/m³ for June), with consistent agreement at low to moderate Chl-a concentration levels. Although Himawari-8 generally underestimates Chl-a concentration, the degree of underestimation remains modest, as seen in the consistently small negative biases. The mean Chl-a

concentration values between the two sensors also remain close, with December showing the narrowest gap (0.191 mg/m³ for Himawari-8 vs. 0.197 mg/m³ for Sentinel-3). However, December has the lowest data availability, with a number of valid matchups of 244,295 only. Therefore, for further temporal analysis in this study, it is more appropriate to use September data, which not only have the highest data availability of 404,194 but also exhibit a high correlation between sensors and the lowest RMSE.

The density distribution in the four cases shows a generally similar pattern, with most of the points clustered along the 1:1 reference line. However, at higher Chl-a concentration (>3 mg/m³), data from all
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** The spatial distribution of Chl-a concentration misestimate by Himawari-8 for March 19–21, 2019, compared to Sentinel-3 data. Regions where Himawari-8 overestimated Chl-a concentrations are highlighted in red, while areas of underestimation are shown in blue. The gray circles represent different viewing zenith angle ranges.</figcaption></figure>

four cases begin to show increased scatter, especially noticeable for the case of March. For further analysis, we identified cases where the satellite-derived Chl-a concentrations were either underestimated or overestimated compared to Sentinel-3 by defining specific zones on a 2D density scatter plot for this period. Chl-a concentration is considered as underestimated when Himawari-8 reports value of <0.9 mg/m³ while Sentinel-3 reports value of >3 mg/m³. Whereas overestimation is when Himawari-8 reports value of >5 mg/m³ while Sentinel-3 reports value of >7 mg/m³. The result is depicted in Fig. 6.

Fig. 6 highlights regions where the Himawari-8 data either overestimates (red) or underestimates (blue) Chl-a concentrations compared to Sentinel-3 Chl-a concentration. The result clearly shows that, when quality control steps are applied, most differences found are located around the coastal regions. The areas of overestimation dominated at low viewing zenith angle (<40°) with low latitude of the Northern hemisphere (<25°) and the Southern hemisphere, which include coastal region of northern Australia, Southeast Asia, and parts of the Indonesian archipelago. These areas are characterized by complex oceanographic conditions, including turbulent mixing of high sedimentary loads and different water optical properties (Gordon, 2005; Nagai et al., 2021). In contrast, the underestimation (blue areas) is prominent in the area with high viewing zenith angle (>40°). This underestimation may be the result of the limitations in the ability to detect the fine gradients of Himawari-8 in the low Chl-a concentration waters due to its high spatial resolution (Huang et al., 2022). In addition, retrieval algorithms may not fully account for the Earth's curvature in these areas, since this area is located at high latitude >35°, with a longer solar light path (Murakami, 2016).

### 3.3. Angular sensitivity analysis

This section focuses on further investigating the effect of the viewing zenith angle on the accuracy of the Chl-a measurement from Himawari-8. In order to implement that, Fig. 7 is plotted based on the collocation of data between Sentinel-3 and Himawari-8. Since the Chl-a concentration in open sea and coastal areas can be vastly different in

terms of magnitude, the mean relative difference has been used instead of absolute mean difference. By utilizing data from four different periods in 2019 and dividing the dataset into subgroups based on 5° viewing zenith angle ranges, this approach aims to comprehensively assess the spatial and temporal variability of the Chl-a concentration difference between two satellites.

Accordingly, in the angle from 0° to 35°, the mean relative difference is fairly low and stable, with the value of less than 5%, and the standard deviation is also low (below 14%). However, starting from viewing zenith angle of 35°, the difference increased sharply, reaching a peak of about 19% at viewing zenith angle of 55°, while the standard deviation also increased, especially at the 50° viewing zenith angle, where the standard deviation exceeded 24%, the highest value for range from 0° to 60°. For viewing zenith angle range from 60° to 65°, the standard deviation reaches 56%, which can be caused by limited data availability, where there are only 143 matchups available for this range.

