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For the first time the vertical variation in light quality in the global ocean is quantified with a single parameter--the hue angle (αE, in degree) in chromaticity of downwelling irradiance. For oceanic waters, αE is ~140° at surface, but it becomes ~230° at the bottom of the euphotic zone; αE changes rapidly near the surface, and we term this layer of rapid change in light quality as chromocline, analogous to the thermocline or pycnocline in oceanography. The 3-D variations in light quality are further highlighted with data from satellite ocean color measurements, where global distributions of αE for depths of 99%, 37%, and 1% of surface photosynthetically available radiation (PAR) are presented. As an example to demonstrate the importance to consider the change in light quality, the relationship between the light quality and the ratio of phytoplankton absorbed light to PAR is presented where this ratio may vary by a factor of 3 or more under different chlorophyll-a concentrations; otherwise, the ratio would be constant vertically. We advocate quantitative measurement and report the light quality in the upper ocean with such a single and objective parameter to accompany the routine measurement and report the light intensity, which will greatly improve our understanding of light-related processes and further bridge ocean optics and oceanography.

Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance (Rrs), it is found that, for P. globosa blooms, the maximum of the second-derivative ( dλ²Rrs) of Rrs(λ) in the 460-480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll c₃ (Chl-/ c₃) around 466 nm, a prominent feature of P. globosa. This position of dλ²Rrs maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured Rrs. Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl-/ a) concentration and colony numbers of P. globosa were estimated from UAV-derived Rrs, where P. globosa colonies were found in a range of ~3-37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

Himawari-8 (H8) is a geostationary meteorological satellite launched by JAXA (Japan Aerospace Exploration Agency) and is now operated by JMA (Japan Meteorological Agency). It takes measurements at a temporal resolution of 10 min for full disk view. Although designed as a meteorological satellite, the Advanced Himawari Imager (AHI) onboard H8 has three visible (460, 510 and 640 nm), one near infrared (860 nm) and two shortwave infrared bands (1610 and 2257 nm) to observe the Earth system. In this study, the Floating Algae Index (FAI) developed for ocean color satellites (Hu, 2009) is adapted to process AHI data for the first time and applied for waters of Lake Taihu, China. For a total of 18 near-cloud-free images, a correlation coefficient (r) of 0.92 was obtained between the algae area derived from AHI FAI and that from the Moderate Resolution Imaging Spectroradiometer (MODIS) FAI, and the mean percentage difference is ~5% in algae coverage. More concurrent images (n = 80) were collected for a comparison between AHI and Geostationary Ocean Color Imager (GOCI), as GOCI is also a geostationary satellite, resulting in a correlation coefficient of 0.91 and percentage deviation of ~8% in observed algae coverage. These results indicate that H8/AHI can obtain reliable observations of floating algae at ultrahigh temporal resolutions (10 min). Especially, such ultrahigh-frequency measurements show that part of Lake Taihu (e.g., Meiliang Bay) experienced more frequent events of floating algae (mostly >60%) than that observed by GOCI (generally

Creseis acicula is a pteropod species, widely distributed in the world’s ocean. The latest report of its bloom was in the Mediterranean Sea in 1990. On June 12, 2020, an unprecedented bloom of C. acicula was observed in Daya Bay, China, that lasted for over a month and caused economic losses. The maximum abundance of C. acicula was up to 5595 ind. m-3, 1000 times the local record high, and 10 times the highest number recorded elsewhere. Higher abundance (≥ 1000 ind. m-3) was found in the warm water of ≥ 30℃ along the southwest shore of Daya Bay. The initiation of the bloom matched a sharp increase of temperature and chlorophyll a and an abrupt decrease of salinity attributed to heavy rainfall that lasted for more than 20 days. Considering rising occurrences of extreme weather due to global change, possibilities of recurrent C. acicula blooms worldwide certainly deserve attention.

