Search Results (292)

Monitoring coastal and estuarine dynamics is crucial for understanding coupled physical, biogeochemical, and human impacts on coastal waters. Motivated by the availability of high spatial resolution ocean color data from the proof-of-concept SeaHawk-HawkEye ocean color CubeSat, this study assesses the capabilities and limitations of satellite remote sensing in capturing shallow water (<10 m) coastal dynamics by integrating in situ measurements with satellite imagery. A Sea Sciences Acrobat collected detailed transects at the mouth of Masonboro Inlet (Wilmington, NC, USA), with “tow-yo” style profiles from the surface to 10 m. It measured conductivity, temperature, and depth (CTD), chlorophyll a (Chl a), turbidity, and dissolved oxygen. Satellite data from SeaHawk-HawkEye, Aqua-MODIS, and Sentinel 3A/3B-OLCI provided extensive spatial coverage, revealing surface-level physical/biological interactions, but were only available 48 h after in situ sampling due to cloud cover during field sampling. Tow-yo profiles elucidated a three-dimensional phytoplankton plume, the spatial extent of which we further characterize with satellite imagery, demonstrating the value of integrating in situ and satellite data. A spatial matchup comparison between data from each satellite and the in situ sensor package revealed significant discrepancies across all satellite sensors analyzed, attributed to differences in sensor resolution, atmospheric correction approaches, and proximity to land/benthos. This study emphasizes key challenges with study design and data interpretation in dynamic nearshore environments. In particular, results suggest that meaningful comparisons of satellite vs. in situ observations in such systems require near-synchronous sampling, careful consideration of spatial scale, and improved characterization of optical complexity. Full article

We developed an explainable machine learning framework combining 22 years (2003–2024) of multi-sensor satellite data (MODIS Aqua, CMEMS, C3S) with zone-specific SHAP attribution to quantify chlorophyll-a (Chl-a) mechanisms in the East China Sea. A geography-free XGBoost model achieved $R^2=0.802$ on 1.4 million pixel-month observations, and counterfactual experiments confirmed its superior environmental sensitivity over location-dependent models. Multi-strategy threshold detection identified two critical salinity boundaries—11.62 psu marking the turbidity-to-productivity transition (Cohen’s $d=-2.92$) and 34.03 psu at the Kuroshio Front ($d=-1.04$)—neither of which coincides with traditional physical definitions. Zone-specific SHAP analysis revealed that sea surface salinity (SSS) dominates Chl-a attribution across all zones but through fundamentally different mechanisms. We propose an “SSS Triple-Role Framework” in which salinity serves as turbidity proxy in estuarine waters, nutrient proxy in transitional waters, and dilution signal offshore, resolving the apparent contradiction of simultaneous positive and negative salinity effects. Non-additive interactions—including SSS × SST coupling (61% modulation) and SST × sea level amplification during Kuroshio intrusions—further demonstrate hierarchical controls missed by additive models. These findings provide quantitative benchmarks for ecosystem monitoring in river-dominated marginal seas. Full article

The occurrence of marine heatwaves (MHWs) and phytoplankton blooms is accelerating under climate change, yet the frequency and drivers of their compound co-occurrence remain poorly understood. Using coastal-optimized satellite observations from 2003–2020, we mapped global compound MHW–phytoplankton bloom (MHW-PB) events across coastal large marine ecosystems and quantified their spatiotemporal trends and environmental predictors. Compound events are increasing at 4.8% yr⁻¹, driven primarily by a 6.5% yr⁻¹ rise in MHW frequency; a temporal shuffle test confirms this trend falls below random co-occurrence expectation, indicating biological suppression actively constrains compound event growth. The compound independence factor (CIF) reveals latitudinal heterogeneity: low-latitude upwelling systems show MHW–PB mutual exclusivity, while high-latitude and eutrophic coastal regions show positive co-occurrence tendency. Interpretable machine learning further shows that nutrient availability dominates bloom responses at low latitudes whereas light dominates at high latitudes, with MHW intensity exhibiting nutrient-dependent non-linear associations with bloom probability. Paradoxically, compound frequency accelerates nearly twice as fast in low latitudes (6.1% yr⁻¹) as in high latitudes (3.5% yr⁻¹), driven by rapid tropical MHW acceleration. These diverging regimes signal dual ecological risks: trophic mismatches in upwelling systems and escalating hypoxia and harmful algal bloom hazards in eutrophic coastal waters. Full article

