Remote Sensing for Marine Spatial Planning andAquaculture

Sonia Cristina, Bruno Fragoso, John Icely (SGM)Jon Grant (Dalhousie)

August 27, 2018

Abstract

This text is part of the introduction toa Masters-level course in ‘Planning andManaging the Use of Space for Aquacul-ture’ made by the AquaSpace project. Itintroduces the use of Remote Sensing inMarine Spatial Planning and site selectionfor aquaculture. Brief case studies illus-trate the aquacultural utility of satellite re-mote sensing of the coastal waters of theAlgarve, the northern Adriatic Sea, and abay in northern France; a case study inNova Scotia illustrates the use of low alti-tude imaging. An exercise involves the useof the ESA SeNtinel Application Platform(SNAP) software.

This document may be cited as: Cristina,S., Fragosa, B., Icely, J. & Grant, J. (2018)Remote Sensing for Marine Spatial Plan-ning and Aquaculture, v.4. Aquaspaceproject document, 17 pp. Sagaremisco,Portugal.

Contents

1 Unit Study Guide 2

2 Remote Sensing 2

3 Missions and sensors 3

4 Optically Active Constituents 4

5 Remote Sensing & Aquacul-ture 4

6 Case Study: Algarve 5

7 Case Study: Adriatic 6

8 Case Study: France 8

9 Case Study: Nova Scotia 8

10 Exercises and SAQ 11

A Use of SNAP with Sentinel-3data 12A.1 Install SNAP . . . . . . . . 12A.2 Get Sentinel-3 data . . . . . 12A.3 Open data in SNAP . . . . 13

B NASA OceanColor site 15

1

7: Remote Sensing 2

1 Unit Study Guide

This text was written during the H2020Aquaspace project (2015-2018, contract no.633476) for a Masters-level course in ‘Plan-ning and Managing the Use of Space forAquaculture’. The course consists of anumber of units; this unit provides an in-troduction to the use of Remote Sensing inmarine spatial planning for aquaculture.

The unit comprises a text (the documentyou are now reading), a set of slides, re-quired further reading, and some exercises.The text complements the slides, which canbe used as the basis for a class-room lecture.The reading provides details of case studies.The exercises illustrate some of the methodsused in processing remotely sensed data

The learning outcomes for this unit areto be able to

• demonstrate an understanding of Re-mote Sensing of the marine environ-ment and its use for the spatial plan-ning of Aquaculture;

• explain and critically discuss at leastone application of Remote Sensing foraquacultural site selection or manage-ment;

• apply skills in the processing of re-motely sensed data, using at least onefreely available software package

2 Remote Sensing

Remote sensing is the science of obtaininginformation about objects or areas from adistance, typically from aircraft or satel-lites. Remote sensors collect data by de-

tecting either passive or active energy thatis reflected from Earth.

Passive sensors respond to externalstimuli in the form of natural energythat is reflected or emitted from theEarth’s surface. The most commonsource of radiation detected by passivesensors is reflected sunlight.

Active sensors use internal stimuli to col-lect data about Earth. For example,a laser remote sensing system projectsa laser onto the surface of Earth andmeasures the time that it takes for thelaser to reflect back to its sensor.

Early passive sensors were essentiallycameras, and these are still used for exam-ple to provide the basis of the maps usedby Google etc. Sensors for sea-surface tem-perature or ocean colour mostly use a scan-ning mechanism, whereby visible and near-IR light from strip of planetary surface isimaged along an array of photodiodes, usu-ally after splitting into several colours plusinfra-red. A picture of the land or sea isbuilt up as the satellite advances along itstrack.

There are numerous technical challengesin interpreting data provided by sensors on-board satellites. These include compensat-ing: for the signal absorbed or added by theatmosphere; for the influence on upwellinglight leaving the sea surface by the reflec-tion from the bottom in shallow waters; forthe absorption and scattering effects of var-ious suspended and dissolved water com-ponents; for the influence of the water it-self (IOCCG, 2000; Pozdnyakov and Graßl,2003); for the rapid changes in signal at theland-sea boundary; and for the roughness

7: Remote Sensing 3

of the water surface. These influences onwater-leaving light have to be corrected by‘sea-truthing’ of the remote sensing mea-surements against observations from shipsor moorings before they can used to de-rive the water products from remote sens-ing measurements (e.g. chlorophyll a con-centration, suspended particulate matter)(IOCCG, 2000).

