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Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine
... In this context, the Moderate Resolution Imaging Spectroradiometer sensor onboard NASA's Aqua satellite (Aqua MODIS) emerges as a vital tool. The Aqua MODIS provides an invaluable resource, providing unprecedented, extended temporal coverage of sea surface temperature and Chl-a concentration data from a single sensor [34][35][36][37]. ...
... To achieve this, Geographic Information System (GIS) technology and the remote sensing dataset from the Aqua MODIS sensor are used in this study. The Aqua MODIS comprehensive dataset, including Chl-a concentration, will provide essential information on environmental conditions [12,22,34,35,39]. ...
... Indonesia's estuarine cities [25,40,34,35,39,41], specifically densely populated metropolitan areas such as Jakarta, confront formidable challenges of deteriorating estuarine water quality. Anthropogenic pollutants, including nutrients, stand as pivotal contributors to the issue of estuarine water pollution. ...
Similar to many estuaries worldwide with sources receiving nitrogen and phosphorus, i.e., nutrients, from point and diffuse sources, the waters in Jakarta Bay, Musi Estuary, and Rokan Estuary in Indonesia are facing negative impacts on water quality and ecosystems, i.e., eutrophication, because of rapid urbanization and human activities. The transport of nutrients through rivers and tributaries depends on rainfall and climate phenomena, ultimately dictating chlorophyll-a (Chl-a) concentrations and trophic levels in estuaries. The relationship between trophic level, Chl-a concentration, rainfall, and climate phenomena was explored in this study by examining monthly Chl-a concentrations from 2003 to 2021 in the three estuaries. Remote sensing Chl-a concentrations data from the NASA Aqua MODIS mission was subjected to Geographic Information System (GIS) and statistical analyses. The dynamic fluctuations of Chl-a concentrations in all estuaries showed eutrophic zones appearing at specific times, influenced by local rainfalls and their patterns. The first principal components of the Empirical Orthogonal Function (EOF) analysis of Chl-a concentration anomalies showed significant correlations with rainfall anomalies and the Indian Ocean Dipole (IOD) index. These relationships exhibited distinct patterns influenced by unique climate factors in each estuary. The study highlights the crucial role of wide-area continuous monitoring and early warning systems, facilitated by satellite remote sensing, in preserving the health of coastal ecosystems. The findings also offer valuable insights for designing future monitoring programs and targeted conservation efforts.
... Over the past century, the Earth's average surface temperature has been rising (Hansen et al., 2022;Katlane et al., 2023). Evidence indicates that extreme weather events, including heat waves, are increasing in both frequency and intensity (Walsh et al., 2020). ...
Backgrounds
As a conserved signaling pathway, mitogen-activated protein kinase (MAPK) cascade regulates cellular signaling in response to abiotic stress. High temperature may contribute to a significant decrease in economic yield. However, research into the expression patterns of StMAPKK family genes under high temperature is limited and lacks experimental validation regarding their role in supporting potato plant growth.
Methods
To trigger heat stress responses, potato plants were grown at 35°C. qRT-PCR was conducted to analyze the expression pattern of StMAPKK family genes in potato plants. Plant with StMAPKK5 loss-of-function and gain-of-function were developed. Potato growth and morphological features were assessed through measures of plant height, dry weight, and fresh weight. The antioxidant ability of StMAPKK5 was indicated by antioxidant enzyme activity and H2O2 content. Cell membrane integrity and permeability were suggested by relative electrical conductivity (REC), and contents of MDA and proline. Photosynthetic capacity was next determined. Further, mRNA expression of heat stress-responsive genes and antioxidant enzyme genes was examined.
Results
In reaction to heat stress, the expression profiles of StMAPKK family genes were changed. The StMAPKK5 protein is located to the nucleus, cytoplasm and cytomembrane, playing a role in controlling the height and weight of potato plants under heat stress conditions. StMAPKK5 over-expression promoted photosynthesis and maintained cell membrane integrity, while inhibited transpiration and stomatal conductance under heat stress. Overexpression of StMAPKK5 triggered biochemical defenses in potato plant against heat stress, modulating the levels of H2O2, MDA and proline, as well as the antioxidant activities of CAT, SOD and POD. Overexpression of StMAPKK5 elicited genetic responses in potato plants to heat stress, affecting heat stress-responsive genes and genes encoding antioxidant enzymes.
Conclusion
StMAPKK5 can improve the resilience of potato plants to heat stress-induced damage, offering a promising approach for engineering potatoes with enhanced adaptability to challenging heat stress conditions.
... Additionally, they can sometimes be time-consuming since they need to be applied separately for each time interval if the temporal scale is long. In recent years, Google Earth Engine (GEE), a cloud-based geospatial analysis platform, has been employed to monitor the effects of climate change and anthropogenic activities on the Earth's features [22][23][24][25][26][27][28][29]. The integration of the GEE platform has significantly enhanced research accessibility, offering robust computational resources at no cost [25,30]. ...
In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables.
... Chlorophyll plays a pivotal role as a pigment in the photosynthetic processes of phytoplankton and algae, providing crucial insights into the growth conditions of marine vegetation [1]. Monitoring the concentration of chlorophyll on the sea's surface serves is a valuable tool for assessing water quality [2], particularly in relation to nutrient levels. Anomalous chlorophyll concentrations can cause ecosystem changes, such as eutrophication and occurrences of cyanobacterial blooms, often attributed to human activities, agricultural runoff, and urban pollution. ...
Gaining insights into the space–time variations in the long-range dependence of sea surface chlorophyll is crucial for the early detection of environmental issues in oceans. To this end, 12 locations were selected along the Yangtze River and Pearl River estuaries, varying in distances from the Chinese coastline. Daily satellite-observed sea surface chlorophyll concentration data at these 12 locations were collected from the Copernicus Marine Service website, spanning from December 1997 to November 2023. The main objective of the current study is to introduce a multi-fractional generalized Cauchy model for calculating the values of Hurst exponents and quantitatively assessing the long-range dependence strength of sea surface chlorophyll at different spatial locations and time instants during the study period. Furthermore, ANOVA was utilized to detect the differences of calculated Hurst exponent values among the locations during various months and seasons. From a spatial perspective, the findings reveal a significantly stronger long-range dependence of sea surface chlorophyll in offshore regions compared to nearshore areas, with Hurst exponent values > 0.5 versus <0.5. It is noteworthy that the values of Hurst exponents at each location exhibit significant differences during various seasons, from a temporal perspective. Specifically, the long-range dependence of sea surface chlorophyll in summer in the nearshore region is weaker than in other seasons, whereas that in the offshore region is stronger than in other seasons. The study concludes that long-range dependence is inversely related to the distance from the coastline, and anthropogenic activity plays a dominant role in shaping the long-range dependence of sea surface chlorophyll in the coastal regions of China.