Regarding the data availability, the chart shows that the number of matchups (shown in gray bars) is unevenly distributed in each interval of 5° of the viewing zenith angle. In the range of 25° to 45°, the number of matchups reaches the highest, ranging from 140,000 to over 175,000 per bin. In contrast, at the viewing zenith angle of less than 10° and greater than 55°, the amount of matchup is significantly low. For viewing zenith angle less than 10°, data deficiency can be explained by the observed geographical location. A significant part of this area is the mainland of Papua New Guinea, which is often covered by clouds, leading to limited number of matchups. For the viewing zenith angle of 60° to 65°, fewer than 20,000 matchups are recorded. This may be due to a significant reduction in data filtering of the quality control process in these areas.

### 3.4. Time series of Chlorophyll-a concentration

This section focuses on analyzing time series data of Chl-a concentration for predominant coastal and open ocean areas in case studies A and B based on viewing zenith angle ranges with high correlation with Sentinel-3 data, as shown in Fig. 8. The dataset comprises observations spanning six days, from September 16 to 22, 2019, acquired by Himawari-8 and Sentinel-3. Data selection is performed using the matching scheme method, after which the Chl-a concentration is calculated by averaging the selected 3 × 3 pixel box.

#### 3.4.1. Case A: Papua New Guinea

In the dominated coastal region, Chl-a concentration fluctuates irregularly between 0.15 mg/m³ and 0.35 mg/m³. This concentration changes significantly day by day, with distinct peaks that appear at different times (Fig. 9). A noticeable cycle for many days is that the Chl-a concentration usually reaches the highest value around noon. This noon peak appears for days, but its magnitude is not always the same. After these peaks, Chl-a concentration usually decreases till the end of the day. High standard deviation value from the graph, ranging from 0.025 to 0.1 mg/m³, emphasizes the high level of variation in the extracted boxes, reflecting regular changes in the observation phase. The standard deviation value is usually the highest at the beginning and the end of the day, and at noon is the time with the lowest standard deviation. Although the general cycle shows that the peaks of Chl-a concentration normally appear at noon time and then drop, some days also exhibit multiple rises and drops. Data from Sentinel-3, shown by black dots, is generally consistent with the data of Himawari-8, although sometimes there is a minor difference, possibly due to different measurement methods or spatial resolution.

In the open ocean area, the fluctuation of Chl-a concentration has a smaller amplitude, with a lower value, ranging from 0.03 mg/m³ to 0.09 mg/m³. Unlike the dominated coastal region, where the variation takes place strongly, the change of Chl-a concentration in the open ocean waters takes place more slowly. A remarkable similarity in this
<figure><img src="image_9.png" /><figcaption>Fig. 7. The mean relative difference of Chl-a concentration between Sentinel-3 and Himawari-8 (S3-H8)/S3) respect to viewing zenith angle (°). The blue line indicates mean relative difference (%) for each 5°, while the red line represents the corresponding standard deviation (left x-axis). The gray column (right y-axis) shows the number of valid matchups in each angle.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Two case study regions (red rectangles) selected based on viewing zenith angle from Himawari-8. Case A (Papua New Guinea) is for the range of 0° to 20°, and Case B (Coral Sea) is for the range of 20° to 40°. The gray circles represent different ranges of viewing zenith angle of Himawari-8.</figcaption></figure>

area is that the Chl-a concentration tends to reach its peak at noon. However, due to the strong interference of the sunglint effect during the measurement of the satellite, this area often lacks data at noon. However, the existing observations still show that the Chl-a concentration tends to increase before noon and then gradually decrease afterwards. Overall, the fluctuations in the offshore area are much smaller than the dominated coastal region of Papua New Guinea. The standard deviation value is also lower, showing a more uniform distribution of Chl-a

concentration over time. However, the timeseries in both cases still shows the same diurnal cycle of Chl-a concentration.

