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Deriving inherent optical properties (IOPs) and other water quality parameters from satellite remote sensing data covering optically shallow environments has historically been problematic due to difficulties in separating the benthic signal from that of the water column. While recent advances have improved such retrievals, most methods have high uncertainties for very shallow (

The impoundment of the Three Gorges Dam (TGD) has led to significant inundation shrinkage and water level decrease in China's largest freshwater lake, Poyang Lake. However, little is known about the influence on the lake's wetland landscapes. Here, using Moderate Resolution Imaging Spectroradiometer (MODIS) observations and a phenology-based decision tree approach, we present the spatial and temporal changes of the major wetland cover types from 2000 to 2014. Over the 15-year period, both total coverage of vegetation ( Carex spp., Triarrhena lutarioriparia L. - Phragmites, sparse grass, and Zizania latifolia communities) and the area of each community (excluding floating aquatic macrophytes community or FAM) showed significantly increasing trends, with vegetation expanded towards the lake center. In contrast, the areas of water and mudflat have decreased significantly since the TGD impoundment, and they were mainly replaced by prominently expanded vegetated areas. The transition maps during 2000–2014 show that the vegetation community transitions occurred mainly from hydrophilic cover types to those adapted to dryer conditions. Despite the significant changes in wetland cover types, the most preferable water depth for each cover type remained stable before and after the TGD. In conclusion, the vegetation compositions are primarily controlled by water depth, indicating that the recent wetland changes can be directly linked to the TGD-induced hydrological regime-shift. These results provide a critical reference for local authorities to assess the potential influence of the newly proposed dam in this lake and to optimize its future operations with respect to modulating water levels.

Carbon-based fuels burned at industrial facilities account for a large proportion of greenhouse gas emissions, and an up-to-date spatiotemporally detailed inventory is essential for a better understanding of global carbon emission patterns. The Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product offers a quantitative estimation of the temperatures of sub-pixel heat sources, providing the potential for detecting thermal anomalies from industrial sectors across the globe. However, identifying subcategories of various industrial heat sources is challenging because there are scarcely any stable and typical characteristics for their classification at a single thermal anomaly scale. Specifically, these nighttime thermal anomalies exhibit a strong spatiotemporal heterogeneity (e.g., fluctuations in retrieved temperature, spatial shifts in position, and presence of false positives), even in industrial heat sources that do not vary through time. Here, we demonstrate an object-oriented approach to robustly segment and accurately classify various industrial heat sources from a time-series of the VIIRS Nightfire product. The approach operates from the cluster level of spatially adjacent nighttime thermal anomalies (i.e., nighttime-heat-source objects rather than individual thermal anomalies) to generate fingerprint-like characteristics and to address the challenge of spatiotemporal heterogeneity. Specifically, the spatial-aggregation characteristic of nighttime thermal anomalies from continuously operating industrial heat sources and the temporal-aggregation characteristics of biomass burnings were incorporated to differentiate industrial nighttime-heat-source objects from ubiquitous biomass burnings. Subsequently, the similarity of the thermal signals of nighttime thermal anomalies from identical industrial heat sources was used to generate highly recognizable characteristics for their identification. A spatiotemporally detailed inventory of industrial heat sources across the globe was then established from this object-oriented classification. The inventory included a total of 15,199 industrial heat sources, representing 49.52% of all higher confidence nighttime thermal anomalies in the VIIRS Nightfire product. Validation of the results showed that only 218 objects (1.43%) were biomass burnings or active volcanoes that were misclassified as industrial heat sources. Further validation of sub-categories indicated an overall classification accuracy of ~ 77%. Our findings suggest that the VIIRS Nightfire product has great potential for monitoring the global distribution and dynamics of industrial heat sources, and combined with the object-oriented approach developed here the methodology is simple, robust, and cost-effective.

The Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instrument has been used as a precursor for a hyperspectral instrument on the future geostationary satellite, yet its ability to “measure” ocean reflectance needs to be evaluated. Here, we demonstrate its capacity through vicarious calibration and atmospheric correction of data collected during flight campaigns over the Louisiana shelf in September 2013 and over the North Atlantic Ocean in November 2015. GCAS-measured at-sensor radiance was first vicariously calibrated using concurrent measurements by the Moderate Resolution Imaging Spectrometer (MODIS) and radiative transfer simulations with the MODerate resolution atmospheric TRANsmission (MODTRAN). Then, atmospheric correction has been implemented using MODTRAN-developed lookup tables and the traditional Gordon and Wang “black pixel” approach but with nonzero water-leaving radiance in the near-infrared accounted for through iteration. The atmospheric correction algorithm was applied to the vicariously calibrated GCAS imagery, with resulting $R_{\mathrm{ rs}}$ compared with concurrent MODIS $R_{\mathrm{ rs}}$ and in situ $R_{\mathrm{ rs}}$ . The comparison shows a mean relative difference of about 25% ( $N = 11$ ) between GCAS and in situ $R_{\mathrm{ rs}}$ in the blue–green bands for clear to moderately turbid waters.

In recent years, massive blooms of pelagic Sargassum have occurred in the Atlantic Ocean, Caribbean Sea, and Gulf of Mexico, and satellite imagery have been used operationally to monitor and track the blooms. However, limited by the coarse resolution and other confounding factors, there is often a data gap in nearshore waters, and the uncertainties in the estimated Sargassum abundance in offshore waters are also unclear. Higher-resolution satellite data may overcome these limitations, yet such a potential is hindered by the lack of reliable methods to accurately detect and quantify Sargassum in an automatic fashion. Here, we address this challenge by combining large quantities of high-resolution satellite data with deep learning. Specifically, data from the Multispectral Instrument (MSI, 10–20 m), Operational Land Imager (OLI, 30 m), WorldView-II (WV-2, 2 m), and PlanetScope/Dove (3 m) are used with a deep convolution neural network (DCNN) to extract Sargassum features and quantify Sargassum biomass density or areal coverage. By utilizing the U-net architecture and the pre-trained weights from the VGG16 model, the DCNN (i.e., the VGGUnet model) can extract Sargassum features while discarding other confusing features (waves, currents, phytoplankton blooms, clouds, cloud shadows, or striping noise). For Sargassum biomass estimated from OLI and MSI images, results indicate an accuracy of ~92% and 90%, respectively, when evaluated using images from the same sensor. When Sargassum areal coverage is estimated from WV-2 and Dove images, there is an accuracy of ~98% and 82%, respectively. When different sensors are cross-compared, OLI reveals ~30% more Sargassum biomass than MODIS from 14 OLI images collected in the Caribbean Sea (path/row: 001/050) for their commonly viewed observable areas, and ~ 180% more Sargassum biomass than MSI (N = 15, path/row: 001/050); such differences appear systematic (R2 = 0.98 and 0.73, respectively). Compared to the quasi-simultaneous MSI, OLI, and MODIS images, Dove shows higher Sargassum coverage. Higher-resolution sensors tend to observe more Sargassum because they can detect smaller-scale features that are missed by the coarser-resolution sensors, although the difference varies with time and location. The morphological characteristics of Sargassum features from these high-resolution data are also reported to facilitate management actions. The findings here not only fill the knowledge gaps and coverage gaps from previous studies, but more importantly pave the road toward operational monitoring and tracking Sargassum features in nearshore waters.