... In terms of data acquisition, a large amount of data support is required for large-scale ecological environment assessment, resulting in a geometric growth in the complexity and time consumption of data preprocessing. In order to address this issue, a planetary-scale geospatial analysis using cloud systems like Google Earth Engine (GEE) [50] is needed. It can provide rich open-source data and powerful computing services, greatly shortening the time for remote sensing data collection and processing. ...
Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.
The proxy for phytoplankton biomass, Chlorophyll a (Chl a), is an important variable to assess the health and state of the oceans which are under increasing anthropogenic pressures. Prior to the operational use of satellite ocean-colour Chl a to monitor the oceans, rigorous assessments of algorithm performance are necessary to select the most suitable products. Due to their inaccessibility, the oligotrophic open-ocean gyres are under-sampled and therefore under-represented in global in situ data sets. The Atlantic Meridional Transect (AMT) campaigns fill the sampling gap in Atlantic oligotrophic waters. In-water underway spectrophotometric data were collected on three AMT field campaigns in 2016, 2017 and 2018 to assess the performance of Sentinel-3A (S3-A) and Sentinel-3B (S3-B) Ocean and Land Colour Instrument (OLCI) products. Three Chl a algorithms for OLCI were compared: Processing baseline (pb) 2, which uses the ocean colour 4 band ratio algorithm (OC4Me); pb 3 (OL_L2M.003.00) which uses OC4Me and a colour index (CI); and POLYMER v4.8 which models atmosphere and water reflectance and retrieves Chl a as a part of its spectral matching inversion. The POLYMER Chl a for S-3A OLCI performed best. The S-3A OLCI pb 2 tended to under-estimate Chl a especially at low concentrations, while the updated OL_L2M.003.00 provided significant improvements at low concentrations. OLCI data were also compared to MODIS-Aqua (R2018 processing) and Suomi-NPP VIIRS standard products. MODIS-Aqua exhibited good performance similar to OLCI POLYMER whereas Suomi-NPP VIIRS exhibited a slight under-estimate at higher Chl a values. The reasons for the differences were that S-3A OLCI pb 2 Rrs were over-estimated at blue bands which caused the under-estimate in Chl a. There were also some artefacts in the Rrs spectral shape of VIIRS which caused Chl a to be under-estimated at values >0.1 mg m⁻³. In addition, using in situ Rrs to compute Chl a with OC4Me we found a bias of 25% for these waters, related to the implementation of the OC4ME algorithm for S-3A OLCI. By comparison, the updated OLCI processor OL_L2M.003.00 significantly improved the Chl a retrievals at lower concentrations corresponding to the AMT measurements. S-3A and S-3B OLCI Chl a products were also compared during the Sentinel-3 mission tandem phase (the period when S-3A and S-3B were flying 30 sec apart along the same orbit). Both S-3A and S-3B OLCI pb 2 under-estimated Chl a especially at low values and the trend was greater for S-3A compared to S-3B. The performance of OLCI was improved by using either OL_L2M.003.00 or POLYMER Chl a. Analysis of coincident satellite images for S-3A OLCI, MODIS-Aqua and VIIRS as composites and over large areas illustrated that OLCI POLYMER gave the highest Chl a concentrations and percentage (%) coverage over the north and south Atlantic gyres, and OLCI pb 2 produced the lowest Chl a and % coverage.
Multispectral and hyperspectral sensor data of the bio-optical parameters with a high spatial resolution are important for monitoring and mapping of the coastal ecosystems and estuarine areas, such as the Kneiss Islands in the Gulf of Gabes. Sentinel 2 S2A and Hyperion Earth observing-1 (EO1) imaging sensors reflectance data have been used for water quality determination and mapping of turbidity TU and chlorophyll Chl-a in shallow waters. First, we applied a tidal swing area mask based on uncorrelated pixel via 2D scatter plot between 665 nm and 865 nm to eliminate the overestimation of the concentration of water quality parameters due to the effect of the bottom reflection. The processing for mapping and validating Chl-a, Turbidity S2A, and EO1 were performed using a relation between reflectance bands and in situ measurements. Therefore, we were able to validate the performance of the case 2 regional coast colour processor (C2RCC) as well as our region-adapted empirical optical remote sensing algorithms. Turbidity was mapped based on the reflectance of 550 nm band for EO1 (R2 = 0.63) and 665 nm band for S2A (R2 = 0.70). Chlorophyll was mapped based on (457/528 nm) reflectance ratio (R2 = 0.57) for EO1 and (705/665 nm) reflectance ratio (R2 = 0.72) for the S2A.
A comparative approach of hydrological variables in order to clarify the abundance and diversity of the zooplankton community was investigated on a spring cruise in the Northern and Southern coastal waters of Sfax coupled with water-quality. Environmental and zooplankton data were taken across the tidal front TF, covering well-mixed MZ and frontal interface FI waters. Zooplankton abundance, structure, specific composition and life histories were analyzed in relation to depth, temperature, salinity, orthophosphate, major and trace elements and chlorophyll-a. Zooplankton-specific composition did not vary across the different zones but, due to differences in relative abundance in Southern and Northern part. Three main copepod assemblages were revealed and characterized by the distribution of physico-chemical variables and chl-a. A strong divergence of zooplankton group was deduced through both multivariate analyses (NMDS, PCA and CCA), reflecting changes in zooplankton between the three typical zones. Results suggest that environmental parameters related differential responses of zooplankton should be considered when zooplankton monitoring is performed to assess the effect of climate forcing and pollution in coastal environments. Data on the distribution of copepod assemblages in the sfax coastline are scarce makes our contribution useful for further global effect of climate forcing and pollution in coastal environments in the Mediterranean Sea.
As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.
Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.
