It is challenging that accurate assessment of chlorophyll-a concentration by remote sensing in coastal waters. Chla concentration is commonly retrieved by blue-green ratio in open ocean waters. And this method is efficient in open ocean waters. But this method is confined when applied to coastal or inland waters, because of abundant variable CDOM and tripton. It is very difficult to retrieve chla of coastal or estuary waters because of overlap of absorption and backscattering caused by CDOM and tripton. Dall’Olmo et al put forward a semi-analytical retrieval model of chla, three-band model. The conceptual three-band model has been successfully applied to estimate chla in turbid and eutrophic waters by tuning the band position in accordance with the spectral properties.The aim of this paper is to testify the three-band model that could resolve this problem. The three-band model was tuned in accord with optical properties and the bands were optimized for accurate estimation. Finally, we found a good linear relationship between chlorophyll-a and three-band model, with the determination coefficient of 0.63 and the RMSE of 2.22μg·L^-1. Furthermore, the in situ spectral data was averaged to the band range of MERIS (band7, band9 and band10) and developed a simulated threeband model. A good linear relationship could be found between [(B7^-1-B9^-1)×B10] and chlorophyll-a, with the determination coefficient of 0.59 and the RMSE of 0.72μg·L^-1. The findings demonstrated that the three-band model of MERIS could be applied to retrieve chlorophyll-a concentration of Yantai coastal waters.

As the reflectivity is very low in near-infrared spectral band for water, it is easy to extract water bodies in remote sensing images with the use of NDVI (Normalized Difference Vegetation Index).The problem is that we always have to try several times to find the appropriate threshold to separate water bodies from land. In this paper, a particular method was developed to solve this problem by automatically determining the threshold that was used to extract the specific water body. We select both Chaohu Lake and Taihu Lake as the study regions. First, we generate NDVI image of the study region from GF-1 data after several pre-processing procedures. Then we resize the NDVI image to make it contain approximately the same number of water and land pixels. Because the NDVI value is lower for water than that for land, there will appear two peaks in the histogram which we derived from the resized NDVI image. The threshold locates at the lowest place between the two peaks can be chosen as the proper threshold used for land/water delineation. With the use of a reasonable threshold range we can finally get the threshold by calculating the minimum value in it, and extract the water body successfully.

Ulva prolifera, a kind of green macroalgae, is nontoxic itself, however, its bloom has bad effects on the marine environment, coastal scene, water sports and seashore tourism. Monitoring of the Ulva prolifera by remote sensing technology has the advantages of wide coverage, rapidness, low cost and dynamic monitoring over a long period of time. The GF-1 satellite was launched in April 2013, which provides a new suitable remote sensing data source for monitoring the Ulva prolifera. At present, segmenting image with a threshold is the most widely used method in Ulva prolifera extraction by remote sensing data, because it is simple and easy to operate. However, the threshold value is obtained through visual analysis or using a fixed statistical value, and could not be got automatically. Facing this problem, we proposed a new method, which can obtain the segmentation threshold automatically based on the local maximum gradient value. This method adopted the average NDVI value of local maximum gradient points as the threshold, and could get an appropriate segmentation threshold automatically for each image. The preliminary results showed that this method works well in monitoring Ulva prolifera by GF-1 WFV data.

The transmittance of upwelling radiance at the air-water interface is a critical quantity in ocean color remote sensing and is usually approximated as a constant (~0.54) for nadir-viewing geometries. Despite its important role, the radiance transmittance has never been measured and validated. In this paper, we present direct measurements of the spectral radiance transmittance in calm seas. The measurements were obtained by a customized instrument package equipped with two collocated radiometers. One radiometer measures the upwelling radiance just below the surface (L_u(0-)) while the other one directly records the water-leaving radiance (Lw) simultaneously. The ratio of measured L_w to L_u(0-) provides the transmittance. Our analyses suggest that the transmittance remains constant within ultraviolet and visible domain (350–700 nm) and is generally consistent with the theoretical approximations. In particular, the observed transmittance is within ±10% of the theoretical value for most portions of the spectral bands (350–600 nm). Within the red portion of the spectrum, the deviations are larger but are still less than 20%. The field observations suggest an optical closure is reached.

Primary production, PP, or the quantity of organic matter synthesized by phytoplankton per unit of surface and time, depends on the photo-synthetically available radiation absorbed by live phytoplankton, APAR. Computing APAR requires knowledge of the absorption coefficient of live phytoplankton and the total absorption coefficient, quantities that are difficult to retrieve accurately from satellite ocean-color data. In the proposed approach, APAR is estimated directly from a linear combination of marine reflectance in the PAR spectral range. Feasibility is demonstrated theoretically from simulations using a marine reflectance model, and experimentally using data collected at 19 biooptical stations during the February-March 2011 R/V Melville oceanographic cruise in the Southern Atlantic and Southeastern Pacific. Improvements in APAR accuracy are quantified in comparisons with estimates obtained from absorption coefficients or chlorophyll concentration determined from marine reflectance via standard satellite algorithms. The linear combination of marine reflectance is fairly robust to atmospheric correction errors. Due to the linear nature of the algorithm, their impact may be further reduced when using space- or time-averaged reflectance. The methodology is applied to actual MODIS imagery over the Southern Atlantic, and variability in the resulting APAR field is analyzed. The study suggests that determining APAR directly from marine reflectance has the potential to improve PP estimates from space.

This study proposes a method for estimating the spectral characteristics of aerosol scattering using the Korean Geostationary Ocean Color Imager (GOCI). GOCI three-hourly observations around noon are used to separate ocean and aerosol reflectance with the assumption that the aerosol amount changes but that the spectral shape of the aerosol scattering and ocean reflectance do not change within the three hours. No assumption of the spectral shape of the ocean color and aerosol scattering is required using this method, however it cannot use sometimes because of availability of the assumption. The results were validated using in situ optical measurements from the East China Sea in March 2013.