Various approaches and models have been proposed to remotely estimate surface pCO2 in the ocean, with variable performance as they were designed for different environments. Among these, a recently developed mechanistic semi-analytical approach (MeSAA) has shown its advantage for its explicit inclusion of physical and biological forcing in the model, yet its general applicability is unknown. Here, with extensive in situ measurements of surface pCO2, the MeSAA, originally developed for the summertime East China Sea, was tested in the northern Gulf of Mexico (GOM) where river plumes dominate water's biogeochemical properties during summer. Specifically, the MeSAA-predicted surface pCO2 was estimated by combining the dominating effects of thermodynamics, river-ocean mixing and biological activities on surface pCO2. Firstly, effects of thermodynamics and river-ocean mixing (pCO2@Hmixing) were estimated with a two-endmember mixing model, assuming conservative mixing. Secondly, pCO2 variations caused by biological activities (ΔpCO2@bio) was determined through an empirical relationship between sea surface temperature (SST)-normalized pCO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) 8-day composite chlorophyll concentration (CHL). The MeSAA-modeled pCO2 (sum of pCO2@Hmixing and ΔpCO2@bio) was compared with the field-measured pCO2. The Root Mean Square Error (RMSE) was 22.94 µatm (5.91%), with coefficient of determination (R2) of 0.25, mean bias (MB) of − 0.23 µatm and mean ratio (MR) of 1.001, for pCO2 ranging between 316 and 452 µatm. To improve the model performance, a locally tuned MeSAA was developed through the use of a locally tuned ΔpCO2@bio term. A multi-variate empirical regression model was also developed using the same dataset. Both the locally tuned MeSAA and the regression models showed improved performance comparing to the original MeSAA, with R2 of 0.78 and 0.84, RMSE of 12.36 µatm (3.14%) and 10.66 µatm (2.68%), MB of 0.00 µatm and − 0.10 µatm, MR of 1.001 and 1.000, respectively. A sensitivity analysis was conducted to study the uncertainties in the predicted pCO2 as a result of the uncertainties in the input variables of each model. Although the MeSAA was more sensitive to variations in SST and CHL than in sea surface salinity (SSS), and the locally tuned MeSAA and the empirical regression models were more sensitive to changes in SST and SSS than in CHL, generally for these three models the bias induced by the uncertainties in the empirically derived parameters (river endmember total alkalinity (TA) and dissolved inorganic carbon (DIC), biological coefficient of the MeSAA and locally tuned MeSAA models) and environmental variables (SST, SSS, CHL) was within or close to the uncertainty of each model. While all these three models showed that surface pCO2 was positively correlated to SST, the MeSAA showed negative correlation between surface pCO2 and SSS and CHL but the locally tuned MeSAA and the empirical regression showed the opposite. These results suggest that the locally tuned MeSAA worked better in the river-dominated northern GOM than the original MeSAA, with slightly worse statistics but more meaningful physical and biogeochemical interpretations than the empirical regression model. Because data from abnormal upwelling were not used to train the models, they are not applicable for waters with strong upwelling, yet the empirical regression approach showed ability to be further tuned to adapt to such cases.

A floating algae bloom in the East China Sea was observed in MODIS imagery in May 2017. Using satellite imagery from MODIS, VIIRS, GOCI, and OLI, and combined with numerical particle tracing experiments and laboratory experiments, we examined the history of this bloom as well as similar blooms in previous years, and attempted to trace the bloom source and identify the algae type. Results suggest that one bloom origin is offshore Zhejiang coast where algae slicks have appeared in satellite imagery almost every February – March since 2012. Following the Kuroshio Current and Taiwan Warm Current, these “initial” algae slicks are first transported to the northeast to reach South Korea (Jeju Island) and Japan coastal waters (up to 135oE) by early April 2017, and then transported to the northwest to enter the Yellow Sea by the end of April. The transport pathway covers an area known to be rich in Sargassum horneri, and spectral analysis suggests that most of the algae slicks may contain large amount of S. horneri. The bloom covers a water area of ~160,000 km2 with pure algae coverage of ~530 km2, which exceeds the size of most Ulva blooms that occur every May – July in the Yellow Sea. While blooms of smaller size also occurred in previous years and especially in 2015, the 2017 bloom is hypothesized to be a result of record-high water temperature, increased light availability, and continuous expansion of Porphyra aquaculture along the ECS coast.

Sea surface salinity (SSS) is an important parameter to characterize physical and biogeochemical processes, yet its remote estimation in coastal waters has been difficult because satellite sensors designed to “measure” SSS lack sufficient resolution and coverage, and higher-resolution ocean color measurements suffer from optical and biogeochemical complexity when used to estimate SSS. In the northern Gulf of Mexico (GOM), this challenge is addressed through modeling, validation, and extensive tests in contrasting environments. Specifically, using extensive SSS datasets collected by many groups spanning > 10 years and MODIS (Moderate Resolution Imaging Spectroradiometer) and SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) estimated remote sensing reflectance (Rrs) at 412, 443, 488 (490), 555, and 667 (670) nm and sea surface temperature (SST), a multilayer perceptron neural network-based (MPNN) SSS model has been developed and validated with a spatial resolution of ~ 1 km. The MPNN was selected over many other empirical approaches such as principle component analysis (PCA), multi-nonlinear regression (MNR), decision tree, random forest, and supporting vector machines (SVMs) after extensive evaluations. The MPNN was trained by a back-propagation learning technique with Levenberg-Marquardt optimization and Bayesian regularization. The model showed an overall performance of root mean square error (RMSE) = 1.2, with coefficient of determination (R2) = 0.86, mean bias (MB) = 0.0, and mean ratio (MR) = 1.0 for SSS ranging between ~ 1 and ~ 37 (N = 3640). Validation using an independent dataset showed a RMSE of 1.1, MB of 0.0, and MR of 1.0 for SSS ranging between ~ 27 and ~ 37 (N = 412). The model with its original parameterization has been tested in the Mississippi-Atchafalaya coastal region, Florida's Big Bend region, and in the offshore Mississippi River plume, with satisfactory performance obtained in each case. Comparison with concurrent Aquarius-derived SSS maps (110-km resolution) showed similar agreement in offshore waters as indicated above, but the new 1-km resolution SSS maps revealed more finer-scale features as well as salinity gradients in coastal waters. The sensitivity of the model to realistic model input errors in satellite-derived SST and Rrs was also thoroughly examined, with uncertainties in the model-derived SSS being always 30. The extensive validation, evaluation, and sensitivity test all indicated the robustness of the MPNN model in estimating SSS in most, if not all, coastal waters and offshore plumes in the northern GOM. Thus, the model provided a basis for generating near real-time 1-km resolution SSS maps from satellite measurements. However, the model showed limitations when applied to regions with known algal blooms or upwelling as they both led to low Rrs in the blue bands that may be falsely recognized as caused by low SSS.

