Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats

Satellite retrievals of particulate backscattering (bbp) are widely used in studies of ocean ecology and biogeochemistry, but have been historically difficult to validate due to the paucity of available ship-based comparative field measurements. Here we present a comparison of satellite and in situ bbp using observations from autonomous floats (n = 2,486 total matchups across three satellites), which provide bbp at 700 nm. With these data, we quantify how well the three inversion products currently distributed by NASA ocean color retrieve bbp. We find that the median ratio of satellite derived bbp to float bbp ranges from 0.77 to 1.60 and Spearman’s rank correlations vary from r = 0.06 to r = 0.79, depending on which algorithm and sensor is used. Model skill degrades with increased spatial variability in remote sensing reflectance, which suggests that more rigorous matchup criteria and factors contributing to sensor noisiness may be useful to address in future work, and/or that we have built in biases in the current widely distributed inversion algorithms

An algorithm for detecting <i>Trichodesmium</i> surface blooms in the South Western Tropical Pacific

<i>Trichodesmium</i>, a major colonial cyanobacterial nitrogen fixer, forms large blooms in NO₃-depleted tropical oceans and enhances CO₂ sequestration by the ocean due to its ability to fix dissolved dinitrogen. Thus, its importance in C and N cycles requires better estimates of its distribution at basin to global scales. However, existing algorithms to detect them from satellite have not yet been successful in the South Western Tropical Pacific (SP). Here, a novel algorithm (TRICHOdesmium SATellite) based on radiance anomaly spectra (RAS) observed in SeaWiFS imagery, is used to detect <i>Trichodesmium</i> during the austral summertime in the SP (5° S–25° S 160° E–170° W). Selected pixels are characterized by a restricted range of parameters quantifying RAS spectra (e.g. slope, intercept, curvature). The fraction of valid (non-cloudy) pixels identified as <i>Trichodesmium</i> surface blooms in the region is low (between 0.01 and 0.2 %), but is about 100 times higher than deduced from previous algorithms. At daily scales in the SP, this fraction represents a total ocean surface area varying from 16 to 48 km² in Winter and from 200 to 1000 km² in Summer (and at monthly scale, from 500 to 1000 km² in Winter and from 3100 to 10 890 km² in Summer with a maximum of 26 432 km² in January 1999). The daily distribution of <i>Trichodesmium</i> surface accumulations in the SP detected by TRICHOSAT is presented for the period 1998–2010 which demonstrates that the number of selected pixels peaks in November–February each year, consistent with field observations. This approach was validated with in situ observations of <i>Trichodesmium</i> surface accumulations in the Melanesian archipelago around New Caledonia, Vanuatu and Fiji Islands for the same period

Significant Contribution of Large Particles to Optical Backscattering in the Open Ocean

The light scattering properties of oceanic particles have been suggested as an alternative index of phytoplankton biomass than chlorophyll-a concentration (chl-a), with the benefit of being less sensitive to physiological forcings (e.g., light and nutrients) that alter the intracellular pigment concentrations. The drawback of particulate scattering is that it is not unique to phytoplankton. Nevertheless, field studies have demonstrated that, to first order, the particulate beam-attenuation coefficient (c(p)) can track phytoplankton biomass. The relationship between c(p) and the particulate backscattering coefficient (b(bp)), a property retrievable from space, has not been fully evaluated, largely due to a lack of open-ocean field observations. Here, we present extensive data on inherent optical properties from the Equatorial Pacific surface waters and demonstrate a remarkable coherence in b(bp) and c(p). Coincident measurements of particle size distributions (PSDs) and optical properties of size-fractionated samples indicate that this covariance is due to both the conserved nature of the PSD and a greater contribution of phytoplankton-sized particles to b(bp) than theoretically predicted. These findings suggest that satellite-derived b(bp)could provide similar information on phytoplankton biomass in the open ocean as c(p)

An improved bio-optical model for the remote sensing of Trichodesmium spp. blooms

