Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was 40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

As climate change intensifies the frequency and magnitude of extreme weather events, such as storm surges, understanding how extreme weather events alter mangrove carbon dynamics is critical for predicting the resilience of blue carbon ecosystems under climate change. Mangrove forests are generally recognized for their resilience to natural disturbances, a characteristic largely attributed to the evolutionary development of species-specific functional traits. However, limited research has explored the impacts of storm surges on carbon flux dynamics in both natural and restored mangrove ecosystems. In this study, we analyzed short-term responses of storm surges on carbon dioxide flux and methane flux in natural and restored mangroves. The results revealed that following the storm surge, CO₂ uptake decreased by 51% in natural mangrove forests and increased by 20% in restored mangroves, while CH₄ emissions increased by 14% in natural mangroves and decreased by 22% in restored mangroves. GPP is mainly driven by PPFD and negatively affected by VPD and RH, while Reco and CH₄ flux respond to a combination of temperature, humidity, and hydrological factors. NEE is primarily controlled by GPP and Reco, with environmental variables acting indirectly. These findings highlight the complex, site-specific pathways through which extreme events regulate carbon fluxes, underscoring the importance of incorporating ecological feedbacks into coastal carbon assessments under climate change.

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies.

Visual perception in Unmanned Surface Vehicles (USVs) suffers from drastic lighting changes and missing texture features. These factors lead to depth scale drift and motion estimation bias. Moreover, existing multi-modal fusion models are computationally complex and unfit for resource-limited edge devices. To address these problems, a lightweight Radar–Vision Fusion (Geo-RVF) algorithm is proposed. To supplement spatial information, point clouds are projected to build sparse depth maps. A probability consistency-based depth correction module is designed to suppress water noise. This helps extract accurate geometric anchors to guide visual depth propagation. Subsequently, a Recurrent Autoregressive Network (RAN) fuses radar and image features in the temporal dimension. This resolves dynamic positional deviations caused by texture degradation and distant small targets. After real-time optimization, Geo-RVF achieves 23 FPS on the Jetson Orin NX. On a collected dataset, the method attains a mean average precision (mAP) 50–90 of 44.2% and a mean intersection over union (mIoU) of 99%, outperforming HybridNets and Achelous.

Satellite ocean-color remote sensing is used to detect harmful algal blooms (HABs), yet detection capability does not necessarily ensure reliable interpretation or transferable performance. If these limitations are overlooked when developing cross-regional or global automated HAB early-warning systems, biological claims may be overstated and regional changes in false-alarm or missed-detection rates may go unnoticed, potentially compromising warning reliability and related management decisions. We develop an evidence-constrained framework to evaluate both dimensions: reliability, defined by whether the observation–processing–inference chain supports the intended biological claim, and transferability, defined by whether task-relevant performance is retained under changes in time, region, sensor, or optical environment. A structured Web of Science search yielded 1212 unique records, from which 28 purposively prioritized full-text core studies underwent coding for validation design, cross-domain evidence, error, and uncertainty. The synthesis identifies an evidentiary gap between optical detection capability and biological claim specificity, while fixed-configuration cross-domain evidence was scarce and performance retention remained unquantified. Satellite observations can support bloom presence, optical phenotypes, and selected taxon-targeted applications, but specific claims require specific independent biological references. Reliability is constrained by reference and labeling practices, preprocessing, observation availability, and incomplete uncertainty characterization, while evidence for transferability is limited by insufficient source–target evaluation. We propose a framework linking claim–evidence alignment, independent validation, domain-shift assessment, and uncertainty reporting. Reliable and transferable HAB remote sensing should move beyond detection outputs toward evidence-supported, uncertainty-aware products for monitoring, early warning, and decision-making.