A new algorithm for estimating particulate inorganic carbon (PIC) concentrations from ocean color measurements is presented. PIC plays an important role in the global carbon cycle through the oceanic carbonate pump, therefore accurate estimations of PIC concentrations from satellite remote sensing are crucial for observing changes on a global scale. An extensive global dataset was created from field and satellite observations for investigating the relationship between PIC concentrations and differences in the remote sensing reflectance (Rrs) at green, red and near-infrared (NIR) wavebands. Three color indices were defined: two as the relative height of Rrs(667) above a baseline running between Rrs(547) and an Rrs in the the NIR (either 748 nm or 869 nm), and one as the difference between Rrs(547) and Rrs(667). All three color indices were found to explain over 90% of the variance in field-measured PIC. But, due to the lack of availability of Rrs(NIR) in the standard ocean color data products, most of the further analysis presented here was done using the color index determined from only two bands. The new two-band color index algorithm was found to retrieve PIC concentrations more accurately than the current standard algorithm used in generating global PIC data products. Application of the new algorithm to satellite imagery showed patterns on the global scale as revealed from field measurements. The new algorithm was more resistant to atmospheric correction errors and residual errors in sun glint corrections, as seen by a reduction in the speckling and patchiness in the satellite-derived PIC images.

Whiting events, or waters with high concentrations of fine-grained calcium carbonate, have been previously reported in southwest Florida using three years of observations from the Moderate Resoluti...

Phytoplankton size class (PSC), a measure of different phytoplankton functional and structural groups, is a key parameter to the understanding of many marine ecological and biogeochemical processes. In turbid waters where optical properties may be influenced by terrigenous discharge and nonphytoplankton water constituents, remote estimation of PSC is still a challenging task. Here based on measurements of phytoplankton diagnostic pigments, total chlorophyll a, and spectral reflectance in turbid waters of Bohai Sea and Yellow Sea during summer 2015, a customized model is developed and validated to estimate PSC in the two semienclosed seas. Five diagnostic pigments determined through high-performance liquid chromatography (HPLC) measurements are first used to produce weighting factors to model phytoplankton biomass (using total chlorophyll a as a surrogate) with relatively high accuracies. Then, a common method used to calculate contributions of microphytoplankton, nanophytoplankton, and picophytoplankton to the phytoplankton assemblage (i.e., Fm, Fn, and Fp) is customized using local HPLC and other data. Exponential functions are tuned to model the size-specific chlorophyll a concentrations (Cm, Cn, and Cp for microphytoplankton, nanophytoplankton, and picophytoplankton, respectively) with remote-sensing reflectance (Rrs) and total chlorophyll a as the model inputs. Such a PSC model shows two improvements over previous models: (1) a practical strategy (i.e., model Cp and Cn first, and then derive Cm as C-Cp-Cn) with an optimized spectral band (680 nm) for Rrs as the model input; (2) local parameterization, including a local chlorophyll a algorithm. The performance of the PSC model is validated using in situ data that were not used in the model development. Application of the PSC model to GOCI (Geostationary Ocean Color Imager) data leads to spatial and temporal distribution patterns of phytoplankton size classes (PSCs) that are consistent with results reported from field measurements by other researchers. While the applicability of the PSC model together with its parameterization to other optically complex regions and to other seasons is unknown, the findings of this study suggest that the approach to develop such a model may be extendable to other cases as long as local data are used to select the optimal band and to determine the model coefficients.

Rapid assessment of algal blooms in bays and estuaries has been difficult due to lack of timely shipboard measurements and lack of spatial resolution from current ocean color satellites. Airborne measurements may fill the gap, yet they are often hindered by the high cost and difficulty in deployment. Here we demonstrate the capacity of a low-cost, low-altitude unmanned aerial vehicle (UAV) in assessing an intense phytoplankton ( Phaeocystis globosa ) bloom (chlorophyll concentrations ranged from 7.3 to 45.6 mg/m 3 ) in Weitou Bay in the western Taiwan Strait. The UAV was equipped with a hyperspectral sensor to measure the water color with a footprint of 5 m at every 30 m distance along the flight track. A novel approach was developed to obtain remote sensing reflectance ( R rs ) from the UAV at-sensor radiometric measurements. Compared with concurrent and co-located field measured R rs (14 stations in total), the UAV-derived R rs showed reasonable agreement with root mean square difference ranging 0.0028–0.0043 sr − 1 (relative difference ~ 20–32%) of such turbid waters for the six MODIS bands (412–667 nm). The magnitude of the bloom was further evaluated from the UAV-derived R rs . For the bloom waters, the estimated surface chlorophyll a concentration ( Chl ) ranged 6–98 mg/m 3 , which is 3–50 times of the Chl under normal conditions. This effort demonstrates for the first time a successful retrieval of both water color ( i.e. , R rs ) and Chl in a nearshore environment from UAV hyperspectral measurements, which advocates the use of UAVs for rapid assessment of water quality, especially for nearshore or difficult-to-reach waters, due to its flexibility, low cost, high spatial resolution, and sound accuracy.

“Whiting” in oceanography is a term used to describe a sharply defined patch of water that contains high levels of suspended, fine-grained calcium carbonate (CaCO 3 ). Whitings have been reported in many oceanic and lake environments, and recently have been reported in southwest Florida coastal waters. Here, field and laboratory measurements were used to study optical, biological, and chemical properties of whiting waters off southwest Florida. No significant difference was found in chlorophyll a concentrations between whiting and outside waters (non-whiting water), but average particle backscattering coefficients in whiting waters were double those in outside waters, and remote sensing reflectance in whiting waters was higher at all wavelengths (400–700 nm). While other potential causes cannot be completely ruled out, particle composition and biochemical differences between sampled whiting water, contiguous water, and outside water indicate a biologically precipitated mode of whiting formation. Taxonomic examination of marine phytoplankton samples collected during a whiting event revealed a community dominated by autotrophic picoplankton and a small ( Thalassiosira sp. through the use of scanning electron microscopy. Amorphous to fully formed crystals of CaCO 3 were observed along the girdle bands of Thalassiosira sp. cells and autotrophic picoplankton cells. Although carbonate parameters differed from whiting and contiguous to outside water, more sampling is needed to determine if these results are statistically significant.

Remote sensing of water quality in turbid coastal and inland waters requires accurate atmospheric correction, which is technically challenging. While previous efforts have shown the advantage of using the short-wave infrared (SWIR) bands instead of near-infrared (NIR) bands for atmospheric correction, such an approach could only be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite (MODISA). This is because MODIS data from the Terra satellite (MODIST) contain more noise and other sensor artifacts, thus this sensor has been generally regarded by the ocean color research community as not being able to provide science quality data. Here, we address this technical challenge through noise reduction and regional vicarious calibration adjustment, and demonstrate preliminary success using turbid Taihu Lake as an example. The noise in the three SWIR bands was evaluated first, and then reduced through a noise reduction method. The SWIR bands were adjusted over open-ocean waters using the well-calibrated NIR ocean bands (1-km resolution) and radiative transfer, which were then used to adjust the land bands (250-m and 500-m resolution) in the visible and NIR over turbid waters where concurrent field-measured reflectance spectra are available. Of all three combinations of SWIR bands, the combination of 1240 and 1640-nm bands was found to perform the best, showing significantly improved retrieval accuracy for Taihu Lake, leading to recovery of low-quality MODIST data to higher-quality data comparable to MODISA, and thus doubling valid data coverage. Testing of this approach on another highly turbid lake (Chaohu Lake, China) showed similar results. While the general application of this approach to turbid lakes still needs to be tested as local tuning of the calibration coefficients may be required, these results suggest that MODIST may be used as effectively as MODISA for monitoring Taihu Lake water quality.