Sea level and sea surface temperature inter-annual variability and trends in the Mediterranean Sea were investigated during the period 1993–2017. These were carried out using gridded absolute dynamic topography from satellite altimetry, tide gauge (TG) time series from 25 stations and gridded sea surface temperature (SST) from advanced very-high-resolution radiometer (AVHRR) data. The coastal TG data were used to verify the satellite derived sea level. Moreover, the contributions of atmospheric pressure and North Atlantic Oscillation (NAO) to sea level changes were also examined. The results revealed that the Mediterranean Sea exhibits inter-annual spatiotemporal coherent variability in both sea level and SST. The spatial variability in sea level is more significant over the Adriatic and Aegean Seas, most of the Levantine basin, and along the Tunisian shelf. Marked spatial variability in SST occurs over the central part of the Mediterranean Sea with maximum amplitude in the Tyrrhenian Sea. The highest temporal variability of sea level and SST was found in 2010 and 2003, respectively. The inter-annual variability of sea level and SST accounts for about 32% and 3% of the total variance of sea level and SST, respectively. An analysis of sea level anomaly reveled large negative values during the extended winter of 2011–2012, which may be attributed to the strong positive phase of NAO index. Satellite altimetry indicated a significant positive sea level trend of 2.7 ± 0.41 mm/year together with a significant warming of 0.036 ± 0.003 °C/year over the whole Mediterranean Sea for the period 1993–2017.
Monitoring water surface dynamics is essential for the management of lakes and reservoirs, especially those are intensively impacted by human exploitation and climatic variation. Although modern satellites have provided a superior solution over traditional methods in monitoring water surfaces, manually downloading and processing imagery associated with large study areas or long-time scales are time-consuming. The Google Earth Engine (GEE) platform provides a promising solution for this type of “big data” problems when it is combined with the automatic water extraction index (AWEI) to delineate multi-temporal water pixels from other forms of land use/land cover. The aim of this study is to assess the performance of a completely automatic water extraction framework by combining AWEI, GEE, and Landsat 8 OLI data over the period 2014–2018 in the case study of New Zealand. The overall accuracy (OA) of 0.85 proved the good performance of this combination. Therefore, the framework developed in this research can be used for lake and reservoir monitoring and assessment in the future. We also found that despite the temporal variability of climate during the period 2014–2018, the spatial areas of most of the lakes (3840) in the country remained the same at around 3742 km². Image fusion or aerial photos can be employed to check the areal variation of the lakes at a finer scale.
The Google Earth Engine (GEE) is a cloud computing platform designed to store and process huge data sets (at petabyte-scale) for analysis and ultimate decision making [...]
The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.
Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r²), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.
Although aquaculture development is experiencing a rapid growth on the southern Mediterranean shore, studies of its environmental impact are still limited. This study, conducted from January to December 2014, in and around a cage fish farm located in Monastir Bay (eastern coast of Tunisia), investigated the hypothesis that cage fish farming would affect the water column quality and plankton communities. Phytoplankton diversity in addition to 13 physico-chemical parameters and chlorophyll-a were all investigated at 11 experimental stations located inside and around the farm. A total of 106 phytoplankton taxa were identified at genus and/or species level, and assigned to two major (dinoflagellates and diatoms) and four minor (cyanobacteria, euglenophyceae, coccolithophorides, and dictyophyceae) groups, with a remarkable predominance of heterotrophic and mixotrophic species, in particular among dinoflagellates taxa, highlighting a top-down model of phytoplankton regulation. Across seasons, a high Shannon-Wiener diversity index (H′) was recorded. Further, we have demonstrated that nutrients were the most important factors contributing to differences in species abundance, as revealed by N/P and Si/Pi ratios that were factors most affecting phytoplankton distribution. Our findings revealed that fish farming aquaculture became an additional source of nutrients in the bay. In some stations, nutrient loading has little or no trophic impact, while, at others, there is evidence that nutrients are assimilated by primary producers. Our study provides a rich information platform for conservation strategies of the bay of Monastir.
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g., country level) and multiple sensors (e.g., Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km² and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that GEE provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.
The Levantine basin in the Eastern Mediterranean Sea is subject to spatial and seasonal variations in primary production and physical-chemical properties both on a short and long-term basis. In this study, the monthly means of daily MODIS product images were averaged between 2002 and 2015, and used to characterize the phytoplankton blooms in different bioregions of the Levantine basin. The selected products were the sea surface temperature (SST), the chlorophyll-a concentration (Chl-a), the diffuse attenuation coefficient for downwelling irradiance at 490nm (Kd_490) and the colored dissolved organic matter index (CDOM_index). Our results showed that phytoplankton blooms were spatially and temporally variable. They occurred in late autumn at the Nile Delta, in early spring and late summer at the eastern coastline, and in spring at the northeastern coastline. The northern coastline and the open water had a common bloom occurring in winter. The Nile Delta was found to be the most productive area of the Levantine basin showing high Chl-a. Kd_490 and Chl-a present a parallel co-variation indicating a dominance of Case 1 waters in the Levantine basin. The CDOM_index shows a phase shift with the Chl-a fluctuation. A strong inverse correlation was observed between both Chl-a and CDOM_index with SST, connoting an indirect relation represented by a depression of CDOM in summer by photobleaching, and a suppression of the chlorophyll-a concentration due to water stratification, together with nutrient stress. An overestimation of the Chl-a values had been signaled by the use of the CDOM_index, suggesting a correction plan in a latter study.
Playa wetlands in Nebraska provide globally important habitats for migratory waterfowl. Inundation condition is an important indicator of playa wetland functionality. However, there is a lack of long-term continuous monitoring records for playa wetlands. The objective of this study was to determine a suitable index for Landsat images to map the playa inundation status in March and April during 1985–2015. Four types of spectral indices—negative normalized vegetation index, Normalized Difference Water Index (NDWI), modified NDWI, and Tasseled Cap Wetness-Greenness Difference (TCWGD)—were evaluated to detect playa inundation conditions from Landsat images. The results indicate that the TCWGD is the most suitable index for distinguishing playa inundation status. By using Landsat images and Google Earth Engine, we mapped the spring inundation condition of Nebraska playas during 1985–2015. The results show that the total inundated areas were 176.79 km2 in spring migratory season, representing 18.92% of the total area of playa wetlands. There were 9898 wetlands inundated at least once in either March or April during the past 30 years, representing 29.41% of a total of 33,659 historical wetlands. After comparing the historical hydric soil footprints and the inundated areas, the results indicate that the hydrological conditions of the majority of playas in Nebraska have changed. The inundated wetlands are candidates for protection and/or partial restoration, and the un-inundated wetlands need more attention for wetland restoration. Wetlands in areas enrolled in conservation easements had a significantly high level of playa inundation status than non-conserved wetlands during spring migratory seasons in the past decades.These conservation easements only count for 4.29% of the total footprint areas, but they have contributed 20.82% of the inundation areas in Nebraska during the past 30 years.
