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Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Better understanding of the temporal-spatial distribution of chlorophyll-a concentration (Chl-a) is crucial in controlling harmful water blooms. In this study, the dynamical change of Chl-a over the Bohai Sea and Yellow Sea from 2003-2017 were analyzed by using the MODIS/Aqua satellite data, and the effects of sea surface temperature (SST), wind and wave were investigated. The typical distribution modes of long-term surface Chl-a were extracted by using the Self-organizing Mapping (SOM), neural network model. The results showed distinct seasonal variations of the Chl-a along with a gradual increase in the study period. The total Chl-a of the whole area reached the lowest value of 2.41mg/m3 in July, and the highest value 3.43mg/m3 in April; though in Laizhou Bay, the Chl-a concentration was significantly higher than other regions and the value reached at the peak in September. The spatial distribution showed that Chl-a decreased from inshore to offshore. Meanwhile, from clear mode to low, medium, and high concentration modes, the Chl-a gradually increased in coverage and concentration, and modes extracted by the SOM neural network have effectively elucidated the trend of Chl-a in spatial, seasonal, and interannual variability. The Generalized Additive Model (GAM) was used to evaluate the effect of SST, wind, and wave on the changing patterns of Chl-a. It was found that there is a significant nonlinear correlation between Chl-a and SST, wind speed, mean wave direction and significant height of the wave. These influencing factors accounted for 47.9% of the change of Chl-a, which had significant effects on Chl-a change. Compared with wind speed, mean wave direction and significant height of wave, SST can better explain the change of Chl-a. Besides, wind direction and increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in the Bohai Sea and Yellow Sea.

The first geostationary ocean color satellite mission (geostationary ocean color imager, or GOCI) has provided eight hourly observations per day over the western Pacific region since June 2010. GOCI imagery has been widely used to track the short-term dynamics of coastal and inland waters. Few studies have been performed to comprehensively assess the advantages of GOCI images in obtaining valid observations and estimating diurnal changes within the water column. Using the entire mission dataset between 2011 and 2017, these knowledge gaps were filled by comparing the daily percentages of valid observations (DPVOs) between GOCI and MODIS Aqua (MODISA) and by examining the diurnal changes in Chl-a over the East China Sea. The mean DPVOs of GOCI was 152.6% over the clear open ocean, suggesting that a daily valid coverage could be expected with GOCI. The GOCI DPVOs were ~26 times greater than the MODISA DPVOs, this pronounced difference was caused by the combined effects of their different observational frequencies and the more conservative quality flag system for MODISA. Diurnal changes in the GOCI-derived Chl-a were also found, with generally higher Chl-a in the afternoon than the morning and pronounced heterogeneities in the temporal and spatial domains. However, whether such diurnal changes are due to the real dynamics of the oceanic waters or artifacts of the satellite retrievals remains to be determined. This study provides the first comprehensive quantification of the unparalleled advantages of geostationary ocean color missions over polar orbiters, and the results highlights the importance of geostationary ocean color missions in studying coastal and inland waters.

The Western Waters of Indonesian (WWI) present a diverse interaction of ocean-atmosphere dynamics. One of them represents the event of Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), and upwelling. The objective of this study is to determine the dynamics of chlorophyll-a concentration (Chl–a), especially during IOD and ENSO. Also, this study is aimed to examine the temporal and spatial distribution of the upwelling area from 2000 to 2017. The data utilized consisted of Chl–a, wind stress, Sea Level Anomaly (SLA), and Sea Surface Temperature (SST). The technique used to determine the upwelling area was by examining the maximum conditions of Chl–a, the low temperature of SST, and SLA. The results showed the sea surface temperature had a relationship with the concentration of Chl–a. It was obtained if the Directional Movement Index (DMI) and N3.4 (Niño 3.4 Index) moved stably (not too fluctuation) resulting in high concentrations of Chl–a. High standard deviations of SST are recognized around the Sunda Strait (June – October). When the standard deviation of SST is high, there is also a tendency for high Chl–a concentrations, while the results of empirical calculations show that large areas of upwelling occurred in January and September respectively at 12,447.72 km2 and 8,146.20 km2. Based on the results of the analysis, it can be concluded that the upwelling does not only occur at the coastal area of Western Sumatra (coastal upwelling), but it also occurs in the eastern territorial waters of the Indian Ocean. In addition, the upwelling area has the same pattern as the Chl–a concentration in January - October.