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.

Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance, through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of 99.5% of the algae-containing pixels in satellite images). The model was further transferred to MODIS Rayleigh-corrected reflectance (Rrc), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS Rrc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS Rrc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of

Reductions of zooplankton biomasses and grazing pressures were observed during overfishing-induced trophic cascades and concurrent oil spills at global scales. Recent phytoplankton increments followed, once Fe-, P-, and N-nutrient limitations of commensal diazotrophs and dinoflagellates were also eliminated by respective human desertification, deforestation, and eutrophication during climate changes. Si-limitation of diatoms instead ensued during these last anthropogenic perturbations of agricultural effluents and sewage loadings. Consequently, ~15% of total world-wide annual asthma trigger responses, i.e. amounting to ~45 million adjacent humans during 2004, resulted from brevetoxin and palytoxin poisons in aerosol forms of western boundary current origins. They were denoted by greater global harmful algal bloom [HAB] abundances and breathing attacks among sea-side children during prior decadal surveys of asthma prevalence, compiled here in ten paired shelf ecosystems of western and eutrophied boundary currents. Since 1965, such inferred onshore fluxes of aerosolized DOC poisons of HABs may have served as additional wind-borne organic carriers of toxic marine MeHg, phthalate, and DDT/DDE vectors, traced by radio-iodine isotopes to potentially elicit carcinomas. During these exchanges, as much as 40% of mercury poisonings may instead have been effected by inhalation of collateral HAB-carried marine neurotoxic aerosols of MeHg, not just from eating marine fish. Health impacts in some areas were additional asthma and pneumonia episodes, as well as endocrine disruptions among the same adjacent humans, with known large local rates of thyroid cancers, physician-diagnosed pulmonary problems, and ubiquitous high indices of mercury in hair, pesticides in breast milk, and phthalates in urine.

Using simulations, error propagation theory, and measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), we determined the minimal signal-to-noise ratio (SNR) required for ocean color measurements and product uncertainties at different spatial and temporal scales. First, based on typical top-of-atmosphere (TOA) radiance over the ocean, we evaluate the uncertainties in satellite-derived Rrs in the visible wavelengths (ΔRrs(vis)) due to sensor noise in both the near-infrared (NIR) and the visible bands. While the former induces noise in Rrs(vis) through atmospheric correction, the latter has a direct impact on Rrs(vis). Such estimated uncertainties are compared with inherent ΔRrs(vis) uncertainties from in situ measurements and from the operational atmosphere correction algorithm. The comparison leads to a conclusion that once SNR(NIR) is above 600:1, an SNR(vis) better than 400:1 will not make a significant reduction in product uncertainties at pixel level under typical conditions for a solar zenith angle of 45°. Then, such uncertainties are found to decrease significantly in data products of oceanic waters when the 1 km pixels from individual images are binned to lower spatial resolution (e.g., 4 km) or temporal resolution (e.g., monthly). Although these findings do not suggest that passive ocean color sensors should have SNR(vis) around 400:1, they do support the argument for more trade space in higher spatial and/or spectral resolutions once this minimal 400:1 SNR(vis) requirement is met.

Increased loadings of nitrogen (N) from fertilizers, top soil, sewage, and atmospheric deposition are important drivers of eutrophication in coastal waters globally. Monitoring seawater and macroalgae can reveal long-term changes in N and phosphorus (P) availability and N:P stoichiometry that are critical to understanding the global crisis of coral reef decline. Analysis of a unique 3-decade data set for Looe Key reef, located offshore the lower Florida Keys, showed increased dissolved inorganic nitrogen (DIN), chlorophyll a, DIN:soluble reactive phosphorus (SRP) ratios, as well as higher tissue C:P and N:P ratios in macroalgae during the early 1990s. These data, combined with remote sensing and nutrient monitoring between the Everglades and Looe Key, indicated that the significant DIN enrichment between 1991 and 1995 at Looe Key coincided with increased Everglades runoff, which drains agricultural and urban areas extending north to Orlando, Florida. This resulted in increased P limitation of reef primary producers that can cause metabolic stress in stony corals. Outbreaks of stony coral disease, bleaching, and mortality between 1995 and 2000 followed DIN enrichment, algal blooms, and increased DIN:SRP ratios, suggesting that eutrophication interacted with other factors causing coral reef decline at Looe Key. Although water temperatures at Looe Key exceeded the 30.5 °C bleaching threshold repeatedly over the 3-decade study, the three mass bleaching events occurred only when DIN:SRP ratios increased following heavy rainfall and increased Everglades runoff. These results suggest that Everglades discharges, in conjunction with local nutrient sources, contributed to DIN enrichment, eutrophication, and increased N:P ratios at Looe Key, exacerbating P limitation, coral stress and decline. Improved management of water quality at the local and regional levels could moderate N inputs and maintain more balanced N:P stoichiometry, thereby reducing the risk of coral bleaching, disease, and mortality under the current level of temperature stress.

Unmanned Aerial Systems (UAS) are an operational tool for monitoring and assessment of oil spills. At the same time, satellite imagery has been used almost entirely to detect oil presence/absence, yet its ability to discriminate oil emulsions within a detected oil slick has not been fully exploited. Additionally, one of the challenges in the past has been the ability to deliver strategic information derived from satellite remote sensing in a timely fashion to responders in the field. This study presents UAS and satellite methods for the rapid classification of oil types and thicknesses, from which information about thick oil and oil emulsions (i.e., "actionable" oil) can be delivered in an operational timeframe to responders in the field. Experiments carried out at the OHMSETT test facility in New Jersey demonstrate that under specific viewing conditions satellites can record a signal variance between oil thicknesses and emulsions and non-emulsified oil. Furthermore, multispectral satellite data acquired by RADARSAT-2 and WorldView-2 were combined with data from a UAS field campaign to generate an oil/emulsion thickness classification based on a multispectral classification algorithm. Herein we present the classification methods to generate oil thickness products from UAS, validated by sea-truth observations, and quasi-synoptic multispectral satellite images acquired by WorldView-2. We tested the ability to deliver these products with minimum latency to responding vessels. During field operations in the Gulf of Mexico, we utilized the UAS multispectral system to identify areas of shoreline impacted by the oil spill. This proof-of-concept test using multispectral UAS data to detect emulsions and deliver a derived information product to a vessel in near-real-time sheds light on how UAS assets could be used in the near future for oil spill tactical response operations.

Global climate change can significantly influence oceanic phytoplankton dynamics, and thus biogeochemical cycles and marine food webs. However, associative explanations based on the correlation between chlorophyll-a concentration (Chl-a) and climatic indices is inadequate to describe the mechanism of the connection between climate change, large-scale atmospheric dynamics, and phytoplankton variability. Here, by analyzing multiple satellite observations of Chl-a and atmospheric conditions from National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis datasets, we show that high-latitude atmospheric blocking events over Alaska are the primary drivers of the recent decline of Chl-a in the eastern North Pacific transition zone. These blocking events were associated with the persistence of large-scale atmosphere pressure fields that decreased westerly winds and southward Ekman transport over the subarctic ocean gyre. Reduced southward Ekman transport leads to reductions in nutrient availability to phytoplankton in the transition zone. The findings describe a previously unidentified climatic factor that contributed to the recent decline of phytoplankton in this region and propose a mechanism of the top-down teleconnection between the high-latitude atmospheric circulation anomalies and the subtropical oceanic primary productivity. The results also highlight the importance of understanding teleconnection among atmosphere-ocean interactions as a means to anticipate future climate change impacts on oceanic primary production.