The seasonal and year-to-year variability of the phytoplankton size class
(PSC) spatial distribution has been examined in the Mediterranean Sea by
using the entire time series of Sea-viewing Wide Field-of-view Sensor
(SeaWiFS) space observations (1998–2010). Daily maps of PSCs have been
determined using an empirical model based on a synoptic relationship between
surface chlorophyll a and diagnostic pigments referred to different
taxonomic groups. The analysis of micro-, nano- and pico-phytoplankton
satellite time series (1998–2010) describes, quantitatively, the algal
assemblage structure over the basin and reveals that the main contribution to
chlorophyll a in most of the Mediterranean Sea comes from the
pico-phytoplankton component, especially in nutrient-poor environments.
Regions with different and peculiar features are the Northwestern
Mediterranean Sea, the Alborán Sea and several coastal areas, such as the
North Adriatic Sea. In these areas, local interactions between physical and
biological components modulate the composition of the three phytoplankton
size classes. It results that, during the spring bloom season,
micro-phytoplankton dominates in areas of intense vertical winter mixing and
deep/intermediate water formation, while in coastal areas micro-phytoplankton
dominates in all seasons because of the nutrient supply from the terrestrial
inputs. In the Alborán Sea, where the Atlantic inflow modulates the
nutrient availability, any predominance of one class over the other two has
been observed. The nano-phytoplankton component instead remains widespread
over the entire basin along the year, and its contribution to chlorophyll a
is of the order of 30–40 %. The largest inter-annual signal occurs in
the Northwestern Mediterranean Sea, driven by the year-to-year variation in
intensity and extension of the spring bloom, followed by the Alborán Sea,
in which the inter-annual variability is strongly modulated by the Atlantic
inflow.
In absence of sufficient in situ data of community composition, the
satellite-based analysis demonstrated that pico-, nano- and micro-phytoplankton classes
often coexist. The predominance of one group over the other ones is strongly
dependent on the physical and biological processes occurring at the mesoscale. These
processes directly influence the nutrient and light availability, which are
the principal forcing for the algae growth.
A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 μg L-1. Most of the observations are from Case I nonpolar waters, and ~20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal: noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.
Optical remote sensing was used to provide scientific information to support environmental management in the Gulf of Gabes located on the southeastern coast of Tunisia. This region is characterized by a shallow continental shelf with semi-diurnal tides. Since the early 1970s, industrial activity in this area may have contributed to the degradation of ecosystem biodiversity due to problems of eutrophication and the disappearance of benthic and planktonic species. Previous studies assessing the long term effects of anthropogenic and natural discharges on the Gulf of Gabes were based on regular seaborne measurements of total suspended matter, Secchi depth, turbidity, chlorophyll and temperature. However, these are often costly and difficult to obtain. The objective of this study is to carry out an optical remote sensing analysis with supporting in situ measurements of chlorophyll to monitor water quality and the impact of industrial discharges in the Gulf of Gabes. To start with, data provided both by the optical sensor and by our in situ sampling methods is described. Then, semi-empirical algorithms (OC3, OC5) are presented which are used to process Moderate Resolution Imaging Spectrometer MODIS data for retrieval of chlorophyll (CHL) and a validated algorithm for TU. A comparison was made between the derived estimates CHL and TU and the concurrent bio-optical in situ measurements at stations representing the coast and open ocean and produced significant correlations. The results obtained therefore confirm the high sensitivity and useful spatial coverage of MODIS data to provide a significant tool to accurately assess water quality parameters.
The summer spatial distribution of the dinoflagellate community along an open coastal sea gradient in the Gulf of Gabes (Tunisia, Eastern Mediterranean Sea), together with environmental factors, were studied. The most dominant families were represented by Gymnodiniaceae (32%), Peridiniaceae (20%), Prorocentraceae (15%), Ceratiaceae (13%) and Ebriaceae (10%). The dinoflagellate community was spatially more concentrated along the coast of the gulf than in the open sea. Eight toxic dinoflagellates were recorded, including Karenia cf. selliformis (37% of total toxic dinoflagellates) which was evenly distributed in both the neritic and open sea areas. Dinocysts contributed 33% of the total motile cells and were more abundant along the coast than in the open sea. This high concentration may be ascribed to nitrogen inputs in the coastal waters of Gabes. The Modified Atlantic Water governed dinoflagellate development in the open sea. The degradation of the water quality due to eutrophication in the Gulf of Gabes may have significant socioeconomic consequences. We suggest that a management framework, similar to that used in freshwater ecosystems, should be developed for the Gulf coast in order to drastically reduce urban interferences.
RESUME Les données du Réseau National de Surveillance Phytoplanctonique ont été examinées dans le golfe de Gabes durant la période 1995-2005. Un intérêt particulier a été attribué au phénomène de bloom phytoplanctonique. En outre, nous avons examiné plus spécifiquement les variations spatio-temporelles des espèces responsables et les relations avec la variabilité des conditions abiotiques, essentiellement la température, la salinité et le pH. Les résultats ont montré que la période allant d'août à janvier, avec des pics en septembre et janvier, semble être propice à des efflorescences algales. Les dinoflagellés sont responsables de 89% des cas ; l'espèce Karenia selliformis détient à elle seule 64% des occurrences de ce phénomène. Les blooms de K. selliformis, bien que relativement fréquents dans des zones spécifiques, ne montrent pas de différence de concentrations significative selon les zones d'échantillonnage. Ceci nous amène à suggérer que les blooms de cette espèce auraient plusieurs foyers. Cette espèce fluctue aussi indépendamment des années et des mois, mais elle montre une nette exigence pour des salinités supérieures à 42 g/l. Ce paramètre pourrait être utilisé comme marqueur pour prévoir l'apparition de K. selliformis dans le golfe de Gabes, à l'encontre des autres espèces qui ne présentent pas de spécificité pour aucun facteur abiotique. Mots clés : blooms, Karenia selliformis, golfe de Gabes, Réseau National de Surveillance Phytoplanctonique. ABSTRACT The algal blooms in the gulf of Gabes (Tunisia) a ten years long monitoring of phytoplankton : The data of the National Phytoplankton Monitoring Program are examined in the Gulf of Gabes during the period 1995-2005. A particular interest is assigned to the blooms events. We particularly examine the spatial and temporal variations of the responsible species and the relationships with the variability of temperature, salinity and pH. The results show that the period from August to January, with peaks in September and January, seems to be favourable to algal blooms. The dinoflagellates are responsible of 89% of the recorded blooms with the species Karenia selliformis representing 64% of the occurrences. Blooms of K. selliformis, although relatively frequent in a specific zone, their concentrations do not show statistically significant variations among sampled zones. This leads to suggest that blooms of this species would be initiated in different zones in the Gulf of Gabes. K. selliformis fluctuates also independently of years and months, but it shows a specific requirement for salinity (higher than 42 g/l). This parameter can be used as a marker to predict the appearance of K. selliformis in the Gulf of Gabes. The others phytoplankton species do not have any specific requirements for the studied abiotic variables.
