scholarly journals MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2069 ◽  
Author(s):  
Saleh Daqamseh ◽  
A’kif Al-Fugara ◽  
Biswajeet Pradhan ◽  
Anas Al-Oraiqat ◽  
Maan Habib
Keyword(s):  
Linear Regression ◽  
Chlorophyll A ◽  
Red Sea ◽  
High Potential ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Chl A ◽  
Modis Data ◽  
The Impact

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

scholarly journals An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2769
Author(s):  
Yingying Gai ◽  
Dingfeng Yu ◽  
Yan Zhou ◽  
Lei Yang ◽  
Chao Chen ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Wind Waves ◽  
Biological Indicator ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
New Model ◽  
Chl A ◽  
Improved Model

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

scholarly journals Arctic Ocean CO<sub>2</sub> uptake: an improved multi-year estimate of the air–sea CO<sub>2</sub> flux incorporating chlorophyll-a concentrations

2017 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Eko Siswanto ◽  
Are Olsen ◽  
Mario Hoppema ◽  
Eiji Watanabe ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Co2 Uptake ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
Sea Ice Concentration ◽  
Chl A

Abstract. We estimated monthly air–sea CO2 fluxes in the Arctic Ocean and its adjacent seas north of 60° N from 1997 to 2014, after mapping partial pressure of CO2 in the surface water (pCO2w) using a self-organizing map (SOM) technique incorporating chlorophyll-a concentration (Chl-a), sea surface temperature, sea surface salinity, sea ice concentration, atmospheric CO2 mixing ratio, and geographical position. The overall relationship between pCO2w and Chl-a is negative in most regions when Chl-a ≤ 1 mg m−3, whereas there is no significant relationship when Chl-a > 1 mg m−3. In the Kara Sea and the East Siberian Sea and the Bering Strait, however, the relationship is typically positive in summer. The addition of Chl-a as a parameter in the SOM process enabled us to improve the estimate of pCO2w via better representation of its decline in spring, which resulted from biologically mediated pCO2w reduction. Mainly as a result of the inclusion of Chl-a, the uncertainty in the CO2 flux estimate was reduced, and a net annual Arctic Ocean CO2 uptake of 180 ± 130 TgC y−1 was determined to be significant.

scholarly journals MEASURING SEA SURFACE SALINITY OF THE JAKARTA BAY USING REMOTELY SENSED OF OCEAN COLOR DATA ACQUIRED BY MODIS SENSOR

2015 ◽  
Vol 36 (2) ◽  
pp. 51-70 ◽  
Author(s):  
Sam Wouthuyzen
Keyword(s):  
Chlorophyll A ◽  
Sediment Resuspension ◽  
Ocean Color ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Minor Role ◽  
Water Runoff ◽  
Surface Salinity ◽  
Chl A

Observations on oceanographic parameters using remote sensing techniques intensively have been done for more than 3 decades for estimating and mapping the sea surface temperature (SST) and the abundance of phytoplankton expressed as the concentration of chlorophyll-a and applied them in studying the ocean phenomenon. As a result, the product of these 2 parameters for all over the oceans in the world has been established and available in daily basis. However, on the contrary, there is still limited application for sea surface salinity (SSS) which is also one of the most important oceanographic features. This paper describes a novel method of deriving SSS from remotely sensed ocean color. The method is based on two important observations of optical properties in regions of freshwater influences. The first is the strong effect of Colored Dissolved Organic Matter (CDOM or yellow substance) on ocean color when present in relatively high concentrations. The second is the close relationship between salinity and CDOM originating from fresh water runoff. In this paper, these relationships are demonstrated for the Jakarta Bay, Indonesia. The MODIS sensor in Terra and Aqua satellites imageries and 10 in situ measurements conducted near-simultaneously with the satellites over flight over the bay in 2004 and 2006 were implemented for deriving CDOM and SSS. The empirical relationships demonstrated in this study allow the satisfactory prediction of CDOM and SSS in the Jakarta Bay from remotely sensed ocean color. The root mean square (r.m.s) error difference between the observed and predicted parameters are 0.14 m-1 and 0.93 psu for CDOM g440 g and SSS, respectively, over a range of salinity from 24 to 33 psu. This range is in good agreement with field surveys. Parameters that may influence CDOM, such as Chlorophyll-a (CHL-a) and total suspended material (TSM) concentrations were also analyzed. Results showed that there were no relationship at all between CDOM and CHL-a, and between CDOM and TSM. These indicate that phytoplankton plays a minor role in regulating CDOM abundance, and also suggest that CDOM contribution from sediment and/or from sediment resuspension is negligible. Thus, CDOM sources in the Jakarta Bay are mainly from riverine inputs. SSS maps created from the satellite-retrieved ocean color identify features in the surface salinity distribution such as salinity front of > 32 psu that migrated in and out of the bay according to seasons. Therefore, the ability to obtain synoptic views of SSS such as presented in this paper provides great potential in furthering the understanding of coastal environments.