3 Missions and sensors

Airborne remote sensing of ocean colour hasa long history. In his book, ‘The OpenSea’, Hardy (1956) refers to “a sharp lineseparating the green water of the EnglishChannel from the deep blue of the Atlantic”that he observed from the air in 1923. Inthe early 1970s oceanographers used sens-ing of sea-surface temperature, based oninfra-red emission measured by low-flyingaircraft, to fix the locations of tidal mix-ing fronts in coastal waters (Simpson andHunter, 1974). Aircraft sensing was soonextended by images (Simpson et al., 1978)from satellites flown by the US agenciesNASA and NOAA. The discovery of thesefronts changed understanding of the physi-cal and biological dynamics of seas on con-tinental shelves (Holligan, 1981).

The first ocean colour sensor, the CZCS,was flow by NASA between 1978 and1986, and allowed mapping of chlorophyll incoastal waters, such as the North Sea (Holli-gan et al., 1989). It was followed by the Sea-WIFS sensor carried on the SeaStar satel-lite (1997-2010), and then by the MODISsensor carried on several NASA satellites,including EOS PM-1 (now Aqua) launchedin 2002. Increasing numbers of spectral

bands allows better estimation of the ‘opti-cally active constituents’ in seawater, espe-cially chlorophyll in nearshore waters wherethe optical signal is made complicated bythat from suspended solids and dissolved‘yellow-substances’ from freshwater.

A large number and variety of Earth ob-servation satellites are currently in orbit,placed there by government agencies andprivate enterprise, and serving a variety ofpurposes: including: military; weather fore-casting; observation of land and sea. Wefocus here on European sensors measur-ing properties of coastal waters relevant toaquaculture.

The European Space Agency (ESA) wasfounded in 1975. Envisat was launched2002 into polar orbit, and ceased func-tioning in 2012. Sensors included MERIS(MEdium Resolution Imaging Spectrom-eter, measuring Earth reflectance in 15bands of visible and near-infrared light).The subsequent Copernicus programmecomprises a sequence of Sentinel missions,each with paired satellites in a variety oforbits.1

Sentinel-1 is a polar-orbiting radar imag-ing mission, with the first satel-lite launched on the 3rd April 2014(Sentinel-1A) and the second on the25th April 2016 (Sentinel1-B);

Sentinel-2 is a polar-orbiting multispec-tral imaging mission covering missioncovering land as well as coastal waters,the first satellite was launched on 22ndJune 2015 and the second on the 7thMay 2017;

1 Notice the distinction between programme,mission, platform (satellite or aircraft) and sensor.

7: Remote Sensing 4

Sentinel-3 is a polar-orbiting multispec-tral imaging mission with observationsfocusing on the oceans to include sea-surface topography, temperature andocean colour; the latter uses OLCI (theOcean and Land Colour Instrument).The 3A satellite was launched on the16th February 2016, with Sentinel-3Bplanned for launching in April 2018.

4 Optically Active Con-

stituents

The Optically Active Constituents (OAC)of seawater are the components that scat-ter, reflect, or absorb underwater light, thuscontributing to the amount and colour ofsea-leaving radiance. They are:

seawater itself

phytoplankton pigments especiallychlorophyll but including carotenoidsand phycobilins

SPM or Suspended Particulate Matter,both living and non-living

CDOM or Colour Dissolved OrganicMatter or ‘gelbstoff’

These interact in complex ways to changethe colour, amount and angular distribu-tion of light, as photons penetrate into theocean (e.g. Bowers et al., 2000; Devlin et al.,2009). They also affect the small fraction ofphotons that is diverted from a downwardscourse and so leaves the sea in an upwardsdirection. It is by comparing the colour andamount of sea-leaving radiance with that ofsunlight falling on the sea-surface, that it is

possible to estimate the concentrations ofthe OAC.