Gelatinous zooplankton species composition, distribution and abundance were investigated in the bay and the lagoon of Bizerte (North Tunisian coast) from January 2004 to December 2005. In total, 48 species were identified in the study area: 24 medusae, 11 siphonophores, four appendicularians, six chaetognaths, two pteropods and one doliolid. The hydromedusa Eutima mira was recorded for the first time in the Western Mediterranean Sea. The diversity in the Bay of Bizerte was greater than the diversity in the Bizerte lagoon. There was a loss of species diversity. Siphonophores were the most sensitive group to anthropogenic discharge. Only two species of siphonophores were recorded in the lagoon. Four species of gelatinous zooplankton, Muggiaea kochi, Lensia conoidea, Oikopleura longicauda and Podocorynoides minima were the most frequent species in the lagoon and may benefit from eutrophication. Appendicularians were numerically the most dominant group in the bay and the lagoon of Bizerte. Mesozooplankton density declined significantly in autumn and winter of 2004 and 2005 in relation to the dense aggregations of the scyphomedusae Pelagia noctiluca. Statistical analysis divided the study area into three zones: lagoon zone, bay zone and channel zone.
Few studies have focused on the use of ocean colour remote sensors in the Gulf of
Gabes (southeastern Tunisia). This work is the first study to evaluate the ocean colour
chlorophyll-a product in this area. Chlorophyll-a concentrations were measured during
oceanographic cruises performed off the Gulf of Gabes. These measurements
were used to validate satellite data acquired from the Moderate Resolution Imaging
Spectroradiometer (MODIS) on the Aqua satellite. First, two atmospheric correction
procedures (standard and shortwave infrared) were tested to derive the remote-sensing
reflectance, and then a comparison between two bio-optical (OC3M and MedOC3) algorithms
were realized using the in situ measurements. Both atmospheric correction
procedures gave similar results when applied to our study area indicating that most pixels
were non-turbid. The comparison between bio-optical algorithms shows that using
the regional bio-optical algorithm MedOC3 improves chlorophyll-a estimation in the
Gulf of Gabes for the low values of this parameter.
Abstract: Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable for estimating chlorophyll-a concentrations in tropical coastal waters with abundant phytoplankton using Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra imagery and to improve the spatial resolution of MODIS/Terra-based estimation from 1 km to 100 m by geostatistics. A model based on the ratio of green and blue band reflectance (rGBr) is proposed considering the bio-optical property of chlorophyll-a. Tien Yen Bay in northern Vietnam, a typical phytoplankton-rich coastal area, was selected as a case study site. The superiority of rGBr over two existing representative models, based on the blue-green band ratio and the red-near infrared band ratio, was demonstrated by a high correlation of the estimated chlorophyll-a concentrations at 40 sites with values measured in situ. Ordinary kriging was then shown to be highly capable of predicting the concentration for regions of the image covered by clouds and, thus, without sea surface data. Resultant space-time maps of concentrations over a year clarified that Tien Yen Bay is characterized by natural eutrophic waters, because the average of chlorophyll-a concentrations exceeded 10 mg/m3 in the summer. The temporal changes of chlorophyll-a concentrations were consistent with average monthly air temperatures and precipitation. Consequently, a combination of rGBr and ordinary kriging can effectively monitor water quality in tropical shallow waters.
Keywords: bio-optical properties; water quality; estimation algorithm; phytoplankton; ordinary kriging
We investigated the phytoplankton biomass and its relationships with nutrient, suspended sediment and hydrographic characteristics, during three oceanographic cruises in May-June 2006, September 2006 and March 2007 in the Gulf of Gabes (south-eastern Mediterranean). The May-June cruise coincided with the beginning of the summer stratification, a strong stratification occurred in September and a well-mixed water column marked March cruise. Results allow characterizing the phytoplankton biomass potential limitation during hydrological transition between vertical mixing and strong stratification. The potential nutrient limitation on the phytoplankton biomass showed seasonal and spatial patterns. N-limitation was preponderant during winter mixing conditions. Potential Si and P-limitation were associated with oligotrophic signatures such as, low chlorophyll and high organic suspended solid during enhanced summer stratification. A transition period characterized by N-limitation in the coastal area and Si-limitation in the offshore area characterized the beginning of the water stratification. General opposition patterns existed between, organic suspended matter and chlorophyll a vertical distributions suggesting a potential control of phytoplankton biomass by top down processes. Our observations revealed the seasonal scale variations in the physical/biological coupling in the Gulf of Gabes and the implications for nutrient, organic suspended solid and phytoplankton standing stock.
Optical remote sensing was used to provide scientific information to support environmental management in the Gulf of Gabes that is located in the southeastern coast of Tunisia. This region is characterized by shallow continental shelf subjected to semi-diurnal tides. Industrial activities in this area since the early 1970s may have contributed to the degradation of the biodiversity of the ecosystem with eutrophication problems and disappearance of benthic and planktonic species. To assess the long-term effect of anthropogenic and natural discharges on the Gulf of Gabes, the optical environment of the coastal waters is assessed from in situ measurements of total suspended matter concentration (TSM), Secchi depth and turbidity (TU). This monitoring requires regular seaborne measurements (monthly), which is very expensive and difficult to obtain. The objective of the present study is the evaluation of the Moderate Resolution Imaging Spectrometer (MODIS) AQUA data compared with two sampling campaigns realized at the study area. To map turbidity data from MODIS images, a semi-empirical algorithm was applied at band 667 nm. This bio-optical algorithm has already been calibrated and validated on the Belgian coast. The validation of this algorithm on the Gulf of Gabes using in situ measurements of turbidity and remotely sensed turbidity obtained from MODIS imagery shows a correlation coefficient of 68.9%. Seasonal and annual average maps for TSM and TU were then computed over the Gulf of Gabes using MODIS imagery. The obtained results of TSM and TU from remotely sensed data are conformable with those obtained through the analysis of in situ measurements. Therefore, remote sensing techniques offer a better and efficient tool for mapping and monitoring turbidity over the whole region.
