Mesoscale Patterns Identiﬁcation through SST Image Processing

Marco Reggiannini

1 a

, Jo

ao Janeiro

2 b

, Fl

avio Martins

3 c

, Oscar Papini

1 d

and Gabriele Pieri

1 e

Institute of Information Science and Technologies, National Research Council of Italy,

Via G. Moruzzi, Pisa, Italy

University of Algarve, Centre for Marine and Environmental Research – CIMA,

Campus de Gambelas, Faro, Portugal

University of Algarve, Centre for Marine and Environmental Research – CIMA, Institute of Engineering ISE,

Campus de Gambelas, Faro, Portugal

Keywords:

Image Processing, Remote Sensing, Mesoscale Patterns, Sea Surface Temperature.

Abstract:

Mesoscale marine phenomena represent important features to understand and include within predictive mod-

els, which provide valuable information for proper environmental policy making. For example the rearrange-

ment of the organic substances, consequent to the dynamics of the water masses affected by the mentioned

phenomena, meaningfully modiﬁes the actual condition of local habitats. Indeed it may facilitate the onset of

non resident living species at the expense of resident ones, eventually affecting related human activity, such

as commercial ﬁshery. Objective of this work is the detection and identiﬁcation of mesoscale events, in terms

of speciﬁc marine surface patterns that are observed throughout such events, e.g. water ﬁlaments, counter-

currents, meanders due to upwelling wind actions stress. These phenomena can be studied and monitored

through the analysis of Sea Surface Temperature images captured by satellite missions, such as Metop, and

MODIS Terra/Aqua. A quantitative description of such events is proposed, based on dedicated algorithms that

extract temporal and spatial features from the images, and exploit them to provide a signature discriminat-

ing different observed scenarios. Preliminary results of the application of the proposed approach to a dataset

related to the southwestern region of the Iberian Peninsula are presented.

1 INTRODUCTION

The impact of climate change on marine ecosys-

tems is often expressed by simpliﬁed warming trends

(Hansen et al., 2010). Although this approximation

may be valid for oceanic regions, in coastal areas the

impact of warming on the ecosystems is far from be-

ing homogeneous. This is mainly due to the fact that

coastal regions host some of the most biodiverse and

variable environments of the ocean.

Near the coast, global drivers are modiﬁed by

topography and by local atmospheric and oceano-

graphic circulation patterns, including upwelling. Ek-

man dynamics and large-scale thermocline processes

control the coastal upwelling occurring at the Eastern

Boundary Upwelling Ecosystems (EBUEs) (Messi

https://orcid.org/0000-0002-4872-9541

https://orcid.org/0000-0002-6241-8520

https://orcid.org/0000-0002-9863-6255

https://orcid.org/0000-0003-2069-5068

https://orcid.org/0000-0001-5068-2861

et al., 2009; Ramajo et al., 2020). Winds directed

towards the Equator drive upwelling by transporting

deeper, colder and nutrient-rich waters to the surface,

where phytoplankton production is triggered by sun-

light (Sydeman et al., 2014). As a result, these areas,

through the upwelling, give strength to the most pro-

ductive ecosystems in the global ocean (FAO, 2018),

playing a major role in the marine primary produc-

tion and the worldwide ﬁsheries (7% of global ma-

rine production and more than 20% of global ﬁsh

catches), thus providing a high number of subsistence

and beneﬁts to human society (Levin and Le Bris,

2015). Apart from the nutrient load, it was recently

shown that upwelled water’s low long-term warming

rates may provide thermal refugia, stabilize changes

in species distributions and enhance local biodiversity

(Varela et al., 2018).

According to related literature, more than 71% of

coastal zones are experiencing a net heat gain due

to global warming (IPCC, 2019). Yet, it is difﬁ-

cult to systematize the trends observed in different

upwelling ecosystems across the global oceans, as

Reggiannini, M., Janeiro, J., Martins, F., Papini, O. and Pieri, G.

Mesoscale Patterns Identiﬁcation through SST Image Processing.

DOI: 10.5220/0010714600003061

In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2021), pages 165-172

ISBN: 978-989-758-537-1

165

positive trends were observed in the coastal areas

of Benguela, Peru, northern California, and Canary

while signiﬁcant negative trends were found along

Chile, Somalia, and southern and central California

coasts (Varela et al., 2015). Therefore, it is surmised

that every upwelling ecosystem reacts differently to

the changing climate.