Doing so requires complex calculations,combining measurements of light at severalwavelengths, after correction for absorptionand scattering in the atmosphere throughwhich a satellite views the sea.

5 Remote Sensing and

Aquaculture

Remote sensing can provide informationabout the distribution in space and timeof many properties relevant to aquaculture.These properties include

• chlorophyll concentration, allowing es-timation of food for shellfish and ob-servation of the occurrence and spreadof Harmful Algal Blooms;

• sea-surface temperature, which con-trols the growth rate of organisms

Although the costs of launching andmaintaining satellites is high, they can bespread over many users. In addition, somedata from satellite remote sensing is in thepublic domain, providing a low-cost way fordevelopers and planners to plan, monitor,manage and rationally exploit marine re-sources.

The remainder of this text, and the corre-sponding slides, exemplify some of the usesof remotely sensed data for the managementand spatial planning of aquaculture and themonitoring of environmental conditions rel-evant both to aquaculture and to marineecosystem health in general. The exercisessuggested in section 10 and appendix A pro-

7: Remote Sensing 5

vide an introduction to obtaining and pro-cessing such data.

6 Case Study: Algarve

The expansion of aquaculture is a strategicpriority for the Portuguese government toimprove the use of the extensive exclusiveeconomic zone occupied by Portugal (Gov-erno Portugal, 2013). Marine Spatial Plan-ning, involving the linking of Remote Sens-ing images with Geographical InformationServices (GIS), has been used to define ar-eas off the Algarve coast that are availablefor developing aquaculture (Fig. 1). Thisdevelopment has to comply with nationaland EU legal requirements for coastal ar-eas. These include

• the Marine Strategy Framework Di-rective (MSFD), aiming to preventthe deterioration of the coastal andoceanic ecosystems by requiring mon-itoring for, and maintenance of, GoodEnvironmental Status (GES);

• the Maritime Spatial Planning Frame-work Directive (MSPFD), aiming toregulate the human uses of these wa-ters.

Christina et al. (2015) used Algarvecoastal waters, near Cape Saint Vincentand the town of Sagres, as a case studyto test how MERIS Algal Pigment Index1 (API1) could contribute to the mon-itoring for the Good Environmental Sta-tus(GES). The MERIS product extractedwas the API1 that corresponds to the to-tal concentration of chlorophyll a and itsdegradation products.

Figure 1: Image showing the location ofareas available for offshore aquaculture inblue; offshore areas currently under use ingreen; offshore artificial reefs in brown andwhite; and fish ponds on land in brown andwhite (Source: www.eaquicultura.pt).

The satellite data were obtained fromMERIS Level 2 Full Resolution (FR) andReduced Resolution (RR) satellite im-ages, with a spatial resolution of 290m × 260 m and 1.2 km × 1.04 km,respectively. They were analysed, andAPI1 extracted, with the Basic ERS andENVISAT (A) ATSR and MERIS Tool-box (BEAM version 4.9; www.brockmann-consult.de/cms/web/beam).

The results shows that MERIS API1product decreased from inshore to offshore,with higher values occurring mainly be-tween early spring and the end of the sum-mer, and with lower values during winter.By using the satellite images for API1, itwas possible to detect and track the de-velopment of algal blooms in coastal andmarine waters (Fig. 2) that are food forshellfish but also have the potential to pro-duce toxins from Harmful Algal Blooms.The study demonstrated the usefulness ofremote sensing for supporting the aqua-

7: Remote Sensing 6

Figure 2: MERIS Reduced Resolution im-ages of Algal Pigment Index 1 (API 1) show-ing the development of an algal bloom betweenFebruary and March 2012 in the coastal watersof southern Portugal. Higher algal pigmentconcentrations shown in red. The inset zoomsin on Cape St Vincent. Source: Christina et al.(2015).

culture economy that is developing in thisstudy area and for the implementation ofthe MSFD and MSPFD.

7 Case Study: Adriatic

Valentini et al. (2016) created a map of thesuitability of different parts of the northernAdriatic Sea for the farming of sea-bass andsea-bream. They used remote sensing, in-sea measurements, and models to provideinformation on the spatial distribution, andseasonal variation, of water characteristicsimportant for fish-farming. The data ob-tained from satellite sensors were sea sur-face temperature (NASA MODIS and ESACopernicus products) and chlorophyll and

other OAC - from ESA MERIS).