An algorithm is proposed to perform atmospheric correction of ocean-color imagery in the presence of semi-transparent clouds. The atmospheric “path” reflectance, due to scattering by molecules, aerosols, and droplets, absorption by aerosols, and reflection by the surface, including coupling terms, is modeled by a polynomial with three terms, i.e., three unknown coefficients. The marine reflectance is modeled as a function of chlorophyll concentration and a backscattering coefficient that accounts for scattering by non-algal particles (or deviation from the backscattering coefficient specified for typical phytoplankton), i.e., two additional unknown variables. The cloud transmittance, assumed constant spectrally, is estimated separately from top-of-atmosphere reflectance in the near infrared. The five unknowns are retrieved by an iterative, spectral matching scheme. The methodology, including the decomposition of the top-of-atmosphere signal and the modeling of the path reflectance, is evaluated theoretically and applied to actual MODIS imagery acquired over relatively thin clouds. Chlorophyll concentration is retrieved adequately under the clouds, and continuity is good between the cloudy and adjacent clear regions. Values are similar to those obtained with the SeaDAS algorithm in clear sky conditions, but cloud coverage is increased considerably. The algorithm is applicable operationally, but needs to be further evaluated in varied cloudy situations.

Mesoscale eddies are widely found in the ocean. They play important roles in heat transport, momentum transport, ocean circulation and so on. The automatic detection of mesoscale eddies based on satellite remote sensing images is an important research topic. Some image processing methods have been applied to identify mesoscale eddies such as Canny operator, Hough transform and so forth, but the accuracy of detection was not very ideal. This paper described a new algorithm based on watershed segmentation algorithm for automatic detection of mesoscale eddies from sea level anomaly(SLA) image. Watershed segmentation algorithm has the disadvantage of over-segmentation. It is important to select appropriate markers. In this study, markers were selected from the reconstructed SLA image, which were used to modify the gradient image. Then two parameters, radius and amplitude of eddy, were used to filter the segmentation results. The method was tested on the Northwest Pacific using TOPEX/Poseidon altimeter data. The results are encouraging, showing that this algorithm is applicable for mesoscale eddies and has a good accuracy. This algorithm has a good response to weak edges and extracted eddies have complete and continuous boundaries. The eddy boundaries generally coincide with closed contours of SSH.

The Suomi National Polar-orbiting Partnership (SNPP) was successfully launched on October 28, 2011. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP, which has 22 spectral bands (from visible to infrared) similar to the NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), is a multi-disciplinary sensor providing observations for the Earth’s atmosphere, land, and ocean properties. In this paper, we provide some evaluations and assessments of VIIRS ocean color data products, or ocean color Environmental Data Records (EDR), including normalized water-leaving radiance spectra nLw(λ) at VIIRS five spectral bands, chlorophyll-a (Chl-a) concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm Kd(490). Specifically, VIIRS ocean color products derived from the NOAA Multi-Sensor Level-1 to Level-2 (NOAA-MSL12) ocean color data processing system are evaluated and compared with MODIS ocean color products and in situ measurements. MSL12 is now NOAA’s official ocean color data processing system for VIIRS. In addition, VIIRS Sensor Data Records (SDR or Level- 1B data) have been evaluated. In particular, VIIRS SDR and ocean color EDR have been compared with a series of in situ data from the Marine Optical Buoy (MOBY) in the waters off Hawaii. A notable discrepancy of global deep water Chl-a derived from MODIS and VIIRS between 2012 and 2013 is observed. This discrepancy is attributed to the SDR (or Level-1B data) calibration issue and particularly related to VIIRS green band at 551 nm. To resolve this calibration issue, we have worked on our own sensor calibration by combining the lunar calibration effect into the current calibration method. The ocean color products derived from our new calibrated SDR in the South Pacific Gyre show that the Chl-a differences between 2012 and 2013 are significantly reduced. Although there are still some issues, our results show that VIIRS is capable of providing high-quality global ocean color products in support of science research and operational applications. The VIIRS evaluation and monitoring results can be found at the website: http://www.star.nesdis.noaa.gov/sod/mecb/color/index.html.

This paper is aiming at the problem that the MODIS surface reflectance product (MOD09) does not offer an accurate aerosol correction for inland water, for the constraints of MODIS atmospheric correction algorithm. In-situ data collected in Taihu Lake and Yuqiao Reservoir were used to validate and assess the quasi-synchronous MOD09 product. The results showed that there is linear relationship on the whole between MOD09 bands and in-situ data in inland water with acceptable deviation level. The reason for the deviations is analyzed primarily and a simple correction for MOD09 product in Lake Taihu is introduced based on bands calculation. The results also illustrated that it is possible to monitor inland water quality globally with MOD09 product by providing validation evidence in typical inland waters. And it would be most accurate by using bands ratio algorithm for the water quality retrieval using MOD09. The validation is also important to improve atmospheric algorithms of MODIS.

The National Satellite Meteorological Center (NSMC)/CMA global sea surface temperature (SST) data are derived from measurements made by the Visible and Infrared Radiometer (VIRR) on board the FY-3 series polar orbiting satellites. Quality controlled in situ data from iQUAM (STAR/NESDIS/NOAA) is used in FY-3B/C VIRR matching procedure. The monthly matchup database (MDB) is created from FY-3C VIRR measurements paired with coincident SST measurements from buoys since November 2013. The satellite sensor’s brightness temperature and buoy SST pairs are included in the MDB if they are coincident within 3km in space and 1 hour in time. Least-Square Regression is used for estimating the first-guess coefficient and SST residuals. Outliers are removed using Median±2STD, and the final coefficients of robust regression are estimated. A set of SST regression formalisms are tested base on NOAA- 19/AVHRR 2010 MDB. The test shows that, for daytime split-window nonlinear SST (NLSST) is the best, for nighttime triple-window MCSST (TCSST) is the best, which is agree with STAR/NESDIS’s. The same regression analysis method also used on FY-3C/VIRR MDB. Compare with the three daytime SST algorithms and five nighttime SST algorithms, the best algorithm to retrieve FY-3C/VIRR SST for daytime is NLSST and for nighttime is TCSST. Compare with the coefficients of nighttime algorithm TCSST, it shows that for FY-3B/C VIRR SST, the contribution of 3.7μm band is smaller than split-window bands. The performance of 3.7μm band of FY-3C/VIRR is better than FY-3B/VIRR, but worse than NOAA-19/AVHRR.