### 3.4.2. Case B: Coral Sea

As shown in the top panel of Fig. 10, in the dominated coastal area, Chl-a concentration varies greatly through time, similar to the coastal dynamic characteristics that have been observed in the previous case. The Chl-a concentration in this area ranges from 0.1 mg/m³ to 0.36 mg/m³, with noticeable increases and decreases throughout the examined period. Similar to the dominated coastal area in Case A, this area also recorded the highest Chl-a concentration around noon time, though these peaks are not consistent and may differ in magnitude. The standard deviation from the graph indicates a strong fluctuation in extracted 3-by-3 boxes of Chl-a concentration, reflecting that the data in this coastal environment is highly dynamic and responsive to various environmental influences.

In the open ocean area shown in the bottom panel of Fig. 10, the Chl-a concentration shows a more stable cycle with a smaller fluctuation. The concentration remains quite low, from about 0.06 mg/m³ to 0.12 mg/m³, and the fluctuations are also more contained than in the dominated coastal area. A peak at noon time is also observed. However, due to the cloud coverage, part of the data is missing at noon for some days, making it difficult to find the peaks of Chl-a concentration on these days. However, the general variation still shows that the open ocean area experiences smaller and more gradual changes in Chl-a concentration throughout the day.

## 3.5. Spatial distribution of Chl-a diurnal variation

In this section, a broader spatial and temporal analysis of Chl-a concentration was performed using Himawari-8 satellite-derived data. By calculating the variation between the highest and lowest Chl-a concentration for every locations, the maps in Fig. 11 show how much Chl-a concentration varies over a day across the western Pacific for four distinct periods: March 19–21, June 19–21, September 16–18, and December 20–22, 2019. During this same period, other statistical parameters, including minimum Chl-a concentration, maximum Chl-a
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Timeseries of Chl-a concentration for case A: Papua New Guinea from September 16 to September 22, 2019, comparing the open ocean (lower) with the dominated coastal waters (upper). The black line depicts the extracted Chl-a concentrations. The red error bars represent the standard deviation for each data box. Black dots indicate Sentinel-3 satellite-derived Chl-a concentrations, and vertical dot lines show local solar noon time.</figcaption></figure>

concentration and standard deviation value, were also provided in Fig-
ure S6, S7, and S8 of Supplementary Materials, respectively. Over the
open ocean, nutrient-poor areas (the subtropical gyre), the daily change
is very small, usually less than 0.05 mg/m³, which matches global
studies that found similar low daily variability in such oligotrophic wa-
ters (Signorini et al., 2015). On the other hand, closer to the coast and
near places with active upwelling, daily changes are much larger. These
higher amplitudes in daily changes coincide with areas characterized
by high Chl-a concentration, showing a possible relationship between
the available nutrients and greater daily changes. Similar findings were
reported in studies of the Indonesian seas, showing noticeable daily
shifts in Chl-a concentration due to tides (Zaron et al., 2023).

In March, the largest daily variations, around 0.10–0.20 mg/m³, ap-
pear mainly around the Indonesian Archipelago and northern Australia.
By June, the daily Chl-a concentration variations in the Arafura and
Coral Seas rise to above 0.25 mg/m³. In September, the area with the
largest daily change shifts to the northeast towards the Bismarck and
Solomon Seas and near the Eastern equator of New Guinea, consistent
with strong noon peaks observed previously in Cases A and B. In
December, this area of highest variability shifts southward, around
Tasmania and Southern Sea, while the northern regions become calmer
during winter. These seasonal shifts follow the annual cycle of sunlight,
water layering, and nutrient availability.