Global ocean colour algorithms, used to extract chlorophyll concentration in the ocean surface, normally overestimate pigment values in coastal regions, due to optical interference of water components. The objective of the present investigation was to test the performance of both empirical (SeaWiFS OC4v4) and semi‐analytical (GSM01 and CARDER) algorithms in the south‐western Atlantic. In situ pigment and optical data have been collected in waters influenced by continental discharge from La Plata River and Patos Lagoon. The data was used to develop a regional version of the empirical SeaWiFS OC2v4 algorithm (termed OC2‐LP). The relative percentage difference (RPD) between in situ and algorithm‐derived chlorophyll was 11% in the regional version as compared to the global OC4v4 (RPD = 27%). The GSM01 and CARDER showed RPD of 14% and 31%, respectively. We have also tested the accuracy of the four algorithms (OC4v4, OC2‐LP, GSM01 and CARDER) on SeaWiFS images taken over two cruise periods in the study region (winter of 2003 and summer of 2004). A seasonal difference was observed, where both OC4v4 and OC2‐LP overestimate chlorophyll in summer at a higher magnitude than in winter, and the GSM01 algorithm showed a marked underestimation of chlorophyll in winter. The CARDER model showed a good performance both in winter and summer, when applied to satellite‐retrieved radiances. Our results show that the use of semi‐analytical models does not improve significantly the accuracy of chlorophyll retrievals in coastal areas when not properly tuned with regional inherent optical properties measurements.
The spatial distribution of chlorophylls and carotenoids was recorded throughout the Gulf of Gabes (South Ionian Sea) in March 2007, and was related to patterns of the physical structure and the nutrient concentrations.Two distinct water masses were identified based on the temperature and salinity (T–S) analysis: a cool and less salty Modified Atlantic Water (MAW) and a saltier Mediterranean Mixed Water (MMW). There was no significant difference in the mean nitrogen and phosphate concentrations between MMW and MAW, although the silica values were significantly higher in MAW. The Integrated chlorophyll a mean value was about 4 mg m−2, with a maximum of 13 mg m−2 at MAW stations.Higher Chlorophyll a records in typical MAW stations were mainly due to chlorophytes, which contributed up to 58% of the pigments concentrations in the MAW and about 46% in the MMW. The contribution of chlorophytes to total Chlorophyll a was found to be relatively stable throughout the water column. The contribution of diatoms, which were twofold higher in the MMW than in the MAW, did not exceed 17% of chlorophyll a and was mainly due to subsurface maxima. The chlorophytes, pelagophytes, prymnesiophytes and cryptophytes all together accounted for more than 77% of total chlorophyll a in the MAW and about 67% in the MMW.There were statistically significant differences between MMW and MAW in the pigment contribution of cyanobacteria and pelagophytes. These two taxa accounted for 13% and 24% of chlorophyll a respectively in the MAW and MMW indicating that these differences concerned phytoplankton classes at relatively low contributions to total chlorophyll a.
The hydrological structure and nutrient dynamics of the Bay of Tunis (south-western Mediterranean), and the abundance of its principal plankton groups (diatoms, dinoflagellates, tintinids, rotifers, appendicularians, copepods, medusae, siphonophores, chaetognaths and cladocer- ans), were studied over 2 years. Despite the small size of the Bay (361 km2), the nutrient concentra- tions varied greatly between the sampling stations. We distinguished spatial from temporal variations using a newly-developed method for regionalization based on multivariate cluster analysis, and the changes over time of any similarities between stations. The method allowed us to obtain an optimal geographical division of any degree of grouping of the sampling stations (i.e. for a chosen number of zones). We also calculated the optimum partition of stations using a multivariate matrix obtained from multi-parameter sampling over time. The application of the method to a time-series of nutrient concentrations in the Bay of Tunis produced four zones of similar nutrient dynamics. Each zone was then characterized by the median and the variability of its nutrient, physical and biological parameters. A statistical test was used to assess the significance of the differences between zones for the various parameters. Comparison of the medians of the various parameters allowed us to synthe- size the underlying differences in nutrient dynamics and also in plankton ecosystem components. This analysis revealed a relative nutrient enrichment in the west and south-western areas of the Bay. Silicates did not contribute to the high variability of nutrients in the Bay. The pattern of phyto- plankton biomass (chlorophyll a) and the abundance of diatoms and dinoflagellates were similar to those of the sampled nutrients. The highest values were measured in the first zone, where the highest nutrient concentrations were also recorded. The coastal zones were characterized by zooplankton groups of small size (tintinids, rotifers and small copepods) with a limited diversity. These zones were also very turbid and shallow. Central zones, on the other hand, were rich in zooplankton species. Meso-zooplankton groups (e.g. siphonophres, chaetognaths) were also more abundant in this zone.
The summer spatial distribution of the dinoflagellate community along an open coastal sea gradient in the Gulf of Gabes (Tunisia, Eastern Mediterranean Sea), together with environmental factors, were studied. The most dominant families were represented by Gymnodiniaceae (32%), Peridiniaceae (20%), Prorocentraceae (15%), Ceratiaceae (13%) and Ebriaceae (10%). The dinoflagellate community was spatially more concentrated along the coast of the gulf than in the open sea. Eight toxic dinoflagellates were recorded, including Karenia cf. selliformis (37% of total toxic dinoflagellates) which was evenly distributed in both the neritic and open sea areas. Dinocysts contributed 33% of the total motile cells and were more abundant along the coast than in the open sea. This high concentration may be ascribed to nitrogen inputs in the coastal waters of Gabes. The Modified Atlantic Water governed dinoflagellate development in the open sea. The degradation of the water quality due to eutrophication in the Gulf of Gabes may have significant socioeconomic consequences. We suggest that a management framework, similar to that used in freshwater ecosystems, should be developed for the Gulf coast in order to drastically reduce urban interferences.
A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 mugL-1. Most of the observations are from Case I nonpolar waters, and ~20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal:noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.