Deforestation and mining activities have proven to be very damaging to rivers because these activities disturb the environmental characteristics of rivers. Thus, the concentrations of dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN), and Chlorophyll-a (Chl-a) were measured monthly during 2 hydrological years in the Maroni and Oyapock Rivers to assess the dynamics and fluxes of organic carbon and nitrogen in these 2 Guiana Shield basins, which have been strongly (Maroni) and weakly (Oyapock) impacted by deforestation and mining activities. The 2-year time series show that DOC, POC, PN, and Chl-a concentrations vary seasonally with discharge in both rivers, indicating a hydrologically dominated control. Temporal patterns of DOC, POC, and PN indicate that these variables show maximum concentrations in rising waters due to the yield of organic matter and nitrogen accumulated in soils, which are incorporated into the rivers during rainfall. However, the Chl-a concentrations were at a maximum during low-water stages. The C/N and C/Chl-a ratios also showed a seasonal trend, with lower values during the low water periods due to an increase in algal biomass. During high water, the POC in both rivers is the result of terrestrial organic matter, whereas during low-water autochthonous organic matter can reach up to 34% of the POC. The mean annual fluxes of TOC and PN were higher (4.56 x 10(5) tonC year(-1) and 1.77 x 10(4) tonN year(-1), respectively) in the Maroni River than those (1.84 x 10(5) tonC year(-1) and 0.54 x 10(4) tonN year(-1), respectively) in the Oyapock River. However, the specific fluxes of DOC, POC, and PN from both basins were nearly the same. Although gold mining activities are performed in both basins, there is no conclusive evidence regarding the impact of these activities on the dynamics of organic matter and particulate nitrogen in the Maroni and Oyapock Rivers.

Particulate organic carbon (POC) plays an important role in the global carbon cycle. The POC in the Changjiang Estuary and adjacent coastal region of the East China Sea (ECS) is dominated by riverine input and marine production and is significantly influenced by the three gorges project (TGP). A coupled physical&ndash, biogeochemical model was used to evaluate TGP&rsquo, s impact on POC. The results demonstrate that TGP regulates the area influenced by diluted water and POC through direct river and sediment discharge and affects the ecosystem. From the early to later TGP construction periods, the surface region with high-POC concentration (&gt, 40 &mu, mol L&minus, 1) decreases by 20.5% in area and 11.5% in concentration. Meanwhile, POC in the whole water column decreases from 19.5 to 17.8 &mu, 1. By contrast, the concentrations of chlorophyll-a (Chl-a) and related nutrients increase. A three end-member mixing model based on quasi-conservative temperature and salinity is used to quantify relative contributions of different water sources to POC in our research area. We also estimate the biological POC production by the difference between the physical-biogeochemical model predicted POC and three end-member model mixing POC. The result demonstrate that under the regulation of TGP in the later period, the decrease of sediment load increases water transparency, which favors photosynthesis and oceanic biological produced POC. In addition, over 70% of the areas have C/Chl-a &gt, 200 and high C/N ratios, which are circumstantial evidences that organic detritus and terrestrial input sources still dominate in the Changjiang Estuary and adjacent coastal ECS but are influenced by TGP&rsquo, s regulation.

