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Lakes in the Northeast Plains-Mountain Lake Region (NPLR) of China face severe risks of eutrophication due to climate change and intensive anthropogenic pressures. As a vital indicator for eutrophication status, the dynamics of chlorophyll-a (Chl-a) concentrations in NPLR lakes were, for the first time, comprehensively investigated in this study. A support vector regression (SVR)-based model was established and applied to the MERIS (2003–2011) and OLCI (2016–2019) observations to derive a long-term Chl-a record for 33 NPLR lakes. The NPLR lakes exhibited a climatological annual mean Chl-a of 12.3 mg m−3, ranging from 6.8 to 18.6 mg m−3 among the 33 studied lakes. During the study period, 11 lakes exhibited statistically significant increases in Chl-a concentrations (p

Hurricane Idalia formed on 26 August 2023 and three days later rapidly intensified from a Category 1 to Category 4 strength storm in less than 24 h over the west Florida shelf. On August 30, it made landfall along Florida’s Big Bend area as a Category 3 hurricane. Strikingly, despite Idalia’s moderate intensity and favorable vortex structure, neither upper ocean thermal energy nor environmental vertical wind shear conditions were as favorable during its intensification from Category 2 to Category 4 as earlier in its path, raising the question of what external factors contributed to its extreme intensification during this phase. Using satellite data, underwater glider observations, and numerical model outputs, this study reveals that, in addition to the 2023 marine heatwave, an extensive riverine plume in the eastern Gulf of Mexico, extending from the Mississippi-Alabama-Florida shelf to the Straits of Florida, produced a ∼20 m thick low-salinity layer (∼34–34.5 psu) and a corresponding warm upper ocean (>29 °C, ∼25–30 m thick). This defined a 10–20 m thick strongly stratified barrier layer below the surface layer with buoyancy frequencies exceeding 10 ^−3 s ^−1 that suppresses vertical mixing and became a critical factor contributing to Idalia’s rapid intensification under the relatively less than favorable thermal and wind field environments. Therefore, incorporating the river plume in future forecast models appears to be essential to improve the accuracy of intensity predictions, especially in the areas affected by the plume, where stratification plays an important role in the intensification dynamics.

Abstract The oceanographic ecology of pelagic Sargassum, and the means by which these floating macroalgae thrive in the nutrient-poor waters of the open ocean, have been studied for decades. Beginning in 2011, the Great Atlantic Sargassum Belt (GASB) emerged, with Sargassum proliferating in the tropical Atlantic and Caribbean where it had not previously been abundant. Here we show that the nutritional status of Sargassum in the GASB is distinct, with higher nitrogen and phosphorus content than populations residing in its Sargasso Sea habitat. Moreover, we find that variations in arsenic content of Sargassum reflect phosphorus limitation, following a hyperbolic relationship predicted from Michaelis-Menten nutrient uptake kinetics. Although the sources of nutrients fueling the GASB are not yet clear, our results suggest that nitrogen and phosphorus content of Sargassum, together with its isotopic composition, can be used to identify those sources, whether they be atmospheric, oceanic, or riverine in origin.

Natural disasters such as earthquakes and/or tsunamis may cause disturbance to the ocean, which can possibly lead to changes in the ocean properties. Here, we review the literature for the reported pre- or post-event changes of such properties, which include chlorophyll-a concentration, temperature, and turbidity in the surface ocean. Most of the reported changes were based on remotely sensed ocean properties, and such changes were attributed to the ocean’s response to the events. Here, by using the same remote sensing data collected in non-event years as the ‘control’ experiments or by analyzing the same remote sensing data at different spatial scales, however, it is found that similar changes also occurred in non-event years or could not be observed at different spatial scales. Therefore, the before-after changes detected in remote sensing imagery do not appear to be sufficient to infer causality but are more likely a result of natural variability.

Abstract The cyanobacterium Trichodesmium is responsible for approximately half of the ocean's nitrogen input through nitrogen fixation. Although it was first recorded near Australia in the 18th century, the knowledge of where and when large quantity of Trichodesmium around Australia could be found is still lacking. Here, using multi‐band satellite imagery acquired between 2012 and 2021, we fill this knowledge gap through the use of deep learning, designed to recognize both the spectral shapes of individual pixels and spatial morphology of surface aggregations (scums) of Trichodesmium. Trichodesmium scums were found nearly everywhere around Australia, with a cumulative footprint (i.e., where the 10‐year average density is >0.001‰) exceeding 4.6 million km2. Strong seasonality was found, with peak months between September and November. Furthermore, temperature, iron‐rich dust and black carbon aerosols, with the latter being a result of frequent bushfires, play major roles in determining the spatial distributions and seasonality of Trichodesmium.