Water-leaving albedo (αw), defined as the ratio of water-leaving irradiance to downwelling irradiance just above the surface, is a major component of ocean surface albedo (α) but has long been ignored or underrepresented. A semi-analytical scheme based on inherent optical properties (IOPs), termed IOPs-αw, is proposed in this study to estimate spectral αw(λ) from ocean color measurements. Evaluations with numerical simulations of radiative transfer show that IOPs-αw outperforms the conventional scheme based on chlorophyll-a (Chl) concentration. The median absolute percentage difference (MAPD) of derived αw(λ) from IOPs-αw is generally less than 3% in the blue-green spectral domain, in comparison to MAPD of over 40% for estimated αw(λ) from the Chl-based scheme. IOPs-αw is later implemented to monthly composite data of the Visible Infrared Imaging Radiometer Suite (VIIRS), where reasonable spatial distributions and seasonal patterns of αw(λ) are obtained. In particular, broadband αw in the visible domain, termed αw_VIS, obtained via IOPs-αw is over 50% higher than the previous estimation by the Chl-based scheme in most oceanic waters. Furthermore, this study concludes that αw_VIS could contribute up to 20% to α in oceanic waters under low solar-zenith angles. Thus, we suggest that neither the spatial variability of αw_VIS nor the contribution of αw_VIS to α shall be neglected, and it is necessary to incorporate IOPs-αw into current parameterizations of α in coupled ocean-atmosphere and climate models.

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (Rrs(λ)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate Rrs(λ) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr−1 and 16.2%, respectively. In contrast, the Rrs(λ) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as ~0.0018 sr−1 and ~38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is ~0.0062 sr−1 and MAPD is ~228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain.

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.

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.

The spectral absorption coefficient of pure seawater (aw(λ)) in published studies differ significantly in the blue domain, yet the impacts of such discrepancies on the inherent optical properties (IOPs) derived from ocean color have been scarcely documented. In this study, we confirm that changes in aw(λ) may have significant impacts on retrieved IOPs in oligotrophic waters, especially for the phytoplankton absorption coefficient (aph(λ)). Two sets of aw(λ) data, aw_PF97 (Appl. Opt. 36, 8710, 1997) and aw_Lee15 (Appl. Opt. 54, 546, 2015), were selected for optical inversion analysis. It is found that aph(λ) retrieved with aw_Lee15 agree better with the in-situ measurements in oligotrophic waters. Further applications to satellite images show that the derived aph(λ) using aw_Lee15 can be up to 238% higher than the retrievals using aw_PF97 in the core zone of the subtropical ocean gyres. Given that aw_PF97 is commonly accepted as the “standard” aw(λ) by the ocean color community in the past decades, this study highlights the need and importance to update aw(λ) with aw_Lee15 for IOPs retrievals in oligotrophic waters.

Visibility, a property measured based on human vision, is a common and widely used measurement to represent the quality of air and water. To visibility, theories have been developed in the past century for measurements in both air and water. This includes the century-old Koschmieder model for visibility in air and the Duntley-Preisendorfer model for visibility (Secchi disk depth) in water, as well as recent model by Lee et al. Here we present a general relationship for the Law of Contrast Reduction based on radiative transfer, which is the key to deduce visibility in both media. And from this general relationship, it suggests the Koschmieder model is applicable only to situations when a common-size object can be viewed 10's of kilometers away, while the Duntley-Preisendorfer model is not applicable for a common size Secchi disk when viewed in water. We further highlight the difference in "visibility" between "simple detection" and "clear recognition", a key difference of "visibility" in air and water.

To achieve a smooth transition between algorithms for “clear” water and “turbid” water, we propose a single formula to calculate the input parameter (ip) used for empirical retrieval of absorption coefficients (a) or chlorophyll concentration ([Chl]) from remote-sensing reflectance (R r s ). This formula for ip takes the ratio of the maximum R r s in the blue-green bands to the sum of R r s (green) and the scaled R r s in the red and infrared bands (termed as ipMax-Sum). We found that, compared to the widely used OC4-type formula for ip, ipMax-Sum can improve the coefficient of determination from ∼0.88 to 0.99 for absorption coefficient at 440 nm [a(440)] in ∼0.01–20.0 m–1 ([Chl] ∼0.01–500 mg m–3). Especially, the sensitivity of ipMax-Sum to the change in a(440) is about five times greater than that of OC4-type for a(440)>∼1.0 m−1 ([Chl]>∼10 mg m−3). These results indicate an advantage of ipMax-Sum for generating robust and seamless a(440) or [Chl] from clear to highly turbid waters. The inclusion of such a scheme in a quasi-analytical algorithm is also presented.