The extensive blooms of the pelagic Sargassum in the Atlantic raised the question of whether this brown seaweed may play an important role in climate change mitigation through carbon fixation and carbon sequestration, as argued in several recent papers. Using simple calculations and published values on Sargassum coverage, biomass density, carbon/biomass ratio, primary productivity, and carbon sequestration efficiency, we show that the total carbon stock in pelagic Sargassum of the entire Atlantic, even during the peak month, is unlikely to exceed 3.61 × 10−3 Pg C, and carbon fixation cannot exceed 6.0 million tons C month−1. While the carbon fixation estimate represents an upper bound, it is still

Thermal remote sensing has been used in assessing oil spills in the ocean, mostly based on empirical interpretations. This study designs a ground-based experiment to measure brightness temperatures (BTs) of oil-in-water (OW) emulsions with different concentrations and oil-free water as a function of time in 32 consecutive hours. Compared with a previous thermal experiment to measure oil slicks (i.e., non-emulsified oil) with different thicknesses and considering the similarity between oil slicks and water-in-oil (WO) emulsions, it is found that (1) the diurnal response of brightness temperature difference (BTD, between oil samples and oil-free sample) to oil emulsion types (OW or WO) is similar, making it difficult to classify oil emulsion types using thermal remote sensing; (2) in contrast, BTD under thermal balance during the optimal time window appears to be a function of equivalent oil thickness (EOT) (oil volume per area, mm) regardless of oil emulsion type, suggesting that EOT could be estimated from BTD. Application of such experimental results to Landsat imagery over the Deepwater Horizon oil spill in the Gulf of Mexico suggests that although their ability to quantify oil footprint is limited, thermal data show potentials in providing unique information (e.g., estimating EOTs for up to 4 mm without the need of differentiating sub-pixel heterogeneity) to complement optical data in characterizing oil type and oil quantity when the spilled oil is thick (> 0.4 mm).

Generation of consistent multi-sensor datasets is critical to the assessment of long-term global changes using satellite-borne instruments. Recent research suggests, however, that a fundamental assumption in satellite ocean color data processing concerning the calibration of the long near infrared band (i.e., 865 nm for MODIS) may introduce sensor-specific biases in space and/or time, which may also contribute to cross-sensor inconsistency in the derived reflectance data products. As such, it is necessary to assess the calibration of this band across sensors – performed here for MODIS/Aqua and VIIRS/SNPP using ‘simultaneous same view’ matchups (SSV; similar to simultaneous nadir overpass, but allowing for non-nadir measurements). Towards that end, we assess geometric, temporal, and spatial homogeneity metrics to identify SSVs, and develop a band-shifting approach applicable within standard satellite data processing routines to resolve expected spectral differences in the radiometry. We find top-of-atmosphere (TOA) radiance data from VIIRS/SNPP long near infrared band to be approximately 3% higher than the corresponding MODIS/A data. With the expectation that cross-calibrating the NIRL should improve cross-sensor continuity of downstream geophysical products (e.g., chlorophyll-a), we reprocessed VIIRS data using updated calibration coefficients. While we noticed many minor improvements in cross-sensor continuity in such data products, large-scale geographic and temporal biases between these two datasets still remain. These discontinuities may be the result of disparate errors in polarization correction or atmospheric correction, both of which are modulated by radiant path geometry.

Despite the importance of remote detection of marine debris, nearly all published studies are focused on either controlled experiments, or Sentinel-2 data with mixed band resolutions that are subject to large uncertainties. To date, key questions such as the following have not been addressed adequately: To what extent can the various forms of marine debris be remotely detected and differentiated through satellite observations in the visible and near infrared (NIR) spectral range, and how? Here, using published reflectance spectra of various types of floating matters, I address these questions through sensitivity analyses, simulations, and spectral analyses of satellite images. While the study is by no means comprehensive, several observations can still be made. First, it appears impossible to remotely detect marine microplastics from all existing and planned optical sensors. This is simply because the contribution of these particles to the sensor signal, even when they are aggregated on the water surface at the reported maximum particle density, is at least 60 times lower than the required signal (~0.2% subpixel coverage) and 20 times lower than the sensor noise for a sensor with a signal-to-noise ratio (SNR) of 200. In contrast, detecting macroplastics and other debris is possible when they form large patches along ocean fronts or windrows. Second, assuming a SNR of 200, discriminating large patches of marine debris from floating algae is only possible with a subpixel coverage of >0.3%. These threshold values are based on the sensor SNRs only, and they represent the lower bounds of detection and discrimination, respectively. The real threshold values above which a detection or discrimination is possible also depend on the observing conditions, and therefore higher. Third, currently, Sentinel-2 MSI (Multi Spectral Instrument) sensors provide an optimal trade between resolution and coverage, yet MSI sensors have SNRs

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Blooms of floating macroalgae have been reported around the world, among which are recurrent blooms of Ulva prolifera and Sargassum horneri in the Yellow Sea and East China Sea. While satellite remote sensing has often been used to estimate their distributions and abundance as well as to trace their origins, because the algae mats are often much smaller than the size of an image pixel, it is unclear to what extent they can be detected and discriminated from each other in satellite imagery. Using data collected from laboratory experiments and by the Sentinel-3 OLCI (Ocean and Land Colour Instrument) and Sentinel-2 MSI (Multi Spectral Instrument) satellite instruments, we conduct simulated experiments to determine the lower detection limit and discrimination limit for these two macroalgae in different water environments and under different atmospheric conditions. For OLCI, the detection limit for both macroalgae is about 0.5% of a pixel, while the discrimination limit varies between 0.8% for clear water and 2% for turbid water. For MSI, the detection limit is about 2%, while the discrimination limit is about 6% for all water types. Below these two limits, detection and discrimination of macroalgae in these regions using the two sensors are subject to large uncertainties, thus requiring additional caution when interpreting algae areas and tracing algae origins.

Phytoplankton pigments significantly affect photosynthesis and play a crucial role in regulating marine ecological and biogeochemical processes. Assessment of phytoplankton pigments through optical means is desirable as it may be extended to satellite remote sensing. Here, using an extensive data set of high performance liquid chromatography (HPLC) phytoplankton pigment concentrations and phytoplankton absorption spectra (aph(λ)) collected through five cruise surveys of the Chinese marginal seas during 2016 and 2017, we explore the potentials of using aph(λ) to estimate twenty pigments. Specifically, the first and second derivatives of aph(λ) are used to construct an aph(λ) - pigment model. The validation of the aph(λ)-derived pigment classes, specific to individual phytoplankton community and size groups, shows a generally satisfactory model performance. Additionally, hierarchical cluster analysis also exhibits high similarity within the classification results based on the measured and modeled pigments, where only two pigments were decided into different clusters. Although still preliminary in nature, this proof-of-concept study for the Chinese marginal seas shows the potentials of using satellite remote sensing to assess phytoplankton pigment composition once hyperspectral satellite data are available and aph(λ) inversion algorithms are developed and validated.