Murat River is the most important tributary of the Euphrates River, which has the highest water potential in Turkey. The seasonal variation of water quality parameters and water quality status of Murat River were determined according to the “Regulation on Surface Water Quality Management” criteria. 19 different parameters were measured in water samples taken from four different stations during the research. These parameters were temperature, pH, dissolved oxygen, electrical conductivity, total hardness, total alkalinity, Biochemical Oxygen Demand (BOD), total phosphorus, fluoride, chloride, nitrite, nitrate, ammonium, sulfate, sodium, potassium, calcium, magnesium, and chlorophyll-a (Chl-a). According to “Regulation on Surface Water Quality Management”, physical and chemical analysis data showed that Murat River waters had "first-class water quality" in terms of temperature, dissolved oxygen, BOD, total phosphorus, fluoride, chloride, nitrate, ammonium, sulfate, "third-class water quality" for sodium, pH and “fourth class water quality” for nitrite. The electrical conductivity and alkalinity values were within normal limits and as "medium-hard" water. Considering all the results, it has been evaluated that there is no significant pollution problem in the Murat River., Murat Nehri, Türkiye'de en yüksek su potansiyeline sahip Fırat Nehri’nin en önemli koludur. Murat Nehri su kalitesi parametrelerinin mevsimsel değişimi ve su kalitesi durumu “yüzey suyu kalite yönetmeliği” kriterlerine göre tespit edilmiştir. Araştırma sürecinde dört farklı istasyondan alınan su örneklerinde sıcaklık, pH, çözünmüş oksijen, elektriksel iletkenlik, toplam sertlik, toplam alkalinite, Biyokimyasal Oksijen İhtiyacı (BOİ), toplam fosfat, florür, klorür, nitrit, nitrat, amonyum, sülfat, sodyum, potasyum, kalsiyum, magnezyum ve klorofil-a (Chl-a) ölçülmüştür. Yüzey suyu kalite yönetmeliğine göre, Murat Nehri sıcaklık, çözünmüş oksijen, BOİ, toplam fosfat, florür, klorür, nitrat, amonyum, sülfat açısından "birinci sınıf" , sodyum, pH için "üçüncü sınıf" , ve nitrit için "dördüncü sınıf" su kalitesine sahipti. Elektriksel iletkenlik ve alkalilik değerleri normal sınırlarda ve "orta sert" su olarak kabul edildi. Tüm sonuçlar dikkate alındığında, Murat Nehrinde önemli bir kirlilik sorunu olmadığı tespit edilmiştir.

Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems. RESUMO A estimativa da concentração de Clorofila–a (Chl-a) em ambientes aquáticos através de técnicas de sensoriamento remoto é complexa devido as diferenças entre as propriedades óticas da água. O objetivo deste trabalho foi estimar concentrações de Chl-a a partir das combinações de bandas espectrais do sensor MODIS, aplicando análise de regressão linear múltipla (MLR), redes neurais artificiais (ANN), regressão multiplicativa não paramétrica e quatro modelos (Appel, Kahru, FAI e O14a). Os modelos de regressão linear múltipla (MLR), NPMR e ANN foram calibrados e validados utilizando medições in-situ de Clorofila–a. Os resultados demostraram que um simples e eficiente modelo desenvolvido e validado através de regressão linear múltipla (MLR) produz vantagens (i.e., melhor performance e poucas variáveis de entrada) em comparação com os modelos ANN, NPMR e quatro modelos (Appel, Kahru, FAI e O14a). Além disso, foi observado que em um grande lago raso subtropical, no qual o vento (intensidade e direção) e a hidrodinâmica são fatores essenciais na heterogeneidade espacial (distribuição de Chl-a), o modelo MLR desenvolvido utilizando um ponto específico do lago teve melhor performance que utilizando o total de dados, sugerindo que não seria apropriado generalizar um único modelo para estimar Clorofila– a nesses grandes lagos rasos utilizando o total de dados. Esta abordagem é uma ferramenta útil para estimar concentrações de Chl- a em ambientes rasos meso – oligotróficos e corroborar a heterogeneidade espacial desses ecossistemas.

International audience; The present work seeks to assess the relationship between daily Sea Surface Temperature (SST) and thermal front (Gradient Magnitude method) using AMSR-E, daily Sea Surface Chlorophyll-a (SSC) using MODIS, and daily long line (LL) bigeye (BG), albacore (ALB), yellowfin (YF), and southern bluefin (SBF) tuna catch data expressed as catch per unit effort (CPUE), which was calculated as the number of fish caught by 1000 hooks in 1° latitude by 1° longitude square grid, and integrated into a month of fishing activity for the period of March–December 2010 in South Java (Indian Ocean). Results obtained showed evidences of non-linear relationships between catch yields and environmental data. BG and ALB show largest CPUE values which occurred mainly in the area with SSTs of 26 – 27 °C and SSCs of 0.15 – 0.3mg/m3. CPUEs appear to be “randomly” dispersed and have a slight positive (negative) correlation with SSC (SST) i.e., 0.33 (-0.38) but not with GM values i.e., -0.02. The weak correlation between CPUE, SST, SSC and GM lead to assume that CPUEs might be linked to other influential parameters that are not assessed in the present study but are needed to give complete prediction of potential fishing ground areas, knowing that the length of observation period is less than 1 year.