Secchi disk depth (ZSD) and Forel-Ule index (FUI) are the two oldest and easiest measurements of water optical properties based on visual determination. With an overarching objective to obtain water inherent optical properties (IOPs) using these historical measurements, this study presents a model for associating remote-sensing reflectance (Rrs) with FUI and ZSD. Based upon this, a scheme (FZ2ab) for converting FUI and ZSD to absorption (a) and backscattering coefficients (bb) is developed and evaluated. For a data set from HydroLight simulations, the difference is

Water clarity, which is commonly and widely represented by the Secchi depth (Zsd), is an important quality parameter for all aquatic environments. To evaluate the variation of water clarity of the Bohai Sea (BHS) in the past decade (2003–2014), Zsd is derived from MODIS-Aqua ocean color data with an innovative mechanistic model. The resulted Zsd product from MODIS is found within 23% differences of match-up in situ measurements that span a range of 2–12 m. Based on this validated MODIS Zsd product of the BHS, valuable findings regarding water clarity of the BHS over the period of 2003–2014 are obtained for the first time. For the central BHS, the climatological mean Zsd in August of 2003–2014 (3.9 m) is found strikingly lower than that observed in situ in August of 1982–1983 (8.7 m), 1959–1979 (8–12 m) and 1972–1987 (8–14 m). This implies substantial deterioration of water clarity in the central BHS after the late 1980s, an unpleasant byproduct of rapid economic development in the BHS surrounding regions initiated in the mid-1980s. On the other hand, no significant variations in Zsd in the central BHS is detected for the period of 2003–2014, suggesting deterioration of water clarity of this water body is not continued. Results from this effort highlight the value of both the analytically-derived Zsd product and satellite ocean color remote sensing in monitoring water quality of coastal seas.

Koschmieder proposed that visibility is inversely proportional to the extinction coefficient of air, and this model has been widely adopted during the past century. Using radiative transfer theory, the authors present a general relationship for the law of contrast reduction and point out that the Koschmieder model is workable only to situations when a common-size object can be viewed tens of kilometers away. However, the Koschmieder model is not applicable for viewable distances of hundreds of meters when the angular dimension of an object is significantly greater than the eye resolution of the human being. The authors further separate the term “visible” into “simple detection” or “detectability” and “clear identification” or “identifiability” and point out that the Koschmieder model is applicable to identifiability, but not necessarily for detectability. In addition, the way of calculating contrast is revised to follow the concept of brightness constancy. The results of this effort advocate the measurement and distribution of detectability in harsh weather conditions, as such data offer more useful and important information for daily life.

Ultraviolet (UV) radiation has a profound impact on marine life, but historically and even currently, most ocean color satellites cannot provide radiance measurements in the UV, and thus UV penetration, in the global ocean. We develop a system (termed as UVISRdl) in this study, based on deep learning, to estimate remote sensing reflectance (Rrs) at 360, 380, and 400 nm (collectively termed as near-blue UV bands, nbUV) from Rrs in the visible bands that are obtained by ocean color satellites. This system is tested using both synthetic and field-measured data that cover a wide range and large number of values, with the resulted coefficient of determination close to 1.0 and bias close to 0 between UVISRdl estimated and known Rrs(nbUV). These results indicate excellent predictability of Rrs(nbUV) from Rrs(visible) via UVISRdl. The system was further applied to VIIRS (the Visible Infrared Imaging Radiometer Suite) data with the estimated Rrs(nbUV) evaluated using matchup field measurements, and obtained a mean absolute relative difference (MARD) at 360 nm of ~14% for oceanic waters and ~ 50% for coastal waters. These results are equivalent to those reported in the literature for satellite Rrs(visible) in oceanic and coastal waters. Examples of the global distribution of Rrs(nbUV), and subsequently the diffuse attenuation coefficient at the nbUV bands (Kd(nbUV)), are generated after applying UVISRdl to Rrs(visible) from the VIIRS data. The system lays the groundwork to generate decade-long Rrs(nbUV) and Kd(nbUV) from satellite ocean color data, which will be useful and important for both ocean color remote sensing and biogeochemical studies.