Light absorption properties of colored dissolved organic matter (CDOM) in adjacent waters of the Changjiang Estuary were investigated during the summer of 2013. CDOM absorption showed a substantial portion of the total absorption and clearly dominant among most investigation stations. It generally decreased from the northwest to the southeast, which controlled by physical mixing of fresh water and seawater as was indicated by a conservative behaviour of CDOM. CDOM absorption sharply increased during phytoplankton blooms. Similarly, dissolved organic carbon (DOC) also peaked during blooms period. However, DOC exhibited a more complex behavior relative to a simple conservative mixing, possibly attributed to multiple origins of DOC. CDOM absorption and DOC co-varied to some degree, implying a potential way of DOC estimation from CDOM absorption. However, more detailed information such as CDOM and DOC composition and more validation data were required to obtain a stable CDOM – DOC pattern. Lastly, empirical algorithms with limited data were developed to retrieve CDOM absorption. Further validation of the algorithms were needed when they were to be commonly applied.

Time series of satellite-derived sea surface chlorophyll concentration (SSC) from 2002 to 2012 were used to investigate the phenology of phytoplankton bloom in the Sergipe-Alagoas Basin, located in Northeast Brazil, Western Tropical Atlantic. The seasonal phytoplankton cycle is the dominant mode of temporal variability. The use of a Gaussian function to fit the temporal variability of SSC allowed the characterization of the timing and magnitude of the annual phytoplankton bloom in the slope and continental shelf areas. Modeled SSC showed a few differences in relation to mean MODIS-derived temporal curves. The maximum error was 0.14 mg.m^-3 in September on the shelf and 0.006 mg.m- 3 in February on the slope. In both areas, SSC data showed that the maximum surface bloom occurs in June, having initiated in March. This cycle is typical of tropical waters of low latitudes where bloom is initiated at lower vertical stability of the water column allowing nutrients from deeper layers to fertilize usually poor and warm waters of the mixed layer. High rainfall increases the continental drainage into the shelf in autumn-winter, which may affect the timing of bloom. However, the flow regulation of the most important river in the region (Sao Francisco River) decreases the potential impact of river inflow in the coastal region. As the shelf and slope showed very similar patterns, it is likely that the processes of wind mixing and water heating/cooling are the most determining factors for the annual cycle of phytoplankton bloom in this region.

Ocean color of tropical lagoons is dependent on bathymetry and bottom type, as well as input of coastal living and mineral particles and chromophoric dissolved organic matter (CDOM). The New Caledonia lagoon lies in the Southwestern Tropical Pacific around 21° 30’S and 166° 30’E, with a great marine biodiversity in UNESCO Heritage coral reefs, benthic sea grass, and benthic communities. They are largely connected to the open ocean in the southern and eastern parts, but only by narrow passes in the southwest part. The trophic state is linked to spatial variations in flushing times. High run offs due to rain carrying abundant chromophoric dissolved organic matter (CDOM) and particle loads may greatly impact the functioning of ecosystems while rivers and sewage effluents may induce localized impacts. Two oceanographic cruises (CALIOPE 1 in 2011 and CALIOPE 2 in 2014) were carried out off the Eastern Coast of New Caledonia during a calm dry period and during high winds, respectively. Multi- and hyper-spectral marine reflectance was measured with a SIMBADA instrument and a TRIOS radiometer system, together with inherent optical properties (total and CDOM absorption coefficients with a PSICAM, in situ absorption and scattering with an AC9, backscattering with a Hydroscat-6). Fluorescence of CDOM (EEM/PARAFAC) was measured on collected 0.2 μm filtered samples. In 2014, Satlantic and FieldSpec hyper-spectral radiometers were available for in-water profiling of upwelling radiance and downwelling irradiance and above-water reflectance measurements, respectively. Inherent and apparent optical data from the two cruises are compared and used to estimate ocean color algorithms performance and evaluate a Linear Matrix Inversion method, providing tools for remote sensing on this highly under-sampled coastal region of New Caledonia.

Spermonde islands located in the Makassar Strait, South-West side of the peninsula of Sulawesi Island. Kondongbali island as one of eight small island in outer zone Spermonde Archipelago is study site of this research. Images of Landsat MSS, Landsat TM, Landsat ETM, Landsat ETM+, and Landsat 8 data were used to examine changes on seagrass on the small islands for forty one year from 1972 to 2013. The image fusion technique were done to combain relevant information for two images into single image to produce the high spatial resolution and fill gap processing was done on Landsat ETM+ SLC-off. Subsequently, a multi-component change detection procedure was applied to these indices to define changes. Seagrass cover classification devided into seagrass percent cover 0-24.9%, 25-59,9%, <60%), and sand. The percentage of seagrass change based on the image classification in outer zone islands from 1972 to 2013 has decrease. Eventhough, seagrass coverage as observed in Kondongbali Island is not so serious compared to other ecosystems (coral reef).. This research will be contributed to baseline information on spasial dynamic of coastal benthic communities in tropical area.

