Many lakes around the world have faced eutrophication and frequent cyanobacterial harmful algal blooms (cyanoHABs), where chlorophyll-a concentration (Chla) is often used to determine the trophic status of the lake and evaluate the effectiveness of nutrient reduction strategies. However, limitedin situsampling and the absence of robust remotely sensed data product often hinder the analysis of long-term Chla changes in such lakes. Here, using Lake Okeechobee (LO) as an example, we addressed this challenge by developing a novel method to harnesses the power of both multi-sensor remote sensing and machine learning. The method first usedin situChla to estimate Chla from OLCI imagery (ChlaOLCI), which then served as a surrogate forin situdata toward obtaining the large matchup datasets for MODIS model training. Subsequently, two deep neural network models, DNNSand DNNNS, were trained for the MODIS-saturated pixels with high algal biomass waters and the non-saturated pixels with lower Chla but more turbid waters, respectively. The daily and monthly Chla derived from MODIS (ChlaMODIS) were validated usingin situChla (R2~0.77, MAPD ~25%) and monthly ChlaOLCI(R2~0.90, MAPD ~11%), respectively. Monthly ChlaMODIS derived from Terra in 2000 — 2023 was used to analyze the spatiotemporal changes in LO. We observed a significant long-term increasing trend of Chla (~2 mg m-3 decade-1) and a ~30% increase relative to the multiple-year average. To explore the potential factors driving long-term Chla changes, water temperature, precipitation, inflow, stage, and the multivariate ENSO index were tested through multi-variative analysis. Although further test is required, the approach of using one sensor as a “bridge” to bring limitedin situdata and another sensor together to overcome the data scarcity and lack of spectral bands is believed to be extendable to other eutrophic lakes around the world.

Satellite ocean color remote sensing provides an effective tool for monitoring and tracking harmful algal blooms (HABs) in oceanic, coastal, and inland waters. However, traditional medium-resolution (300 m – 1 km) satellite data do not have the required spatial resolution to characterize nearshore HABs, whose harmful impacts are directly relevant to local communities. Here, we show that commercial imagery provided by the >100 SuperDove sensors in PlanetScope constellation can provide ~4 m resolution, 8-band spectral data every 1-2 days (resampled to 10 m in this study for consistency with Sentinel-2) to meet the critical need for detecting blooms of the toxic dinoflagellateKarenia brevis(often called red tides) in nearshore waters off the central west coast of Florida (USA). Despite the relatively low signal-to-noise ratios (SNRs), SuperDove Enhanced Red-Green-Blue (ERGB) images clearly depict the detailed distributions and morphological shapes ofK.brevisblooms in both nearshore coastal waters and small estuaries. These features, challenging or impossible to observe from traditional satellite sensors, are well captured and quantified with an optimized deep learning model that leverages both spectral features and spatial context. Comparison of model results with medium-resolution satellite data indicates a detection limit of 0.055 mW cm-2μm-1sr-1in the normalized fluorescence line height (nFLH) data product, corresponding to around 2-3 mg m-3surface chlorophyll-aconcentration or about 300,000 cells L-1ofK. brevis. Although this detection limit is higher than those offered by traditional satellite sensors with higher SNRs, because of its frequent coverage of global nearshore waters at much higher spatial resolution, the SuperDove constellation is expected to play an essential role in monitoring HABs in nearshore environments to help mitigate the negative impacts of these blooms.

Freshwater cyanobacterial blooms pose a major threat to local ecosystems, economies, and public health. Monitoring these occurrences is essential for water resource managers worldwide. Satellite remote sensing techniques can detect and quantify blooms in large inland and estuarine water bodies but monitoring blooms in small water bodies (300 m) or low re-visit frequency (>10 days) of most operational satellite sensors. The ephemeral nature of cyanobacterial blooms and form dense surface mats (or ‘scums’) that aggregate nearshore further highlight the need for sensors with higher spatial and temporal resolutions. In this study, a deep learning model based on Convolutional Neural Network U-net was developed to detect cyanobacterial blooms (i.e., scums) in the highly modified and managed Caloosahatchee River (i.e., C-43 canal) and Caloosahatchee River Estuary (CRE) (Florida, USA) using Dove imagery (3-m resolution) obtained near-daily from the PlanetScope satellite constellation. The approach consisted of three steps: 1) training and validating the U-net model with “ground truth” images; 2) classifying bloom pixels; and 3) quantifying bloom area using linear unmixing. Validation results indicate an overall F1 score of 89.6% when assessing bloom area. Application of the model revealed the westward expansion of a cyanobacteria bloom from C-43 to CRE in summer 2018, indicating the physical transport of the bloom originating upstream in Lake Okeechobee to the estuary. This approach was tested on other inland water bodies, indicating potential for monitoring cyanobacterial blooms on a global scale.