For waters with stratified chlorophyll concentration (Chl), numerical simulations were carried out to gain insight into the forward models of subsurface reflectance and empirical algorithms for Chl from the ocean color. It is found that the Gordon and Clark (1980) forward model for reflectance using an equivalent homogeneous water with a weighted average Chl ( ⟨ C h l ⟩ ) as the input works well, but depending on the contribution of gelbstoff, the difference in reflectance between stratified and the equivalent homogeneous water can be more than 10%. Further, the attenuation of upward light is better approximated as ∼ 1.5 t i m e s that of the diffuse attenuation coefficient of downwelling irradiance. On the other hand, although the forward model for reflectance developed in Zaneveld et al. [Opt. Express 13, 9052 (2005)] using equivalent homogeneous water with a weighted average of the backscattering to absorption ratio as the input also works well, this model cannot be used to obtain equivalent ⟨ C h l ⟩ for reflectance. Further, for empirical Chl algorithms designed for “Case 1” waters, it has been discovered that, for surface Chl in a range of ∼ 0.06 − 22.0 m g / m 3 , the predictability of surface Chl is basically the same as that of ⟨ C h l ⟩ from the blue-green band ratio or the band difference of reflectance. Because ⟨ C h l ⟩ is wavelength and weighting-formula dependent, and it is required to have profiles of both Chl and the optical properties, these results emphasize that for empirical Chl algorithms, it is easier, less ambiguous, and certainly more straightforward and simple to use surface Chl for algorithm development and then its evaluation, rather than to use ⟨ C h l ⟩ , regardless of whether or not the water is stratified.

This study used MODIS observed phytoplankton absorption coefficient at 443 nm (Aph) as a preferable index to characterize phytoplankton variability in optically complex waters. Aph derived from remote sensing reflectance (Rrs, both in situ and MODIS measured) with the Quasi-Analytical Algorithm (QAA) were evaluated by comparing them with match-up in situ measurements, collected in both oceanic and nearshore waters in the Taiwan Strait (TWS). For the data with matching spatial and temporal window, it was found that the average percentage error (ε) between MODIS derived Aph and field measured Aph was 33.8% (N=30, Aph ranges from 0.012 to 0.537 m−1), with a root mean square error in log space (RMSE_log) of 0.226. By comparison, ε was 28.0% (N=88, RMSE_log = 0.150) between Aph derived from ship-borne Rrs and Aph measured from water samples. However, values of ε as large as 135.6% (N=30, RMSE_log = 0.383) were found between MODIS derived chlorophyll-a (Chl, OC3M algorithm) and field measured Chl. Based on these evaluation results, we applied QAA to MODIS Rrs data in the period of 2003–2009 to derive climatological monthly mean Aph for the TWS. Three distinct features of phytoplankton dynamics were identified. First, Aph is low and the least variable in the Penghu Channel, where the South China Sea water enters the TWS. This region maintains slightly higher values in winter (~17% higher than that in the other seasons) due to surface nutrient entrainment under winter wind-driven vertical mixing. Second, Aph is high and varies the most in the mainland nearshore water, with values peaking in summer (June–August) when river plumes and coastal upwelling enhance surface nutrient loads. Interannual variation of bloom intensity in Hanjiang River estuary in June is highly correlated with alongshore wind stress anomalies, as observed by QuikSCAT. The year of minimum and maximum bloom intensity is in the midst of an El Niño and a La Niña event, respectively. Third, a high Aph patch appears between April and September in the middle of the southern TWS, corresponding to high thermal frontal probabilities, as observed by MODIS. Our results support the use of satellite derived Aph for time series analyses of phytoplankton dynamics in coastal ocean regions, whereas satellite Chl products derived empirically using spectral ratio of Rrs suffer from artifacts associated with non-biotic optically active materials.