The paper does not record the softwareused for the mapping, but the computa-tional method seems to be that of a pixel-by-pixel binary analysis. For each pixelin the map, it was determined whetherit showed space available for aquacultureor already assigned to another activity;whether the annual temperature range wassuitable for fish growth; whether concentra-tions of OAC exceeded thresholds; whetherwater currents were above threshold val-ues.2

The resulting map (Fig. 3) shows thatthe most suitable areas for fish farmingwere offshore and in the eastern part of thenorthern Adriatic. The western Adriaticwas strongly influenced by the dischargeof the Po river, with nutrients that stimu-lated phytoplankton growth and so resultedin chlorophyll concentrations above accept-able limits for sea-bass and sea-bream.These high concentrations, however, makethe western Adriatic particular suitable forthe cultivation of filter-feeding shellfish, asexplored by Brigolin et al. (2017).

Brigolin et al. (2017) studied the poten-tial area for aquaculture along the coastof the of the Emilia-Romagna Italian re-gion (Northern Adriatic Sea) particularlyfor offshore shellfish culture of the Mediter-ranean mussel (Mytilus galloprovincialis).The study assessed: (i) if the establishmentof new farms in deeper areas, beyond the3 nautical miles would be beneficial for theactivity, and if suitable to introduce newlongline technology; (ii) the introduction of

2 Unit 5, ‘Introduction to AquaSpace integrat-ing tools’ explains pixel-based analysis in more de-tail.

7: Remote Sensing 7

Figure 3: Feasibility analysis for fish aqua-culture in northern Adriatic Sea. Green re-gion is suitable and free of other activities.Source: Valentini et al. (2016).

a new species, such the Pacific oyster (Cras-sostrea gigas); and (iii) the risks for a farmbecause of storm events, particularly, in lesssheltered off-shore areas.

Taking these three points into consider-ation, the methodology used to assess thesuitability of the site for shellfish was theSpatial Multi-Criteria Evaluation (SMCE),which provided the framework to com-bine mathematical models and operationaloceanography products. Intermediate levelcriteria (ILC) considered in the analysisincluded optimal growth conditions, envi-ronmental interactions, and socio-economicevaluation (Fig 4).3

Making use of resources provided by re-mote sensing and operational oceanogra-phy in site selection overcame the scarcityof data for model application. The use of

3 The ‘BlueFarm’ tool developed and applied byBrigolin et al. is similar to the ‘AquaSpace Tool’described by Gimpel et al. (2018) and introducedin unit 5.

environmental parameters provided by theremote sensing sensor MODIS (ModerateResolution Imaging Spectroradiometer) be-tween 2003-2012 (e.g. Sea Surface Temper-ature (SST) and concentration of Chloro-phyll a) were used in the dynamic modelsof the present study. The non-linear ef-fects of the different environmental param-eters were integrated over the duration ofthe farming cycle.

The results of the SMCE showed that thewhole coastal area between 0 and 3 nauti-cal miles was highly suitable for farming ofmussel, while the area comprised between 3and 12 nautical miles was divided betweenthe highly suitable northern part, and theless suitable southern part.

In addition, seven different scenarios ofdevelopment of shellfish aquaculture indus-try were explored. The introduction of oys-ter farming did not change dramatically thedegree of suitability for shellfish in the area.The results highlighted that: (i) the growthpotential in this area was high; (ii) the spacewith suitability index > 0.5 increased whenprioritizing the optimal growth conditioncriteria, and (iii) the socio-economic crite-rion was the most restrictive of the ILC.

The results from Brigolin et al. (2017)show that remote sensing can help iden-tify zones for sustainable aquaculture inthe Mediterranean Sea, where the spacefor these activities is becoming increasinglylimited in coastal waters.

7: Remote Sensing 8

Figure 4: Information flow, and frameworkadopted in the SMCE. Colours mark thedifferent Intermediate Level Criteria (ILC):(i) ‘optimal growth’ (orange); (ii) ‘environ-mental interactions’ (red); (iii) ‘socio eco-nomic evaluation’ (green). Constraints areshown in blue. Source: Brigolin et al.(2017).