Uncertainties are an integral part of remote sensing data products in order to quantify changes, yet due to patchiness and spatial heterogeneity, it is difficult to use field measurements to estimate uncertainties in the satellite-derived Ulva prolifera (U. prolifera, also called green tides) amounts in the Yellow Sea. This is perhaps why such estimates are missing in nearly all remote sensing literature on U. prolifera mapping. Here, by comparing all available data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra/Aqua satellites and the MultiSpectral Instrument (MSI) on the Sentinel-2A/ 2B satellites for the period of 2015–2022, we evaluate uncertainties in the MODIS-derived U. prolifera amounts. The relative uncertainties are found to decrease with increasing Ulva amounts in individual images, ranging from 58.8% for Ulva areal coverage (after pixel unmixing) of$>$200 km2. Such uncertainties decrease in the monthly composite data products because of the increased number of observations, reducing to 3% in the total Ulva amount during the peak months. Such uncertainty estimates, in relative terms, are expected to serve as a reference when interpreting temporal changes in long-term Ulva estimates derived from satellite data.

Multi-sensor medium-resolution satellite images from MODIS, VIIRS, OLCI, and MERIS are used to study spatial and temporal distributions of sea snot features in the Marmara Sea between 2000 and 2021. Suspicious image slicks are identified in most years, and spectral diagnostics indicate sea snot features in 2007, 2008, and 2021, with the record-high sea snot event occurring in spring-summer 2021. In other years when similar image slicks are found, they appear to be from surface scums of red Noctiluca scintillans, a heterotrophic dinoflagellate responsible for red tides. Based on the medium-resolution images, the 2021 sea snot event started from March 14 and ended on June 27, with its peak time around May 4 when the sea snot features are found in the entire Marmara Sea covering an area of 1,160 km2. When all sea snots are aggregated together, the estimated areal coverage during the peak time is 50 km2, suggesting significant patchiness in the surface scums.

Sargassumbeaching events have been reported in recent years around the Caribbean Sea and FL, USA, causing numerous environmental and economic problems. Satellite remote sensing has been widely used to monitorSargassumblooms in open waters, yet due to either coarse spatial resolution or low-revisit frequency, it is difficult to provide timely information onSargassuminundation from traditional satellite instruments. In this study, we demonstrate the capacity of 3-m resolution daily Dove imagery in monitoringSargassumbeaching events on Miami beach (FL, USA) and Cancun beach (Mexico). A U-net deep learning (DL) computer model is developed to extractSargassumfeatures from Dove imagery over beaches and nearshore waters. Application of the model to Dove image sequences between May and August 2019 shows two major inundation events on both Miami beach and Cancun beach, consistent with local reports. With the availability of 3-m resolution PlanetScope/Dove and PlanetScope/SuperDove data around the globe, the findings suggest that it is possible to monitor dynamic inundation events of not onlySargassumbut also other macroalgae in many other regions.

Numerous assumptions and approximations are employed when translating satellite-derived radiance to surface remote sensing reflectance (RRS) for ocean color applications. Among these is the vicarious calibration coefficient (g) of the ‘long’ near infrared band (NIRL) used for atmospheric correction. For this band, the pre-launch calibration has always been deemed sufficient [thus g(NIRL) = 1.00] as long as other bands are vicariously calibrated. Recent research, however, suggests that MODIS/AquaRRStime series are quite sensitive to g(NIRL) (and associated vicarious gains in other bands). In this work, we assessed the sensitivity of VIIRS/SNPPRRSto NIRLcalibration, and compared our results to previous MODIS/Aqua and SeaWiFS/OrbView2 analysis. In doing so, we noteg(NIRL) sensitivities of mission-averagedRRStimeseries are lower for VIIRS and SeaWiFS, relative to MODIS. At the scale of monthly climatologies, however, all sensors show prominentg(NIRL) sensitivity, with that of SeaWiFS being the most substantial. These findings informed simulation analyses, whereby we identified signal-to-noise ratio (SNR) and radiant path geometry, as well as their interaction, as having notable impacts ong(NIRL) sensitivity. As such,g(NIRL) sensitivity is a necessary consideration for reflectance uncertainty budgets, especially for sensors with higher NIR SNR or particular prevailing radiant path geometries. Given the geometry components embedded within g(NIRL) sensitivity, such studies should be coupled with cross-sensor intercalibrations (e.g., using simultaneous same view measurements) toward minimizing NIRLerrors between satellite instruments, but such efforts will not completely remediate remaining cross-sensor biases inRRS.

