Climate-driven latitudinal shift in fishing ground of jumbo flying squid (Dosidicus gigas) in the Southeast Pacific Ocean off Peru

The jumbo flying squid Dosidicus gigas is a pelagic squid species extensively distributed in the Eastern Pacific Ocean with climate-related geographical variability. An analysis was carried out to evaluate impacts of climatic and oceanographic variability on spatial distribution of D. gigas in the Southeast Pacific Ocean off Peru. Logbook data of the 2006–2013 Chinese squid-jigging fishery were used to determine latitudinal gravity centres (LATG) of fishing ground of D. gigas in relation to sea surface temperature (SST), chlorophyll-a (chl-a) concentration and sea surface height (SSH), coupled with the SST anomaly (SSTA) in the Niño 1 + 2 region. Results indicated that the SSTA in the Niño 1 + 2 region played crucial influences on SST, chl-a and SSH on the fishing ground of D. gigas. The LATG of D. gigas exhibited seasonal and interannual variability with closely associations with SST, chl-a, and SSH. Significantly positive relationships were found between monthly LATG and the average latitude of the most favourable contour lines of SST, chl-a, and SSH for D. gigas, with time lags at 0, 7, and 0 month, respectively. The spatial pattern of LATG largely responded to climate-induced oceanographic variability on the squid fishing ground: the Niño 1 + 2 SSTA became warm, the most favourable SST and SSH contour lines for D. gigas would move southward, resulting in a southward movement of the LATG; however, the Niño 1 + 2 SSTA shifted into cold episodes, the most favourable SST and SSH contour lines for D. gigas would shift northward, leading to a northward shift of the LATG. Our findings suggested that the SSTA in the Niño 1 + 2 region coupled with the most favourable contour lines of SST and SSH were the major drivers regulating the latitudinal movement of fishing ground of D. gigas in the Southeast Pacific Ocean off Peruvian waters.     

Time-series analysis of chlorophyll-a,sea surface temperature,and sea surface height anomalies during 2003–2014 with special reference to El Niño,La Niña,and Indian Ocean Dipole (IOD) Years

The present study was carried out to find the variability of chlorophyll-a (chl-a) concentration, sea surface temperature (SST), and sea surface height anomalies (SSHa) during 2003–2014, covering the Bay of Bengal (BoB) and Arabian Sea (AS) waters. These parameters were linked with El Niño, La Niña, and Indian Ocean Dipole (IOD) years. The observed results during 2003–2014 were evaluated and it was found that the monthly mean value for 12-year data ranged as follows: chl-a (0.11–0.46 mg m^?3), SST (27–31 °C), and SSHa (?0.2 to 20 cm). The annual mean range of chl-a for 12-year data was 0.1–0.23 mg m^?3, the SST range was 27–28 °C, and the SSHa range was 2.14–13.91 cm. It has been observed that with the SST range of 27–28 °C and the SSHa range of 7–9 cm, the chl-a concentration enhanced to 0.20–0.23 mg m^?3. With a higher SST range of 28–29 °C and with a positive SSHa range of 11–14 cm, the chl-a concentration appeared to be low (0.17–0.18 mgm^?3). During normal years, SSHa was positive with the >5 to <10 cm range during the months of April–June, which coincided with an increase in SST, >2 to <4 °C. During the normal years, SSHa (>?0.2 to a concentration (>0.3 to <0.5 mg m^?3) was noticed during December–February in the BoB and AS. Compared to the BoB chl-a range (<0.4 mg m^?3), a high chl-a concentration was observed in AS (>0.4 mg m^?3). However, during the phenomenon years, the study area had experienced low chl-a (<0.2 mg m^?3), high SST (>5 °C), and more positive SSHa (>10 to <20 cm) during January–March and October–December in AS and BoB. The present study infers that a positive IOD leads to low chl-a concentration (<2 mg m^?3) and low primary productivity in AS. El Niño caused the down-welling process, it results in a low chl-a concentration (<1 mg m^?3) in BoB and AS. La Niña caused the upwelling process, and it results in a high chl-a concentration (>2.0 mg m^?3) in BoB and AS. In the recent past years (2003–2014), the intensity and frequency of El Niño, La Niña, and IOD have been increasing, evidenced with few studies, and have impacts on the Indian Ocean climate. Therefore, the influences of the relative changes of these phenomena on the BoB and AS need to be understood for productivity assessment and ocean state monitoring.     