Abstract Background AKI is related to severe adverse outcomes and mortality with Coronavirus Disease 2019 (COVID-19) patients, that early diagnosed and intervened is imperative. Neutrophil gelatinase-associated lipocalin (NGAL) is one of the most promising biomarkers for detection of acute kidney injury (AKI), but current detection methods are inadequacy, so more rapid, convenient and accuracy methods are needed to detect NGAL for early diagnosis of AKI. Herein, we established a rapid, reliable and accuracy lateral flow immunoassay (LFIA) based on europium nanoparticles (EU-NPS) for the detection of NGAL in human urine specimens. Methods A double-antibody sandwich immunofluorescent assay using europium doped nanoparticles was employed and the NGAL monoclonal antibodies (MAbs) conjugate as labels were generated by optimizing electric fusion parameters. Eighty-three urine samples were used to evaluate the clinical application efficiency of this method. Results The quantitative detection range of NGAL in AKI was 1-3000 ng/mL, and the detection sensitization was 0.36 ng/mL. The coefficient of variation (CV) of intra-assay and inter-assay were 2.57-4.98 % and 4.11-7.83 %, respectively. Meanwhile, the correlation coefficient between europium nanoparticles-based lateral fluorescence immunoassays (EU-NPS-LFIA) and ARCHITECT analyzer was significant (R2 = 0.9829, n = 83, p

Since the first report in 2008, macroalgal blooms of Ulva prolifera (often called green tides) in the Yellow Sea have occurred every year, with their origins, transport pathways, temporal changes, as well as causes and consequences studied extensively. Of these studies, satellite remote sensing has been used widely to detect the bloom presence and quantify the bloom size (i.e., U. prolifera coverage in km2 or biomass in kilotons). However, substantial variability has been found in the refereed literature in the remote sensing methodology, results, and interpretation of the U. prolifera coverage, especially in the attempts to study inter-annual changes or long-term trends. There are often inconsistent or contradicting results even from the same satellite sensor. Such inconsistencies or contradictions create difficulty not only within the remote sensing community when presenting new methodology or results, but also to researchers when attempting to use the remote sensing results to make predictions or perform impact assessments. Here, we review the literature on the remote sensing methodology to detect and quantify U. prolifera blooms, and make recommendations based on physical principles. Specifically, we propose the following conceptual guidelines: 1) a reliable index or algorithm should be relatively tolerant to perturbations by non-optimal observing conditions (thick aerosols, thin clouds, moderate sun glint, cloud-adjacent straylight, which can all be found frequently in the study region) for presence/absence detection, as well as to small errors in the selected thresholds to quantify U. prolifera; 2) a reliable index or algorithm should also make it relatively easy to account for variability in subpixel coverage of U. prolifera (i.e., through pixel unmixing) in order to obtain an accurate estimate of total U. prolifera coverage from an image; 3) a reliable data product (i.e., U. prolifera maps) should be able to account for the variable clouds when interpreting spatial patterns or temporal changes, with uncertainty estimates provided whenever possible; and 4) both the algorithm and the data product should minimize manual work in order to make them more objective and repeatable by other researchers. Finally, we show different types of time series of U. prolifera amounts in the Yellow Sea using the approaches based on these guidelines and Moderate Resolution Imaging Spectroradiometer (MODIS) observations, and discuss their implications on the interpretation of annual changes in interdisciplinary studies.

Absorption coefficient and chlorophyll concentration (Chl) are important optical and biological properties of the aquatic environment, which can be estimated from the spectrum of water color, commonly measured by the remote sensing reflectance (Rrs). In this study, we extended the band-difference scheme for Chl of oceanic waters developed a decade ago to the estimation of absorption coefficient at 440 nm (a(440)). As demonstrated earlier for the estimation of Chl, a(440) product from the band difference of Rrs showed much smoother spatial pattern than that from a semianalytical algorithm. More importantly, it is found that the upper limit of using band difference of Rrs can be extended from −0.0005 sr−1 (the upper limit set a decade ago for the estimation of Chl) to ~0.0005 sr−1 (corresponding to a(440) ~0.08 m−1), which covers ~91% of the global ocean. We further converted a(440) to Chl based on the “Case-1” water assumption and found that the standard Chl product of oligotrophic waters (Chl ~ 0.1 mg/m3) distributed by NASA is generally ~20% higher than Chl converted from a(440), possibly a result of different datasets used to determine the algorithm coefficients. These results not only extended the application of the band-difference scheme for more oceanic waters but also highlighted the need of more accurate field measurements of Chl and Rrs in oligotrophic oceans in order to minimize the discrepancies observed in satellite Chl products derived using the same algorithm concept but different empirical approaches.