A dual-wavelength lidar fluorosensor system for fast diagnosis of chromophoric dissolved matter (CDOM) in water in the Pearl River estuary was discussed. The laser-induced fluorescence (LIF) system used two lasers as excitation sources with wavelength at 355nm and 532 nm, and a hyperspectral CCD spectrometer was used to record the fluorescence signal. The overlapping fluorescence spectra of water Raman scattering and CDOM were separated with fitting bi- Gaussian of the least squares method. High correlation was observed between concentration of CDOM and fluorescence normalized to water Raman scattering. The in situ results demonstrated rapid characterization of dissolved organic matter can be done by the LIF technique.

Satellite remote sensing has been successfully employed to monitor and detect the increasing incidence of harmful algal blooms (HABs) under various water conditions. In this study, to establish a comprehensive monitoring system of HAB outbreaks (particularly Cochlodinium polykrikoides blooms) in the southern coast of Korea (SCK), we tested the several proposed red-tide detection methods using SeaWiFS and MODIS ocean color data. Temporal and spatial information of red tide events from 2002 to 2013 were obtained from the National Fisheries Research and Development of Korea (NFRDI), which were matched with synchronously obtained satellite-derived ocean color data. The spectral characteristics of C. polykrikoides red tides were that increased phytoplankton absorption at 443 nm and pigment backscattering 555 nm resulted in a steeper slope between 488 and 555 nm with a hinge point at 488 (or 490) nm. On the other hand, non-red tide water, typically were presented by broader radiance spectra between the blue and green bands were associated with reduced pigment absorption and backscattering. The analysis of ocean color imageries that captured C. polykrikoides red tide blooms showed discolored waters with enhanced pigment concentrations, high chlorophyll, fluorescence, absorption at 443 nm. However, most red tide detection algorithms found a large number of false positive but only a small number of true positive areas. These proposed algorithms are not useful to distinguish true red tide water from complex non-red tide water. Our proposed method substantially reduces the false signal rate (false positive) from strong absorption at short wavelengths and provide a more reliable and robust detection of C. polykrikoides blooms in the SCK from the space.

Comparative analysis of chlorophyll-a concentrations obtained using flow fluorometric measurements carried out on the board vessel, and chlorophyll-a concentrations calculated by MODIS-Aqua and VIIRS satellite data of ocean color was conducted. The ship data were adjusted to standard spectrophotometric measurements and vertical depth distribution of phytoplankton. Investigations were done in the Bering and Chukchi Seas, De Long Strait in August 2013. In waters of the Russian Eastern Arctic satellite radiometers versus ship-borne measurements of chlorophyll-a concentration were overestimated, which was associated with relatively high content of colored dissolved organic matter at upper layers. In De Long Straight satellite estimation didn't reflect overall viewing on the depth integrated chlorophyll-a concentration, as in this area the bulk of the phytoplankton with chlorophyll-a concentration around 10-20 mg/m³ was located in the depth layer with 3-5% illumination relative to the surface light level. In the analyzed waters VIIRS gave more accurate measurements of chlorophyll-a concentration as compared to using MODIS-Aqua satellite data with processing procedures № 2013.1.

The physical forcing and biological response are highly variable over multi-time scales ranging from biennial to interdecadal in the tropical Pacific Ocean. Satellite provides a systematic view of the coupled biological-physical variability over large spatial scales. Based on the satellite observation data from 1985 to 2011, we analyzed the multi-timescale variabilities of the physical ocean parameters (sea surface temperature, sea level anomaly, wind, rainfall) and the biological ocean parameter (chlorophyll-a) as well as the linkages between them over the tropical Pacific Ocean. The goal of this study was to investigate the multi-timescale variability and spatial-temporal association patterns of the physical-biological paramters in the tropical Pacific Ocean of the year from 1985 to 2011.

Evolution of river delta is highly related to the deposition and re-suspension of sediments. At the interacting zone of fresh river discharge and seawater, suspended sediments concentration (SSC) can vary sharply from a few mg/L to thousands of mg/L; thus, mapping the distribution of SSC will provide the first information about sediments transportation. The high spatial resolution (30 m) and high revisit frequency (2 day) of CCD imager on board the Chinese environment-monitoring satellite constellation: HJ-1A and HJ-1B, enable an effective observation of the fine dynamics of suspended sediments. In this work, three intensive cruises in the flooding season and dry season of Yellow River, were carried out to explore the SSC retrieval algorithms on the basis of HJ-1 CCD imageries. Quasi-simultaneous in-situ SSC data were collected with the pass of HJ-1 over the Yellow River Estuary and its vicinity waters, and a local empirical retrieval algorithm of SSC was established against the TOA (top of atmosphere) reflectance of HJ-1 CCD bands with the correction of Rayleigh scattering. This algorithm can be applied to very turbid waters with thousands of mg/L of SSC.

Satellite-observed daily sea level anomaly (SLA), sea surface temperature (SST) and sea surface salinity (SSS) are used to determine the impact of the typhoon Prapiroon (2012) on the sea surface. The typhoon, Prapiroon, has a unique track when it went through the area (126°E-133°E, 17°N-25.5°N), where it influenced the sea greatly. For example, the lowest SST in this area was 25.2°C on October 8 before it entered, while the SST dropped to 20.7 °C on October 15. On the other hand, the lowest SLA dropped from -20 cm (on October 8) to -53 cm (on October 29). Accompanied with this, the SSS increased about 0.8-0.9 psu in some region. This is qualitative but quantitative agree with the Argo data of 0.2 psu SSS increase. One possible reason is the precipitation of typhoon, as the SSS data by Aquarius are easily affected by precipitation. And such significant SST cooling and SSS increasing were mainly due to vertical mixing, caused by longterm strong wind stirring brought by Parpiroon. We find that both the cold patch and eddies were transported westward. The horizontal advection took the cold water to 100 km away, this non-local effect could have notable impact on ocean dynamics and bio-physical processes. In addition we analyzed the possible factors of the temperature and salinity decreasing below the thermocline by calculating the Ekman pumping velocity due to the typhoon. It reveals that this is caused by Ekman pumping and upwelling in the cyclonic eddy, and Ekman pumping is the main factor.