Among the world’s EBUEs, the Iberia/Canary

Current System (ICCS) is one of the least studied

(Chavez and Messi

e, 2009). Several research stud-

ies have focused on the western Iberian oceanogra-

phy (Relvas et al., 2007). Despite a general circu-

lation similar to other EBUEs, in ICCS the disconti-

nuity imposed by the Mediterranean Sea, combined

with the seasonality of the large-scale atmospheric

circulation, have a profound impact on the regional

oceanography. Time scales of a few tens of days ex-

plain more than 70% of the variability of the coastal

alongshore wind stress, a major factor governing the

regional coastal circulation (

Alvarez-Salgado et al.,

2003). The region’s continental shelf, with less than

10 km wide south of Lisbon, 30–40 km wide off cen-

tral Portugal and somewhat narrower again off north-

ern Portugal and Galicia, is characterized by a large

number of topographical features, such as prominent

capes, promontories and submarine canyons, whose

spatial scales are tens to hundreds of kilometers (Rel-

vas et al., 2007). All the above highlight the impor-

tance of sub-seasonal temporal scales and sub-basin

spatial scales, which explain the observed oceano-

graphic patterns. In a review paper (Relvas et al.,

2007), the physical oceanography of the western

Iberia system is described and characterized through

the main mesoscale features related to that region.

They include a succession of mesoscale structures

such as jets, meanders, ubiquitous eddies, upwelling

ﬁlaments and counter-currents, superimposed on the

more stable variations at seasonal timescales, as sug-

gested by several authors (Relvas et al., 2007).

The identiﬁcation and cataloguing of upwelling

features occurring in an EBUE is an important

achievement towards the characterization of the sys-

tem. Traditionally that task has been performed sub-

jectively by experts, analyzing Sea Surface Temper-

ature (SST) maps of the area of interest. The use of

upwelling indices has also been used as a ﬁrst guess

directing the experts towards the dates and events to

be analyzed (Lamont et al., 2018), but a visual inspec-

tion has been always needed to certify the presence of

an upwelling and, above all, to classify the type of

upwelling. This procedure is manageable if few tens

or even hundreds of images are used but it turns into

an impossible task to analyze thousands of images, as

needed to identify the effects of climate change.

The main objective of this work is to design and

develop automatic methods capable of accepting mas-

sive datasets of oceanographic SST imagery as input

and returning the classiﬁed images as output. The out-

put classiﬁcation labels reﬂect the different regimes of

observable upwelling patterns. The identiﬁcation of a

speciﬁc temperature pattern will be based on the ex-

traction of quantitative features from the SST maps.

Particular attention will be devoted to those features

that reﬂect the signal variability (e.g. gradient-based

indicators). Indeed the emergence of a certain pat-

tern is usually highly correlated with peculiarities

in the temperature spatial arrangement at time ﬁxed

(e.g. the presence of abrupt variations in the tempera-

ture values within a certain neighborhood) as well as

with the observation of speciﬁc temperature trends at

ﬁxed locations, providing insights about the ﬂowing

of water masses between points at different tempera-

ture values. A dedicated visualization tool has been

developed to organize the relevant information in a

single plot, so that SST patterns relative to speciﬁc

mesoscale events are easily recognizable.

The proposed methods will be applied to the South

Iberian region, contributing to the understanding of

the effects of climate change in this particular EBUE.

In this article, a preliminary methodology is proposed,

and demonstrated using SST remote sensing images.

In its preliminary form the metrics used are able to

identify different types of mesoscale features, but dif-

ferent variables and arrangements still need to be

tested, as discussed in the text.

The paper is arranged as follows: Section 2 con-

cerns a detailed description of the employed dataset,

the related ground truth classiﬁcation, visually per-

formed by expert users, and a description of the de-

veloped processing tools; Section 3 presents some

preliminary outcomes resulting from the application

of the proposed methods to the selected dataset; Sec-

tion 4 concludes the paper with a ﬁnal discussion and

future works.

2 MATERIALS AND METHODS

2.1 SST Imagery

Seasonal mesoscale features shape the regional ocean

surface circulation of the South Iberian coast, affect-

ing the physical, chemical and biological processes

that occur at the surface layer (Lopes et al., 2014).