Abstract Blooms and coastal inundations of pelagic Sargassum have caused major problems to coastal communities and ecosystems throughout the tropical Atlantic over the past decade. Understanding Sargassum growth, movement, and mortality is essential to being able to predict and mitigate these events. In principle, tropical cyclones that occur in this area could have an impact on all three processes. In a recent article, Sosa‐Gutierrez et al. (2022, https://doi.org/10.1029/2021GL097484) investigated the potential impacts of tropical cyclones (TCs) on pelagic Sargassum using satellite‐based Sargassum maps and 86 hurricane tracks during 2011–2020. Their statistical analysis showed an average drop of 40% in Sargassum coverage under TC trajectories, which was attributed to possible sinking of Sargassum. Here, we discuss implications of these findings, and advocate continued research on how storms and other physical factors influence the dynamics of growth, movement, and mortality in this ecologically and economically important macroalgae.

Net primary production (NPP) plays an important role in estuarine carbon cycling, which has been increasingly impacted by human activities and global climate change. Spatiotemporal trends of NPP in the open ocean have been well studied using satellite data and standard primary production algorithms such as the Vertically Generalized Production Model (VGPM), but these algorithms are generally not suitable for estuarine and coastal waters. Previous remote sensing studies on estuarine NPP mainly focused on the tuning of the standard VGPM based on extensive local in situ data. Here we make a first attempt to use machine learning algorithms to estimate NPP in an estuarine environment from satellite measurements. Tampa Bay, the largest estuary in Florida (United States), has abundant in situ measurements of NPP, although the spatiotemporal variability of NPP within the bay remains unrevealed. Combining these data with concurrent MODIS/Aqua image data, we developed and evaluated seven machine learning algorithms (support vector regression, random forest, decision tree, bagging, adaptive boosting, gradient tree boosting, and neural network), and applied the one with the least root mean square error (RMSE) and highest correlation coefficient to establish a time-series NPP record for Tampa Bay from 2002 to 2020. Results of the best performing algorithm (Pearson’s r = 0.82, RMSE = 151.0 mgC m−2 d−1) showed substantial improvement over the standard or tuned VGPM (Pearson’s r = 0.51, RMSE = 364.9 mgC m−2 d−1), using the same independent variables for NPP ranging from 157.5 to 1368.8 mgC m−2 d−1. MODIS NPP shows temporal variations that are largely driven by temperature: lowest values in winter, highest values in summer, and an increasing trend from 2003 to 2020, highlighting the impact of global warming on estuarine NPP. The spatial distribution of MODIS NPP shows higher values in Hillsborough Bay, Middle Tampa Bay and Lower Tampa Bay, and relatively lower values in Old Tampa Bay, a pattern that likely reflects differences in river discharge. The long-term NPP product derived from machine learning algorithms and satellite data can complement existing field-based monitoring programs and help to understand estuarine responses to climate changes and human impacts, and design relevant mitigation strategies.