In this paper, the method of monitoring coastal areas affected by thermal discharge of nuclear plant by using remote sensing techniques was introduced. The proposed approach was demonstrated in Daya Bay nuclear plant based on HJ-B IRS data. A single channel water temperature inversion algorithm was detailed, considering the satellite zenith angle and water vapor. Moreover the reference background temperature was obtained using the average environmental temperature method. In the case study of Daya Bay nuclear plant, the spatial distribution of thermal pollution was analyzed by taking into account the influence of tidal, wind and so on. According to the findings of this study, the speed and direction of the ebb tide, is not conducive to the diffusion of thermal discharge of DNNP. The vertically thermal diffusion was limited by the shallow water depth near the outlet.

Ship wake and oil slick signatures imaged by both multispectral ETM (enhanced thematic mapper) onboard Landsat-7 (L7) and multi-polarization SAR (synthetic aperture radar) onboard Radarsat-2 (R2) were analyzed in this paper. Spectral analysis used all the available band data together, not as an image but as a set of spectral features. Multipolarization imagery offers scattering information of the ground surface. Ship wake signatures were readily detected by local Hough transmission. Oil slicks are expected to benefit from spectral reflectance deviation from the background water color or phase differences between polarization channels. The appearance of ship wake in an area surrounded by oil slicks was also considered, although we have not yet conclusion at this moment. Comparing to our former efforts with polarized radar data only, the combined view by multispectral and multi-polarization sensors indicated detailed radiation and scattering characteristics of ship wake and oil slick signatures.

A relationship between depolarization ratio and surface concentration of particulate organic carbon (POC) is developed from the NASA SeaWiFS Bio-optical Archive and Storage System (SeaBASS) in situ measurements and the Cloud- Aerosol Lidar with Orthogonal Polarization (CALIOP) active lidar measurements. This relationship provides an algorithm for estimating global POC from satellite or airborne polarization lidar measurements. Application of this relationship to CALIOP data indicates that the estimates of POC ranges from about 3.3 mg/m³ within the South Pacific Subtropical Gyre to 1.2×10³ mg/m³ in the area near land are in good agreement with Moderate Resolution Imaging spectroradiometer (MODIS) POC products. Our results present depolarization ratio as a valuable tool for evaluating global POC predictions in ocean ecosystem. The application of the algorithm to a 7-year of CALIOP depolarization ratio mean values revealed patters of seasonal and interannual variability of POC. By comparing the results averaged over the entire study region and entire season for each year separately, we found that the lowest POC occurred in 2013 and the highest POC occurred in 2008.

The Information Telecommunication System "Solaris" was created to solve the actual problems of navigation. The system allows analyzing an operational local and synoptic hydrometeorological situation on a vessel route to issue recommendations to navigators about the optimization of the movement from the point of view for the safety of navigation and economic efficiency. ITS "Solaris" represents a combination of two hardware-software complexes one of which is fitted on the vessel, and the other one ashore. The ship complex is based on the use of regular ship radar station to which AD converter and the computer with the software is connected. The coastal complex is a server that collects, processes, analyzes stores and transmits satellite and ship information. To operate the system on a particular ship the proposed model calculates the speed of the vessel, depending on the direction and speed of meteorological parameters such as wind, waves and currents, of ice conditions. The system can be used in the initial planning of the route on the basis of operational and climatic data according to the seasonal variability of the weather conditions. The created system improves the safety of navigation and increase economic efficiency.

This paper primarily proposes simple C-band empirical models between ocean wave significant wave height (Hs) and SAR azimuth cutoff using RADARSAT-2 fine quad-polarization mode data. The empirical models of VV, HH and VH polarization relate the Hs to the cutoff divided by range-velocity-ratio with approximatly linear relationships. Compared from NDBC buoy data, retrieved Hs by empirical models have the root mean square (Rms) errors of 0.62 m, 0.52 m and 0.70 m for VV, HH and VH polarization, respectively. Particularly, HH polarization presents the best Hs retrieval performance.

The classic atmospheric correction algorithm, routinely applied to second-generation ocean-color sensors such as SeaWiFS, MODIS, and MERIS, consists of (i) estimating the aerosol reflectance in the red and near infrared (NIR) where the ocean is considered black (i.e., totally absorbing), and (ii) extrapolating the estimated aerosol reflectance to shorter wavelengths. The marine reflectance is then retrieved by subtraction. Variants and improvements have been made over the years to deal with non-null reflectance in the red and near infrared, a general situation in estuaries and the coastal zone, but the solutions proposed so far still suffer some limitations, due to uncertainties in marine reflectance modeling in the near infrared or difficulty to extrapolate the aerosol signal to the blue when using observations in the shortwave infrared (SWIR), a spectral range far from the ocean-color wavelengths. To estimate the marine signal (i.e., the product of marine reflectance and atmospheric transmittance) in the near infrared, the proposed approach is to decompose the aerosol reflectance in the near infrared to shortwave infrared into principal components. Since aerosol scattering is smooth spectrally, a few components are generally sufficient to represent the perturbing signal, i.e., the aerosol reflectance in the near infrared can be determined from measurements in the shortwave infrared where the ocean is black. This gives access to the marine signal in the near infrared, which can then be used in the classic atmospheric correction algorithm. The methodology is evaluated theoretically from simulations of the top-of-atmosphere reflectance for a wide range of geophysical conditions and angular geometries and applied to actual MODIS imagery acquired over the Gulf of Mexico. The number of discarded pixels is reduced by over 80% using the PC modeling to determine the marine signal in the near infrared prior to applying the classic atmospheric correction algorithm.