A new empirical algorithm is proposed to estimate surface chlorophyll a
(Chl) concentrations in the global ocean for Chl ≤ 0.25 mg
m-3(˜78% of the global ocean area). The algorithm is
based on a color index (CI), defined as the difference between
remote-sensing reflectance (Rrs, sr-1) in the
green and a reference formed linearly between Rrsin the blue
and red. For low-Chl waters, in situ data showed a tighter (and
therefore better) relationship between CI and Chl than between
traditional band ratios and Chl, which was further validated using
global data collected concurrently by ship-borne and Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging
Spectroradiometer (MODIS)/Aqua instruments. Model simulations showed
that for low-Chl waters, compared with the band-ratio algorithm, the
CI-based algorithm (CIA) was more tolerant to changes in
chlorophyll-specific backscattering coefficient and performed similarly
for different relative contributions of nonphytoplankton absorption.
Simulations using existing atmospheric correction approaches further
demonstrated that the CIA was much less sensitive than band-ratio
algorithms to various errors induced by instrument noise and imperfect
atmospheric correction (including sun glint and whitecap corrections).
Image and time series analyses of SeaWiFS and MODIS/Aqua data also
showed improved performance in terms of reduced image noise, more
coherent spatial and temporal patterns, and better consistency between
the two sensors. The reduction in noise and other errors is particularly
useful to improve the detection of various ocean features such as
eddies. Preliminary tests over Medium-Resolution Imaging Spectrometer
and Coastal Zone Color Scanner data indicate that the new approach
should be generally applicable to all past, current, and future ocean
color instruments.
In situ measurements of total suspended matter (TSM) over the period 2003–2006, collected with two autonomous platforms from
the Centre for Environment, Fisheries and Aquatic Sciences (Cefas) measuring the optical backscatter (OBS) in the southern
North Sea, are used to assess the accuracy of TSM time series extracted from satellite data. Since there are gaps in the remote
sensing (RS) data, due mainly to cloud cover, the Data Interpolating Empirical Orthogonal Functions (DINEOF) is used to fill
in the TSM time series and build a continuous daily “recoloured” dataset. The RS datasets consist of TSM maps derived from
MODIS imagery using the bio-optical model of Nechad et al. (Rem Sens Environ 114: 854–866, 2010). In this study, the DINEOF
time series are compared to the in situ OBS measured in moderately to very turbid waters respectively in West Gabbard and
Warp Anchorage, in the southern North Sea. The discrepancies between instantaneous RS, DINEOF-filled RS data and Cefas data
are analysed in terms of TSM algorithm uncertainties, space–time variability and DINEOF reconstruction uncertainty.
KeywordsTotal suspended matter (TSM)–MODIS–Data Interpolating Emprirical Orthogonal Functions (DINEOF)–Cefas–Optical backscatter–TSM algorithm–TSM time series
In connection with the Taparura Project, studies of spatial distribution of the crustacean zooplankton community, nutrients, phytoplankton and ciliates were conducted in July 2007 at 45 stations spread over fifteen transects along the coast north of Sfax. The results showed that the N/P ratio was lower than the Redfield ratio, suggesting potential N limitation. Phytoplankton was characterised by the proliferation of several diatoms, while ciliates were largely dominated by spirotrichs. Copepods were the most abundant zooplankton present during the entire study period, comprising 61% of the total zooplankton community. Twelve copepod families were identified at every station, with a high percentage of Oithonidae (77% of copepods) dominated by Oithona nana. The abundance of this species was correlated with that of diatoms, Cocoolithophorideae and ciliated Colpodea, suggesting that O. nana may feed on a wide range of prey. Despite human pressure and industrial activities, the coastal waters north of Sfax showed a wide diversity of phytoplankton, ciliates and zooplankton.
A study of water quality in connection with circulation patterns in the bay of Tunis is presented. The hydrodynamics aspects were analysed using two numerical models in order to identify the internal circulation patterns. The first ID model, was designed for the lagoon of Tunis and the bay, and the second 2D, was applied to the gulf. The simulations showed that the water residence time in the bay varies from few days to 67 days in relation of the wind. The wind tends to produce relatively strong coastal currents. Tidal currents are rather weak and remain concentrated offshore. Then, an ecological model, describing the phytoplankton feedback in relation with the availability of the nutrients (inorganic Phosphate and Nitrogen), is proposed. Some results of ecological simulations of the littoral are presented. They showed an enrichment of the sediments with nutriments as a result of the sedimentation of dead plankton organisms.
Semianalytical (SA) ocean color models have advantages over conventional band ratio algorithms in that multiple ocean properties can be retrieved simultaneously from a single water-leaving radiance spectrum. However, the complexity of SA models has stalled their development, and operational implementation as optimal SA parameter values are hard to determine because of limitations in development data sets and the lack of robust tuning procedures. We present a procedure for optimizing SA ocean color models for global applications. The SA model to be optimized retrieves simultaneous estimates for chlorophyll (Chl) concentration, the absorption coefficient for dissolved and detrital materials [a(cdm)(443)], and the particulate backscatter coefficient [b(bp)(443)] from measurements of the normalized water-leaving radiance spectrum. Parameters for the model are tuned by simulated annealing as the global optimization protocol. We first evaluate the robustness of the tuning method using synthetic data sets, and we then apply the tuning procedure to an in situ data set. With the tuned SA parameters, the accuracy of retrievals found with the globally optimized model (the Garver-Siegel-Maritorena model version 1; hereafter GSM01) is excellent and results are comparable with the current Sea-viewing Wide Field-of-view sensor (SeaWiFS) algorithm for Chl. The advantage of the GSM01 model is that simultaneous retrievals of a(cdm)(443) and b(bp)(443) are made that greatly extend the nature of global applications that can be explored. Current limitations and further developments of the model are discussed.
Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
This research focuses on the spatial and temporal distribution of seawater quality factors such as dissolved oxygen (DO), chlorophyll a (Chl-a), salinity and temperature in the Persian Gulf (PG). The samplings were carried out on 11-34 stations during September 2018, May and November 2019. The range and average of Chl-a (μg/l) concentration in the surface waters were 0.04-1.10 (0.43 ± 0.25), 0.02-1.19 (0.46 ± 0.39) and 0.58-1.70 (0.93 ± 0.36) in the summer 2018, spring and autumn 2019, respectively. The cross-section of seawater salinity (psu), temperature (°C) and DO (mg/l) indicated strong stratification during late summer and autumn and vertically well-mixed structures in spring. Frequency distributions of DO at different levels indicated the incidence of a layer of minimum oxygen (0.85 mg/l) at depths below 50 meters in autumn. The distribution of Chl-a was also estimated using the remote sensor Moderate Resolution Imaging Spectrometer (MODIS) during 2018-2019. The results confirmed a significant covariation between remotely-sensed surface Chl-a and in situ data. The results showed that Chl-a was greater in regions nearby the coast compared to open sea regions and also significant impacts of the regional 2019 flooded on the Arvand River catchment.