Source at https://doi.org/10.3390/w10101428. The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

After Andrew (Bakun, 1990) formulated his hypothesis of upwelling intensification caused by increasing global warming, contradictory results have been published on whether primary productivity is increasing or decreasing in Eastern Boundary Upwelling Ecosystems (EBUE). The present work is focused in comparing three net primary production (NPP) models -the VGPM (Vertically Generalized Production Model), the Eppley-VGPM and the CbPM (Carbon-based Production Model)- in the Canary Current (CanC) EBUE during the 1998-2015 period, making use of both SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and MODIS (MODerate-resolution Imaging Spectroradiometer) derived data. We looked for the first time for seasonal to interannual trends of NPP under a regional perspective, with the aim of searching for temporal patterns that could support or reject the intensification hypothesis. According to previous studies based on the seasonality of the upwelling regime, the CanC EBUE was divided into three subregions: a seasonal upwelling zone (SUZ; 13-20oN), a permanent upwelling zone (PUZ; 20-26oN) and a weak permanent upwelling zone (WPUZ; 26-33oN). Our analyses do not show significant increasing trends in NPP with any of the productivity models used, challenging Bakun’s hypothesis. Nevertheless, differences in the output of the models are important, both at regional and subregional scales, questioning the accuracy of the models. The comparison made in this work clearly shows the disagreements between some of the best-known NPP models and calls for a validation effort in this region. Contrary to the open-ocean, temperature trends show significant decreases in certain areas over the shelf waters, which might indicate an increase of cool, upwelled waters in those regions. Seasonal to decadal anomalies of NPP and sea-surface temperature (SST) are estimated and analyzed in relation to selected climate indices, yielding only significant correlations between SST and the North Atlantic Oscillation (NAO) indices.

A detailed study of wind-induced coastal upwelling (CU) in the south-eastern Baltic Sea is presented based on an analysis of multi-mission satellite data. Analysis of moderate resolution imaging spectroradiometer (MODIS) sea surface temperature (SST) maps acquired between April and September of 2000⁻2015 allowed for the identification of 69 CU events. The Ekman-based upwelling index (UI) was applied to evaluate the effectiveness of the satellite measurements for upwelling detection. It was found that satellite data enable the identification of 87% of UI-based upwelling events during May⁻August, hence, serving as an effective tool for CU detection in the Baltic Sea under relatively cloud-free summer conditions. It was also shown that upwelling-induced SST drops, and its spatial properties are larger than previously registered. During extreme upwelling events, an SST drop might reach 14 °C, covering a total area of nearly 16,000 km2. The evolution of an upwelling front during such intensive events is accompanied by the generation of transverse filaments extending up to 70 km offshore. An analysis of the satellite optical data shows a clear decline in the chlorophyll-a concentration in the coastal zone and in the shallow Curonian Lagoon, where it drops down by an order of magnitude. It was also shown that a cold upwelling front alters the stratification in the atmospheric boundary layer, leading to a sudden drop of air temperature and near-surface winds.

El Niño—Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals such as ENSO. We provide empirical evidence of an immediate and lagged influence of ENSO on SeaWiFS and MODIS-Aqua derived global Chl-a concentrations. We use 13 years of Chl-a remotely sensed observations along with sea surface temperature (SST) observations across the Tropical and South Pacific to isolate and examine the spatial development of Chl-a anomalies during ENSO: its canonical or eastern Pacific (EP) mode, and El Niño Modoki or central Pacific (CP) mode, using the extended empirical orthogonal function (EEOF) technique. We describe how an EP ENSO phase transition affects Chl-a, and identify an interannual CP mode of variability induced spatial pattern. We argue that when ENSO is analysed as a propagating signal by the EEOF, CP ENSO is found to be more influential on Chl-a interannual to decadal variability than the canonical EP ENSO. Our results cannot confirm the independence of the two ENSO modes but clearly demonstrate that both ENSO flavors manifest a distinct biological response.