8 Case Study: Mont St-

Michel bay

Mont Saint-Michel Bay (North Brittany)was used as a case study by Thomas et al.(2011) to assess the potential of this marineecosystem for shellfish aquaculture and toevaluate its carrying capacity. This studyclarified the response of exploited species toenvironmental variations using robust eco-physiological models and available environ-mental data. It simulated the responseof blue mussel (Mytilus edulis L.) to thespatio-temporal fluctuations of the environ-ment in this study area by forcing a genericgrowth model based on Dynamic EnergyBudgets (DEB) with satellite derived en-vironmental data (i.e. temperature andfood).

The sea-surface temperatures were takenfrom nightime infra-red emission measure-ments by the NOAA-18 satellite, and the

chlorophyll concentrations were calculatedusing the OC4 algorithm from the oceancolour (sea-surface reflectance) measure-ments made by the NASA SeaWiFS.

The model was applied over nine yearson a large area covering the entire bayand the simulations provided an evaluationof the spatio-temporal variability in mus-sel growth and showed the capacity of theDEB model to integrate satellite-deriveddata and to predict the variability of spa-tial and temporal growth of mussels. Inaddition, seasonal, inter-annual and spatialgrowth variations were also simulated. Theresults showed a strong link between food(satellite-estimated chlorophyll) and musselgrowth.

9 Case Study: Nova Sco-

tia

This case study concerns the use of low-altitude aerial photography from balloonsand drones to visualise and map features inthe vicinity of farms.

Delineation of marine habitat types andboundaries is essential to several manage-ment concerns facing aquaculture, mostlyrelated to siting of farms. First, some habi-tats are specific to other activities such asfishery grounds, i.e. hard or vegetated bot-tom may be lobster habitat. Separationof aquaculture from these activities mightlead to avoiding conflicts with other users.Second, some habitats may be sensitive(e.g. eelgrass beds) and avoided to mini-mize near-field influences such as ammoniastreams from fish farms.

A challenge to resolution of these habi-

7: Remote Sensing 9

tats is their coverage by water and there-fore in-availability to many remote sens-ing techniques. Bathymetric lidar is anexception but this method is largely ori-ented only to quantifying depth. In con-trast, ocean colour of the upper water col-umn is a longstanding approach to quanti-ties such as chlorophyll and turbidity, butthese are temporally variable, and usuallynot indicative of benthic habitats. One so-lution to these issues arises via in-water re-mote sensing using echosounding. However,given the emphasis on airborne remote sens-ing in this report, we detail a study of inter-tidal habitats where the substrate is fullyexposed to airborne imaging. The signifi-cance of this approach can be related mosteasily to intertidal bivalve culture, althoughthe following example was not in an aqua-culture area.

The conceptual basis for this researcharises from landscape ecology (Zajac, 2008)wherein the size, patchiness, and arrange-ment of the landscape as a mosaic is quan-tified. Arising from terrestrial ecology, thisapproach is more developed where the land-scapes are visible. In addition, faunal prop-erties are associated with landscape struc-ture, i.e. use of meadows and forest by birdspecies.

Fundamental to this discipline are land-scape metrics that are derived with respectto single patch characteristics (e.g. area toperimeter ratio), components of the patchpopulation (spatial distribution, connec-tivity), and landscape quantities (patchdiversity, fragmentation). Although a de-tailed explanation of landscape statistics isbeyond the scope of this summary, an excel-lent introductory text is Gergel and Turner(2017). Software used to calculate land-

scape indices includes freeware Fragstats(www.umass.edu/landeco/research/fragstats).