Knowledge of cloud cover, frequency, and duration is not only important to study cloud dynamics, but also critical in determining when and where to take ocean measurements from geostationary orbits such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission due to the challenges in achieving complete hemispheric coverage of coastal oceans, estuaries, and inland waters at hourly frequency. Using GOES hourly measurements at 4 km nadir resolution between 2006 and 2011, the number of cloud-free hourly observations per day (N(sub cf)) for solar zenith angle Theta(sub 0) less than 80 degrees was estimated for each 0.1 degree location of the Intra-Americas Sea. The number of Sun-glint-affected hourly observations per day [Ns(sub sg)] was also calculated based on the planned GEO-CAPE observation geometry and realistic wind speed. High-latitude and equatorial oceans showed the lowest N(sub cf) (less than 2.4) in all climatological months, and highest N(sub cf) was observed in the Gulf of Mexico (GoM) and Caribbean (greater than 4.5). Different regions showed differences in seasonality of cloud-free conditions and also showed differences in the hour of a day at which the satellite observations would have the maximal cloud-free and glint-free probability (Temperature maximum). Cloud cover from Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km measurements are greater than 10 degrees higher than those from the MODIS 250 m measurements, supporting ocean color missions at subkilometer resolutions to enhance both spatial coverage and temporal frequency. These findings provide valuable information for GEO-CAPE mission planning to maximize its science value through minimizing the impacts of clouds and Sun glint.

As an image-driven method to correct for atmospheric effects, the cloud shadow CS approach does not require accurate radiometric calibration of the sensor, making it feasible to process remotely sensed data when radiometric calibration may contain non-negligible uncertainties. Using measurements from the Geostationary Coastal and Air Pollution Events Airborne Simulator and from the Moderate Resolution Imaging Spectroradiometer over the Louisiana Shelf, we evaluate the CS approach to airplane measurements in turbid-water environments. The original CS approach somehow produced remote-sensing reflectance Rrs, sr−1 with an abnormal spectral shape, likely a result of the assumption of identical path radiance for the pair of pixels in and out of the shadow, which is not exactly valid for measurements made from a low-altitude airplane. To overcome this limitation, an empirical scheme using an effective wavelength-dependent radiance reflectance for the cloud γ, sr−1 was developed and reasonable GCAS Rrs retrievals are then generated, which were further validated against in situ Rrs. Issues and challenges in applying CS to measurements of low-altitude airplanes are discussed.

Particulate organic carbon (POC) plays an important role in coastal carbon cycling and the formation of hypoxia. Yet, coastal POC dynamics are often poorly understood due a lack of POC observations and the complexity of coastal hydrodynamic and biogeochemical processes that influence POC sources and sinks. Using a dataset of field observations and satellite ocean color products, we developed a new multiple regression algorithm to derive POC from satellite observations in two river-dominated estuaries in the northern Gulf of Mexico: the Louisiana Continental Shelf (LCS) and Mobile Bay. The algorithm had reliable performance with mean relative error (MRE) of ~40%, and root mean square error (RMSE) of ~50% for MODIS and SeaWiFS images in the two systems. Substantial spatio-temporal variability was observed from satellite on the LCS, with higher POC on the inner shelf (< 10 m depth) and lower POC on the middle (10-50 m depth) and outer shelves (50-200 m depth), and with higher POC in winter (January to March), and lower POC in summer to fall (August to October). Correlation analysis between long-term POC time series and several potential influencing factors indicated that river discharge dominants POC dynamics on the LCS. Wind and surface currents also affect POC spatial patterns on short time scales. This study demonstrates that algorithms that can determine coastal POC from satellites greatly increase the spatial and temporal extent of observations available for characterizing POC dynamics and their relations to various dominant physical forcings to the continental shelf and estuaries.

In an effort to more fully employ underutilized satellite observations in ocean modeling, this work demonstrates a method for quantifying the agreement between time-evolving spatial features evident in fields of differing, but functionally related, variables that are more commonly compared qualitatively via visual inspection. This is achieved through application of the Modified Hausdorff Distance metric to the evaluation of ocean model simulations of surface salinity near riverine sources using satellite ocean color data. The Modified Hausdorff Distance is a metric from the field of topology designed to compare shapes and the methodology provides quantitative assessment of similarity of spatial fields. The Modified Hausdorff Distance can be applied for comparison of many geophysical and ecological fields that vary spatially and temporally. Here, the utility of the metric is demonstrated by applying it to evaluate numerical simulations of the time-evolving spatial structure of the surface salinity fields from three ocean models in the vicinity of large riverine sources in the northeast Gulf of Mexico. Using the Modified Hausdorff Distance, quantitative comparison of modeled sea surface salinity contours to contours of a gridded satellite-derived ocean color product is made under the assumption that the modeled fields are related to optically significant quantities that indicate the spatial extent of riverine influenced water. Three different ocean models are evaluated and are compared individually to the satellite data. The sea surface salinity values and ocean color index values that most closely match (lowest Modified Hausdorff Distance score) are identified for each model. The Modified Hausdorff Distance scores for these best pairings are used to both determine the degree to which surface salinity fields from the models match the satellite observations and obtain an empirical relationship between the two variables for each model. Furthermore, the best pairings are compared between models allowing key differences in the simulated riverine water distributions to be distinguished. The Modified Hausdorff Distance proves a robust and useful diagnostic tool that has the potential to be utilized in many geophysical applications and facilitates the use of satellite ocean color data for quantitative evaluation of hydrodynamic ocean models.

Surface pCO2 data from the West Florida Shelf (WFS) have been collected during 25 cruise surveys between 2003 and 2012. The data were scaled up using remote sensing measurements of surface water properties in order to provide a more nearly synoptic map of pCO2 spatial distributions and describe their temporal variations. This investigation involved extensive tests of various model forms through parsimony and Principal Component Analysis, which led to the development of a multi-variable empirical surface pCO2 model based on concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) estimates of surface chlorophyll a concentrations (CHL, mg m−3), diffuse light attenuation at 490 nm (Kd_Lee, m−1), and sea surface temperature (SST, °C). Validation using an independent dataset showed a pCO2 Root Mean Square Error (RMSE) of