The objective of this research was to develop a model for estimating chlorophyll-a, SST and kd(490) on the Northwest Persian gulf water using relatively high resolution landsat8 image. A field compaign were conducted in Feb, March, April, May, July, Agust, Oct and Nov of 2014 concurrent to satellite over-pass. The field work were conducted of Mahshar estuary between Feb, 14th to Nov, 13th, 2014. Temperature, Secchi disk depth, chl-a, kd and zeu was measured in this study. We tested various regression models using combinations of bands. The regression models showed acceptable correlation and accuracy. The Chl-a concentrations ranged from 0.6 to 1.8 mg m−3, with an average of 1.01 ± 0.32 mg m−3. The values for Kd(490) were highest(0.86) in July and lowest (0.59m) in February. Deeper Zeu are observed in Feb (7.8m) and Shallower Zeu were observed in July (5.3m). The study of SST algorithm model for Landsat-8 data has been conducted using the method approached to SST derived from calibrated landsat8 data. The retrieval accuracy (R2) was 0.63, while the root mean square (RMSE) was 0.81. The highest (31c°) temperature was found in July and lowest temperature(15.1c°) was found in Feb. The approach employed in this research indicates that remote sensing is a valuable, low-cost and stable tool for water quality monitoring of estuary health.Read Complete Article at ijSciences: V5201602953 AND DOI: http://dx.doi.org/10.18483/ijSci.953

The overall objective of this study was to use field spectro-radiometers for finding possible spectral regions in which chlorophyll-a (Chl-a) and particulate organic carbon (POC) could be identified so as to assist the assessment and monitoring of water quality using satellite remote sensing technology. This paper presents the methodology adopted in this study which is based on the application of linear regression analysis between the mean reflectance values (measured with the GER1500 field spectro-radiometer) across the spectrum and the concentrations of chlorophyll-a (µg/L) and POC (µg/L) acquired simultaneously on the same day and time in the Lower Thames Valley in West London (U.K.) from old campaigns. Each regression model (512 in total) corresponded to a measured wavelength of the GER1500 field spectro-radiometer. The achieved correlations presented as r2 against wavelength, indicate the regions with high correlation values for both water quality variables. Based on the results from this study and by matching the spectral bands of the field spectro-radiometer with those of the Landsat TM satellite sensor (or any other sensor), it has been found that suitable spectral regions for monitoring water quality in water treatment reservoirs are the following: for chlorophyll-a, the spectral region of 0.45–0.52 μm (TM band 1), and for POC, the region 0.52–0.60 μm (TM bands 1 and 2). Then 12 atmospheric corrected Landsat TM/ETM+ band 1 images acquired from 2001 to 2010 were used for validation purposes to retrieve the Chl-a concentrations.

This is a study of the optical, physical and biological parameters of sea ice and the water below it at stations ( n =25) in the central (>88 ° N) Eurasian sector of the Arctic Ocean during the summer 2012 record low sea-ice minimum extent. Results show that photosynthetically active radiation (PAR) transmittance of the ice was low (0.09) and apparently related to a high degree of backscattering by air-filled brine channels left by brine draining. The under-ice PAR was also low (8.4±4.5 SD µmol photons m −2 s −1 ) and partly related to the low transmittance. There were no significant differences in multi-year and first-year PAR transmittances. In spite of this low under-ice PAR, only 3% of the transmitted PAR through the ice was absorbed by phytoplankton in the water. On average, chlorophyll-a concentrations were low (0.34±0.69 SD mg chl-a m −3 ) in the water compared to the high ( a 375 =0.52 m −1 ) coloured dissolved organic matter (CDOM) absorption coefficient with a strong terrestrial optical signature. Two distinct clusters of stations with waters of Pacific and North Atlantic origin were identified based on significant differences in temperature, salinity and CDOM absorption coefficient between water masses. The under-ice light field for bare ice was parameterized as follows: I z = I o (1−0.55)*(0.09)*exp (−0.17*z) . Keywords: Sea ice; transmittance; PAR; CDOM; chl-a; Eurasian sector (Published: 1 September 2015) Citation: Polar Research 2015, 34 , 23978,http://dx.doi.org/10.3402/polar.v34.23978