4. Discussion

4.1. *Correlation between Himawari-8 and Sentinel-3*

The correlation analysis between Chl-a concentrations retrieved from Himawari-8 and Sentinel-3 has demonstrated a strong overall agreement, with correlation coefficients consistently above 0.8 for three out of four cases in the full-disk assessment and varying slightly in the dominated coastal and open ocean regions. These results confirm the reliability of Himawari-8 in estimating Chl-a concentration, but there are still some significant differences when considering the resolution, retrieval algorithm, viewing zenith angle, and specific environmental conditions.

A key strength of this analysis lies in the selection and pre-
processing, particularly through the application of criteria such as
coefficient of variation (CV = 0.2). This approach ensures that only
the matching data with high reliability is used, thereby improving
the strength of the correlation results in this study. The agreement
between Himawari-8 and Sentinel-3 is particularly high in the open
ocean, where the homogeneous water characteristics help minimize
discrepancies. However, the difference becomes larger in the coastal
areas, where the complexity of bio-optical properties and external
influences such as the river inputs and suspended sediments make it
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Timeseries of Chl-a concentration for case B: Coral Sea from September 16 to September 22, 2019, comparing the open ocean (lower) with the dominated coastal waters (upper). The black line depicts the extracted Chl-a concentrations. The red error bars represent the standard deviation for each data box. Black dots indicate Sentinel-3 satellite-derived Chl-a concentrations, and vertical dot lines show local solar noon time.</figcaption></figure>

difficult to measure the Chl-a concentration from satellite (Gordon, 2005; Nagai et al., 2021; Behrenfeld et al., 2013). In addition, the green band of Himawari-8 has a center at 510 nm, not in the actual green spectrum (Murakami, 2016). Meanwhile, Sentinel-3 uses not only 510 nm band but also band of 560 nm for Chl-a retrieval. This spectral difference can cause errors, especially in areas with high Chl-a concentrations, where the bio-optical response is more sensitive than in oligotrophic open-ocean waters. These differences are in line with the observation results, where Himawari-8 misestimates the Chl-a concentration in coastal waters with high biological productivity, while it better aligns in the open ocean area with low concentrations. This is consistent with known limitations of blue-green band-ratio algorithms (e.g., OC4/OC4ME), which can saturate and/or be perturbed by suspended sediments (TSM) and CDOM in optically complex (Case-2) waters, leading to bias and larger uncertainty (Sathyendranath et al., 2000).

The compositing approach used for generating 5-km resolution Chl-a data also plays a role in the observed differences. Himawari-8 first aggregates its original 0.5–1 km data to get the 2-km resolution product. Then, the resampling process to get 5-km data is by selecting the nearest 2-km data to a 0.05-degree grid point rather than performing an averaging. In contrast, the Sentinel-3 products with 5-km resolution are created by averaging a 5 × 5 grid of 1-km data. The difference in this

method may be the reason why the data from Himawari-8 is smoother and has a higher data availability, as averaging takes into account all elements that appear in 5-km resolution, while nearest neighbor resampling only considers the factors in the nearest pixel. This leads to local inconsistencies when applying the nearest neighbor resampling method.

In addition, the viewing zenith angle can also affect the correlation between Himawari-8 and Sentinel-3 data. The results from Section 3.3 show that when the viewing zenith angle increased, the relative difference between Himawari-8 and Sentinel-3 also increased. This suggests that the accuracy of the Chl-a product from Himawari-8 may be significantly affected by the viewing zenith angle, especially at the limb of the disk, where the large viewing zenith angle causes the influence of optical phenomena such as atmospheric scattering and skewer angle at the limb of the disk of Himawari-8 (Wang et al., 2019; Broomhall et al., 2019). Notably, the increase in mean relative difference at large viewing zenith angle also coincides with the decreasing number of data points, especially from viewing zenith angle of 55° onward. This reduction of data availability shows that the homogeneity also decreases with the increase of viewing zenith angle. From this result, it can be concluded that Chl-a products from Himawari-8 are highly compatible with Sentinel-3 in the small to medium viewing zenith angle (0°–40°). While at higher viewing zenith angle, the mean relative
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** The spatial distribution of Chl-a concentration diurnal variation by Himawari-8 data. The panels represent maps for four different periods in 2019: March 19-21, June 19-21, September 16-18, and December 20-22, respectively. The color scale shows the amplitude of Chl-a concentration from lowest to highest.</figcaption></figure>