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Satellite ocean color missions require accurate system vicarious calibrations (SVC) to retrieve the relatively small remote-sensing reflectance (Rrs, sr-1) from the at-sensor radiance. However, the current atmospheric correction and SVC procedures do not include calibration of the “long” near infrared band (NIRL-869 nm for MODIS), partially because earlier studies, based primarily on simulations, indicate that accuracy in the retrieved Rrsis insensitive to moderate changes in the NIRL vicarious gain (g). However, the sensitivity of ocean color data products to g(NIRL) has not been thoroughly examined. Here, we first derive 10 SVC “gain configurations” (vicarious gains for all visible and NIR bands) for MODIS/Aqua using current operational NASA protocols, each time assuming a different g(869). From these, we derive a suite of ~1.4E6 unique gain configurations with g(869) ranging from 0.85 to 1.2. All MODIS/A data for 25 locations within each of five ocean gyres were then processed using each of these gain configurations. Resultant time series show substantial variability in dominant Rrs(547) patterns in response to changes in g(869) (and associated gain configurations). Overall, mean Rrs(547) values generally decrease with increasing g(869), while the standard deviations around those means show gyre-specific minima for 0.97 <; g(869) <; 1.02. Following these sensitivity analyses, we assess the potential to resolve g(869) using such time series, finding g(869) = 1.025 most closely comports with expectations. This approach is broadly applicable to other ocean color sensors, and highlights the importance of rigorous cross-sensor calibration of the NIRL bands, with implications on consistency of merged-sensor data sets.

Various approaches have been proposed to estimate surface ocean chlorophyll a concentrations (Chl, mg m-3) from spectral reflectance measured either in the field or from space, each with its own strengths and limitations. Here, we develop a machine learning approach to reduce the impact of spectral noise and improve algorithm performance at the global scale for multiple satellite sensors. Among several candidates, the support vector regression (SVR) approach was found to yield the best algorithm performance as gauged by several statistical measures against field-measured Chl. While statistically the performance of the SVR is slightly worse than the empirical color index (CI) algorithm proposed in Hu et al. (2012) for Chl-3, its applicability to global waters is much extended, from the CIs 0.01-0.25 mg m-3(about 75% of the global oceans) to its 0.01-1 mg-3[about 96% of global oceans according to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) statistics]. Within this range, not only does the SVR show much improved performance over the traditional band-ratio OC x approaches, but the SVR leads to much reduced image noise and much improved cross-sensor consistency between SeaWiFS and Moderate Resolution Spectroradiometer (MODIS)/Aqua and between MODIS/Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, compared with the hybrid Ocean CI (OCI) algorithm currently used by the U.S. NASA as the default algorithm for all mainstream ocean color sensors, the SVR avoids the need to merge two different algorithms for intermediate Chl (band subtraction for CI and band ratio for OC x), thus may serve as an alternative approach for global data processing.

Frequent Sargassum beaching in the Caribbean Sea and other regions has caused severe problems for local environments and economies. Although coarse-resolution satellite instruments can provide large-scale Sargassum distributions, their use is problematic in nearshore waters that are directly relevant to local communities. Finer resolution instruments, such as the multispectral instruments (MSIs) on the Sentinel-2 satellites, show potential to fill this gap, yet automatic Sargassum extraction is difficult due to compounding factors. In this article, a new approach is developed to extract Sargassum features automatically from MSI Floating Algae Index (FAI) images. Because of the high spatial resolution, limited signal-to-noise ratio (SNR), and staggered instrument internal configuration, there are many nonalgae bright targets (including cloud artifacts and wave-induced glints) causing enhanced near-infrared reflectance and elevated FAI values. Based on the spatial patterns of these image “noises,” a Trainable Nonlinear Reaction Diffusion (TNRD) denoising model is trained to estimate and remove such noise. The model shows excellent performance when tested over realistic noise patterns derived from MSI measurements. After removing such noise and masking clouds (as well as cloud shadows and glint patterns), biomass density from each valid pixel is quantified using the FAI-biomass model established from earlier field measurements, from which Sargassum morphology (length/width/biomass) is derived. Overall, the proposed approach achieves over 86% Sargassum extraction accuracy and shows preliminary success on Landsat-8 images. The approach is expected to be incorporated in the existing near real-time Sargassum Watch System for both Landsat-8 and Sentinel-2 observations to monitor Sargassum over nearshore waters.