This study analyzed monthly and inter-annual variability of mesoscale phenomena, including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) climate indexes and wind intensity considering their influence on sea surface temperature (SST) and chlorophyll a (Chl-a). These analyses were performed to determine the effects, if any, of climate indexes and oceanographic and environmental variability on the central and southern coastal ecosystem of Sonora in the Gulf of California (GC). Monthly satellite images of SST (°C) and Chl-a concentration were used with a 1-km resolution for oceanographic and environmental description, as well as monthly data of the climate indexes and wind intensity from 2002–2015. Significant differences (p > 0.05) were observed while analyzing the monthly variability results of mesoscale phenomena, SST and Chl-a, where the greatest percentage of anti-cyclonic gyres and filaments was correlated with a greater Chl-a concentration in the area of study, low temperatures and, thus, greater productivity. Moreover, the greatest percentage of intrusion was correlated with the increase in temperature and cyclonic gyres and a strong decrease of Chl-a concentration values, causing oligotrophic conditions in the ecosystem and a decrease in upwelling and filament occurrence. As for the analysis of the interannual variability of mesoscales phenomena, SST, Chl-a and winds, the variability between years was not significant (p > 0.05), so no correlation was observed between variabilities or phenomena. The results of the monthly analyses of climate indexes, environmental variables and wind intensity did not show significant differences for the ENSO and PDO indexes (p > 0.05). Nonetheless, an important correlation could be observed between the months of negative anomalies of the ENSO with high Chl-a concentration values and intense winds, as well as with low SST values. The months with positive ENSO anomalies were correlated with high SST values, low Chl-a concentration and moderate winds. Significant inter-annual differences were observed for climate indexes where the years with high SST values were related to the greatest positive anomaly of ENSO, of which 2002 and 2009 stood out, characterized as moderate Niño years, and 2015 as a strong El Niño year. The years with the negative ENSO anomaly were related to the years of lower SST values, of which 2007–2008 and 2010–2011 stood out, characterized as moderate Niñas. Thus, variability associated with mesoscale oceanographic phenomena and seasonal and inter-annual variations of climate indexes had a great influence on the environmental conditions of the coastal ecosystem of Sonora in the Gulf of California.

The purpose of this study is to assess the performance of an adaptive model (AM) in estimating chlorophyll‑a concentration (Chl‑a) in optically complex inland waters. Chl‑a modeling using remote sensing data is usually based on a single model that generally follows an exponential function. The estimates produced by such models are relatively accurate at high Chl‑a concentrations, but accuracy drops at low concentrations. Our objective was to develop an approach combining spectral response classification and three semi-empirical algorithms. The AM discriminates between three blooming classes (waters poorly, moderately, and highly loaded in Chl‑a), with discrimination thresholds set using the classification and regression tree (CART) technique. The calibration of three specific estimators for each class was achieved using a multivariate stepwise regression. Compared to published models (Floating Algae Index, Kahru model, and APProach by ELimination) using the same data set, the AM provided better Chl‑a concentration estimates (R2 of 0.96, relative RMSE of 23%, relative Bias of −2%, and a relative NASH criterion of 0.9). Moreover, the AM achieved an overall success rate of 67% in the estimation of blooming classes (corresponding to low, moderate, and high Chl‑a concentration classes). This was done using an independent data set collected from 22 inland water bodies for the period 2007–2010 and for which the only information available was the blooming class.