An approach that combines field observations and satellite inferences of Secchi depth could transform how we assess water clarity across the globe and pinpoint key changes over the past century.

Many recent models for retrieval of primary production in the sea from ocean-color data are temperature based. But previous studies in low latitudes have shown that models that include phytoplankton community structure can have improved predictive capability. In this study, we measured photosynthetic parameters from photosynthesis-irradiance (P-E) experiments, phytoplankton absorption coefficients, and phytoplankton community structure derived from algal pigments during four cruises in the northern South China Sea (NSCS). The maximum quantum yield of CO2 ( ΦmC) and the chlorophyll a-normalized P-E curve light-limited slope (αB) varied significantly with the blue-to-red ratio of phytoplankton absorption peaks (aph(435)/aph(676)) (p

Commonly we see the diffuse attenuation coefficient of downwelling irradiance (Kd) expressed as a sum of the contributions of various constituents. We show here that, both theoretically and numerically, because Kd is an apparent optical property (AOP), this approach is not consistent with radiative transfer. We further advocate the application of models of Kd developed in past decades that are not only consistent with radiative transfer but also provide more accurate estimates, in particular for coastal turbid waters.

Radiative transfer modeling of Secchi disk observations has historically been based on conjugated signals of eye response and radiance, where water's attenuation in the entire visible band is included in the attenuation when deciding the Secchi disk depth in water. Aas et al. [Ocean Sci.10(2), 177 (2014)Remote Sens. Environ.169, 139 (2015)] hypothesized that it is actually the attenuation in water's transparent window that matters to the observation of a Secchi disk in water. To test this hypothesis, observations of Secchi disks in blue and green waters were conducted via naked eyes, blue-pass glasses, and green-pass glasses. Measurement results indicate that for blue waters, the observed Secchi depths via naked eyes match the depths obtained with blue-pass glasses and much deeper than the depths with green-pass glasses, although the green-pass glasses match the highest response of human eyes. These observations experimentally support the hypothesis that our eye-brain system uses the contrast information in the transparent window to make a judgement decision regarding sighting a Secchi disk in water.

Since June 2010, the Geostationary Ocean Color Imager (GOCI) has been collecting the first diurnally resolved satellite ocean measurements. Here GOCI retrievals of phytoplankton chlorophyll concentration and fluorescence are used to evaluate daily to seasonal changes in photophysiological properties. We focus on nonphotochemical quenching (NPQ) processes that protect phytoplankton from high light damage and cause strong diurnal cycles in fluorescence emission. This NPQ signal varies seasonally, with maxima in winter and minima in summer. Contrary to expectations from laboratory studies under constant light conditions, this pattern is highly consistent with an earlier conceptual model and recent field observations. The same seasonal cycle is registered in fluorescence data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer Aqua satellite sensor. GOCI data reveal a strong correlation between mixed layer growth irradiance and fluorescence-derived phytoplankton photoacclimation state that can provide a path for mechanistically accounting for NPQ variability and, subsequently, retrieving information on iron stress in global phytoplankton populations.

Dinoflagellate and diatom blooms often occur in the East China Sea (ECS) during spring and summer. Some of the dinoflagellate blooms are toxic, resulting in widespread economic damage. In order to mitigate the negative impacts, remote-sensing methods that can effectively and accurately discriminate between bloom types are demanded for early warning and continuous monitoring of bloom events at large scales. An in situ bio-optical data set collected from diatom and dinoflagellate blooming waters indicates that the two types of blooms exhibited distinctive differences in the shapes and magnitudes of remote-sensing reflectance (Rrs). The ratio of in situ measured Rrs spectral slopes at two spectral ranges (443–488 and 531–555 nm, bands available with the moderate resolution imaging spectrometer (MODIS) sensor), abbreviated as BI (representing bloom index), was found effective in differentiating dinoflagellates from diatoms. Reflectance model simulations, which were carried out using in situ and algal culture data as input, provided consistent results. A classification approach for separating dinoflagellate from diatom blooms in the ECS was then developed: When fluorescence line height (FLH) is doubled over the background level and total absorption coefficient at 443 nm ≥ 0.5 m−1, if 0.0