The scale of the Deepwater Horizon disaster was and is unprecedented: geographic extent, pollutant amount, countermeasure scope, and of most relevance to this Research Topic issue, range of ecotypes affected. These ecotypes include coastal/nearshore, continental shelf, deep benthic, and open-ocean domains, the last of which is the subject of this synthesis. The open-ocean ecotype comprises ~90% of the volume of the Gulf of Mexico. The exact percentage of this ecotype contaminated with toxins is unknown due to its three-dimensional nature and dynamics, but estimates suggest that the footprint encompassed most of its eastern half. Further, interactions between the water column and the deep benthos may be persistent, making this synthesis one of time (a decade) rather than event conclusion. Here we examine key elements of the open-ocean ecosystem, with emphasis on vulnerability and resilience. Of paramount importance relative to the Gulf nearshore and shelf ecotypes, pre-disaster baseline data were lacking for most of the fauna. In such cases, inferences were drawn from post-disaster assessments. Both phytoplankton and mesozooplankton vulnerabilities were quite high, but resilience appeared equally so. The phytoplankton situation was a bit more complex in that toxin-imposed reductions may have been offset by nutrient injection via high freshwater discharge in 2010. Intermediate trophic levels exhibited population-level depressions, ostensibly due to high vulnerability and low resilience. Apex predator impacts were variable. Certain large epipelagic fishes may have avoided the highest concentrations of hydrocarbons/dispersant, and thus larval abundances returned to pre-disaster levels of variability and abundance within a few years after a steep initial decline. Oceanic cetaceans, particularly shallow-diving stenellid dolphins, did not appear to avoid oiled waters and exhibited strong declines in the northern Gulf. Given that population declines of many open-ocean taxa appear to be ongoing a decade later, we conclude that this largest of Gulf ecosystem components, like its deep-benthic counterpart, is as fragile as it is voluminous. This is particularly concerning given the rapid, and likely irreversible, shift to deeper waters by the US and Mexican oil industries in concert with the higher likelihood of accidents with increasing platform depth.

Uncertainties in the retrieval of the remote sensing reflectance, Rrs, from Ocean Color (OC) satellite sensors have a strong impact on the performance of algorithms for the estimation of chlorophyll-a, mineral concentrations, and inherent optical properties (IOPs). The uncertainties are highest in the blue bands. The total radiance measured at the top of the atmosphere captures the instantaneous state of the atmosphere-ocean system: the in-water conditions, sky and Sun glint reflected from the wind-roughened ocean surface, as well as light scattered from molecules and aerosols in the atmosphere. Each of these components has associated uncertainties, and when combined with the additional uncertainties from the instrument noise and the atmospheric correction process, they contribute to the total uncertainty budget for the retrieved Rrs. We analyzed the contribution of each component uncertainties to the total Rrs uncertainties in SNPP-VIIRS level 2 products, taking advantage of the spectral differences between the components. We examined multiple scenes in the open ocean and coastal waters at spatial resolutions ranging from 2250 to 5250 m by comparing the retrieved Rrs to in situ measurements made at several AERONET-OC sites and at the MOBY site. It was shown that uncertainties associated with the molecular (Rayleigh) scattering play the most significant role, while the contributions of other components are usually smaller. Uncertainties in Rayleigh scattering are primarily attributed to the variability of Rayleigh optical thickness (ROT) with a standard deviation of approximately 1.5% of ROT, which can largely explain the frequency of negative Rrs retrievals as observed using the current standard atmospheric correction process employed by NASA. Variability of the sky light reflected from the ocean surface in some conditions also contributed to uncertainties in the blue; water variability proportional to Rrs had a very pronounced peak in the green at coastal sites.

Ocean eddies along the Loop Current (LC)/Florida Current (FC) front have been studied for decades, yet studies of the entire evolution of individual eddies are rare. Here, satellite altimetry and ocean color observations, Argo profiling float records and shipborne acoustic Doppler current profiler (ADCP) measurements, together with high-resolution simulations from the global Hybrid Coordinate Ocean Model (HYCOM) are used to investigate the physical and biochemical properties, 3-dimensional (3-D) structure, and evolution of a long-lasting cyclonic eddy (CE) in the Straits of Florida (SoF) along the LC/FC front during April–August 2017. An Angular Momentum Eddy Detection Algorithm (AMEDA) is used to detect and track the CE during its evolution process. The long-lasting CE is found to form along the eastern edge of the LC on April 9th, and remained quasi-stationary for about 3 months (April 23 to July 15) off the Dry Tortugas (DT) until becoming much smaller due to its interaction with the FC and topography. This frontal eddy is named a Tortugas Eddy (TE) and is characterized with higher Chlorophyll (Chl) and lower temperature than surrounding waters, with a mean diameter of ∼100 km and a penetrating depth of ∼800 m. The mechanisms that contributed to the growth and evolution of this long-lasting TE are also explored, which reveal the significant role of oceanic internal instability.

The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys.