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998–2011. Globally, diatoms contributed the most to the total phytoplankton production (~50%, the equivalent of ~20 PgC∙y−1). Coccolithophores and chlorophytes each contributed ~20% (~7 PgC∙y−1) of the total primary production and cyanobacteria represented about 10% (~4 PgC∙y−1) of the total primary production. Primary production by diatoms was highest in the high latitudes (>40°) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998–2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4% (1–2 PgC∙y−1). We assessed the effects of climate variability on group-specific primary production using global (i.e., Multivariate El Niño Index, MEI) and “regional” climate indices (e.g., Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p < 0.05) between the MEI and the group-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatoms/cyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on group-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

Chlorophyll-a (Chl-a) concentration is considered as a key indicator of the eutrophic status of inland water bodies. Various algorithms have been developed for estimating Chl-a in order to improve the accuracy of predictive models. The objective of this study is to assess the potential of hyperspectral multi-band indices to estimate the Chl-a concentration in Dianshan Lake, which is the largest lake in Shanghai, an international metropolis of China. Based on field spectral measurements and in-situ Chl-a concentration collected on 7–8 September 2010, hyperspectral multi-band indices were calibrated to estimate the Chl-a concentration with optimal wavelengths selected by model tuning. A three-band index accounts for 87.36% (R2 = 0.8736) of the Chl-a variation. A four-band index, which adds a wavelength in the near infrared (NIR) region, results in a higher R2 (0.8997) by removing the absorption and backscattering effects of suspended solids. To test the applicability of the proposed indices for routinely monitoring of Chl-a in inland lakes, simulated Hyperion and real HJ-1A satellite data were selected to estimate the Chl-a concentration. The results show that the explanatory powers of these satellite hyperspectral multi-band indices are relatively high with R2 = 0.8559, 0.8945, 0.7969, and 0.8241 for simulated Hyperion and real HJ-1A satellite data, respectively. All of the results provide strong evidence that hyperspectral multi-band indices are promising and applicable to estimate Chl-a in eutrophic inland lakes.

This study evaluated the ability to improve Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a retrieval from optically shallow coastal waters by applying algorithms specific to the pixels’ benthic class. The form of the Ocean Color (OC) algorithm was assumed for this study. The operational atmospheric correction producing Level 2 SeaWiFS data was retained since the focus of this study was on establishing the benefit from the alternative specification of the bio-optical algorithm. Benthic class was determined through satellite image-based classification methods. Accuracy of the chl-a algorithms evaluated was determined through comparison with coincident in situ measurements of chl-a. The regionally-tuned models that were allowed to vary by benthic class produced more accurate estimates of chl-a than the single, unified regionally-tuned model. Mean absolute percent difference was approximately 70% for the regionally-tuned, benthic class-specific algorithms. Evaluation of the residuals indicated the potential for further improvement to chl-a estimation through finer characterization of benthic environments. Atmospheric correction procedures specialized to coastal environments were recognized as areas for future improvement as these procedures would improve both classification and algorithm tuning.

The occurrence and extent of intense harmful algal blooms (HABs) have increased in inland waters during recent decades. Standard monitor networks, based on infrequent sampling from a few fixed observation stations, are not providing enough information on the extent and intensity of the blooms. Remote sensing has great potential to provide the spatial and temporal coverage needed. Several sensors have been designed to study water properties (AVHRR, SeaBAM, and SeaWIFS), but most lack adequate spatial resolution for monitoring algal blooms in small and medium-sized lakes. Over the last decade, satellite data with 250-m spatial resolution have become available with MODIS. In the present study, three models inspired by published approaches (Kahru, Gitelson, and Floating Algae Index (FAI)) and a new approach named APPEL (APProach by ELimination) were adapted to the specific conditions of southern Quebec and used to estimate chlorophyll-a concentration (Chl-a) using MODIS data. Calibration and validation were provided from in situ Chl-a measured in four lakes over 9 years (2000–2008) and concurrent MODIS imagery. MODIS bands 3 to 7, originally at 500-m spatial resolution, were downscaled to 250 m. The APPEL, FAI, and Kahru models yielded satisfactory results and enabled estimation of Chl-a for heavy blooming conditions (Chl-a > 50 mg∙m−3), with coefficients of determination reaching 0.95, 0.94, and 0.93, respectively. The model inspired from Gitelson did not provide good estimations compared to the others (R2 = 0.77). However, the performance of all models decreased when Chl-a was below 50 mg∙m−3.