scholarly journals STUDY ON CYCLONE INDUCED PHYTOPLANKTON BLOOM IN THE ARABIAN SEA THROUGH GAP-FREE RECONSTRUCTED CHLOROPHYLL-A DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1391-1396
Author(s):  
R. Shunmugapandi ◽  
S. Gedam ◽  
A. B. Inamdar
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Arabian Sea ◽  
Phytoplankton Bloom ◽  
Suitable Condition ◽  
Sea Surface ◽  
Cyclone Track ◽  
Chl A ◽  
The Impact

Abstract. Ocean surface phytoplankton responses to the tropical cyclone (TC)/storms have been extensively studied using satellite observations by aggregating the data into a weekly or bi-weekly composite. The reason behind is the significant limitations found in the satellite-based observation is the missing of valid data due to cloud cover, especially at the time of cyclone track passage. The data loss during the cyclone is found to be a significant barrier to efficiently investigate the response of chl-a and SST during cyclone track passage. Therefore it is necessary to rectify the above limitation to effectively study the impact of TC on the chlorophyll-a concentration (chl-a) and the sea surface temperature (SST) to achieve a complete understanding of their response to the TC prevailed in the Arabian Sea. Intending to resolve the limitation mentioned above, this study aims to reconstruct the MODIS-Aqua chl-a, and SST data using Data Interpolating Empirical Orthogonal Function (DINEOF) for all the 31 cyclonic events occurred in the Arabian Sea during 2003-2018 (16 years). Reconstructed satellite retrieved data covering all the cyclonic events were further used to investigate the chl-a and SST dynamics during TC. From the results, the exciting fact has been identified that only two TC over the eastern-AS were able to induce phytoplankton bloom. On investigating this scenario using sea surface temperature, it was disclosed that the availability of nutrients decides the suitable condition for the phytoplankton to proliferate in the surface ocean. Relevant to the precedent criterion, the results witnessed that the 2 TC (Phyan and Ockhi cyclone) prevailed in the eastern AS invoked a suitable condition for phytoplankton bloom. Other TC found to be less provocative either due to less intensity, origination region or the unsuitable condition. Thereby, gap-free reconstructed daily satellite-derived data efficiently investigates the response of bio-geophysical parameters during cyclonic events. Moreover, this study sensitised that though several TC strikes the AS, only two could impact phytoplankton productivity and SST found to highly consistent with the chl-a variability during the cyclone passage.

scholarly journals Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2975
Author(s):  
Huabing Xu ◽  
Rongzhen Yu ◽  
Danling Tang ◽  
Yupeng Liu ◽  
Sufen Wang ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Vertical Mixing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Ekman Pumping ◽  
Surface Salinity ◽  
Argo Data ◽  
Before And After ◽  
The Impact

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

scholarly journals Adjusting Neural Network to a Particular Problem: Neural Network-Based Empirical Biological Model for Chlorophyll Concentration in the Upper Ocean

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Vladimir Krasnopolsky ◽  
Sudhir Nadiga ◽  
Avichal Mehra ◽  
Eric Bayler
Keyword(s):  
Neural Network ◽  
Chlorophyll A ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Biological Model ◽  
Upper Ocean ◽  
Wide Field ◽  
Biological Processes ◽  
Chl A ◽  
The Impact

The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great variety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually requires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a NN application that demonstrates the importance of such adjustments; moreover, in this case, the adjustments applied to a generic NN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll “a” (chl-a) variability—primarily driven by biological processes—with the physical processes of the upper ocean using a NN-based empirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and sea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures of upper-ocean dynamics. Chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the root-mean-square error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the impact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is constructed. This study demonstrates that the NN technique provides an accurate, computationally cheap method to generate long (up to 10 years) time series of consistent chl-a concentration that are in good agreement with chl-a data observed by different satellite sensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and time. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction systems, and data assimilation systems to dynamically consider biological processes in the upper ocean.

Removing the impact of wind direction on remote sensing of sea surface salinity

2006 ◽  
Vol 51 (11) ◽  
pp. 1368-1373 ◽  
Author(s):  
Xiaobin Yin ◽  
Yuguang Liu ◽  
Hande Zhang
Keyword(s):  
Remote Sensing ◽  
Wind Direction ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Impact

scholarly journals Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

2019 ◽  
Vol 11 (17) ◽  
pp. 1964 ◽  
Author(s):  
Jorge Vazquez-Cuervo ◽  
Jose Gomez-Valdes ◽  
Marouan Bouali ◽  
Luis Miranda ◽  
Tom Van der Stocken ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Current System ◽  
California Current ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
California Current System ◽  
The Impact ◽  
Buoy Measurements

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

scholarly journals Biogeographic classification of the Caspian Sea

2014 ◽  
Vol 11 (22) ◽  
pp. 6451-6470 ◽  
Author(s):  
F. Fendereski ◽  
M. Vogt ◽  
M. R. Payne ◽  
Z. Lachkar ◽  
N. Gruber ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Caspian Sea ◽  
Benthic Invertebrate ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Agglomerative Clustering ◽  
The Caspian Sea ◽  
Surface Salinity ◽  
Chl A

Abstract. Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl a concentrations between the different ecoregions shows significant differences (one-way ANOVA, P < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence–absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics.

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