The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color satellite sensor launched in June 2010 on board the South Korean Communication, Ocean and Meteorological Satellite (COMS). The GOCI has a local coverage area of the western Pacific Ocean including Bohai, Yellow Sea and East Sea of China. Jiaozhou Bay is a semienclosed basin in the western part of the Yellow Sea, which is an important representative of gulf ecosystem in the North Temperate Zone. The GOCI data can provide useful information with an-hour temporal and 500-m spatial resolutions for monitoring oceanic and atmospheric process in Jiaozhou Bay. The performance of the atmospheric and optical algorithms of GOCI was evaluated by comparing with the simultaneous data from MODIS/Aqua. The match-up products include remote sensing reflectance (R_rs) and chlorophyll-a concentration ([chl-a]) and they were derived by GDPS software for GOCI data and SeaDAS software for MODIS and GOCI data, respectively, using default atmospheric correction and bio-optical algorithms. Our analyses show that GOCI products (i.e., R_rs(490), R_rs(555) and [chl-a]) are compared well with MODIS products. GOCI shows a potential capability of oceanography investigations in Jiaozhou Bay and Qingdao coastal area. The consistency of R_rs between GOCI and MODIS is relatively good but significant difference was observed in [chl-a] especially in Jiaozhou Bay. Moreover, it is suggested to improve GOCI standard atmospheric correction algorithm for high turbid water and cloud detection to increase data utilization.

Radiative transfer modelling in atmosphere, water, and on the air-water surface was used to create an algorithm and computer code for satellite monitoring Chinese estuarine and coastal waters. The atmospheric part of the algorithm is based on the Reference Evaluation of Solar Transmittance (REST) model for calculation of optical properties of the atmosphere from the top of the atmosphere to the target; for modelling optical properties from target towards satellite's sensor, an optical reciprocity principle has been used. An algorithm uses estimates derived from three different sources: 1) the MODIS-based software; 2) radiative transfer equations, and 3) well-known empirical relationships between measured parameters and optical depths and transmittances for such atmospheric components as molecules, aerosols, ozone, nitrogen dioxide, precipitable water vapor and uniformly mixed gases. Using this model allowed us to derive a reliable relationship relating an important parameter, the diffuse-to-global solar incoming irradiance ratio, to the aerosol optical thickness, solar zenith angle and wavelength. The surface and underwater parts of the algorithm contained theoretical and semi-empirical relationships between inherent (such as absorption, scattering and backscattering coefficients) and apparent (remote-sensing reflectance and diffuse attenuation coefficient, Kd) optical properties, and suspended sediment concentration (SSC) measured in the Yangtze River Estuary and its adjacent coastal area. The first false colour maps of SSC and K_d demonstrated a well accordance with the multi-year field observations in the region, and suggest promise for use of this algorithm for the regular monitoring of Chinese and worldwide natural waters.

Secchi disk depth (SDD) is an important optical property of water related to water quality and primary production. The traditional sampling method is not only time-consuming and labor-intensive but also limited in terms of temporal and spatial coverage, while remote sensing technology can deal with these limitations. In this study, models estimating SDD have been proposed based on the regression analysis between the HJ-1 satellite CCD image and synchronous in situ water quality measurements. The results illustrate the band ratio model of B3/B1 of CCD could be used to estimate Secchi depth in this region, with the mean relative error (MRE) of 8.6% and root mean square error (RMSE) of 0.1 m, respectively. This model has been applied to one image of HJ-1 satellite CCD, generating water transparency on June 23, 2009, which will be of immense value for environmental monitoring. In addition, SDD was deeper in offshore waters than in inshore waters. River runoffs, hydrodynamic environments, and marine aquaculture are the main factors influencing SDD in this area.

The propagation of millimeter wave through offing fog is affected by the size distribution, droplet shape, dielectric parameters and frequency. Based on the Gamma distribution of droplets size, the property of offing fog is analyzed. Using the Monte Carlo method to establish the propagation model, the propagation process of millimeter wave is simulated at different frequencies. The transmission and attenuation of millimeter wave through offing fog at different frequencies （35、94、140、220GHz） and different sea conditions is obtained which can reflect the influence of offing fog on the propagation of electromagnetic wave.

Global monitoring of marine surfactants is important for understanding the heat, particle, momentum, and gas exchanges between the ocean and atmosphere. Observing the sunglint, i.e., the specular reflection between Sun, sea surface, and sensor, which intensity depends on the geometry of acquisition, has the potential to improve surfactants detection. The basis of the approach is that surfactants modify surface roughness, therefore the probability distribution of observing reflection by wave facets in the sensor field-of-view. An automatic method to detect surfactants from optical imagery under Sun glint conditions is presented. The method consists in comparing modeled normalized radiance (for clean water and surfactant contaminated water) with observed values. Application to MODIS imagery acquired off the Southwest Coast of Brazil in January 2003 demonstrates the method’s feasibility, revealing the presence of biogenic films formed by Trichodesmium colonies. The presence of the N2 fixing cyanobacteria was verified independently from in-situ measurements and ocean color remote sensing. An advantage of the glint-based method over detection methods using SAR imagery is the good spatial resolution (250 m) and the capability to detect surfactants at low wind speeds (< 2 m/s), impractical to SAR sensors due to the absence of Bragg scattering. Discriminating the type of surfactant, i.e., biogenic versus mineral, requires additional information, such as spectral marine reflectance.

El Niño continues the most important coupled ocean-atmosphere phenomenon to cause global climate variability on seasonal to inter annual time scales. The first independent spatial mode which carried out by EOF analysis of tropical and north Pacific sea surface temperature (SST) for the period of 1985-2009 in AVHRR dataset is found to be associated with well-known regional climate phenomena: the El Niño. This paper addresses the need for a reliable El Niño index that allows for the historical definition of El Niño events in the instrumental record back to 1985-2009 with a new perspective. For quantitative purposes, possible definitions are explored that match the El Niño identified historically in 1985-2009, and it is suggested that an El Niño can be said to occur if difference of sea surface temperature (SST) anomalies between the tropical and north Pacific exceeds 0.6 times standard deviation for 5 months or more. An advantage of such a definition is that it combines the characteristics between tropical and north Pacific. Through seasonal analysis of SST in El Niño event, we found that the El Niño events are almost beginning in boreal spring or perhaps boreal summer and peak from November to February. It provides a more complete and flexible description of the El Niño phenomenon than single area in tropical Pacific.