Despite the well-recognized importance in understanding the long term impact of anthropogenic release of atmospheric CO2 (its partial pressure named as pCO2air) on surface seawater pCO2 (pCO2sw), it has been difficult to quantify the trends or changing rates of pCO2sw driven by increasing atmospheric CO2 forcing (pCO2swatm_forced) due to its combination with the natural variability of pCO2sw (pCO2swnat_forced) and the requirement of long time series data records. Here, using a novel satellite-based pCO2sw model with inputs of ocean color and other ancillary data between 2002 and 2019, we address this challenge for a mooring station at the Hawaii Ocean Time-series Station in the North Pacific subtropical gyre. Specifically, using the developed pCO2sw model, we differentiated and separately quantified the interannual-decadal trends of pCO2swnat_forced and pCO2swatm_forced. Between 2002 and 2019, both pCO2sw and pCO2air show significant increases at rates of 1.7 ± 0.1 μatm yr–1 and 2.2 ± 0.1 μatm yr–1, respectively. Correspondingly, the changing rate in pCO2swnat_forced is mainly driven by large scale forcing such as Pacific Decadal Oscillation, with a negative rate (-0.5 ± 0.2 μatm yr–1) and a positive rate (0.6 ± 0.3 μatm yr–1) before and after 2013. The pCO2swatm_forced shows a smaller increasing rate of 1.4 ± 0.1 μatm yr–1 than that of the modeled pCO2sw, varying in different time intervals in response to the variations in atmospheric pCO2. The findings of decoupled trends in pCO2swatm_forced and pCO2swnat_forced highlight the necessity to differentiate the two toward a better understanding of the long term oceanic absorption of anthropogenic CO2 and the anthropogenic impact on the changing surface ocean carbonic chemistry.

Optical remote sensing (ORS) of reflected sun light has been used to assess oil spills in the ocean for several decades. While most applications are toward simple presence/absence detections based on the spatial contrast between oiled water and oil-free water, recent advances indicate the possibility of classifying oil types and quantifying oil volumes based on their spectral contrasts with oil-free water. However, a review of the current literature suggests that there is still confusion on whether this is possible and, if so, how. Here, based on the recent findings from numerical models, laboratory measurements, and applications to satellite or airborne imagery, we attempt to clarify this situation by summarizing (1) the optics behind oil spill remote sensing, and in turn, (2) how to interpret optical remote sensing imagery based on optical principles. In the end, we discuss the existing limitations and challenges as well as pathways forward to advance ORS of oil spills.

The pelagic Gulf of Mexico (GoM) is a complex system of dynamic physical oceanography (western boundary current, mesoscale eddies), high biological diversity, and community integration via diel vertical migration and lateral advection. Humans also heavily utilize this system, including its deep-sea components, for resource extraction, shipping, tourism, and other commercial activity. This utilization has had impacts, some with disastrous consequences. The Deepwater Horizon oil spill (DWHOS) occurred at a depth of ∼1500 m (Macondo wellhead), creating a persistent and toxic mixture of hydrocarbons and dispersant in the deep-pelagic (water column below 200 m depth) habitat. In order to assess the impacts of the DWHOS on this habitat, two large-scale research programs, described herein, were designed and executed. These programs, ONSAP and DEEPEND, aimed to quantitatively characterize the oceanic ecosystem of the northern GoM and to establish a time-series with which natural and anthropogenic changes could be detected. The approach was multi-disciplinary in nature and included in situ sampling, acoustic sensing, water column profiling and sampling, satellite remote sensing, AUV sensing, numerical modeling, genetic sequencing, and biogeochemical analyses. The synergy of these methodologies has provided new and unprecedented perspectives of an oceanic ecosystem with respect to composition, connectivity, drivers, and variability.

Water transparency, usually denoted by Secchi disk depth (SSD), represents the first-order description of water quality and has important implications for the diversity and productivity of aquatic life. In China, lakes supply freshwater and ecosystem services to nearly a billion people. Therefore, real time monitoring of lake transparency is of great significance. Moreover, understanding how and why transparency varies in space and time in response to different driving forces is needed to understand, manage, and predict lake water quality. Based on the time-saving and low-cost Google Earth Engine cloud platform, this study developed a new algorithm for quickly mapping SDDs in Chinese lakes. SDDs were retrieved for 412 Chinese lakes (> 20 km2) for the period 2000–2018. Results demonstrated that lake water depth spatially differentiated transparency. Deep lakes usually had high transparency and water depth explained 88.81 % of the spatial variations. With increasing catchment vegetation coverage and lake water depth, 70.15 % of lakes witnessed increasing transparency during 2000–2018. Of these 42.72 % were significant (p < 0.05). Transparency of deep lakes was generally determined by phytoplankton density not sediment resuspension. Minimum transparency occurred in summer. Future increases in lake water levels in response to factors such as climate change may contribute to further improvements in transparency. Management should focus on controlling eutrophication and increasing vegetation cover in catchments.