To look for seasonal mesoscale circulation pat-

terns, satellite images from both NASA’s Aqua and

EUMETSAT’s METOP satellites were selected start-

ing from 2009. Aqua’s satellite data was retrieved

ROBOVIS 2021 - 2nd International Conference on Robotics, Computer Vision and Intelligent Systems

166

Figure 1: Mesoscale patterns in the South-Western Iberian Peninsula.

from NASA’s Ocean Color webpage and it consists of

a dataset of 470 binned 4 km (night time) SST images

obtained through its MODIS sensor. The data was

visually selected and only swaths with relevant cov-

erage were downloaded. The METOP dataset com-

prised approximately 7800 daily world SST images

from its Advanced Very-High-Resolution Radiome-

ter (AVHRR) sensor with 1 km resolution and was

retrieved from OSI-SAF webpage for the period of

interest. The data was pre-processed applying a ﬁlter

to select the images covering the geographical limits

of the study area.

2.2 Patterns of Interest

A visual inspection of the combined MODIS/METOP

SST dataset has been performed by experts, in or-

der to identify a minimal set of temperature patterns,

whose occurrence is repeatedly observed throughout

the dataset time range. As a result of this label-

ing generation process, ﬁve typologies of mesoscale

events have been recognized as the most representa-

tive within the South Iberian coast area (see Figure 1).

The ﬁrst mesoscale pattern, here deﬁned E1, is as-

sociated with the meander of the southward upwelling

jet to the west, near Cape S. Vicente, alongside oc-

curring the development of upwelling ﬁlaments. Pat-

tern E2 is depicted by the southwards ﬂow of the

upwelling jet overpassing the Cape S. Vicente form-

ing an extended meridional ﬁlament. A clear signal

of cool water throughout the southern Iberian coast

without detachment is what deﬁnes pattern E3. After

a careful analysis of the dataset, it has been veriﬁed

that pattern E3 usually takes place in a twofold mode.

When the signal of cool water throughout the south-

ern Iberian coast was associated with a small thermal

gradient within the adjacent Gulf of Cadiz waters, the

pattern has been classiﬁed as E3i. In the satellite im-

age dataset, this event is more frequent during winter

with the warm water along the Iberian shelf edge and

slope being associated with the upsurge of a poleward

ﬂow (Peliz et al., 2005), a direct effect of the shift in

wind direction veriﬁed during wintertime. The sec-

ond type of E3 pattern, named E3u, is related to the

occurrence of a signiﬁcant thermal gradient within the

Gulf of Cadiz waters. The cool water signal was as-

sociated with the upwelling jet turning Cape S. Vi-

cente while ﬂowing through the south Iberian coast.

Finally, in pattern E4, a warm counter-current (Rel-

vas and Barton, 2005) develops near the south Iberian

coast, turning around Cape S. Vicente and ﬂowing

northwards near the coast.

Based on this ground truth labeling, dedicated

image processing methods have been designed and

preliminary developed with the objective of auto-

matically detect and classify the above mentioned

mesoscale patterns in the South-Western Iberian

coast.

Mesoscale Patterns Identiﬁcation through SST Image Processing

167

36.0

◦

36.5

◦

8.5

◦

W 8.0

◦

24-Jul

31-Jul

07-Aug

Temperature (

◦

Figure 2: Example of a spaghetti plot. Each square in the reference grid (on the left) corresponds to the plot of the same color

in the graph on the right, so it is easy to associate SST trends with a geographical area.

2.3 Processing Methods

In order to extract and organize the information from

the satellite SST data a small suite of custom Python

scripts has been developed.

The main goal of the analysis is to detect a speciﬁc

event at a given time, recognize its typology, but also

inspect the dynamic evolution preceding the consid-

ered observation time. The occurrence and the type

of an event is analyzed by looking at the SST trend in

small selected areas off the coast.

The developed tool takes as input a folder contain-

ing ﬁles from the datasets, a time range and the coor-

dinates of a rectangular area and returns a spaghetti

plot obtained in the following way:

1. the area is divided into a grid of very small squares

of ﬁxed dimension (typically 0.02–0.05 degrees of

latitude/longitude);

2. for each square, a mean temperature value is com-

puted by taking the ﬁle corresponding to a given

time and averaging all the SST data with geo-

graphical coordinates contained in the square;

3. for each square, a series of temperature values is

computed, following the instructions of the pre-

vious step, for every step in the considered time

range and the resulting temperature series is plot-

ted against time;

4. the spaghetti plot is composed by superimposing

the plots relative to all the squares.