Seasonal evolution of the interannual variability of chlorophyll-a concentration and its forcing factors in the northwestern Pacific from 1998 to 2010

The northwestern Pacific Ocean is a complex region with significant biological spatial variations on a seasonal timescale. To investigate the joint variation patterns on both seasonal and interannual timescales, a season-reliant empirical orthogonal function (S-EOF) analysis was applied to seasonal mean chlorophyll-a concentration (chl-a) anomalies in the northwestern Pacific Ocean during the period 1998–2010. The first two dominant modes accounted for nearly 31% of the total interannual variance, with the second S-EOF mode (S-EOF2) lagging behind the first S-EOF mode (S-EOF1) by one year. S-EOF1 featured a strong variation pattern to the north of 30° N, with maximum chl-a in winter and minimum chl-a in summer. However, S-EOF2 indicated an opposite seasonally evolving pattern compared with S-EOF1, with chl-a increasing along the Kuroshio and extension current from boreal winter to autumn. Both these modes revealed significant relationships with climate-related indices. The two modes corresponded to the central Pacific (CP) La Niña developing episodes and the turnaround from eastern Pacific (EP) La Niña to CP El Niño, respectively. Both modes were associated with the cold phase of the Pacific Decadal Oscillation, which played an important role in prolonging the impact of the El Niño/Southern Oscillation on chl-a seasonal evolution from 1998 to 2010. In addition, we discuss the possible factors dominating chl-a seasonal variation, in terms of the subregions of the northwestern Pacific Ocean. In the subtropical northwestern Pacific Ocean (15° N – 30° N), the chl-a growth was primarily nutrient-limited, whereas in the mid-latitude northwestern Pacific Ocean (35° N – 50° N), the chl-a growth was mainly light-limited.     

Variability of chlorophyll-a off the southwest coast of India

Coastal upwelling off the southwest coast of India during the southwest monsoon is a well-known phenomenon that enhances the chlorophyll-a (chl-a) biomass. The present study explores this property and examines the variability of surface chl-a using satellite data obtained from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for the period from September 1997 to December 2010. Spatial variability showed substantial cross-shore as well as along-shore gradients during the southwest monsoon. Temporal variability in chl-a was studied in conjunction with satellite observations on sea-surface temperature, sea-surface height anomaly, winds, and currents. The results revealed the dominant influence of the West India Coastal Current on chl-a variability during the upwelling and downwelling periods. Moreover, noticeable intra- and inter-annual variability was observed in the parameter. Therefore, an empirical mode decomposition (EMD) method was used to identify the oscillations influencing variability. SeaWiFS chl-a data for the period 2008–2010 were omitted from this analysis due to gaps in the record. EMD analysis revealed oscillations ranging from seasonal to a five-year periodicity. Quasi-biennial oscillations are identified as the dominant factor causing inter-annual variability in chlorophyll in the study area, compared with the El-Niño Southern Oscillation and Indian Ocean Dipole. Chl-a was also studied in two smaller grids of size 0.5° × 0.5° separated by around 300 km and representing coastal and offshore areas, to understand the nature of variability in these areas. The annual range of variability was high (0.1–8.0 mg m^?3) near the coast consequent on high upwelling intensity, and very low (about 0.1 mg m^?3) in the offshore grid due to the absence of upwelling.     