The investigation of submesoscale coastal eddies requires the products of high spatiotemporal resolution. MODIS color index (CI) products can improve spatial coverage in coastal regions to detect submesoscale signals, when observations of high-frequency radars are limited in small areas. In our study, a gradient parameter derived by CI images is used to identify submesoscale eddies by the eddy detection algorithm based on the vector geometry in the case of Subei Shoal. The results show that gradient parameters have the ability to detect submesoscale eddies. The mean radius values of anticyclonic and cyclonic eddies are 1.85 and 1.56 km, respectively. This method makes it potentially useful for investigations of ecosystem distributions in other coastal regions.

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels “contaminated” by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.

With a five-day revisit frequency over coastal regions and a spatial resolution of 10-60 m, the Sentinel-2 multispectral instrument (MSI) has shown its capacity to provide a reasonably accurate remote sensing reflectance (Rrs) data product over water when the standard “black pixel” (BP) atmospheric correction algorithm was applied to the top-ofatmospheric (TOA) reflectance data. Alternative atmospheric correction approaches, such as the POLYnomial-based algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) (POLYMER), may show advantages under nonoptimal observation conditions (e.g., in the presence of strong sun glint). Here, POLYMER is implemented to process the data collected by both MSI and the Moderate Resolution Imaging Spectroradiometer (MODIS) with the resulting Rrsevaluated with concurrent and colocated in situ Rrsdata collected from the AERONET-OC platforms. The results indicate less uncertainties in the MSI Rrs than those in the MODIS Rrs, and also less uncertainties in the MSI Rrs than those reported earlier. This is possibly attributed to the spatial heterogeneity of coastal waters where MODIS coarseresolution data may suffer, and to the high-quality AERONETOC data. In addition, for the evaluation data set, MSI Rrsdoes not appear to suffer from adjacency effects from the AERONETOC platform and clouds, leading to more coverage than MODIS in nearshore waters. However, MSI Rrsis noisy in relatively clear waters, possibly due to the noisy TOA reflectance in the atmospheric correction bands over clear waters.

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.

Optical remote sensing is one of the most commonly used techniques to detect oil in the surface ocean. This is because oil has optical properties that are different from water to modulate oil-water spatial and spectral contrasts. However, understanding these contrasts is challenging because of variable results from laboratory and field experiments as well as from different observing conditions and spatial/spectral resolutions of remote sensing imagery. Here, through reviewing published oil-water spectral contrasts and analyzing remotely sensed spectra collected by several satellite and airborne sensors (MERIS, MODIS, MISR, Landsat, and AVIRIS) from the Deepwater Horizon oil spill, we provide the interpretation of the spatial/spectral contrasts of various oil slicks and discuss the challenges in such interpretations. In addition to oil thickness, several other factors also affect oil-water spatial/spectral contrasts, including sun glint strength, oil emulsification state, optical properties of oil covered water, and spatial/spectral resolutions of remote sensing imagery. In the absence of high spatial- and spectral-resolution imagery, a multistep scheme may be used to classify oil type (emulsion and non-emulsion) and to estimate relative oil thickness for each type based on the known optical properties of oil, yet such a scheme requires further research to improve and validate.

The atmospheric correction approach currently being used operationally by NASA [termed as NASA standard atmospheric correction (NSAC) approach] to process ocean color data relies on traditional “black pixel” approach, with additional modifications to account for nonnegligible water-leaving radiance in the near-infrared (NIR) bands. The NSAC approach underestimates remote-sensing reflectance (Rrs, sr-1) in blue wavelengths in the presence of absorbing aerosols. Addressing this issue requires realistic absorbing-aerosol model and knowledge of the vertical distribution of aerosols, which are currently difficult to achieve. An alternative atmospheric correction approach has been evaluated in this paper for Moderate Resolution Imaging Spectroradiometer (MODIS) data. The approach is based on a previously developed spectra-matching optimization [POLYnomial-based approach established for the atmospheric correction of MERIS data (POLYMER)], where polynomial functions are used to express atmospheric contribution to the measured radiance and where a bio-optical model is used to estimate the water contribution. Evaluation against in situ data measured over the regions frequently affected by absorbing aerosols indicates that, compared with the NSAC approach, the POLYMER approach improves the Rrsretrievals in blue wavelengths while having a slightly worse performance in other wavelengths. Evaluation using NSAC-retrieved Rrs in adjacent days free of absorbing aerosols suggests that the POLYMER approach could improve the spectral shape and increase valid spatial coverage. When applied to time-series MODIS data, the POLYMER approach could generate more temporary coherent daily and monthly Rrspatterns than the NSAC approach. These results suggest that the POLYMER approach could be an alternative approach to partly correct for absorbing aerosols in the absence of explicit information on the aerosol type and the vertical distribution.