WOS: 000298099400021 The aim of the study is to determine effects of Izmir Big Channel Wastewater Treatment Plant on inorganic nutrients such as NO(3)(-)-N, NO(2)(-)-N, NH(4)(+)-N, PO(4)(-3)-P, Si and Chl-a levels in Izmir bay. Variations of inorganic nutrients and Chl-a were investigated weekly during 1 year period in 2003-2004 in Izmir bay. In addition, physico-chemical environmental parameters such as pH, Dissolved Oxygen (DO), water temperature and salinity were measured. The parameters mentioned above have been carried on in different depths of the 3 sampling stations which were located in middle and inner parts of Izmir bay. The concentration ranges of nutrients and chlorophyll-a were from BDL (Below Detection Limit) to 40.94 mu M for NH(4)(+)-N, BDL to 21.35 mu M for NO(3)(-)-N, BDL to 28.99 mu M for NO(2)(-)-N, BDL to 31.43 mu M for PO(4)(-3)-P, 0.16 to 54.12 mu M for Si and BDL to 66.13 mu g L(-1) for Chlorophyll-a. When the results were compared with those obtained before Izmir waste water treatment plant had been operated, concentrations of Chl a and nitrogen forms declined while it was not the case for PO(4)(-3)-P. TUBITAK (Scientific and Technical Research Council of Turkey)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Izmir Municipality Gulf Control StaffBelediyeler; Science and Technology Research Centre of Ege UniversityEge University The researchers would like to thank TUBITAK (Scientific and Technical Research Council of Turkey), Izmir Municipality Gulf Control Staff and Science and Technology Research Centre of Ege University for their efforts to participate in this project as well as their scientific and financial supports.

WOS: 000269883900033, PubMed: 18821023, This paper presents an application of water quality mapping through real-time satellite and ground data. The Lake Beysehir which is the largest freshwater lake and drinking water reservoir in Turkey was selected as the study area. Terra ASTER satellite image is used as remote sensing data source for water quality mapping in addition to simultaneously performed in-situ measurements. Ground data is collected simultaneously with the ASTER overpass on June 09, 2005 over the Lake Beysehir. The spatial distribution map is developed by using multiple regression (MR) technique for water quality parameter, which is chlorophyll-a (chl-a). The results indicate that simultaneous ground and satellite remote sensing data are highly correlated (R (2) > 0.86). In the image processing step, geometric correction, image filtering and development of water quality map procedures are performed with the ERDAS Imagine and ArcGIS 9.0 software. The trophic status of Lake Beysehir is considered to be oligotrophic with an average 1.55 A mu g/l chl-a concentration., The Scientific and Technological Research Council of Turkey (TUBITAK) [105Y086]; Selcuk University Scientific Research [2004/102], This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) with project No: 105Y086 and Selcuk University Scientific Research Fund with project No: 2004/102.

Apparent Spectral properties (AOPs) of phytoplankton pigment concentration were analyzed in Shitoukoumen Reservoir, the Changchun city drinking water resource, in order to investigate the feasibility of the using remote sensing to monitor chlorophyll a (Chl-a) concentration in Northeast China. 225 samples were collected for laboratory Chl-a analysis during 12 field campaigns from 2006 to 2008. Correlation analysis between Chl-a and spectra and derivative spectral reflectance revealed that derivative at spectral band between 420 to 700 nm is more sensitive to Chl-a concentration, however correlation coefficient on reflectance is relatively low as Chl-a is not the dominating optical active component in the reservoir. A combination of Genetic Algorithms and Partial Least Square (GA-PLS) model was established for Chl-a estimation in this study. A preprocessing procedure was conducted to selected the comparatively high correlated spectral variable by application of correlation analysis between Chl-a with each spectral band, reflectance derivative ranging from 350 to 1000 nm and all possible band ratios (101, 250 combinations all together). 100 sensitive spectral variables were selected for GA-PLS modeling with above mentioned preprocessing procedure. A number of 2/3 samples were selected to train the GA-PLS model, and the rest was utilized to validate the performance of the model. It is found that the relationship between the most sensitive reflectance band, reflectance derivative and band ratio and Chl-a concentration agreed well with linear function with R-Square range from 0.45 to 0.78. However, the GA-PLS model for Chl-a estimation performs much better, with model validation R-Square of 0.81. As our results were derived from large number of ground truth samples, representing a spatio-temporal variation of pigment conditions, so the GA-PLS model has great potential for Chl-a estimation for inland water bodies with similar background.