difference can affect the accuracy of the analysis. Xu et al. (2024) have shown that the earth curvature can lead to 5.36–10.06% error in TOA radiances with solar zenith angle higher than 70° (Xu et al., 2024). To address this issue, atmospheric correction algorithms should account for large solar and viewing zenith angles more rigorously, especially for aerosol correction at the limb of swaths and at dawn and dusk (IOCCG, 2012; Xu et al., 2024).

To further diagnose whether the decline toward the limb of the disk originates in atmospheric correction or the bio-optical model, we additionally compared green bands remote sensing reflectances by angle. In the green (band 510 nm of Himawari-8 and Sentinel-3), the full set (N = 387,588) gives r ≈ 0.70, improving to r ≈ 0.802 for viewing zenith angle smaller than 40° and dropping to r ≈ 0.63 for viewing zenith angle higher than 40° (as shown in Fig. 12). The same

angular pattern appears in the Chl-a matchups during March period, where r increases from 0.685 to 0.801 and RMSE decreases from 0.305 to 0.203 mg/m³ (bias from -0.036 to -0.016 mg/m³) when we limit to viewing zenith angle smaller than 40° (as shown in Fig. 13). Similar analyses were also performed for the blue band (Himawari-8 470 nm vs. Sentinel-3 490 nm), which showed comparable angular trends. Because the patterns were nearly identical, only the green-band results are presented here for clarity.

These additions clarify the mechanism: beyond ≈ 40° of viewing zenith angle, the decline in agreement is already visible at the remote sensing reflectances level, especially in the green, indicating that the low precision at the limb of the disk is more due to atmospheric corrections/geometry driven (longer atmospheric paths and greater sensitivity to aerosol). By contrast, within viewing zenith angle smaller
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** Density scatterplots of remote sensing reflectance comparing Himawari-8/AHI 510 nm (y) to Sentinel-3/OLCI 510 nm (x) for March 2019. The left plot is for all valid matchups, the middle plot is for subset with viewing zenith angle smaller than 40°, the right plot is for subset with viewing zenith angle higher than 40°. Colors indicate point density. The dotted line is 1:1 and the solid line the linear fit.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 13.** Density scatter plots of chlorophyll-a from Himawari-8/AHI (y) versus Sentinel-3/OLCI (x) for March 2019. The left plot is for all valid matchups, the right plot is for subset with viewing zenith angle smaller than 40°. Colors indicate point density. The dotted line is 1:1 and the solid line the linear fit.</figcaption></figure>

than 40° the green band remote sensing reflectance differences are modest ($r \approx 0.80$ with tight clustering), so the residual Chl-a spread is more plausibly explained by bio-optical algorithm and spectral choices (e.g., AHI's hybrid CI/ratio versus OLCI's OC4Me and the 470 nm vs. 490 nm blue band difference). This is consistent with the angle-stratified mean relative difference of Chl-a plots in Fig. 7. For viewing zenith angle below 40°, a better overall agreement is observed, but atmospheric corrections effects from aerosols, sun angle/glint or high density of suspended particulate matter may still contribute to the precision of Chl-a retrieval.