Each square of the grid is color-coded so that simi-

lar colors are assigned to neighboring squares. The

graphs in the spaghetti plot are colored according to

the corresponding square in the grid. This way it is

easier to identify the behavior of different zones in-

side the target area (see Figure 2).

The main problems encountered while processing

the signals and generating the spaghetti plots relate

to the quantity and quality of the data. In fact, the

satellites provided only a few (two or three) images

per day over the area of interest, and it was very likely

that those images couldn’t be used at all because of

missing data (which may be caused by some external

factor, e.g. presence of clouds). One possible solution

to these issues, also aim of future research activity,

will be to introduce additional datasets (possibly from

different remote sensing missions) to ﬁll the missing

gaps.

Another aspect that has to be taken into account

is the reliability level of the signal in the SST maps,

which sometimes may cause misleading interpreta-

tions. For example, it is usual to observe that the

temperature measured at the boundary of a region of

missing data (marked as “cloud” in the ﬁle) is very

low compared to the temperature that we expect when

looking at the neighboring data. Indeed, the exploited

datasets are provided with a quality label among the

usual metadata, i.e. a quality level that is assigned to

each point for which SST is measured. In the example

above, both the missing data region and the surround-

ing low temperature points are marked in the product

metadata as “bad quality” data. This information can

be used to improve the overall quality of the spaghetti

plot — in fact, we included in our scripts a couple

of control switches that allow the user to adapt the

computation in the above described step 2 by either

discarding completely the bad quality data, or com-

puting a weighted average with lower weight given to

bad quality data.

Once the spaghetti plots have been obtained, an

analytical reasoning is performed in association with

the ground truth deﬁned by experts as deﬁned above

in Section 2.2. The goal is to identify and associate

different dynamic patterns with different events (in-

cluding also a no-event situation), so that an auto-

matic supervised association could be made out of the

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36.5

◦

37.0

◦

9.5

◦

W 9.0

◦

35.0

◦

36.0

◦

37.0

◦

38.0

◦

39.0

◦

40.0

◦

12.0

◦

11.0

◦

10.0

◦

9.0

◦

8.0

◦

7.0

◦

6.0

◦

05-Sep

10-Sep

15-Sep

20-Sep

Temperature (

◦

Figure 3: Event of 19 September 2017. Top left: reference map for the plot; bottom left: SST map at the date of the event;

right: generated spaghetti plot.

described computations. The following section con-

cerns the description of some preliminary results of

this ongoing analysis, including the analytical reason-

ing behind.

3 PRELIMINARY RESULTS

Several processing and analysis have been performed

within the dataset, in particular the year 2017 was

selected as a preliminary study case. Following the

classiﬁcations given by experts, with a series of dif-

ferent events identiﬁed at speciﬁc dates, the dynamic

analysis considering n days before the classiﬁed event

has been processed in a geographical area around the

event of interest. The spaghetti plots produced were

then associated with the classiﬁcations assigned to the

speciﬁc events and then analyzed together with the

experts.

The following table lists three speciﬁc events cho-

sen for this paper as a preliminary result, together

with the subsequent reference ﬁgures, the number of

backward days T

analyzed for the dynamic of the

event, and a brief description of the event.

Table 1: List of analyzed events.

Date (T

) Ref. Fig. Time T

Type

19-Sep-2017 Fig. 3 −14 days E3u

5-Oct-2017 Fig. 4 −16 days E4

27-Jun-2017 Fig. 5 −11 days No event

Following the application of our methodology, the

results in the form of spaghetti graphs, as described

in Section 2.3, are shown for each selected event and

period. The small map in each ﬁgure represent the

color identiﬁcation for the respective spaghetti plot as

described in Section 2.3, where each line in the plot is

relative to the region of the same color.

The ﬁrst example (Figure 3) is a typical mesoscale

event, identiﬁed by the experts as of type E3u rep-

resenting a cold current going southward to the end

of Iberian Peninsula, crossing Cape S. Vicente, and

then moving eastward towards the Mediterranean Sea.