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article

The Ieodo plume is a distinctive suspended sediment plume near the Ieodo Ocean Research Station (I-ORS), located in the middle of the northern East China Sea. Because the Ieodo plume exhibits multiple different spatial scales, this study conducted an integrated remote sensing observation using satellites and unmanned aerial vehicles (UAVs) to observe its development and dispersion. Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) data were used to determine the plume’s spatial characteristics, broad-scale behavior, hourly variability, and turbidity characteristics. Also, TPXO model outputs were employed to evaluate the relationship between plume occurrence and tides, together with satellite imagery. Plume was repeatedly observed near the top of the Ieodo Seamount, with an affected extent of 11.4 ± 3.2 km in the east–west direction and 14.3 ± 4.1 km in the north–south direction. Moreover, hourly variations observed using GOCI-II showed that the Ieodo plume rotated clockwise with shifting tidal currents, forming a counterclockwise curved band or a ring-shaped structure. Total suspended solids (TSSs) in the plume reached their maximum when the southward component of the TPXO tidal current was dominant. Based on UAV optical surveys at the I-ORS, fine-scale morphology at the early stage of plume development was revealed, and it was confirmed that the Ieodo plume can occur even when it is not detected by satellite imagery. Furthermore, the u- and v-velocity vectors of the propagating Ieodo plume were derived by applying large-scale particle image velocimetry (LSPIV) to geometrically corrected sequential UAV imagery obtained in I-ORS. Plume speed was greatest near the source during the initial stage (0.81 ± 0.30 m s⁻¹) and gradually decreased to 0.34 ± 0.29 m s⁻¹ over distance. Based on the results above, we propose that the Ieodo plume is primarily generated by a pressure reduction associated with tidally accelerated currents over topography, driven by the Bernoulli effect. This study shows that an integrated satellite and UAV observation framework can effectively monitor rapidly evolving suspended sediment plumes. It can further help improve our understanding of dynamically driven submesoscale marine events. Full article

Global water environment monitoring urgently requires remote sensing data with high temporal resolution and wide spatial coverage. However, current space-borne ocean color spectrometers still face a significant trade-off among spatial resolution, swath width, and system compactness, which limits the large-scale deployment of satellite constellations. To address this challenge, this study developed a lightweight high-resolution spectral imager (LHRSI) with a total mass of less than 25 kg and power consumption below 80 W. The visible (VIS) camera adopts an interleaved dual-field-of-view and detectors splicing fusion design, while the shortwave infrared (SWIR) camera employs a transmission-type focal plane with staggered detector arrays. Through the field-of-view (FOV) optical design, the instrument achieves swath widths of 207.33 km for the VIS bands and 187.8 km for the SWIR bands at an orbital altitude of 500 km, while maintaining spatial resolutions of 12 m and 24 m, respectively. On-orbit imaging results demonstrate that the spectrometer achieves excellent performance in both spatial resolution and swath width. In addition, preliminary analysis using index-based indicators illustrates LHRSI’s potential for observing chlorophyll-related features in water bodies. This research not only provides a high-performance, miniaturized spectrometer solution but also lays an engineering foundation for developing low-cost, high-revisit global ocean and water environment monitoring constellations. Full article

As an important component of waters constituent that affects ocean color and the underwater ecological environment, the accurate assessment of Chromophoric Dissolved Organic Matter (CDOM) is crucial for observing the continuous changes in the marine ecosystem. However, remote sensing estimation of CDOM remains challenging for both coastal and oceanic waters due to its weak optical signals and complex optical conditions. Therefore, the development of efficient, practical, and robust models for estimating the CDOM absorption coefficient in both coastal and oceanic waters remains an active research focus. This study presents a novel algorithm (denoted as DQAAG) that incorporates ultraviolet bands into the inversion model. The design leverages the distinct spectral absorption characteristics of phytoplankton versus detrital particles in the ultraviolet (UV) region, enabling improved discrimination of water color parameters. Furthermore, the algorithm replaces empirical formulas commonly used in semi-analytical approaches with an artificial intelligence model (deep learning) to achieve enhanced inversion accuracy. Using IOCCG hyperspectral simulation data and NOMAD dataset to evaluates Shanmugam (2011) (S2011), Aurin et al. (2018) (A2018), Zhu et al. (2011) (QAA-CDOM), DQAAG, the results indicate that the a_g(443) derived from the DQAAG exhibit good agreement with the validation data, with root mean square deviation (RMSD) < 0.3 m⁻¹, mean absolute relative difference (MARD) < 0.30, mean bias (bias) < 0.028 m⁻¹, coefficient of determination (R²) > 0.78. The DQAAG algorithm was applied to SeaWiFS remote sensing data, and validation was performed through match-up analysis with the NOMAD dataset. The results show the RMSD = 0.14 m⁻¹, MARD = 0.39, and R² = 0.62. Through a sensitivity analysis of the algorithm, the study reveals that R_rs(670) and R_rs(380) exhibit more significant characteristics. These results demonstrate that UV bands play a crucial role in enhancing the retrieval accuracy of ocean color parameters. In addition, DQAAG, which integrates semi-analytical algorithms with artificial intelligence, presents an encouraging approach for processing ocean color imagery to retrieve a_g(443). Full article