Spatio-temporal distribution of skipjack in relation to oceanographic conditions in the west-central Pacific Ocean

The skipjack tuna, Katsuwonus pelamis, is an economically important oceanic species widely distributed in the west-central Pacific Ocean (WCPO). The spatio-temporal distribution of Katsuwonus pelamis with respect to oceanographic and climatic variables during 1995–2010 in the west-central Pacific was examined in this study using purse seine fishery data from South Pacific Fisheries Commission (SPC). ‘Gravitational centre’ of two temporal scales (i.e. monthly and yearly) of catch per unit effort (CPUE) was calculated to represent the variability of local stock abundance on fishing grounds. Significant inter-annual and seasonal variabilities were observed. Monthly longitudinal ‘centres of gravity’ were correlated with sea surface temperature anomaly (SSTA) in Niño 3.4 region and monthly latitudinal ‘centres of gravity’ reflect a ‘South–North’ migration pattern of Katsuwonus pelamis. The distribution–habitat associations were quantitatively evaluated including SST between 28–30°C, sea surface height (SSH)in the range 90–100 cm, gradient SST between 0.1 and 0.7°C 10 km^?1,and chlorophyll-a(chl-a) between 0.1 and 0.6 mg m^?3 by an empirical cumulative distribution function (ECDF). Four clusters of yearly ‘gravitational centres’ were classified using the k-means method, which could be defined as warmpool fishing ground (WFG) and cold-tongue fishing ground (CFG) according to their oceanographic habitat. The integrated environmental distribution map combined with the developed model (R² = 0.28, p < 0.0001) provides an approach for predicting hotspots of Katsuwonus pelamis. This study improves our understanding of the spatio-temporal dynamics of skipjack tuna, which is critical for sustainable management of this important fisheries resources.     

Spatio-temporal variability of surface chlorophyll-a in the Halmahera Sea and its relation to ENSO and the Indian Ocean Dipole

ABSTRACT

Long-term satellite data are used to investigate the variability of ocean surface chlorophyll-a (chl-a) concentration in the Halmahera Sea (HS) under influence of the Australian-Indonesian Monsoon (AIM), the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). In this study, we first analysed the seasonal variability of chl-a, and then examine the relationship between surface chl-a, sea surface temperature (SST), and sea surface wind stress in the area. Our results suggest that prevailing southeasterly winds play a fundamental role in generating chl-a blooms in the HS. Particularly on a seasonal timescale, through the mechanism of Ekman mass transport, strengthening of southeasterly wind stress during the Southeast Monsoon season (June – August) produces enhanced chl-a concentrations associated with ocean surface cooling in the area of study. On the other hand, the chl-a bloom completely diminishes during the Northwest Monsoon season (December – February) due to weakening of wind stress and Ekman transport. On an interannual timescale, sea level pressure and wind stress are coherent with ENSO and IOD phases. During El Niño and positive IOD events (La Niña and negative IOD events), both sea level pressure and wind stress greatly increase (decrease) over the HS. These conditions cause an anomaly in southerly (northerly) wind stress, which is favourable to an enhancement (reduction) of the chl-a concentration in the region. This study demonstrates that sea level pressure and wind stress are the critical factors in determining the magnitude of chl-a bloom in the HS.     

Spatio-temporal distribution of chlorophyll-a concentration derived from MODIS satellite remote-sensing and in situ observations in Sanya Bay,China

This study examined satellite chlorophyll-a (chl-a) concentration and in situ observations in Sanya Bay (SYB). In situ observation of chl-a was conducted four times per year at 12 sampling stations in SYB from January 2004 to October 2008. Monthly satellite chl-a was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) during 2000–2012. This study compared satellite chl-a values to in situ measurements in SYB. The two data sets match well in the whole region except for two estuaries. Results show that the average in situ chl-a was 1.49 mg m^?3 in SYB. Chl-a was relatively higher (>2 mg m^?3) and more variable in coastal areas, with a tendency to decrease offshore (<0.4 mg m^?3). The chl-a level in summer displayed obviously vertical stratification, with higher values at the bottom and lower values at the surface. Analysis of monthly mean chl-a showed that the highest level (>2 mg m^?3) appeared in December, with the lowest in March (<1 mg m^?3). The gradients are ranked winter, autumn, summer and spring. There was higher chl-a in autumn and winter, which may be associated with the stronger wind monsoon then. Annual mean chl-a from 2000 to 2012 varied from 1.17 to 2.05 mg m^?3, with the minimum in 2001 and the maximum in 2005. The chl-a level presented a roughly increasing tendency from 2000 to 2012, which may be related to the increasing nutrients associated with the development of tourism and fishery.     