Sargassum washing ashore on the beaches of the Caribbean Islands since 2011 has caused problems for the local environments, tourism, and economies. Although preliminary results of Sargassum distributions in the nearby oceans have been obtained using measurements from the Medium Resolution Imaging Spectrometer (MERIS), MERIS stopped functioning in 2012, and detecting and quantifying Sargassum distributions still face technical challenges due to ambiguous pixels from clouds, cloud shadows, cloud adjacency effect, and large-scale image gradient. In this paper, a novel approach is developed to detect Sargassum presence and to quantify Sargassum coverage using the Moderate Resolution Imaging Spectroradiometer (MODIS) alternative floating algae index (AFAI), which examines the red-edge reflectance of floating vegetation. This approach includes three basic steps: 1) classification of Sargassum-containing pixels through correction of large-scale gradient, masking clouds and cloud shadows, and removal of ambiguous pixels; 2) linear unmixing of Sargassum-containing pixels; and, 3) statistics of Sargassum area coverage in pre-defined grids at monthly, seasonal, and annual intervals. In the absence of direct field measurements to validate the results, limited observations from the Hyperspectral Imager for the Coastal Ocean (HICO) measurements and numerous local reports support the conclusion that the elevated AFAI signals are due to the presence of Sargassum instead of other floating materials, and various sensitivity analyses are used to quantify the uncertainties in the derived Sargassum area coverage. The approach was applied to MODIS observations between 2000 and 2015 over the Central West Atlantic (CWA) region (0–22°N, 63–38°W) to derive the spatial and temporal distribution patterns as well as the total area coverage of Sargassum. Results indicate that the first widespread Sargassum distribution event occurred in 2011, consistent with previous MERIS findings. Since 2011, only 2013 showed a minimal Sargassum coverage similar to the period of 2000 to 2010; all other years showed significantly more coverage. More alarmingly, the summer months of 2015 showed mean coverage of > 2000 km2, or about 4 times of the summer 2011 coverage and 20 times of the summer 2000 to 2010 coverage. Analysis of several environmental variables provided some hints on the reasons causing the inter-annual changes after 2010, yet further multi-disciplinary research (including in situ measurements) is required to understand such changes and long-term trends in Sargassum coverage.

Particle size distribution (PSD), a measure of particle concentrations at different sizes, is of great importance to the understanding of many biogeochemical processes in coastal marine ecosystems. Here, a hybrid method, including analytical, semi-analytical, and empirical steps, is developed to estimate PSD through the median diameter of suspended particles (Dv50). Four cruise surveys were conducted to measure optical scattering properties, particle concentrations, spectral reflectance, and particle size distributions (obtained with a LISST instrument covering a size range of 2.5-500 μm) in coastal waters of Bohai Sea, Yellow Sea, and Jiangsu coastal region. Based on the Mie scattering theory, Dv50 is closely related to mass-specific backscattering coefficient of suspended particles (bbp*), and their relationship is calibrated through a power model (R2=0.796, n=67, p

In recent decades, the technology used to detect and quantify harmful algal blooms (commonly known as red tides) and characterize their physicochemical environment has improved considerably. A remaining challenge is effective delivery of the information generated from these advances in a user-friendly way to a diverse group of stakeholders. Based on existing infrastructure, we establish a Web-based system for near-real-time tracking of red tides caused by the toxic dinoflagellate Karenia brevis , which annually threatens human and environmental health in the eastern Gulf of Mexico. The system integrates different data products through a custom-made Web interface. Specifically, three types of data products are fused: 1) near-real-time ocean color imagery tailored for red tide monitoring; 2) K. brevis cell abundance determined by sample analysis; and 3) ocean currents from a nested and validated numerical model. These products are integrated and made available to users in Keyhole Markup Language (KML) format, which can be navigated, interpreted, and overlaid with other products in Google Earth. This integration provides users with the current status of red tide occurrence (e.g., location, severity, and spatial extent) while presenting a simple way to estimate bloom trajectory, thus delivering an effective method for near-real-time tracking of red tides.

The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Nino events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast.

Thermal remote sensing has been used to detect oil slicks, yet estimation of slick thickness has largely remained unfeasible, mainly because the optimal detection time during a day, the minimum detectable thickness (MDT), and the relationship between the thermal response and thickness all remain largely unknown. Here, a ground-based experiment is used to address some of these uncertainties. The experiment measured the brightness temperatures (BTs) of oil slicks (with different known thicknesses) and oil-free water as a function of time of the day for both clear and turbid waters. The BT differences (BTDs) between oil slicks and oil-free water were further simulated using a diurnal temperature cycle (DTC) model. The results demonstrate that: (1) for an oil slick that is in thermal equilibrium with the water, the optimal time for thermal detection is around local noon (positive BTDs) with midnight (negative BTs) being the next best time; the periods shortly before sunrise and after sunset are not suitable for the thermal detection of oil slicks; (2) a better linear relationship between slick thickness and BTD is found during daytime than night-time and the type of background water also plays a role in this; and (3) assuming a detection limit of 0.3 °C for a thermal sensor, the MDT at noon is approximately 40 μm for both clear and turbid waters, while for other times of the day the MDT is higher (e.g., 75 and 150 μm for clear and turbid waters, respectively, at midnight). Detection limits for several existing satellite sensors and for other observation scenarios are also discussed.

Hydrocarbons that originated from Deepwater Horizon were observed at the surface along west Florida’s northern coastline in June 2010. The farthest eastward advance nearly to Cape San Blas occurred during the last week of June before the surface oil retreated back westward and dissipated. Surface oil was not observed on the portion of the West Florida Continental Shelf (WFS) situated to the southeast of Cape San Blas. Nevertheless, there were numerous anecdotal occurrences of reef fish caught on the WFS with lesions and other deformities. Subsequent systematic sampling of WFS reef fish provided additional evidence for damage that extended as far south as the Dry Tortugas. Here we examine the possibility that hydrocarbons of Deepwater Horizon origin transited to the WFS beneath the surface. We use a numerical circulation model simulation run for the entirety of 2010 and quantitatively gauged against in situ observations. A passive tracer is introduced into the model to mimic the movement of subsurface hydrocarbons, either dissolved or of sufficiently small particle size to effectively be dissolved. The tracer, driven primarily by an anomalously strong and persistent upwelling circulation, eventually covered most of the WFS. Using reasonable estimates of what the initial tracer concentration may have been with respect to hydrocarbons, we conclude that the transport of subsurface hydrocarbons to the WFS is both plausible and consistent with the observed distribution of fish lesions, fish liver chemistry and other chemical and ecological evidence.

Sunlight induced chlorophyll a fluorescence (SICF) can be used as a probe to estimate chlorophyll a concentrations (Chl) and infer phytoplankton physiology. SICF at ∼685 nm has been widely applied to studies of natural waters. SICF around 740 nm has been demonstrated to cause a narrow reflectance peak at ∼761 nm in the reflectance spectra of terrestrial vegetation. This narrow peak has also been observed in the reflectance spectra of natural waters, but its mechanism and applications have not yet been investigated and it has often been treated as measurement artifacts. In this study, we aimed to interpret this reflectance peak at ∼761 nm and discuss its potential applications for remote monitoring of natural waters. A derivative analysis of the spectral reflectance suggests that the 761 nm peak is due to SICF. It was also found that the fluorescence line height (FLH) at 761 nm significantly and linearly correlates with Chl. FLH(761 nm) showed a tighter relationship with Chl than the relationship between FLH(∼685 nm) and Chl mainly due to weaker perturbations by nonalgal materials around 761 nm. While it is not conclusive, a combination of FLH(761 nm) and FLH(∼685 nm) might have some potentials to discriminate cyanobacteria from other phytoplankton due to their different fluorescence responses at the two wavelengths. It was further found that reflectance spectra with a 5 nm spectral resolution are adequate to capture the spectral SICF feature at ∼761 nm. These preliminary results suggest that FLH(761 nm) need to be explored more for future applications in optically complex coastal and inland waters.

Harmful algal blooms of the dinoflagellate Karenia brevis require an upwelling circulation to manifest along the coastline of the West Florida Continental Shelf. Too much upwelling, however, can impede bloom formation by increasing inorganic nutrient levels to the point where faster growing phytoplankton such as diatoms may out-compete the slower growing K. brevis, as occurred in 1998 and 2010. Both 2012 and 2013 experienced persistent upwelling, but only 2012 exhibited a robust harmful algal bloom. Here we examine the subtle differences in the coastal ocean circulation between those two years that led to the disparate bloom evolutions.