Sensor noise can also contribute to these effects. According to OSCAR/Space specifications, OLCI exhibits very high blue-green SNRs ($\approx 1541$ at 490 nm and $\approx 1488$ at 510 nm at the reference radiances), whereas AHI visible bands are specified at SNR $\ge 300$ (0.47, 0.51, 0.645 µm) at 100% albedo (World Meteorological Organization, 2024a,b). Over dark-ocean scenes and at oblique geometries, this lower effective SNR might amplify atmospheric corrections residuals such as IOCCG (2019). This is in line with the observed remote sensing reflectance degradation for viewing zenith angle higher than 40°. Conversely, within viewing zenith angle smaller than 40°, where green band remote sensing reflectance agreement remains tight, the residual Chl-a spread is better explained by algorithm/spectral differences than

by sensor noise. In practice, spatial aggregation in the AHI Level-3 products (from native $\sim 0.5-1$ km to 2 km and 5 km) has already partially mitigated random noise by averaging, increasing the effective SNR for Himawari-8 data. Noise-tolerant atmospheric-correction strategies such as spectrum-matching and neural-network-based atmospheric corrections may further improve high-angle, low-radiance cases.

## 4.2. Time series of Chlorophyll-a concentration

The time series analysis of Chl-a concentration provides an important understanding of temporal variation in the ocean and coastal waters. The open ocean areas, such as the Philippine Sea (case A) and the Coral Sea (case B), display a small and stable variation of Chl-a concentration. This stability is typical of oligotrophic waters, in which low nutrient level leads to limited phytoplankton growth. Although these areas show the noon vertices, their intensity is significantly lower than the coastal water dominated. In contrast, dominated coastal water, such as the coastal area of Papua New Guinea (case A) and Australia's East Coast near the coral reefs (case B), shows a higher variation in Chl-a concentration. These variations also show peaks of Chl-a concentration at noon time and are followed by a decline. Although these cycles might align with known diurnal variations, in
which phytoplankton reacts with the availability of sunlight and mixing
nutrients for productivity (Li et al., 2025; Marra, 1992; Köhler et al.,
2017), there is currently no direct evidence proving that the diurnal
variation of Chl-a concentration observed by Himawari-8 is linked
specifically to phytoplankton growth associated with these factors. One
possible hypothesis may be a photophysiological effect rather than a
true increase in biomass. Under strong sunlight, non-photochemical
quenching (NPQ) acts as a protective response and reduces fluores-
cence. This change can shift the apparent Chl-a signal over the course
of the day even if biomass stays nearly constant (Browning et al., 2014;
Carberry et al., 2019). A second explanation can be short-term trans-
port. Such movement can reorganize surface optics and create a midday
maximum in the satellite record, as shown in geostationary studies with
GOCI (Hu et al., 2016; He et al., 2013). Besides, this observed diurnal
variation of Chl-a concentration can also be caused by the diel cycles in
beam attenuation and backscattering, which directly affect blue/green
band ratios (Kheireddine and Antoine, 2014). These results suggest
that further refinement or validation of atmospheric corrections under
varying solar-view geometries could improve the stability of retrieved
Chl-a concentration and help separate true biogeochemical signals from
geometry or illumination-driven artifacts.

The broader spatial analysis presented in Section 3.5 complements these findings. The result shows a strong diurnal variation in Chl-a concentration data from Himawari-8, which is mainly located in coastal areas and shallow waters. This analysis indicates that areas near the coast, and upwelling zones, especially around the Sea of Indonesia and northern Australia, often have a higher daily fluctuation due to favorable conditions such as shallow mixing water and high nutrient availability (Zaron et al., 2023). Moreover, the seasonal changes of areas with high diurnal variability are closely aligned with the change in solar radiation and water stratification throughout the year. This link suggests that the noon peaks seen in coastal case studies are part of a wider spatial trend, not just isolated events.