Sun glint is commonly observed as interference in the imaging process of ocean color satellite sensors, making the extraction of water color information in sun glint-affected areas challenging and often leading to significant data gaps. The remote sensing baseline indices, calculated based on Rayleigh-corrected reflectance ($R_{\mathrm{rc}}$), are recognized as effective in reflecting water color variability in sun glint-affected regions. However, the accurate extraction of the $R_{\mathrm{rc}}$ baseline indices requires sun glint correction. The determination of sun glint correction coefficients for different bands lacks a clear methodology, and the currently available correction coefficients are not applicable to different sea regions. Therefore, this study focuses on the South China Sea, where VIIRS imagery is significantly affected by sun glint. Based on paired datasets comprising sun glint-affected and -unaffected images acquired over the same region on adjacent dates, sun glint correction coefficients for each spectral band were derived by maximizing the cosine similarity of histograms constructed from three baseline indices: SS486 (Spectral Shape index at 486 nm), CI551 (Color Index at 551 nm), and SS671 (Spectral Shape index at 671 nm). To further evaluate the effectiveness of the proposed correction, chlorophyll-a concentrations were retrieved using a Random Forest regression model trained with baseline indices derived from sun glint-free $R_{\mathrm{rc}}$ data and subsequently applied to baseline indices after sun glint correction. Comparative analyses of both baseline index extraction and chlorophyll-a retrieval demonstrate that the proposed optimal-value and mean-value correction approaches effectively mitigate sun glint effects. The mean sun glint correction coefficients $\alpha$(443), $\alpha$(486), $\alpha$(551), $\alpha$(671) and $\alpha$(745) were determined to be 0.75, 0.83, 0.89, 0.95 and 0.94, respectively. These coefficients can be applied as sun glint correction coefficients for the VIIRS $R_{\mathrm{rc}}$ data in the South China Sea region. Furthermore, the proposed method for determining sun glint correction coefficients offers a transferable framework that can be extended to other sea areas. Full article

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article

The vertical attenuation coefficient of photosynthetically active radiation ($K_d\left(PAR\right)$) is essential for characterizing the underwater light field and for operational marine monitoring. Although there have been efforts to use the standard satellite light attenuation product at 490 nm ($K_d\left(490\right)$) to estimate ($K_d\left(PAR\right)$) over a decade, earlier approaches were constrained by limited data. This study used a globally representative robust database of in-situ and satellite observations spanning diverse marine optical conditions and applied rigorous quality control. Three empirical models (linear, power, and a higher-order polynomial) were developed using four $K_d\left(490\right)$ satellite variants validated against an independent dataset and benchmarked against six published algorithms (36 total approximations). Performance was assessed using a Model Performance Index (MPI), where values closer to 1 indicate a better model. The best model was a power regression driven by the standard satellite $K_d\left(490\right)$, which yielded an MPI of 0.8704, indicating a robust performance under a wide variability of marine optical conditions. These results highlight the value of multisensor products, which with a rigorous quality control protocol, could be used to estimate the $K_d\left(PAR\right)$ from the standard satellite $K_d\left(490\right)$. The objective of the proposed algorithm is to generate long-term $K_d\left(PAR\right)$ time series. This algorithm will be operational for implementation in marine ecosystem monitoring systems and can contribute to strengthening decision-making. Full article

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) vertical profile is essential for understanding global biogeochemical processes; however, no dedicated retrieval models currently exist. This study developed a novel PON profile retrieval model using the eXtreme Gradient Boosting (XGBoost) algorithm, based on a comprehensive global dataset that includes in situ PON measurements, MODIS-Aqua bio-optical data, and 3D reanalysis physical data. The XGBoost-retrieved PON profiles were compared with those derived from Copernicus particulate backscattering coefficient ($b_{\mathit{bp}}$) profiles and were further used to estimate the euphotic-zone PON stocks through an optimally performing regression model. The results showed that the proposed model significantly outperformed models constructed without physical inputs, achieving $R^2$ of 0.83, RMSE of 1.49 $\mathrm{mg} {\mathrm{m}}^{-3}$ and MAPE of 18.07%. Compared to the $b_{\mathit{bp}}$-based profiles, the XGBoost-retrieved profiles exhibited higher accuracy. The model also provided reliable estimates of euphotic-zone PON stocks, with $R^2$ of 0.76, RMSE of 200.31 mg ${\mathrm{m}}^{-2}$ and MAPE of 15.09%. These findings demonstrate the potential of the proposed retrieval model for investigating oceanic nitrogen dynamics and biogeochemical cycles. Full article