From extensive evaluations, it is found that, of all satellite data products of sea surface temperature (SST), MODIS SST is the most appropriate in assessing long-term trends of water temperature in the South Florida estuaries. Long-term SST data show significant warming trends in these estuaries during both daytime (0.55 °C/decade) and nighttime (0.42 °C/decade) between 2000 and 2021. The warming rates are faster during winter (0.70 °C/decade and 0.67 °C/decade for daytime and nighttime, respectively) than during summer (0.48 °C/decade and 0.28 °C/decade for daytime and nighttime, respectively). Overall, the South Florida estuaries experienced rapid warming over the past two decades, 1.7 and 1.3 times faster than the Gulf of Mexico (0.33 °C/decade and 0.32 °C/decade for daytime and nighttime), and 6.9 and 4.2 times faster than the global oceans (0.08 °C/decade and 0.10 °C/decade for daytime and nighttime).

Abstract Recent data have shown that the expression of lysosome-associated membrane protein type 2 A (LAMP2A), the key protein in the chaperone-mediated autophagy (CMA) pathway, is elevated in breast tumor tissues. However, the exact effects and mechanisms of CMA during breast cancer metastasis remain largely unknown. In this study, we found that the LAMP2A protein level was significantly elevated in human breast cancer tissues, particularly in metastatic carcinoma. The increased LAMP2A level was also positively correlated with the histologic grade of ductal breast cancer. High LAMP2A levels also predicted shorter overall survival of breast cancer patients. Downregulation of CMA activity by LAMP2A knockdown significantly inhibited the growth and metastasis of both MDA-MB-231 and MDA-MB-468 breast cancer cells in vivo and in vitro, while upregulation of CMA activity by LAMP2A overexpression had the opposite effect. Mechanistically, we found that elevated CMA activity mediated increased growth and metastasis of human breast cancer cells by downregulating the activity of autophagy-related gene 5 (ATG5)-dependent macroautophagy. Collectively, these results indicate that the anti-macroautophagic property is a key feature of CMA-mediated tumorigenesis and metastasis and may, in some contexts, serve as an attractive target for breast cancer therapies.

In coastal waters around the world, the dominant primary producers are benthic macrophytes, including seagrasses and macroalgae, that provide habitat structure and food for diverse and abundant biological communities and drive ecosystem processes. Seagrass meadows and macroalgal forests play key roles for coastal societies, contributing to fishery yields, storm protection, biogeochemical cycling and storage, and important cultural values. These socio-economically valuable services are threatened worldwide by human activities, with substantial areas of seagrass and macroalgal forests lost over the last half-century. Tracking the status and trends in marine macrophyte cover and quality is an emerging priority for ocean and coastal management, but doing so has been challenged by limited coordination across the numerous efforts to monitor macrophytes, which vary widely in goals, methodologies, scales, capacity, governance approaches, and data availability. Here, we present a consensus assessment and recommendations on the current state of and opportunities for advancing global marine macrophyte observations, integrating contributions from a community of researchers with broad geographic and disciplinary expertise. With the increasing scale of human impacts, the time is ripe to harmonize marine macrophyte observations by building on existing networks and identifying a core set of common metrics and approaches in sampling design, field measurements, governance, capacity building, and data management. We recommend a tiered observation system, with improvement of remote sensing and remote underwater imaging to expand capacity to capture broad-scale extent at intervals of several years, coordinated with stratified in situ sampling annually to characterize the key variables of cover and taxonomic or functional group composition, and to provide ground-truth. A robust networked system of macrophyte observations will be facilitated by establishing best practices, including standard protocols, documentation, and sharing of resources at all stages of workflow, and secure archiving of open-access data. Because such a network is necessarily distributed, sustaining it depends on close engagement of local stakeholders and focusing on building and long-term maintenance of local capacity, particularly in the developing world. Realizing these recommendations will produce more effective, efficient, and responsive observing, a more accurate global picture of change in vegetated coastal systems, and stronger international capacity for sustaining observations.