Water quality monitoring programs have been widely implemented worldwide to monitor and assess water quality and to understand its trends. However, water quality analysis based on point-source field observations is difficult to perform at large spatial and temporal scales. In this paper, a fully automated Google Earth Engine (GEE) application algorithm was developed to estimate the total suspended solids (TSS) concentration in the Chesapeake Bay based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. Combining long-term archived satellite data (2002–2020) with field observations, the concentrations and spatiotemporal patterns of TSS in the bay water were evaluated. Time series analysis showed a statistically significant decreasing trend in TSS concentration between 2002 and 2020, suggesting that the sediment concentration in the bay has gradually been decreasing over the last two decades. The decreasing trend was observed in 49 out of 60 segments of the bay, implying that substantial progress has been made toward attaining the Chesapeake Bay water quality standards. Based on the monthly TSS analysis, 12 major peak events of TSS were identified in the Chesapeake Bay, which coincided with extreme winter blizzards and summer hurricane events. The GEE application and the results presented herein complement the existing monitoring program in attaining the water quality standards of the bay.
Oceans play a key role in energy storage in the global Earth–Ocean–Atmosphere system. Within this framework, the knowledge of past evolution and future trends of sea surface temperature is crucial for the future climate scenarios. Previous studies have highlighted the role of sea surface temperature as an important ingredient for the development and/or intensification of heavy precipitation events in the Western Mediterranean basin but have also highlighted its role in heat waves in Europe. In this study, a consistent warming trend has been found for daily sea surface temperature data series derived from satellites (1982–2016) for the whole Mediterranean region and for different temporal scales, from daily to monthly, seasonal and decadal estimates. Additionally, spatial clustering analysis has been run to look for its spatial structure. Two main distribution modes have been found for sea surface temperature in winter and summer, while spring and fall show transitional regimes. Winter mode shows a north-to-south increasing gradient banded structure while summer regime presents a set of well-differentiated areas.
This study examined the first results about the occurrence of epiphytic dinoflagellate species in the Gulf of Tunis. Three potentially harmful marine species were found: Prorocentrum lima (Ehrenberg) Dodge 1975, Ostreopsis siamensis Schmidt, 1901 and Coolia monotis Meunier 1919. From June 2001 to May 2002, the annual distribution of these species was studied on the seagrass Posidonia oceanica (Linnaeus) Delile. In the area where P, lima was the most abundant species, two peaks were recorded in fall and spring. The maximum density of O. siamensis was recorded in August until September whereas the maximum density of C. monotis was recorded in September and May. The abundance of epiphytic dinoflagellates was higher from August to October. These species displayed host substratum preference on Cymodocea nodosa (Ucria) Ascherson where the cell abundance was higher than on P. oceanica, particularly for P. lima.
The apparent optical properties (AOPs) of oceanic case 1 waters were previously analyzed [Morel, 1988] and statistically related to the chlorophyll concentration ([Chl]) used as a global index describing the trophic conditions of water bodies. From these empirical relationships a bio-optical model of the upper layer was developed. With objectives and structure similar to those of the previous study the present reappraisal utilizes AOPs determined during recent Joint Global Ocean Flux Study cruises, namely, spectral attenuation for downward irradiance Kd(lambda) and irradiance reflectance R(lambda). This revision also benefits from improved knowledge of inherent optical properties (IOPs), namely, pure water absorption coefficients and particle scattering and absorption coefficients, and from better pigment quantification (via a systematic use of high-performance liquid chromatography). Nonlinear trends, already observed between optical properties and algal biomass, are fully confirmed, yet with numerical differences. The previous Kd(lambda) model, and subsequently the R(lambda) model, is modified to account for these new relationships. The R(lambda) values predicted as a function of [Chl] and the predicted ratios of reflectances at two wavelengths, which are commonly used in ocean color algorithms, compare well with field values (not used when developing the reflectance model). This good agreement means that semianalytical ocean color algorithms can be successfully applied to satellite data. Going further into purely analytical approaches, ideally based on radiative transfer computations combined with a suite of relationships between the IOPs and [Chl], remains presently problematic, especially because of the insufficient knowledge of the phase function and backscattering efficiency of oceanic particles.
The NASA Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), known as GSFC-ECS in the Earth Observing System Data Gateway, distributes three major groups of MODIS products: Level 1 Radiometric and Geolocation data, and Level 2 and higher level of Atmosphere and Ocean products. The Atmosphere data types are aerosol, water vapor, cloud, temperature and moisture profiles, and cloud mask. The 107 (at present) Ocean data types include such parameters as normalized water-leaving radiances, chlorophyll and pigment concentrations (“ocean color”), total absorptions, sea surface temperatures, and ocean primary productivity. GES DAAC provides a broad spectrum of MODIS support, covering; data access, visualization tools, tools for search and order of the aforementioned data, documentation, data content, troubleshooting, and science and software support for the Earth Observing System Core System (ECS). The Web gateway for MODIS data products and services is http://daac.gsfc.nasa.gov/MODIS/
Global satellite ocean color instruments provide the scientific community a high-resolution means of studying the marine biosphere. Satellite data product validation and algorithm development activities both require the substantial accumulation of high-quality in-situ observations. The NASA Ocean Biology Processing Group maintains a local repository of in-situ marine bio-optical data, the SeaWiFS Bio-optical Archive and Storage System (SeaBASS), to facilitate their ocean color satellite validation analyses. Data were acquired from SeaBASS and used to compile a large set of coincident radiometric observations and phytoplankton pigment concentrations for use in bio-optical algorithm development. This new data set, the NASA bio-Optical Marine Algorithm Data set (NOMAD), includes over 3400 stations of spectral water-leaving radiances, surface irradiances, and diffuse downwelling attenuation coefficients, encompassing chlorophyll a concentrations ranging from 0.012 to 72.12 mg m− 3. Metadata, such as the date, time, and location of data collection, and ancillary data, including sea surface temperatures and water depths, accompany each record. This paper describes the assembly and evaluation of NOMAD, and further illustrates the broad geophysical range of stations incorporated into NOMAD.
Insights on 2017 marine heat waves in the Mediterranean Sea, Copernicus marine service ocean state
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Insights on 2017 marine heat waves in the Mediterranean Sea, Copernicus marine service ocean state
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