The flow of marine debris in the ocean causes extensive damage to coastal environments. In addition to local rivers, a large proportion of the marine debris that washes up along the coastline of the Sea of Japan originates in neighboring countries. It is considered important to understand the flow of marine debris in the Sea of Japan for environmental research purposes and for international relations. This study describes the results of monitoring marine debris flows using multi-spectral satellite images. The small size of most marine debris means that it cannot be confirmed directly, even when using high spatial resolution satellite imagery. Thus, to extract candidate pixels containing possible marine debris, pixels with spectra that differ from those of the surrounding ocean and the wave crests were identified. As a first step towards monitoring marine debris, a method for identifying marine debris floating on the Sea of Japan has been proposed using two-dimensional scatter diagrams of satellite spectral bands.

The spatial and temporal variability of sea surface temperature (SST) in the Zhejiang Coastal Waters of the East China Sea is investigated with long time series of cloud-gap free SST imagery. The SST dataset is reconstructed with Data INterpolating Empirical Orthogonal Function (DINEOF) method using daily MODIS Aqua SST images. An EOF analysis technique is further used to reveal the regional temporal and spatial SST variability at seasonal to inter-annual timescales. The first three EOF modes cumulatively account for more than 84% of the total SST variance. The first mode explains 71.5% of the total SST variability and it is dominated by an annual cycle. The second mode accounts for 10.3% of the SST variance and it reveals a warm/cold pattern in the coastal shelf sea. The third mode, accounting for 2.4% of the SST variance, indicates a pattern describing the synoptic-scale variability.

The NOAA Sea-viewing Data Analysis System (NOAA-SeaDAS) is an Interactive Data Language (IDL)-based satellite data visualization, analysis, and processing system based on the version 6.4 of the NASA’s Sea-viewing Wide Field-ofview (SeaWiFS) Data Analysis System (SeaDAS) released in 2012. NOAA-SeaDAS inherited all the original functionalities of SeaDAS 6.4 and was upgraded with many new functions and new sensor supports, particularly the support of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (SNPP). The main goal of the NOAA-SeaDAS development is primarily in support of NOAA ocean color team’s calibration and validation activities. The current version of NOAA-SeaDAS can visualize, analyze, and process VIIRS Sensor Data Records (SDR or Level-1B data) produced by the NOAA Interface Data Processing System (IDPS), ocean color Environmental Data Records (EDR or Level-2 data) produced by the NOAA Multi-Sensor Level-1 to Level- 2 (MSL12) ocean color data processing system, and Level-3 data binned or mapped from Level-2 data produced by NOAA-MSL12. NOAA-SeaDAS is currently serving an active IDL user group at NOAA and will serve other institutions and universities in the future. The goal is to allow various scientific users to visualize, analyze, and process VIIRS data from Level-1B through Level-2 and Level-3. In addition, NOAA-SeaDAS can also visualize satellite images from the Korean Geostationary Ocean Color Imager (GOCI), as well as many other satellite ocean color sensors, e.g., SeaWiFS, the Moderate Resolution Imaging Spectroradiometer (MODIS), etc. NOAA-SeaDAS is under constant development to create new system functionalities and enhance user experience. With constantly increasing volume in the global ocean color data archive, NOAA-SeaDAS will play an important role in support of global marine environment data analysis and various scientific applications.

The Visible Infrared Imaging Radiometer Suite (VIIRS) ocean color (OC) Environmental Data Records (EDR) (or Level-2 data) are one of the most important product sets derived from the visible and near-infrared (NIR) moderate resolution (M) bands of VIIRS Sensor Data Records (SDR or Level-1B) data. NOAA Interface Data Processing System (IDPS) only produces the operational SDR from Raw Data Records (RDR or Level-0 data) in the forward mode (no reprocessing). As the ocean color EDR are highly sensitive to the quality of the SDR, while the forward processed SDR by nature have large uncertainty, VIIRS SDR need to be reprocessed from RDR periodically with improved instrument calibration algorithm and look-up tables (LUTs), primarily the F-factors LUTs (F-LUTs), for ocean color EDR. The NOAA VIIRS ocean color team has been building the capability of processing the RDR to SDR efficiently for VIIRS entire mission as well as any selected areas with any selected time periods. In this paper, we describe our effort to develop two approaches in VIIRS RDR to SDR data processing. The first one is to process the raw RDR data into SDR using an internal offline-processing tool, which is based on Joint Polar Satellite System (JPSS) Algorithm Development Library (ADL) with updated calibration LUTs. This tool allows one to select the IDPS/ADL version and input auxiliary/ancillary data for the VIIRS RDR to SDR reprocessing. The second approach, however, is for the case where only the F-LUTs are updated (most cases). We have developed a ratio approach, which is based on the linear relationship between the SDR radiance/reflectance and the F-factors. This approach requires only about one hundredth computational effort compared to the ADL approach and can also significantly reduce the storage requirements. With the ratio approach, the updated SDR are generated from the old SDR and only the bands that are interested are processed.

