An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea

The consistency of satellite and in situ time series of Chlorophyll-a (Chl-a), Turbidity and Total Suspended Matters (TSM) was investigated at 17 coastal stations throughout the year 2017. These stations covered different water types, from relatively clear waters in the Mediterranean Sea to moderately turbid regions in the Bay of Biscay and the southern bight of the North-Sea. Satellite retrievals were derived from MODIS/AQUA, VIIRS/NPP and OLCI-A/Sentinel-3 spectral reflectance. In situ data were obtained from the coastal phytoplankton networks SOMLIT (CNRS), REPHY (Ifremer) and associated networks. Satellite and in situ retrievals of the year 2017 were compared to the historical seasonal cycles and percentiles, 10 and 90, observed in situ. Regarding the sampling frequency in the Mediterranean Sea, a weekly in situ sampling allowed all major peaks in Chl-a caught from space to be recorded at sea, and, conversely, all in situ peaks were observed from space in a frequently cloud-free atmosphere. In waters of the Eastern English Channel, lower levels of Chl-a were observed, both in situ and from space, compared to the historical averages. However, despite a good overall agreement for low to moderate biomass, the satellite method, based on blue and green wavelengths, tends to provide elevated and variable Chl-a in a high biomass environment. Satellite-derived TSM and Turbidity were quite consistent with in situ measurements. Moreover, satellite retrievals of the water clarity parameters often showed a lower range of variability than their in situ counterparts did, being less scattered above and under the seasonal curves of percentiles 10 and 90.

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Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data

Open Geosciences ◽

10.1515/geo-2020-0204 ◽

2021 ◽

Vol 13 (1) ◽

pp. 85-97

Author(s):

Ioannis Moutzouris-Sidiris ◽

Konstantinos Topouzelis

Keyword(s):

Mediterranean Sea ◽

Chlorophyll A ◽

Time Window ◽

Pearson Correlation ◽

Neural Net ◽

Data Set ◽

Chl A ◽

In Situ Data ◽

The Mediterranean

Abstract The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated (r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN.

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An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

Remote Sensing ◽

10.3390/rs12244123 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4123

Author(s):

Michela Sammartino ◽

Bruno Buongiorno Nardelli ◽

Salvatore Marullo ◽

Rosalia Santoleri

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Temperature Fields ◽

Spatial And Temporal Variability ◽

Dynamic Topography ◽

In Situ Data ◽

Artificial Neural ◽

The Mediterranean ◽

3D Fields

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

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Use of the quasi-analytical algorithm to retrieve backscattering from in-situ data in the Mediterranean Sea

Remote Sensing Letters ◽

10.1080/2150704x.2016.1171922 ◽

2016 ◽

Vol 7 (6) ◽

pp. 591-600 ◽

Cited By ~ 10

Author(s):

Jaime Pitarch ◽

Marco Bellacicco ◽

Gianluca Volpe ◽

Simone Colella ◽

Rosalia Santoleri

Keyword(s):

Mediterranean Sea ◽

In Situ Data ◽

Analytical Algorithm ◽

The Mediterranean

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Computation of a new Mean Dynamic Topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in-situ data

Ocean Science Discussions ◽

10.5194/osd-11-655-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 655-692 ◽

Cited By ~ 11

Author(s):

M.-H. Rio ◽

A. Pascual ◽

P.-M. Poulain ◽

M. Menna ◽

B. Barceló ◽

...