For the thin horizontal stripes visible in the June, as shown in
Figure S9 of Supplementary Materials, and December maps, they are
similar to stray light effects sometimes reported from the Himawari-8
satellite (Shao et al., 2016). Normally, stray light is strongest around
the equinoxes and is typically seen in the infrared band (3.9 µm). To
minimize this, the Japan Meteorological Agency employed an auto-
matic sun avoidance function. This function predicts when sunlight
might directly enter the sensor, particularly around midnight during
equinox seasons, and adjusts observations accordingly to prevent image
degradation. This may involve skipping parts of the observation area
or omitting entire observations if interference is anticipated across the
entire domain (Japan meteorological Agency, 2015). However, since
the data come from the visible light spectrum and were collected
outside these eclipse periods, and no similar stripes are visible during
equinox months (March and September), there are high chances that
these stripes come from calibration or atmospheric correction process.

5. Conclusion and summary

The results of this study showed that there is a high correlation
between Chl-a concentration observations from satellite Himawari-8
and Sentinel-3, with a correlation coefficient of 0.79 when applying the
quality control criteria, such as the coefficient of variation threshold
of 0.2. This agreement is particularly strong at low viewing zenith
angle area (0°-40°) with relative mean difference smaller than 5%,
since this area light path is shorter and atmospheric correction error is
less significant. Whereas, at higher viewing zenith angle areas, where
higher discrepancies in data are found, there are still many significant
challenges in the coastal area, where complex biological properties, in-
cluding high concentrations of suspended sediments, dissolved organic
matter, contribute to misestimation between datasets. These deviations
are likely to originate from the difference in retrieval algorithms,
spectral response functions, spatial resolution, and viewing geometry.

Although the temporal analysis in this study showed a significant diurnal variation of the Chl-a concentration, especially in coastal areas with values varying from 0.1 to 0.2 mg/m³, these fluctuations have not been well understood and cannot be confidently attributed to phytoplankton dynamics. The presence of data gaps at noon due to the sunglint and cloud cover, combined with unexplained variability, limits the suitability of the current Himawari-8 Chl-a product for the diurnal cycle in this study. Nevertheless, Himawari-8 still provides valuable high-frequency, wide-area coverage and compares well with Sentinel-3 under stable conditions, particularly in oligotrophic waters. The ability to provide data near real time with high frequency makes Himawari-8 a promising tool for operating supervision and recording temporary surface phenomena.

With the similarities of the sensor characteristics between
Himawari-8 and the MTG, such as geostationary platform, high-time
resolution (repeat every 10 min), multi-spectral sensor in the visible
to infrared range (eg, 443 nm, 555 nm, and 670 nm), the evaluation
conducted in this study can also be applied to support the use of MTG in
monitoring Chl-a concentrations. The study emphasized the importance
of developing and refining a rigorous atmospheric correction model,
especially for geostationary satellite missions like MTG at high viewing
zenith angle and in coastal areas with complex optical properties.
The lessons learned from Himawari-8 can help to improve not only
the retrieval algorithm but also the critical atmospheric correction
development, thereby improving the efficiency and accuracy of MTG
in ocean color monitoring in the future.

CRediT authorship contribution statement

Quang-Tu Bui: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. Stéphane Saux Picart: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization. Jerome Vidot: Writing – review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Funding

This work was supported by the OSI SAF that is funded by EUMET-SAT, Germany and Météo-France.

Declaration of competing interest

The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
influence the work reported in this paper.

Acknowledgments

The authors gratefully acknowledge the support from Dr. Hiroshi
Murakami for providing access to Himawari-8 data, which was essential
for this research. We would also like to thank Dr. Ewa Kwiatkowska,
Dr. Juan Ignacio Gossn, and David Dessailly from EUMETSAT for their
valuable support and advice throughout this work. We are also grateful
to Dr. Thibault Guinaldo for his careful review and insightful feedback,
which helped improve the quality of this manuscript.

Appendix A. Supplementary data

Supplementary material related to this article can be found online
at https://doi.org/10.1016/j.srs.2025.100320.
# Data availability

Satellite data used in this article are retrieved from EUMETSAT Open Access Hub and JAXA Himawari-monitor. These are open-sources, which can be accessed through the link: https://data.eumetsat.int/ and https://www.eorc.jaxa.jp/ptree/.

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