A feature present in shallow waters ismarine vegetation, usually seaweeds or sea-grasses. Fauna are often associated withvegetated bottoms, as nursery grounds oradult habitat, and seagrasses are knownas sensitive habitat relative to ecosystemhealth. In the following example from Bar-rell and Grant (2015), we illustrate howremote sensing was able to detect inter-tidal features to map habitat. Fig. 5 showsthe intersection of eelgrass (Zostera ma-rina) and blue mussel (Mytilus edulis) bedsin Halifax Harbour. We have emphasizedmarine plants, but bivalve reefs are signif-icant biogenic features that provide a vari-ety of ecosystem services. Remote sensingvia a helium blimp was used to capture theimages, but recent developments in droneshave made this older technique less relevantfor thus type of work. Image manipulationwas used to highlight each habitat. It is ob-vious that the eelgrass grows as distinct fea-tures with repeating patterns. Other partsof the seagrass meadow are more continu-ous. The bivalve bed has more subtle patch-iness. In this study, images captured at dif-ferent times were used to describe changesin the eelgrass patches via landscape statis-tics.

The integration of landscape ecology intothe FAO’s Ecosystem Approach to Aqua-culture is only in its initial stages, but con-tinued applications in remote sensing willencourage progress. Consideration of thefar field involves understanding the distri-bution of habitats and potential interac-tion with farm sites. A systematic ap-proach for characterizing and describinghabitats is necessary, and likely achieved

7: Remote Sensing 10

through methodologies developed for land-scape ecology. Remote sensing in underwa-ter habitats remains an important compo-nent requiring further research.

Figure 5: Aerial images of intertidal eel-grass and bivalve beds in Halifax Harbour.Source: Barrell and Grant (2015).

7: Remote Sensing 11

10 Exercises and SAQ

Exercise

The exercise involve the use of SNAP (Sen-tinel Application Platform) software to ex-amine a Sentinel-3 image obtained fromEUMETSAT. It is detailed in Appendix A.

Further reading

Study one of the papers associated with theCase Studies:

Algarve : Christina et al. (2015) (OpenAccess);

Adriatic : Valentini et al. (2016) orBrigolin et al. (2017) (both Open Ac-cess);

Mont St Michel : Thomas et al. (2011);

Nova Scotia : Barrell and Grant (2015).

Self Assessment Questions

The SAQs that follow test your achievementof the learning outcomes and help you thinkactively about the points raised in this lec-ture. No answers are given. The (paren-thetical) numbers refer to sections and theletters to appendices.

1. What satellite data would help anaquacultural planner or developer tofind a favourable environment for thecultivation of mussels? (7)

2. How are optical measurements madeby a satellite converted to maps ofchlorophyll concentration? (4)

3. What useful information was gainedby using remote sensing to monitoringaquaculture concessions in the coastalwaters of the Algarve coast and in thebays of Nova Scotia? (6, 9)

4. What were the contributions of remotesensing data to the case studies in theNorth Adriatic and Mont Saint-Michelbay? (7, 8)

5. Describe the main steps of the methodfor obtaining a processed image ofChlorophyll-a concentrations. (A)

7: Remote Sensing 12

Appendices

A Use of SNAP with

Sentinel-3 data

SNAP is the ESA Sentinel ApplicationPlatform that provides analysis of the sci-entific data from the ERS-ENVISAT mis-sions, the Sentinels 1/2/3 missions and arange of national and third party missions.Remotely sensed images are stored as arraysof data relating to pixels in maps. The ex-ercise involves data that have already beensubjected to some processing, to correct foratmospheric changes to the sea-leaving sig-nal and to convert the latter into variablesof interest, such as sea surface temperature(SST) or – in the present exercise – the phy-toplankton photosynthetic pigment Chloro-phyll a (Chla). SNAP allows such data ar-rays to be explored and manipulated at dif-ferent processing levels.

The exercise will have been completedwhen the student has obtained at least onechlorophyll image for a coastal region oftheir choice.

A.1 Install SNAP

Install the SNAP softwarefrom the ESA toolbox site atstep.esa.int/main/toolboxes/snap, whichprovides guidance. Chose the ‘SentinelToolboxes’ and then follow the normalpractice for your computer’s OS to installthe programme. Note where it was placed.4

4In the case of Mac OS X, it is most likely inApplications/snap/bin.

A.2 Get Sentinel-3 data

Although the SNAP app includes the abil-ity to access ESA data as one of its func-tions, it is suggested, here, that the studentvisits the EUMETSAT site and downloadsdata independently of SNAP. In some casesthis will require a user name and password.5

A directory or folder should be set up tohold the (large) files before they are loadedinto SNAP.