The Great Lakes contain 84% of the fresh water in North America and provide many critical and valuable ecosystem services to lakeside communities. In the past decade, a rapid ecosystem regime shift has occurred where changing precipitation and runoff patterns, along with invasive zebra and quagga mussels, have contributed to dramatic lake-specific responses in water clarity, reductions in phytoplankton, increases in toxic algal blooms, and disruptions in the food chain. Focusing on Lake Michigan, this study applies a newly improved remote sensing algorithm for water clarity and integrates meteorological data and in-situ sampling of mussels to understand the spatial and temporal linkages between historical weather patterns, water clarity and mussel abundance. Results from a decade of satellite-derived ocean color images reveal the complex spatio-temporal patterns of `bio-clarification' occurring in Lake Michigan. Mussel biomass was negatively correlated with phytoplankton concentrations and turbidity, but association was likely weakened by unmeasured variables such as nearshore runoff, patterns of plankton and nutrient dynamics, thermal structure and mixing.

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 Rrscompared with concurrent MODIS Rrsand in situ Rrs. The comparison shows a mean relative difference of about 25% (N = 11) between GCAS and in situ Rrsin the blue-green bands for clear to moderately turbid waters.

Ocean color satellite missions to measure the biophysical and geochemical properties of the surface ocean need to consider not only the spectral and spatial requirements of the sensors but also the satellite overpass time to maximize valid observations. The valid observations are impacted not only by cloud cover but also by other perturbations such as sun glint and stray light. Using Level-3 global composites of three ocean products (chlorophyll a or Chl-a, normalized florescence line height or nFLH, and sea surface temperature or SST), the daily percentage valid observations (DPVOs) over the global oceans were calculated, from which the differences between MODIS Aqua (afternoon pass) and MODIS Terra (morning pass) have been analyzed. For all three products, Aqua shows more valid observations than Terra over the Southern Ocean, the ocean near Peru and Chile, and the ocean around Angola and Namibia, with relatively >30% more valid observations in boreal winter months due to lower cloud coverage in the afternoon. In contrast, more than 20% of valid Chl-a and nFLH observations are obtained by Terra in the North Indian Ocean, and 10%-30% more valid observations by Terra are also found for the Equatorial Pacific and Atlantic oceans. These can be possibly linked to the lower presence of sun glint for Terra. Compared with Chl-a and nFLH, SST retrievals are more tolerant to sun glint and other perturbation factors, leading to much higher DPVOs. The implications of these findings to future satellite mission design and field campaigns are also discussed.

The Visible Infrared Imager Radiometer Suite (VIIRS) is not equipped with a fluorescence band, which may affect its ability to detect and quantify harmful algal blooms (HABs) in coastal waters rich in colored dissolved organic matter. Such a deficiency has previously been demonstrated for a bloom of the toxic dinoflagellate Karenia brevis in the northeastern Gulf of Mexico (NEGOM) in summer 2014. Here, using data collected in the field and by VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS), we show that such a deficiency may be partially overcome using a red-green-chlorophyll-a index (RGCI). A relationship between near-concurrent (±4 hours) VIIRS RGCI (Rrs(672)/Rrs(551)) and field-measured chlorophyll-a (Chla; in mg m-3) was developed and evaluated using calibrated Chla obtained by a flowthrough system. A mean relative uncertainty, which was approximately twofold lower than VIIRS OC3M Chla, was obtained for VIIRS RGCI Chla (mean relative error: ~56%) over a large range (0.5-20 mg m-3). Similar spatial patterns between near-concurrent MODIS-Aqua (MODISA) normalized fluorescence line height (nFLH) and VIIRS RGCI Chla imagery indicate that VIIRS RGCI may be used as a surrogate for MODISA nFLH in the absence of a fluorescence band. The success of this newly developed data product may be partially attributed to the 20-nm bandwidth of the VIIRS 672-nm band (662-682 nm) that covers a portion of the solar stimulated fluorescence region. However, whether such observations from a simple case study can be extended to other turbid coastal or inland waters still remains to be tested.