University of Massachusetts Boston; NASA Ocean Biology and Biogeochemistry and Water and Energy Cycle Programs; JPSS VIIRS Ocean Color Cal/Val Project; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]

Chlorophyll a (Chl) concentrations derived from satellite measurements have been used in oceanographic research, for example to interpret eco-responses to environmental changes on global and regional scales. However, it is unclear how existing Chl products compare with each other in terms of accuracy and consistency in revealing temporal and spatial patterns, especially in the optically complex marginal seas. In this study, we examined three MODIS (Moderate Resolution Imaging Spectroradiometer) Chl data products that have been made available to the community by the US National Aeronautics and Space Administration (NASA) using community-accepted algorithms and default parameterization. These included the products derived from the OC3M (ocean chlorophyll three-band algorithm for MODIS), GSM (Garver–Siegel–Maritorena model) and GIOP (generalized inherent optical properties) algorithms. We compared their temporal variations and spatial distributions in the northern South China Sea. We found that the three products appeared to capture general features such as unique winter peaks at the Southeast Asian Time-series Study station (SEATS, 18° N, 116° E) and the Pearl River plume associated blooms in summer. Their absolute magnitudes, however, may be questionable in the coastal zones. Additional error statistics using field measured Chl as the truth demonstrated that the three MODIS Chl products may contain high degree of uncertainties in the study region. Root mean square error (RMSE) of the products from OC3M and GSM (on a log scale) was about 0.4 and average percentage error (ε) was ~ 115% (Chl between 0.05–10.41 mg m−3, n = 114). GIOP with default parameterization led to higher errors (ε = 329%). An attempt to tune the algorithms based on a local coastal-water bio-optical data set led to reduced errors for Chl retrievals, indicating the importance of local tuning of globally-optimized algorithms. Overall, this study points to the need of continuous improvements for algorithm development and parameterization for the coastal zones of the study region, where quantitative interpretation of the current Chl products requires extra caution.

Based upon 14 field surveys conducted between 2003 and 2008, we showed that the seasonal pattern of sea surface partial pressure of CO2 (pCO(2)) and sea-air CO2 fluxes differed among four different physical-biogeochemical domains in the South China Sea (SCS) proper. The four domains were located between 7 and 23 degrees N and 110 and 12 degrees E, covering a surface area of 1344 x 10(3) km(2) and accounting for similar to 54% of the SCS proper. In the area off the Pearl River estuary, relatively low pCO(2) values of 320 to 390 mu atm were observed in all four seasons and both the biological productivity and CO2 uptake were enhanced in summer in the Pearl River plume waters. In the northern SCS slope/basin area, a typical seasonal cycle of relatively high pCO(2) in the warm seasons and relatively low pCO(2) in the cold seasons was revealed. In the central/southern SCS area, moderately high sea surface pCO(2) values of 360 to 425 mu atm were observed throughout the year. In the area west of the Luzon Strait, a major exchange pathway between the SCS and the Pacific Ocean, pCO(2) was particularly dynamic in winter, when northeast monsoon induced upwelling events and strong outgassing of CO2. These episodic events might have dominated the annual sea-air CO2 flux in this particular area. The estimate of annual sea-air CO2 fluxes showed that most areas of the SCS proper served as weak to moderate sources of the atmospheric CO2, with sea-air CO2 flux values of 0.46 +/- 0.43 mol m(-2) yr(-1) in the northern SCS slope/basin, 1.37 +/- 0.55 mol m(-2) yr(-1) in the central/southern SCS, and 1.21 +/- 1.48 mol m(-2) yr(-1) in the area west of the Luzon Strait. However, the annual sea-air CO2 exchange was nearly in equilibrium (-0.44 +/- 0.65 mol m(-2) yr(-1)) in the area off the Pearl River estuary. Overall the four domains contributed (18 +/- 10) x 10(12) g Cyr(-1) to the atmospheric CO2.