Ocean color remote sensing is important for monitoring marine biogeochemical processes. The accuracy of remote sensing reflectance (R_rs), a fundamental data product, is critical yet challenged by the scale mismatch between in situ point measurements and satellite-based areal observations from pixels. This mismatch introduces uncertainty, notably from the non-uniform spatial response within a pixel—a potential error source at pixel boundaries that remains poorly quantified. To address this issue, we introduced the pixel-level spatial mismatch index (PSMI) to assess spatial representativeness errors induced by the pixel boundary effect (PBE). Using AERONET-OC (AErosol RObotic NETwork-Ocean Color) data alongside MODIS/Aqua and OLCI/Sentinel-3A observations, we showed that the PSMI effectively identified a systematic R_rs deviation peak when a site lay within a pixel’s edge attenuation zone. This phenomenon, observed across sensors with different resolutions (MODIS and OLCI), exhibited sensor- and band-dependent peak characteristics. We further proposed a quantitative framework called a Riemann Stieltjes integral-based index to measure the spatial concentration of this deviation peak, and a baseline method to objectively define the PBE window. Our analyses revealed that PBE not only acts as an independent error source but also interacts with atmospheric and geometric errors, forming new multifactor interactions that significantly modulate the overall uncertainty in R_rs products. Consequently, pixel-scale effects should be incorporated into future validation protocols, and the PSMI framework can provide an intrinsic tool for this purpose. Full article

Harmful algal blooms (HABs) pose increasing threats to marine ecosystems and fisheries worldwide, creating an urgent need for efficient wide-area monitoring schemes. Satellite remote sensing offers a promising approach. However, quantitative, real-time HAB monitoring via satellites remains underdeveloped. Here, we evaluated the applicability of the Normalized Red Tide Index (NRTI), originally developed for Korean waters using the Geostationary Ocean Color Imager (GOCI), in detecting and quantifying HAB in the southern California Current. Our integrated monitoring encompassed two distinct regions of the California Current—Monterey Bay (central California) and La Bocana (Baja California)—separated by a 1470-km stretch of coastline and characterized by blooms of multiple HAB species. Our objectives were threefold: (1) to validate the relationship between NRTI and HAB cell densities through field measurements, (2) to evaluate the performance of hyperspectral NRTI derived from in situ reflectance measurements compared to existing multispectral indices including MODIS ocean color products, and (3) to assess the capability of multispectral sensors to represent NRTI by comparing multispectral-derived indices against hyperspectral NRTI measurements. We found species-specific relationships between hyperspectral NRTI and in situ HAB cell densities, with Prorocentrum gracile in Baja California showing a robust logarithmic fit (R² = 0.92) and multi-species assemblage (dominated by Akashiwo sanguinea) in Monterey Bay displaying a weak, positive correlation. MODIS-derived NRTI values were consistently lower than hyperspectral estimates due to reduced spectral resolution, but the two datasets were strongly correlated (R² = 0.97), allowing for reliable tracking of relative bloom intensity. MODIS applications further captured distinct bloom dynamics across regions, with localized nearshore blooms in Baja California and broader offshore expansion in Monterey Bay. These results suggest that the NRTI-based monitoring scheme can effectively quantify HAB intensity across broad geographic scales, but its application requires explicit consideration of regional HAB assemblages. Full article

We present a gradient boosting framework for reconstructing hyperspectral signatures in the visible spectrum (400–700 nm) of satellite-based ocean scenes from limited multispectral inputs. Hyperspectral data is composed of many, typically greater than 100, narrow wavelength bands across the electromagnetic spectrum. While hyperspectral data can offer reflectance values at every nanometer, multispectral sensors typically provide only 3 to 11 discrete bands, undersampling the visible color space. Our approach is applied to remote sensing reflectance ($R_{rs}$) measurements from a set of ocean color sensors, including Suomi-National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), Hyperspectral Imager for the Coastal Ocean (HICO), and NASA’s Plankton, Aerosol, Cloud, Ocean Ecosystem Ocean Color Instrument (PACE OCI), as well as in situ $R_{rs}$ data from National Oceanic and Atmospheric Administration (NOAA) calibration and validation cruises. By leveraging these datasets, we demonstrate the feasibility of transforming low-spectral-resolution imagery into high-fidelity hyperspectral products. This capability is particularly valuable given the increasing availability of low-cost platforms equipped with RGB or multispectral imaging systems. Our results underscore the potential of hyperspectral enhancement for advancing ocean color monitoring and enabling broader access to high-resolution spectral data for scientific and environmental applications. Full article

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article