Using data collected from two eutrophic lakes located in eastern China (Taihu Lake, 2330 km2and Chaohu Lake, 760 km2), the influence of variable particle composition on remote sensing reflectance (Rrs, in sr-1) properties and on the Medium Resolution Imaging Spectrometer (MERIS) maximum chlorophyll index (MCI) algorithm for estimating near-surface chlorophyll-a concentrations (Chla, in μg · L-1) is demonstrated. Although separated by a distance of only ~200 km, the two lakes showed dramatic differences in particle composition, with Taihu Lake dominated by inorganic particles and Chaohu Lake dominated by organic particles. Such differences led to variable Rrs spectral slopes in the red and near-IR bands and perturbations to the MCI algorithm. A modified MCI algorithm (MCIT) was then developed to reduce the impact of turbidity caused by inorganic particles. Root-mean-square errors in Chla retrievals decreased from 129.5% to 43.5% when using this new approach compared with the MCI algorithm in Taihu Lake for Chla ranging between ~5 and 100 μg · L-1. Application of this approach to other turbid water bodies, on the other hand, requires validation and possibly further tuning.

Satellite images of reflected sunlight have been used to detect and monitor oil spills in oceans. However, such a capacity is often hindered by the image noise due to either a low signal-to-noise ratio or other image features such as clouds or cloud shadows. The problem is particularly severe for nighttime images captured by the Visible Infrared Imager Radiometer Suite (VIIRS). This letter proposes a practical method to extract oil slick features in a semiautomatic fashion from VIIRS nighttime images and other noisy optical remote sensing images. The method is based on statistical information and morphological operators, and it is demonstrated to be able to effectively remove the noise and identify line features with the appropriate selection of threshold values. Testing this method over VIIRS nighttime images shows the preliminary success of oil slick feature extraction. Experiments on daytime data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) also suggest the applicability of this method to other optical remote sensing images. However, the requirement of human intervention to determine optimal parameters points to the need for improved automation in future works.

Addressing critical earth science questions often requires time scales beyond the life of any single satellite sensor. Overlap between satellite-based datasets allows for the quantification of continuity (and discrepancies) between sensors. Toward that end, collocated matchups between Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imager Radiometer Suite (VIIRS) water clarity data from the Gulf of Mexico were analyzed at simultaneous, daily, and monthly time scales. Simultaneous data indicated strong agreement between sensors, with unbiased percent difference (UPD) generally less than 10% for both SeaWiFS/MODIS and VIIRS/MODIS matchups, with no apparent temporal trends. Spatially, UPD was highest near frontal boundaries and at high sensor zenith angles, while bias showed nearshore/offshore trends. UPD and bias statistics did not diminish for daily matchups; however, large degradation was seen for comparisons of monthly means between sensors, particularly SeaWiFS/MODIS matchups. Data coverage represented an important factor contributing to uncertainties in monthly mean data, as higher UPD was observed when fewer valid satellite measurements were recorded. Requiring a minimum of 15 samples per pixel per month minimizes the uncertainties in monthly mean products, with UPD between satellites roughly equivalent to that for simultaneous matchups. Overall, these findings demonstrate high consistency between three satellite instruments for most locations, while several “hot spots” of inconsistency are also revealed, which should be avoided in time-series studies. The findings also highlight the need to quantify uncertainties in often-used satellite products (particularly monthly mean composites) as well as the need to have a sufficient number of observations to assure the fidelity of monthly means.

The advantages of geostationary observations of sediment plumes and phytoplankton blooms have been reported for coastal waters in the southern North Sea and west Pacific. So far, similar observations have not been possible for the Gulf of Mexico where blooms of Trichodesmium erythraeum often occur. Here, using data collected by the Geostationary Operational Environmental Satellite (GOES) Imager, we document diurnal changes of a Trichodesmium bloom first identified by the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite the low-signal-to-noise ratio ( ~ 46 : 1 for typical ocean radiance), the 550-750-nm band revealed clear patterns of Trichodesmium mats floating on the ocean surface and their temporal changes between 14:15 and 22:30 GMT on May 22, 2004. Normalization of the delineated bloom against the ocean background provided an effective atmospheric correction that enabled quantification of the changes in bloom size (i.e., area) and bloom intensity over the course of a day. The area coverage increased by about eightfold from midmorning (14-15 GMT) to reach its maximum around 18:30 GMT, whereas the mean intensity of the bloom area increased by ~ 22% from midmorning to 17:30 GMT. In the afternoon, while the bloom area remained relatively stable on the water surface, bloom intensity sharply decreased. These temporal patterns may be caused by physical aggregation and/or vertical migration of the Trichodesmium cells, and they agree well with the diurnal changes of a harmful algal bloom of the dinoflagellate Prorocentrum donghaiense in the East China Sea observed by the Geostationary Ocean Color Imager.