NASA; JPSS VIIRS; National Natural Science Foundation of China [41071223, 40976068, 41121091]; Ministry of Science and Technology of China [2013BAB04B00]; CNRS; INSU

Mangroves are valuable contributors to coastal ecosystems; knowledge of the dynamics of mangrove ecosystems is important in the context of global change. To obtain this knowledge, remote sensing is an indispensable means, yet it poses challenges since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this letter, we proposed a modified artificial immune algorithm (AIA), in which the antibodies represent the candidate solutions and the antigens are expressed by the fitness function. Multiclass coevolution was combined with the concept of clonal selection to ensure computation of an optimal clustering center in parallel for each land cover type. A cluster-center-oriented decimal encoding method for antibodies was adopted, and the inner class variance and the between-class difference together were used to formulate the fitness function. Furthermore, a design of the antibody solubility-based selection operator and nonuniform mutation operator was undertaken. Applying this modified AIA to a Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in southeastern China, we found that the AIA substantially improved classification accuracy over traditional methods, showing an overall accuracy of 90% (kappa coefficient = 0.88) and was capable to discern mangrove well (commission of 10% and omission of 22%).

We extended the quasi-analytical algorithm (QAA) architecture to analytically derive absorption coefficient of chromophoric dissolved organic matter (ag). Specifically, we used an empirical formula based on total absorption and particle backscattering coefficients to estimate and then remove detritus absorption coefficient (ad), and developed a scheme to use absorption coefficients at three wavelengths (412, 443, and 490 nm) for the separation of ag and aph (absorption coefficient of phytoplankton). The algorithm was tested using an in situ data set collected in the South China Sea and the Taiwan Strait and a global in situ data set—the NASA Bio-Optical Marine Algorithm Data set (NOMAD). Our results indicated that this new analytical algorithm for retrieving ag performed reasonably well with a mean absolute percentage error of approximately 45% for ag(412), while it also presented a satisfactory performance for aph and ad in both coastal and oceanic waters. Furthermore, the applicability of this new algorithm for general oceanographic studies was briefly illustrated by applying it to MODIS measurements over the Taiwan Strait and the shelf region near the Mississippi River delta. Nevertheless, more independent tests with in situ and satellite data are needed to further validate and improve this innovative approach.

Spectral remote sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio-optical properties. Since Rrs from in-situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs datasets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD dataset as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ datasets can improve the development and validation of bio-optical algorithms and its application to ocean color satellite data can improve the short- and long-term products by objectively excluding questionable Rrs data. This article is protected by copyright. All rights reserved.

Blooms of the green macroalga Ulva prolifera in the western Yellow Sea occurred every year since 2008, and they have been reported and studied extensively using a variety of means including remote sensing. However, to date, long-term bloom patterns have not been reported except for a few case studies showing examples in different years. Here, using MODIS observations and an objective method to perform statistical analysis, mean Ulva coverage in the western Yellow Sea has been derived and analyzed between 2007 and 2015 at both monthly and annual scales. On annual scale, mean Ulva coverage decreased after 2008, but increased rapidly after 2012 from 8km2 in 2012 to 116km2 in 2015 (the largest ever reported in history for this region). In the month of June the mean coverage increased from 18km2 in 2012 to 363km2 in 2015. Other than 2009 and 2010, the month of June showed maximum Ulva coverage in every year. These coverage estimates are significantly lower than previously reported values as they represent "pure" algae coverage after taking into account of partial pixel coverage. Several environmental factors were examined in an attempt to determine the reasons behind such long-term changes, yet the results are inconclusive, suggesting a strong necessity of further coordinated and multi-disciplinary researches.