Keyword(s):

Mediterranean Sea ◽

External Validation ◽

Dynamic Topography ◽

Crucial Issue ◽

Mean Dynamic Topography ◽

In Situ Data ◽

Velocity Variance ◽

Sensitivity Studies ◽

The Mediterranean

Abstract. The accurate knowledge of the ocean Mean Dynamic Topography (MDT) is a crucial issue for a number of oceanographic applications and in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new Mean Dynamic Topography (MDT) that was computed for the Mediterranean Sea. It takes profit of improvements made possible by the use of extended datasets and refined processing. The updated dataset spans the 1993–2012 period and consists of: drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This has required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous version, SMDT-MED-2014 features shorter scales structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

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Combined use of ERS, TOPEX/POSEIDON, AVHRR and in-situ data to study the mesoscale dynamics in the Mediterranean Sea

10.1117/12.298165 ◽

1997 ◽

Author(s):

Rosalia Santoleri ◽

Bruno Buongiorno Nardelli ◽

Daniele Iudicone ◽

Simona Zoffoli ◽

Salvatore Marullo

Keyword(s):

Mediterranean Sea ◽

Mesoscale Dynamics ◽

In Situ Data ◽

Combined Use ◽

The Mediterranean

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Mediterranean Sea climatic indices: monitoring long term variability and climate changes

10.5194/essd-2018-51 ◽

2018 ◽

Author(s):

Athanasia Iona ◽

Athanasios Theodorou ◽

Sarantis Sofianos ◽

Sylvain Watelet ◽

Charles Troupin ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Mediterranean Sea ◽

Climate Changes ◽

Salt Content ◽

Climatic Indices ◽

In Situ Observations ◽

The Mediterranean

Abstract. We present a new product composed of a set of thermohaline climatic indices from 1950 to 2015 for the Mediterranean Sea such as decadal temperature and salinity anomalies, their mean values over selected depths, decadal ocean heat and salt content anomalies at selected depth layers as well as their long times series. It is produced from a new high-resolution climatology of temperature and salinity on a 1/8° regular grid based on historical high quality in situ observations. Ocean heat and salt content differences between 1980–2015 and 1950–1979 are compared for evaluation of the climate shift in the Mediterranean Sea. The spatial patterns of heat and salt content shifts demonstrate in greater detail than ever before that the climate changes differently in the several regions of the basin. Long time series of heat and salt content for the period 1950 to 2015 are also provided which indicate that in the Mediterranean Sea there is a net mean volume warming and salting since 1950 with acceleration during the last two decades. The time series also show that the ocean heat content seems to fluctuate on a cycle of about 40 years and seems to follow the Atlantic Multidecadal Oscillation climate cycle indicating that the natural large scale atmospheric variability could be superimposed on to the warming trend. This product is an observations-based estimation of the Mediterranean climatic indices. It relies solely on spatially interpolated data produced from in-situ observations averaged over decades in order to smooth the decadal variability and reveal the long term trends with more accuracy. It can provide a valuable contribution to the modellers' community, next to the satellite-based products and serve as a baseline for the evaluation of climate-change model simulations contributing thus to a better understanding of the complex response of the Mediterranean Sea to the ongoing global climate change. The product is available here: https://doi.org/10.5281/zenodo.1210100.

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SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽

10.15809/irriga.2021v1n3p585-598 ◽

2021 ◽

Vol 1 (3) ◽

pp. 585-598

Author(s):

Pedro Henrique Jandreice Magnoni ◽

Cesar De Oliveira Ferreira Silva ◽

Rodrigo Lilla Manzione

Keyword(s):

Remote Sensing ◽

Water Budget ◽

Irrigation Management ◽

Irrigation Scheduling ◽

In Situ Data ◽

Joint Application ◽

E Mail ◽

Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP* PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil, [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores. 1 RESUMO Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados. Palavras-chave: evapotranspiração, momento de irrigar, agriwater. MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP 2 ABSTRACT Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data. Keywords: evapotranspiration, irrigation scheduling, agriwater.

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Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽

10.3390/s19163609 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3609 ◽

Cited By ~ 11

Author(s):

Kyryliuk ◽

Kratzer

Keyword(s):

Remote Sensing ◽

Baltic Sea ◽

Attenuation Coefficient ◽

Secchi Depth ◽

Local Algorithm ◽

The Baltic Sea ◽

In Situ Data ◽

The Baltic ◽

Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

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