Ocean color continuity calls for consistent observations from multiple sensors in order to establish a seamless data record to address earth science questions. Currently, both Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites are being operated well beyond their designed five-year mission life, and they have shown signs of sensor degradation. It is thus urgent to evaluate whether the most recently launched Visible Infrared Imager Radiometer Suite (VIIRS) instrument (2011 to present) can provide consistent observations should MODIS instruments stop functioning. In this study, the consistency between MODIS/Aqua and VIIRS measurements over the Tampa Bay estuary ( ~ 1000 km2) is assessed for remote sensing reflectance (Rrs, sr-1), chlorophyll-a concentrations (Chla, mg·m-3), and absorption coefficient of colored dissolved organic matter (ag(443), m-1). While Rrs was derived as a standard National Aeronautics and Space Administration product from the SeaDAS software package (reprocessing version R2013.0), Chla and ag(443) were estimated using the recently developed regional algorithms for Tampa Bay. Time-series analysis and statistics both showed that the two sensors provided consistent measurements for most products evaluated, with unbiased mean percentage differences of 25% and mean annual biases within -9% (except for one of the eight cases) for large dynamic ranges in Chla (1.0-20 mg·m-3) and ag(443) (0.1-1.5 m-1) in all four bay segments. These estimates are comparable or better than those derived from satellite-in situ comparisons, suggesting that VIIRS will provide observations consistent with MODIS, ensuring ocean color continuity and seamless data records for Tampa Bay. Such observations are crucial in establishing a long-term satellite-based water quality decision matrix for Tampa Bay.

In teaching introductory computer programming courses, problem solving with computers is an important topic and algorithm design is essential. We developed a team-based project to teach students solving real-world problem. Students are provided with six satellite images of the Deepwater Horizon oil spill in the Gulf of Mexico and asked to develop computer programs to outline oil contaminated areas. Working on the project in a team, students conducted initial analysis of the problem, identified patterns of oil slicks by visualizing satellite images, and designed algorithms to delineate the oil slicks. The open-ended problem allowed the students to gain experiences in user interface design, use of arrays, decision-making, and repetition with hands-on experience. The project has also been adapted in teaching classes of computer science general education and digital image processing.

Cloud contamination can lead to significant biases in sea surface temperature (SST) as estimated from satellite measurements. The effectiveness of four cloud detection algorithms for the Moderate Resolution Imaging Spectroradiometer (MODIS) in retaining valid SST data and masking cloud-contaminated data was assessed for all 2125 daytime and nighttime images during 2010 over the eastern Gulf of Mexico and including the east coast of Florida. None of the cloud detection algorithms was found to be sufficient to reliably differentiate clouds from valid SST, particularly during anomalously cold events. The strengths and weaknesses of each algorithm were identified, and a new hybrid cloud detection algorithm was developed to maximize valid data retention while excluding cloud-contaminated pixels. The hybrid algorithm was based on a decision tree, which includes a set of rules to use existing algorithms in different ways according to time and location. Comparing with >10000 concurrent in situ SST measurements from buoys, images processed with the hybrid algorithm showed increases in data capture and improved accuracy statistics over most existing algorithms. In particular, while keeping the same accuracy, the hybrid algorithm resulted in nearly 20% more SST retrievals than the most accurate algorithm (Quality SST) currently being used for operational processing. The increases in both data coverage and SST range should improve MODIS data products for more reliable SST retrievals in near real time, thus enhancing the ocean observing capacity to detect anomaly events and study short- and long-term SST changes in coastal environments.

The Ocean-Colour Climate-Change Initiative (OC-CCI) aims to create a long-term, consistent, error-characterised time series of ocean-colour products, for use in climate change studies. Climate Change Initiative is a programme of the European Space Agency devoted to using satellites to generate climate quality time series data of Essential Climate Variables (ECVs) identified by the Global Climate Observing System (GCOS). Within the ocean colour CCI project, a user consultation was undertaken, targeting both the climate modelling community and the Earth Observation community. Taking the user requirements into account, a set of criteria was developed, for selecting the best ocean-colour algorithms for climate research. Candidate atmospheric correction algorithms and in water algorithms have been submitted to a round robin comparison. The overall best performers are being used to generate test products, to be evaluated further.

Cloud filters developed for high-resolution (1-km) Advanced Very High Resolution Radiometer (AVHRR) satellite-derived sea surface temperature (SST) observations are generally inadequate to capture extreme cold events. Such events impacted shallow waters in Florida Bay and other coastal regions in January 2010 with fatal consequences for large numbers of corals and associated organisms. Raw AVHRR images were reprocessed to understand whether historical knowledge of daily and interannual SST variations could be used to derive a practical cloud-filtering technique. This approach, however, misidentified valid water temperature pixels in nearly 20% of 2703 images collected during the month of January for each year between 1995 and 2010. To create an improved SST climatology, this cloud-filtering method was combined with manually delineated overrides of falsely masked regions. During the January 2010 cold event, this climatology indicated negative SST anomalies of up to 11.6°C in the Big Bend region and 14°C in Florida Bay, with high spatial heterogeneity throughout. Our findings highlight the need for improved autonomous cloud-masking techniques to detect cold events in near real time.