Detection of cyclone-induced rapid increases in chlorophyll-a with sea surface cooling in the northwestern Pacific Ocean from a MODIS/SeaWiFS merged satellite chlorophyll product

We performed a comprehensive analysis of satellite chlorophyll-a concentration (chl-a) data to detect all events in which chl-a rapidly increased on a time scale of 10 days or less. Our analysis could successfully detect cases in which chl-a increased and sea surface temperature (SST) decreased rapidly after the passages of tropical and extratropical cyclones. The events with large SST decreases tended to occur south of Japan, in the Sea of Okhotsk, and in the regions between 35° and 45° N, especially the Kuroshio–Oyashio Extension (KOE) region. Although the contribution of cyclones on the yearly total chl-a increase was basically small, the cyclone-induced chl-a increases accounted for a few tens of per cent of the total chl-a increase in some areas. In oligotrophic regions, the increases in chl-a tended to become larger as the corresponding SST decrease became larger, although the relationship between them is opposite in mesotrophic and eutrophic regions.     

Multitemporal analysis of land surface temperature using NOAA-AVHRR: preliminary relationships between climatic anomalies and forest fires

Advanced Very High Resolution Radiometer (AVHRR) National Oceanic and Atmospheric Administration (NOAA)-14 imagery was used to analyse changes in land surface temperature in an area of Central Mexico during the course of each dry season (November–April) for the period 1996–2000. Daily surface temperature was obtained by the split-window method and cloud-free monthly composites were subsequently built. This value was related to maximum air temperatures recorded at meteorological stations and to forest fires detected from night-time images. During 1996–1997 and 1997–1998 (El Niño) dry seasons, monthly surface temperature ranged from 35°C to 46°C and from 33°C to 51°C, respectively; during 1998–1999 (La Niña) and 1999–2000 it was lower, ranging from 28°C to 47°C, and from 28°C to 41°C, respectively. At the end of El Niño, land surface temperatures higher than 50°C were registered, and 730 forest fires were detected, suggesting that this temperature increment also contributed to the vulnerability of vegetation to fire. It is concluded that land surface temperature during the first four months of the dry season can be used as a variable for modelling the probability of forest fire occurrence, in combination with other environmental variables. Similarities between land surface temperature and maximum air temperature suggest the potential use of NOAA-AVHRR imagery for evaluating El Niño/La Niña effects on the continental surface.     

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

Climatic oscillations in aerosol optical depth over tropical oceanic regions pertaining to genesis of the Indian Ocean Dipole

Data on aerosol optical depth (AOD) derived from the ocean colour sensor of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from September 1997 to December 2010 over the western tropical Indian Ocean (WTIO) (10° S to 10° N; 50° E to 70° E) and southeastern tropical Indian Ocean (SETIO) (10° S to equator; 90° E to 110° E) were analysed with a view to understanding its response to climatic oscillations in regard to the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study demonstrates the existence of a bimodal distribution pattern of AOD in the atmosphere over both WTIO and SETIO, with the highest values being around 1.1 during the period of primary maximum during August over the WTIO and during October over the SETIO. A secondary maximum (～0.9) appeared during March over both areas. In addition, the existence of a see-saw oscillation in the distribution of AOD between the atmospheric columns over the study regions was revealed, with higher values during August–December over the SETIO. AOD data over the SETIO captured very well the influence of these atmospheric modes, whereas the influence was not as significant over the WTIO. Stronger El Niño (Niño index > 0.80) events produced a significantly positive (more than +0.03) anomaly in AOD values over the SETIO during October, whereas the lone mode of IOD events and La Niña were not sufficient to induce any significant change in the aerosol distribution over the area. The mode of El Niño co-occurring with a positive IOD (PIOD) strengthens this anomalous behaviour. A significantly negative anomaly (≤0.03) in AOD was observed with concurrent La Niña (Niño index < ?1.1) and negative IOD (NIOD) (dipole mode index ≤ 1.1) events. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) winds were utilized to verify these observations.     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.     