An approach to semianalytically derive waters' inherent optical properties (IOPs) from remote sensing reflectance (Rrs) and at the same time to take into account the residual errors in satellite Rrs is developed for open-ocean clear waters where aerosols are likely of marine origin. This approach has two components: (1) a scheme of combining a neural network and an algebraic solution for the derivation of IOPs, and (2) relationships between Rrs residual errors at 670 nm and other spectral bands. This approach is evaluated with both synthetic and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data, and the results show that it can significantly reduce the effects of residual errors in Rrs on the retrieval of IOPs, and at the same time remove partially the Rrs residual errors for “low-quality” and “high-quality” data defined in this study. Furthermore, more consistent estimation of chlorophyll concentrations between the empirical blue-green ratio and band-difference algorithms can be derived from the corrected “low-quality” and “high-quality” Rrs. These results suggest that it is possible to improve both data quality and quantity of satellite-retrieved Rrs over clear open-ocean waters with a step considering the spectral relationships of the residual errors in Rrs after the default atmospheric correction procedure and without fixing Rrs at 670 nm to one value for clear waters in a small region such as 3 × 3 box.

Several methods have been proposed in previous studies to map macroalgal blooms (MABs) in the Yellow Sea (YS) and East China Sea (ECS), yet some of the required spectral bands are not available on the new HJ-1 satellite sensors although they do provide optimal resolution and temporal coverage (30-m resolution with 2-day revisit frequency) for bloom mapping. In this study, an index of Virtual-Baseline Floating macroAlgae Height (VB-FAH) is proposed to use the green and red bands as the baseline to measure the height of the near-infrared (NIR) reflectance. Cross-sensor comparison with Landsat TM and ETM + data suggests that for several images evaluated here VB-FAH appears to be comparable to the previously proposed Floating Algae Index (FAI) even in the absence of a shortwave-infrared (SWIR) band. VB-FAH is applied to 30-m resolution TM and ETM + data for the YS during 1995-2006 and to HJ-1 data for the ECS during 2009-2014 to map MABs. Results show bloom history in the YS back to 1999 and early bloom occurrence in the ECS in winter and spring (e.g., February and March 2013). MABs are also found to extend to the ECS as far south as 26 degrees N near Fujian Province and Taiwan, and as far east as the Kuroshio Current. These new findings provide important information for exploring the origins, causes, and consequences of MABs in the YS and ECS. (C) 2016 Elsevier Inc. All rights reserved.

Observed bio-optical water quality data collected from 2009 to 2011 in Pensacola Bay, Florida were used to develop empirical remote sensing retrieval algorithms for chlorophyll a (Chla), colored dissolved organic matter (CDOM), and suspended particulate matter (SPM). Time-series of the three bio-optical water quality variables were generated from MEdium Resolution Imaging Spectrometer (MERIS) observations from 2003 to 2011. Bio-optical water quality in this estuary exhibited spatial and temporal variations that were correlated to river discharge and wind. Both annual mean and monthly mean bio-optical water quality variables were positively correlated to river discharge. Monthly mean bio-optical water quality variables were also positively correlated to wind speed and wind density (defined by the number of days with daily mean wind speed > 3 m s −1 in a month) over this estuary. These results indicate that bio-optical water quality dynamics in this estuary are vulnerable to changes in river discharge and river constituent loads and local weather conditions such as winter storms and hurricanes.

Wetlands provide important ecosystem functions for water alteration and conservation of bio-diversity, yet they are vulnerable to both human activities and climate changes. Using four decades of Landsat and HJ-1A/1B satellites observations and recently developed classification algorithms, long-term wetland changes in Poyang Lake, the largest freshwater lake of China, have been investigated in this study. In dry seasons, while the transitions from mudflat to vegetation and vice versa were comparable before 2001, vegetation area increased by 620.8 km 2 (16.6% of the lake area) between 2001 and 2013. In wet seasons, although no obvious land cover changes were observed between 1977 and 2003, ~ 30% of the Nanjishan Wetland National Nature Reserve (NWNNR) in the south lake changed from water to emerged plant during 2003 and 2014. The changing rate of the Normalized Difference Vegetation Index (NDVI) in dry seasons showed that the vegetation in the lake center regions flourished, while the growth of vegetation in the off-water areas was stressed. Rapid NDVI increase was also found in the NWNNR in the wet seasons. The relationships between the water levels and vegetation coverage also showed two regimes in both dry and wet seasons for the pre-Three Gorges Dam (TGD) period (before 2003) and post-TGD period (after 2003). Analyses of long-term hydrological and meteorological data clearly indicated that while local precipitation remained stable, the water level of Poyang Lake decreased significantly after the impoundment of the TGD, which is likely the main reason for the wetland expansion in recent years.

An atmospheric correction algorithm has been developed for the Airborne Imaging Spectrometer for Applications AISA imagery over optically shallow waters in Sugarloaf Key of the Florida Keys. The AISA data were collected repeatedly during several days in May 2012, October 2012, and May 2013. Non-zero near-infrared NIR remote-sensing reflectance Rrs was accounted for through iterations, based on the relationship of field-measured Rrs between the NIR and red wavelengths. Validation showed mean ratios of 0.94–1.002 between AISA-retrieved and in situ Rrs in the blue to red wavelengths, with uncertainties generally

Satellite-based empirical models explaining hypoxic area and volume variation were developed for the seasonally hypoxic (O2

Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely available. The goal of this study is to quantify such effects on top-of-atmosphere (TOA) radiance and ocean color data products for MODIS/Terra, MODIS/Aqua, and SeaWiFS measurements. The AEs estimation was based on statistics and an objective method applied to carefully selected clear-water scenes (the number of cloud patches was > 100,000 for each instrument) where ocean properties are relatively homogeneous, over both the North Atlantic and South Pacific. The AEs were quantified as the relative difference between the near-cloud pixels and pixels at least 20 km away from any cloud. Results show that the AEs on TOA radiance share similar patterns among the three missions. Specifically, the AEs decrease sharply as distance increases from cloud edges, and the AEs increase monotonically with increasing wavelengths because they were evaluated in relative rather than absolute terms. However, while discernable memory effects (MEs) are observed on cloud-adjacency pixels of both MODIS missions, they are insignificant on the SeaWiFS mission, and are found in measurements along the scan direction downstream of the clouds, representing > 15% of the total AEs in TOA radiance. The AEs on the retrieved remote sensing reflectance (Rrs) data products are different among the three missions possibly due to their differences in vicarious calibration and uncertainties in atmospheric correction, leading to different patterns in the chlorophyll-a (Chl-a) and normalized Florescence Line Height (nFLH) data products. Large AEs (> 50%) are observed in nFLH of both MODIS/Terra and MODIS/Aqua, likely due to the opposite AEs on Rrs between 667 and 678 nm. Finally, when the OCI Chl-a algorithm is used, the current MODIS stray-light masking window (7 × 5) used to mask the AE-contaminated pixels may be relaxed to 3 × 3 without sacrificing data quality, leading to > 40% of the previously masked low-quality data being recovered for clear waters.