Red tides pose a significant environmental and economic threat in the Gulf of Mexico. Timely detection of red tides is important for understanding this phenomenon. In this paper, learning approaches based on k-nearest neighbors, random forests and support vector machines have been evaluated for red tide detection from MODIS satellite images. Detection results from our algorithms were compared with ground truth red tide data collected in situ. Our results show that red tide identification methods based on machine learning approaches outperform baseline algorithms based on bio-optical characterization.

Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993-2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and plusmn3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent ( plusmn0.5 h) data from the SEAKEYSinsitustations in the Florida Keys showed near-zero bias errors (<0.05degC) in the weekly anomaly for SST anomalies between -3degC and 3degC, with standard deviations <0.5degC. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near real time into National Oceanic and Atmospheric Administration's (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA.

Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993–2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and$\pm$3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent ($\pm$0.5 h) data from the SEAKEYSin situstations in the Florida Keys showed near-zero bias errors$(≪ 0.05\ ^{\circ}\hbox{C})$in the weekly anomaly for SST anomalies between$-$3$^{\circ}\hbox{C}$and 3$^{\circ}\hbox{C}$, with standard deviations$≪ 0.5\ ^{\circ}\hbox{C}$. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential

Inner shelf sand ridges are common features along many sandy coastlines. Here, I demonstrate that ocean color imagery from the operational satellite instrument Moderate Resolution Imaging Spectroradiometer can clearly show their morphology, including orientation, width, length, spacing, thickness, and distribution, on the inner west Florida shelf between the Big Bend and the Florida Bight (about 500 km N-S) up to 35-m water depth and 150 km from the shoreline. Some of the sand ridges were previously unknown due to lack ofinsitudata. Most of the periodic, parallel, and static features agree well with the existing high-resolution bathymetry. However, there are also mismatches between the bathymetric features and those revealed by the consistent satellite measurements, suggesting possible errors in the bathymetry data. Using a simple optical model, I show that the 500-m resolution imagery can accurately reveal sand ridge thickness from several meters to < 1 m. Because of the repeated and synoptic coverage, these medium-resolution ocean color imageries provide unprecedented capability in studying distributions and changes of the sand ridge morphology, which are otherwise expensive and difficult to obtain.

Daily high-resolution Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images of the central North Atlantic Ocean (1998-2003) show that temporal changes in the absorption coefficient of colored dissolved organic matter (CDOM) or "yellow substance" follow changes in phytoplankton pigment absorption coefficient in time. CDOM peaks (between January and March) and troughs (late summer and fall) followed pigment peaks and troughs by approximately two and four weeks, respectively. This phase shift is additional strong evidence that CDOM in the marine environment is derived from phytoplankton degradation. The common assumption of linear covariation between chlorophyll and CDOM is a simplification even in this ocean gyre. Due to the temporal changes in CDOM, chlorophyll concentration estimated based on traditional remote sensing band-ratio algorithms may be overestimated by about 10% during the spring bloom and underestimated by a similar 10% during the fall. These observations are only possible through use of synoptic, precise, accurate, and frequent measurements afforded by space-based sensors because in situ technologies cannot provide the required sensitivity or synoptic coverage to observe these natural phenomena.

We present satellite imagery that is interpreted as showing extensive lines of floating Sargassum in the western Gulf of Mexico in the summer of 2005. In spite of frequent reports of floating weed covering extended areas in different parts of the world's ocean, this appears to be the first observation of Sargassum from space. Satellite observations were made with the Medium Resolution Imaging Spectrometer (MERIS) on the Envisat satellite launched by the European Space Agency, and subsequently with the Moderate Resolution Imaging Spectroradiometer (MODIS) launched on both the Terra and Aqua satellites by the National Aeronautics and Space Administration. Both instruments cover wide swaths, providing near-daily images. Both have optical spectral bands in the range 670 to 750 nm, which detect the chlorophyll red-edge characteristic of land and marine vegetation, but only MERIS has a band at 709 nm, which was critical to the initial discovery. The combined satellite data from both sensors show the seasonal cycle of weed density in different areas of the Gulf. A wider ranging study is now needed to map its occurrence in other areas, including the Sargasso Sea (named for the weed, but not so far covered in our survey). The satellite observations suggest that Sargassum biomass is greater than previously estimated, and hence plays a more important part in oceanic productivity