As it can be seen from the relative spaghetti plot, the

temperatures in the upper right corner (close to Cape

S. Vicente) are decreasing to a much higher gradi-

ent with respect to the other surrounding areas — this

identiﬁes a possible ﬁrst dynamic pattern.

As a second example we show (Figure 4) a differ-

ent type of event, recognised by the experts as a warm

counter-current moving westward from the Gulf of

Cadiz. For this case study, considering the type of

event, we have selected a narrower shaped rectangle

that allows classifying the trend along the coast. In

this case the spaghetti plot clearly outlines a dynamic

pattern of the easternmost area (red plots) which in-

creases its temperature starting colder than the west-

ernmost (blue plots) but raising up and overcoming it

(i.e. much higher positive gradient of the easternmost

area, with a close-to-zero gradient of the westernmost

one).

Finally, as a third example (Figure 5) we see a sit-

uation where no speciﬁc event has been detected in

Mesoscale Patterns Identiﬁcation through SST Image Processing

169

36.90

◦

36.93

◦

10.0

◦

W 7.0

◦

35.0

◦

36.0

◦

37.0

◦

38.0

◦

39.0

◦

40.0

◦

12.0

◦

11.0

◦

10.0

◦

9.0

◦

8.0

◦

7.0

◦

6.0

◦

20-Sep

25-Sep

30-Sep

05-Oct

Temperature (

◦

Figure 4: Event of 5 October 2017. Top left: reference map for the plot; bottom left: SST map at the date of the event; right:

generated spaghetti plot.

36.5

◦

37.0

◦

9.5

◦

W 9.0

◦

35.0

◦

36.0

◦

37.0

◦

38.0

◦

39.0

◦

40.0

◦

12.0

◦

11.0

◦

10.0

◦

9.0

◦

8.0

◦

7.0

◦

6.0

◦

16-Jun

20-Jun

24-Jun

28-Jun

Temperature (

◦

Figure 5: No event in late June 2017. Top left: reference map for the plot; bottom left: SST map at the date of the event; right:

generated spaghetti plot.

that period, and this can be associated with the rela-

tive spaghetti plot, where large decays or increases in

temperature cannot be identiﬁed (i.e. the gradients of

the plots are not giving a clear indication of speciﬁc

different trends among the regions, also considering

existing outliers).

As it can be seen from this preliminary results dif-

ferent patterns can be identiﬁed for several events. In

more detail “ﬂat” patterns of temperatures can be seen

for the situations where no particular event happens,

where distinct decays/increases of temperature while

moving from speciﬁc regions to others can be identi-

ﬁed in the charts related to speciﬁc events.

4 CONCLUSIONS

The preliminary results of this ongoing study are

promising and show possible patterns of differenti-

ation among different mesoscale events occurring in

the analyzed area. Experts initially supported the pri-

mary analysis, identifying possible events occurring

ROBOVIS 2021 - 2nd International Conference on Robotics, Computer Vision and Intelligent Systems

170

at speciﬁc dates within the data set range we used.

Following this, we applied our method to different

time intervals before the events speciﬁed to under-

stand whether a speciﬁc dynamic pattern could be as-

sociated with them. Moreover, we also performed the

same analysis on periods where no events were iden-

tiﬁed to analyze a different speciﬁc pattern relevant to

a “no-event” period.

Future work will be centred on the extension of

this preliminary results on pattern identiﬁcation to

more distinct and expanded events, with the goal to

deﬁne a more complete collection of patterns.

Another focus will be to tackle the implementa-

tion of higher-level stages, i.e. those concerning to

the classiﬁcation task. In particular, the feature ex-

traction stage will be reﬁned by selecting the features

typologies returning the best possible discriminating

power. Also, different classiﬁers will be devised to

identify the most suitable ones for the purpose of this

analysis. These latter activities are the foundation for

the next step of automatic classiﬁcation of massive

datasets without the need for expert feedback.

The test and validation of the proposed algorithm

is carried out and will continue as part of the activities

of the EU H2020 project NAUTILOS (Pieri, 2020;

Pieri et al., 2021).

ACKNOWLEDGEMENTS

This paper is part of a project that has received fund-

ing from the European Union’s Horizon 2020 re-

search and innovation programme under grant agree-

ment No. 101000825 (Nautilos, 2021).

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