MERIS observations of chlorophyll-a dynamics in Erhai Lake between 2003 and 2009

The Medium Resolution Imaging Spectrometer (MERIS) was used to investigate the spatial and temporal dynamics of chlorophyll-a (chl-a) in Erhai Lake, the second largest freshwater lake in the Yunnan province of China. Six chl-a retrieval models, including four Basic ERS & Envisat (A)ATSR and Meris Toolbox (BEAM) software-incorporated algorithms and MERIS three-band and two-band models, were validated to find the best fit to extract chl-a concentration in Erhai Lake. With a chl-a range of 5–15 mg m^–3, the Lakes/Eutrophic method showed the best performance. The algorithm was then applied to eight-year cloud-free MERIS images between 2003 and 2009, with seasonal and inter-annual variability analysed. Long-term chl-a distributions of Erhai Lake revealed significant seasonal and inter-annual variability. The mean chl-a of the south lake was higher in summer (14.3 mg m^–3) than in spring (10.1 mg m^–3), while generally lower chl-a was found in the north lake with a mean chl-a of 6.4 mg m^–3 in spring and 9.0 mg m^–3 in summer, respectively. An increasing trend was found between 2006 and 2009, and the increasing rate was 12.9% for annual chl-a of the entire lake. While chl-a seasonality was attributed to the seasonal changes of the local temperature, the inter-annual variation was possibly linked to the discharged wastewater from Dali City. This work could provide critical information for decision-makers to manage Erhai Lake’s aquatic ecosystems.     

Evaluation of atmospheric correction and high-resolution processing on SeaDAS-derived chlorophyll-a: an example from mid-latitude mesotrophic waters

Over the last 15 years, great effort has gone into the development of chlorophyll-a (chl-a) retrieval algorithms for case 2 waters, where variations in the water leaving radiance signal are not well correlated with concentrations of chl-a. In this study, we investigate the effectiveness of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived chl-a retrieval algorithms in the less productive coastal waters around Tasmania, Australia. Algorithms were evaluated using matches between satellite imagery and in-situ water samples (number of samples, n = 16–65) derived from a 604 sample data set collected over a 9-year period. Three aerosol correction models and three chl-a retrieval algorithms were evaluated using both standard and high-resolution processing procedures using the National Aeronatics and Space Adminstration’s SeaDAS software package. chl-a retrievals were evaluated in Bass Strait, where in-situ chl-a was less than 1 mg m^?3 and retrievals were less affected by coloured dissolved organic matter. chlor_a, the default SeaDAS chl-a product, with the Management unit of the North Sea Mathematical models aerosol correction algorithm performed best (root mean square error (RMSE) = 0.09 mg m^?3; mean absolute percentage error (MAPE) = 34%; coefficient of determination, R² = 0.75). The fluorescence line height algorithm using Rayleigh corrected top of atmosphere reflectances (RMSE = 0.11 mg m^?3, MAPE = 41%, R² = 0.61) may provide an alternative in waters where full atmospheric correction is problematic and the two-band red/near-infrared algorithm failed to provide a meaningful estimate of chl-a. High-resolution processing of MODIS imagery improved spatial resolution but reduced chl-a retrieval accuracy, reducing the agreement between measured and predicted levels by between 12% and 25% depending on the retrieval algorithm. The SeaDAS default chlor_a product proved superior to the alternatives in mid-latitude mesotrophic coastal waters with low chl-a concentrations. In addition, there appears little benefit in using MODIS high-resolution processing mode for chl-a retrievals.     

Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas,China

In optically complex waters, it is important to evaluate the accuracy of the standard satellite chlorophyll-a (chl-a) concentration algorithms, and to develop accurate algorithms for monitoring the dynamics of chl-a concentration. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing reflectance and concurrent in situ measured chl-a (2010–2013) were used to evaluate the standard OC3M algorithm (ocean chlorophyll-a three-band algorithm for MODIS) and Graver–Siegel–Maritorena model version 1 (GSM01) algorithm for estimating chl-a concentration in the Bohai and Yellow Seas (BYS). The results showed that the chl-a algorithms of OC3M and GSM01 with global default parameters presented poor performance in the BYS (the mean absolute percentage difference (MAPD) and coefficient of determination (R²) of OC3M are 222.27% and 0.25, respectively; the MAPD and R² of GSM01 are 118.08% and 0.07, respectively). A novel statistical algorithm based on the generalized additive model (GAM) was developed, with the aim of improving the satellite-derived chl-a accuracy. The GAM algorithm was established using the in situ measured chl-a concentration as the output variable, and the MODIS above water remote-sensing reflectance (visible bands at 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm) and bathymetry (water depth) as input variables. The MAPD and R² calculated between the GAM and the in situ chl-a concentration are 39.96% and 0.67, respectively. The results suggest that the GAM algorithm can yield a superior performance in deriving chl-a concentrations relative to the standard OC3M and GSM01 algorithms in the BYS.     

Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River,India

ABSTRACT

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.     

Change detection using remote sensing in a reef environment of the UAE during the extreme event of El Niño 2015–2016

ABSTRACT

Coral reefs of the United Arab Emirates (UAE) are living in the world’s hottest sea. Recently, corals harbouring Symbiodinium thermophilum, a thermotolerant microalgae, were found to be prevalent among UAE reefs and were reported to endure extreme sea-surface temperatures. Late 2015–early 2016 was marked with the strongest El Niño on record worldwide, which caused massive coral bleaching (loss of symbiotic microalgae from reef-building corals). In September 2015, the waters flanking UAE coasts were identified to be among the areas facing a thermal stress reaching its highest level liable to cause massive coral bleaching. However, the effect of this thermal stress on UAE corals remained largely unknown. Here, multi-temporal DubaiSat-2 satellite images were used to show that changes in the reef environment of Dalma Island, UAE, between 2014 and 2016, occurred in macroalgae-dominant habitats, whereas live corals remained unaltered. Furthermore, extending the study to a larger area helped in discovering a continuum of live and pristine corals, which was not reported or studied before. While sea-surface temperature anomalies of 1°C were reported to significantly damage coral reefs around the world, the live coral habitat was observed to exhibit no-change despite four consecutive months of +2°C to 3°C anomalies reported during the study period. These findings point to the tolerance of UAE live corals faced with extreme climate conditions.     

Effect of optically active substances and atmospheric correction schemes on remote-sensing reflectance at a coastal site off Kochi