Secchi depth, an important optical characteristic of water, is a useful index of water quality and is widely used in many environmental studies. The Yellow Sea and the East China Sea are typical case 2 waters, where concentrations of suspended matter, phytoplankton pigments, and colored dissolved organic matter are higher than those in other open oceans. Two cruises were conducted to investigate the water optical characteristics in the Yellow Sea and the East China Sea in May and June, 2009. 62 water sampling stations of Secchi disk depth were measured in situ in day time, and their values were in the range of 0.0112 to 15.6 m with the mean of 6.72 m and a standard deviation of 3.18 m. In this paper, we adapted a quasi-analytical algorithm to estimate the Secchi depth from satellite ocean data in both coastal and oceanic waters. The development of the algorithm is based on the use of in situ measurements and 8-day MODIS-Aqua remote sensing reflectance data with 4 km spatial resolution. More than 39 matchups were compiled for the MODIS sensor by spatial-temporal matching. The comparison between water transparency retrievals from remote sensing data and in situ measurements yields showed that the determination coefficient was 0.60 and a root mean square error of 8.4 m. This study suggests that the quasi-analytical algorithm provide a promising result on in situ data. In the future, maps of ocean transparency for this area will be derived using this algorithm.

Colored Dissolved Organic Matter (CDOM, or yellow substance) exists in all natural waters. It can be used as evaluation indexes for inland water pollution condition. Remote sensing data used for CDOM inversion has its significant advantages, but the inversion method usually has obvious regional limitations. At present, there is little CDOM studies have been carried out to the waters in north China. Yuqiao Reservoir, which is in northern Tianjin, was chosen as the study area, and CDOM was inverted through empirical method for the first time. The data used in this paper was the spectral reflectance data collected on September 24 and 25, 2013 over the 23 sampling points in Yuqiao Reservoir and CDOM concentrations (which is represented by the absorption coeffiecnet of CDOM at 440nm, ^aCDOM(440)) of each sampling points. Among the 23 sampling points, 16 points were selected randomly as training samples, and the remaining 7 points were for accuracy test. Four ratios, as R_rs(412)/R_rs(551), R_rs(443)/R_rs(551), R_rs(490)/R_rs(551) and R_rs(531)/R_rs(551) were used to carry out linear regression with aCDOM(440). At the same time, the linear regression was also taken between the logs base 10 of the four ratios and log(^aCDOM(440)).Then 8 inversion models were built. The performance of the model based on log(R_rs(490)/R_rs(551)) and log(^aCDOM(440)) was the best. The correlation coefficient R was 0.65. The Root Mean Square Error (RMSE) was 0.088 and the average relative error (σ) was 11.9%. It showed that the precision of using the ratio of the Remote sensing reflectance of the blue and green band to build inversion models for Yuqiao Reservoir was good, and the method was worth popularization and utilization.

Space-borne imaging spectrometer can be used for ocean color remote sensing and coastal zones monitoring. To meet the demand of quantitative remote sensing inversion, the imaging spectrometer needs precisely spectral calibrated before launching. However, due to the changes of space environment and degradation of the instrument performance, maybe the central wavelength or spectral width of each channel in the spectrometer varies after launching. Thus, on-orbit spectral characteristics check even recalibration for the instrument is necessary. The imaging spectrometer is capable of channel programmable and the spectral width of every pixel line of the array CCD is 2.5nm. In the field imaging experiments, the instrument showed an atmospheric O2 absorption peak near 765 nm. This characteristic peak coordinating with Pr-Nd glass, which belongs to the on-board calibration mechanism, can be used for on-orbit spectral calibration. Also, it can be used independently to calibrate the central wavelength of each channel during the earth observation. Empirical research shows that this method is not only simple and applicable but also can achieve precision of 1.25 nm and meet the practical requirements.

In recent years great progress has been made in global mapping of phytoplankton from space. Two main trends have emerged, the recognition of phytoplankton functional types (PFT) based on reflectance normalized to chlorophyll-a concentration, and the recognition of phytoplankton size class (PSC) based on the relationship between cell size and chlorophyll-a concentration. However, PFTs and PSCs are not decorrelated, and one approach can complement the other in a recognition task. In this paper, we explore the recognition of several dominant PFTs by combining reflectance anomalies, chlorophyll-a concentration and other environmental parameters, such as sea surface temperature and wind speed. Remote sensing pixels are labeled thanks to coincident in-situ pigment data from GeP&CO, NOMAD and MAREDAT datasets, covering various oceanographic environments. The recognition is made with a supervised Support Vector Machine classifier trained on the labeled pixels. This algorithm enables a non-linear separation of the classes in the input space and is especially adapted for small training datasets as available here. Moreover, it provides a class probability estimate, allowing one to enhance the robustness of the classification results through the choice of a minimum probability threshold. A greedy feature selection associated to a 10-fold cross-validation procedure is applied to select the most discriminative input features and evaluate the classification performance. The best classifiers are finally applied on daily remote sensing datasets (SeaWIFS, MODISA) and the resulting dominant PFT maps are compared with other studies. Several conclusions are drawn: (1) the feature selection highlights the weight of temperature, chlorophyll-a and wind speed variables in phytoplankton recognition; (2) the classifiers show good results and dominant PFT maps in agreement with phytoplankton distribution knowledge; (3) classification on MODISA data seems to perform better than on SeaWIFS data, (4) the probability threshold screens correctly the areas of smallest confidence such as the interclass regions.

We propose a statistical algorithm to assess chlorophyll-a concentration ([chl-a]) using remote sensing reflectance (Rrs) derived from MODerate Resolution Imaging Spectroradiometer (MODIS) data. This algorithm is a combination of two models: one for low [chl-a] (oligotrophic waters) and one for high [chl-a]. A satellite pixel is classified as low or high [chla] according to the Rrs ratio (488 and 555 nm channels). If a pixel is considered as a low [chl-a] pixel, a log-linear model is applied; otherwise, a more sophisticated model (Support Vector Machine) is applied. The log-linear model was developed thanks to supervised learning on Rrs and [chl-a] data from SeaBASS and more than 15 campaigns accomplished from 2002 to 2010 around New Caledonia. Several models to assess high [chl-a] were also tested with statistical methods. This novel approach outperforms the standard reflectance ratio approach. Compared with algorithms such as the current NASA OC3, Root Mean Square Error is 30% lower in New Caledonian waters.