The cyanobacterium Trichodesmium spp. can be an important ecological and biogeochemical component for both the coastal and open ocean ecosystems by way of its nitrogen fixation ability. However, information regarding its spatial and temporal distribution remains sparse. Trichodesmium has unique optical properties that should allow for its spectral signature to be detectable in satellite ocean color data sets. Here, a global data set of concurrent measurements of Trichodesmium abundance and radiometric reflectance was compiled and used to develop bio-optical models for Trichodesmium. The most robust global model related the water-leaving radiance signal to the identification of an occurrence of a Trichodesmium bloom above a threshold value of 3200 trichomes L−1. Using the in situ data set, this model is trained to successfully predict Trichodesmium blooms (∼92%) while minimizing false positive retrievals (∼16% of nonbloom observations). A validation of the approach applied to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery shows that the model correctly predicts 76% of the bloom occurrences of an independent validation data set of in situ Trichodesmium observations. Ultimately, maps of Trichodesmium bloom occurrence will provide a means of addressing the ecology of Trichodesmium and its contribution to new production of the world oceans

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (&lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Application of this method to spectral &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m&lt;sup&gt;−3&lt;/sup&gt; and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6-1.5 1/4μm. The inferred carbon biomass of these cells was about 5.2-6.0 mg mg -\u273 and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity

An algorithm for detecting Trichodesmium surface blooms in the South Western Tropical Pacific

Trichodesmium, a major colonial cyanobacterial nitrogen fixer, forms large blooms in NO₃-depleted tropical oceans and enhances CO₂ sequestration by the ocean due to its ability to fix dissolved dinitrogen. Thus, its importance in C and N cycles requires better estimates of its distribution at basin to global scales. However, existing algorithms to detect them from satellite have not yet been successful in the South Western Tropical Pacific (SP). Here, a novel algorithm (TRICHOdesmium SATellite) based on radiance anomaly spectra (RAS) observed in SeaWiFS imagery, is used to detect Trichodesmium during the austral summertime in the SP (5° S–25° S 160° E–170° W). Selected pixels are characterized by a restricted range of parameters quantifying RAS spectra (e.g. slope, intercept, curvature). The fraction of valid (non-cloudy) pixels identified as Trichodesmium surface blooms in the region is low (between 0.01 and 0.2 %), but is about 100 times higher than deduced from previous algorithms. At daily scales in the SP, this fraction represents a total ocean surface area varying from 16 to 48 km² in Winter and from 200 to 1000 km² in Summer (and at monthly scale, from 500 to 1000 km² in Winter and from 3100 to 10 890 km² in Summer with a maximum of 26 432 km² in January 1999). The daily distribution of Trichodesmium surface accumulations in the SP detected by TRICHOSAT is presented for the period 1998–2010 which demonstrates that the number of selected pixels peaks in November–February each year, consistent with field observations. This approach was validated with in situ observations of Trichodesmium surface accumulations in the Melanesian archipelago around New Caledonia, Vanuatu and Fiji Islands for the same period

An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe

Abstract. Nearly half of the earth’s photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters

Improbability mapping: a metric for satellite-detection of submarine volcanic eruptions

Submarine volcanic eruptions can result in both real and apparent changes in marine algal communities, e.g., increases in phytoplankton biomass and/or growth rates that can cover thousands of square kilometers. Satellite ocean color monitoring detects these changes as increases in chlorophyll and particulate backscattering. Detailed, high resolution analysis is needed to separate the optical effects of volcanic products from the response of the marine algal community. It is possible to calculate an index, which maps the magnitude of improbable change (relative to long term average conditions) following known volcanic eruptions by using low resolution, initial estimates of chlorophyll and backscatter along with an archived history of satellite data. We apply multivariate probability analysis to changes in global satellite ocean chlorophyll and particulate backscatter data to create a new metric for observing apparent biological responses to submarine eruptions. Several examples are shown, illustrating the sensitivity of our improbability mapping index to known submarine volcanic events, yielding a potentially robust method for the detection of new events in remote locations.Keywords: Improbability mapping index, Submarine volcanic eruptions, Satellite detectio