The present study focused on understanding the variability of optically active substances (OASs) and their effect on spectral remote-sensing reflectance (R_rs). Furthermore, the effect of atmospheric correction schemes on the retrieval of chlorophyll-a (chl-a) from satellite data was also analysed. The OASs considered here are chl-a, coloured dissolved organic matter (CDOM), and total suspended matter (TSM). Satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite was used for this study. The two atmospheric correction schemes considered were: multi-scattering with two-band model selection NIR correction (hereon referred as ‘A1’) and Management Unit of the North Sea Mathematical Models (MUMM) correction and MUMM NIR calculation (hereafter referred as ‘A2’). The default MODIS bio-optical algorithm (OC3M) was used for the retrieval of chl-a. Analysis of OASs showed that chl-a was the major light-absorbing component, with highly variable distribution (0.006–25.85 mg m^–3). Absorption due to CDOM at 440 nm (a_CDOM440) varied from 0.002 to 0.31 m^–1 whereas TSM varied from 0.005 to 33.44 mg l^–1. The highest concentration of chl-a was observed from August to November (i.e. end of the southwest monsoon and beginning of the northeast monsoon), which was attributed to coastal upwelling. The average value of a_CDOM440 was found to be lower than the global mean. A significant negative relationship between a_CDOM440 and salinity during the southwest monsoon indicated that much of the CDOM during this season was derived from river discharge. Spectral R_rs was found to be strongly linked to the variability in chl-a concentration, indicating that chl-a was the major light-absorbing component. Satellite-derived spectral R_rs was in good agreement with that in situ when chl-a concentration was lower than 5 mg m^–3. The validation of chl-a, derived from in situ R_rs, showed moderate performance (correlation coefficient, R² = 0.64; log₁₀(RMSE) = 0.434; absolute percentage difference (APD) = 43.6% and relative percentage difference (RPD) = 42.33%). However the accuracy of the algorithm was still within acceptable limits. The statistical analysis for atmospheric correction schemes showed improved mean ratio of measured to estimated chl-a (‘r’ = 1.6), log₁₀(RMSE) (0.49), APD (25.46%), and RPD (17.57%) in the case of A1 as compared with A2, whereas in the case of A2, R² (0.56), slope (0.26), and intercept (0.27) were better as compared with A1. The two atmospheric correction schemes did not show any significant statistical difference. However the default atmospheric correction scheme (A1) was found to be performing comparatively better probably due to the fact that the concentration of TSM and CDOM was much lower to overcome the impact of chl-a.     

Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network

Ommastrephes bartramii is a short-lived species of squid and reacts rapidly to changes in the regional environmental conditions of the fishing ground. Understanding the preferred range of key environmental variables and predicting potential resource distributions are critical to conserve and manage its resources. Commercial fishery data for the western winter–spring cohort of O. bartramii from Chinese squid-jigging vessels during 2003–2013 were used to evaluate a suitable range of three key environmental variables, sea surface temperature (SST), sea surface height (SSH), and chlorophyll-a (chl-a) concentration, and to explore potential fishing zones (PFZs) using an artificial neural network. The neural interpretation diagram and independent variable relevance analysis indicate that month, latitude, and SST had significant influences on the PFZ distribution of O. bartramii, yielding 21.78%, 23.91%, and 26.04% of contribution rates, respectively. Based on the sensitivity analyses, a high abundance of O. bartramii mainly occurred in the waters between 150°–165° E and 37°–42° N during July to August. Suitable ranges of environmental variables for O. bartramii were 11–18°C for SST, ?10 to 60 cm for SSH, and 0.1–1.7 mg/m³ for chl-a concentration, respectively. The back-propagation network model was well developed and could be used to predict the PFZ with 80% accuracy. The actual fishing grounds coincided with the predicted PFZ, suggesting that the established model of PFZ is effective in forecasting the potential habitat of O. bartramii in the Northwest Pacific Ocean.     

A classified El Niño index using AVHRR remote-sensing SST data

A process-orientated El Niño index (PEI) is constructed to determine the detailed classification of two major types of El Niño events during 1985–2009 from remote-sensing monthly sea surface temperature (SST) anomaly data sets. Four revised Niño regions are defined in the tropical Pacific, and the index uses SST anomalies and their duration in each region. Based on their varying evolution, the index allows eastern Pacific (EP) El Niño events to be either weak or strong EP type, and central Pacific (CP) El Niño events to be either weak CP, strong CP, or mixed EPCP type. The El Niño types identified by this index are compared to those obtained using previously published methods, and the differences are examined and discussed. The results suggest that the PEI is optimal for monitoring El Niño events of weaker amplitude and shorter duration. The PEI is focused on the development process of El Niño events, providing a novel perspective for classifying El Niño types. Analysis of the SST evolution of the El Niño events identified here reveals several misclassifications in previous studies, stressing the disadvantage of relying solely on single areas or short time periods. Existing EP and CP El Niño events in the study period were reclassified by the PEI into weak, strong, and mixed events. The listing of El Niño types produced here can be used for selecting El Niño events for further study of their dynamics and climatic impacts.