Tutorials are available from the SNAPwebsite at step.esa.int/main/doc/tutorials.Or, go directly to YouTube for How to Vi-sualise Sentinel 3 Data. As shown in thisvideo,

• log in to the EUMETSAT site atcoda.eumetsat.int/#/home;

• select region of interest on the mapthat is presented, and in the search cri-teria menu select OL 2 WFR from ‘Prod-uct Type’, OLCI from ‘Instrument’,and L2 from ‘Product Level’, to getSentinel-3 Level 2 Full Resolution wa-ter and atmosphere geophysical prod-ucts from OLCI;

• you may be presented with many datasets, each from one pass of the satellite;identify the data set you want;

• download the data, which will be pro-vided as a zip file: decompress it andstore the resulting folder in the loca-tion you have identified for these data.

5 In the case of EUMETSAT, a new user can setup an account when first visitng the web site.

7: Remote Sensing 13

A.3 Open data in SNAP

Sentinel-3 products are provided as a collec-tion of files contained within a folder. Thefolder name is the actual product name,ending in .SEN3. Each folder containsa metadata file named xfdumanifest.xml

and at least one netcdf-file. Each netcdf-filecontains a subset of a Sentinel-3 product’scontent.

There are several ways to open a Sentinel-3 product within SNAP. Use this one:

• Choose File → Open Product,

• navigate to the xfdumanifest.xml filein your downloaded folder, and clickOpen ;

• the name of the folder will appear inthe Product Explorer window, withsubfolders that include Bands;

• ctrl-click or right click on the foldername to bring up a menu;

• select Open RGB Image Window , ac-

cept Tristimulus profile, and click

OK to bring up an image in SNAP’sright-hand window.

The result may look different from thatin the video; a cloud-covered image, for ex-ample, will be mainly white.6 Continue asfollows to output a chlorophyll image:

• in the Product Explorer window, →Bands → CHL → CHL OC4ME

• ctrl-click or right click on the filename to bring up a menu;

6If the image is cloud-covered, it will be desir-able to return to EUMETSAT for another data set.

• select Open Image Window to dis-play the chlorophyll image;

• select (in main menus) Layer→ Layer

Manager;

• in the Layer Manager window selectMasks → WQSF Isb CLOUD; close LMwindow;

• select (in main menus) View → Tool

Windows → Colour Manipulation;

• In Colour window, select Table andclick on colours to change the colourscale displayed for chlorophyll.

By this time, your SNAP screen shouldlook something like Figure 6. The choro-phyll image can be exported using the menugenerated by ctrl-click or right click .

7: Remote Sensing 14

Figure 6: Screen grab from SNAP run-ning under Mac OS X, showing an imagecentered on the Strait of Gibraltar on 16November 2017

7: Remote Sensing 15

B NASA OceanColor

site

The NASA OceanColor Web site providesaccess to NASA data from a variety of mis-sions. If you use that link to enter the site’sfront page, chose Data → Level 1 and 2

browsers. Or follow the instructions be-low to see global maps of chlorophyll con-centration or sea-surface temperature fora selected month and year, derived fromMODIS.

• Go to NASA Ocean Color sitepage at oceancolor.gsfc.nasa.gov/cgi/browse.pl?sen=am

• set options to to CHL , MODIS

aqua and day ;

• select a month (in this case, August)and year (in this case, 2017), to displaya global map for that month;

• set option to SST for a temperaturemap;

• if you want to do more, use the helpfor guidance.

This NASA web page also provides accessto older data, including those from SeaW-iFS (select MLAC) and CZCS.

7: Remote Sensing 16

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Bowers, D., Harker, G., Smith, P., and Tett,P. (2000). Optical properties of a re-gion of freshwater influence (the ClydeSea). Estuarine, Coastal and Shelf Sci-ence, 50:717–726.

Brigolin, D., Porporato, E. M. D., Pri-oli, G., and Pastres, R. (2017). Mak-ing space for shellfish farming along theAdriatic coast. ICES Journal Of Ma-rine Science, 74(6):1540–1551. DOI:10.1177/0309133315578943.

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