Satellite Based Sea Ice Advisory for Antarctic Expedition · The technique of multi-sensor remote sensing data analysis to derive sea ice characteristics has been explained. The actual

i

Scientific Report SAC/EPSA/AOSG/SR/22/2014

Sea Ice Advisory using Earth Observation Data

for Ship Routing during Antarctic Expedition

D. Ram Rajak, R.K. Kamaljit Singh, Megha Maheshwari, Jayaprasad P., Sandip R. Oza, Javed M. Beg*,

Rashmi Sharma and Raj Kumar

Atmospheric and Oceanic Sciences Group, EPSA.

Space Applications Centre, Indian Space Research Organization,

Ahmedabad – 380015, India.

December 2014

Current Location & Next Destination of Ship

SIE

SIC

SIT

SID

SIDr

SITr

Ship Entering Sea Ice ?

Next Shorter Route with Lowest SIC & Lowest SIT

SIC=100% & High SIT ?

Polynya Or Deformation?

Refreezing ?

DriftHindrance ?

No Sea Ice Advisory

Likely Safe Route

Yes

No

No

No

No

No

Wait ?

Yes

Yes

YesYes

ii

Scientific Report SAC/EPSA/AOSG/SR/22/2014

Sea Ice Advisory using Earth Observation Data for Ship Routing during Antarctic Expedition

D. Ram Rajak, R.K. Kamaljit Singh, Megha Maheshwari, Jayaprasad P., Sandip R. Oza, Javed M. Beg*,

Rashmi Sharma and Raj Kumar

Atmospheric and Oceanic Sciences Group, EPSA. Space Applications Centre,

Indian Space Research Organization, Ahmedabad – 380015, India.

December 2014

iii

GOVERNMENT OF INDIA

INDIAN SPACE RESEARCH ORGANISATION SPACE APPLICATIONS CENTRE

AHMEDABAD – 380 015 DOCUMENT CONTROL AND DATA SHEET

1 Date December 15, 2014

2 Title Sea Ice Advisory using Earth Observation Data for Ship Routing during Antarctic Expedition

3 Version 1.0

4 Document No. SAC/EPSA/AOSG/SR/22/2014 5 Type of Report Scientific 6 No. of pages 30 7 Authors D. Ram Rajak, R.K. Kamaljit Singh, Megha

Maheshwari, Jayaprasad P., Sandip R. Oza, Javed M. Beg*, Rashmi Sharma and Raj Kumar

8 Originating Unit OSD/AOSG/EPSA, SAC, Ahmedabad, India. 9 Abstract A brief description on the potential of earth observation

data to derive sea ice parameters needed to provide sea ice

advisory for safer shipping has been presented. Extraction

of near real time information needed for ship routeing has

been discussed with respect to a case study of successful

delivery of sea ice condition (derived from satellite data

analysis) during 33rd Indian Scientific Expedition to

Antarctica (33ISEA). The technique of multi-sensor remote

sensing data analysis to derive sea ice characteristics has

been explained. The actual route followed by the 33ISEA

voyage ship has been compared with the route advised

based on satellite derived sea ice conditions.

10 Classification Non Restricted 11 Circulation General

* Javed M. Beg from NCAOR, Goa

Satellite Based Sea Ice Advisory for Antarctic Expedition Rajak et al, 2014

1

Sea Ice Advisory using Earth Observation Data for Ship Routing during Antarctic Expedition

1. Introduction

Safe shipping depends on a number of factors related to sea state, weather condition,

and ship’s own characteristics. The ship routing for a scientific expedition may differ a

lot than that for a cargo ship and is a little more complicated due to an additional factor

of achieving scientific objectives. Research Vessels or ships on a scientific mission may

not necessarily follow the shortest route or the most economic route with least fuel

consumption because of the scientific objectives / targets that are to be met. Navigation

in polar regions through sea ice is perhaps most tedious task and requires a lot of extra

information on sea ice condition for routing through path of least resistance. Continuous

processes of sea ice melting, freezing and drifting warrants near real time information

on sea ice condition. Traditional ship routing does not provide adequate real time

information on sea ice status. The availability of remote sensing data over ocean offers

an opportunity to derive and use near real time information needed for ship Routing.

–

Attempts have been made to develop algorithm and to numerically model the problem

of ship routing (Tsou and Cheng, 2013; Mannarini et al, 2013; Al-Hamad et al, 2012;

Shigeaki et al, 2010; Kotovirta et al, 2009). A prototype Decision Support System for an

operational ship routing using time‐dependent meteo‐oceanographic fields was

presented by Mannarini et al, 2013. Another prototype system for optimizing routes

through the ice field was presented by Kotovirta et al, 2009. Vlachos, 2004 presented a

method for manipulating the POSEIDON system forecasts for the Greek seas and for

producing optimal routes for small and medium size ships. Shigeaki et al, 2010 studied

a numerical navigation system for a small ship sailing in coastal waters with SWAN,

RIOS and MMG models and concluded that it is possible to achieve an optimum route

by numerical simulation if winds, waves, and tidal currents can be predicted. The basic

isochrone method was attempted to obtain an optimal route on the basis of the

dynamically changing weather, which could be forecasted by the coupled atmosphere-

wave-ocean model (Zhang and Huang, 2007).


2

Recently an ice class ship on way to Antarctica got struck in sea ice (Akademic

Shokalskiy, 2013). It was not the only case of ship routing trouble in polar sea ice

regions. Earlier, many polar expedition ships got struck in sea ice (Arctic supply ships,

2012; 1000 ships, 2013; Passenger ferry, 2010; Ships in Baltic Sea ice, 2010; Kapitan

Khlebnikov, 2009; Cold Irony, 2008; Rescue, 2002). To avoid and minimise such

instances, effective ship routing is an important aspect of polar navigation.

2. Factors Affecting Ship Routing

Factors influencing ship routing in polar regions for scientific expeditions may be

categorised in four classes –

–

1. Atmosphere / weather parameters e.g. wind speed, wind direction, atmospheric

pressure, atmospheric temperature, visibility etc.

2. Sea state parameters like significant and maximum wave height, wave direction,

current speed and direction, sea surface temperature etc. Sea ice conditions like

sea ice thickness, sea ice type, sea ice concentration etc.

3. Economics (distance to be travelled, fuel consumption, travel time etc) and Ship

Characteristics (ship type, hull type, speed capability, safety considerations etc).

4. Scientific Objectives to be achieved and expedition time window.

Wind speed affects ship navigation in a complex way. While ships lose speed in light

headwinds (less than 20-knots),) they gain speed slightly in following light winds. In

case of high wind speed, ship speed decreases in both head and following winds. In

such cases, wave action dominates over wind action for determining ship performance.

Additionally, the ship navigation gets influenced by direction, speed and persistence of

wind due to its direct impact on drifting ice, in polar regions. Winds are considered as

either pushing-off or pushing-in factors in such situation. While the pushing-off

contributes in weakening of compression due to concentrated ice, pushing-in does

strengthening of ice. Hence, accurate forecast of wind, which is a critical parameter for

all sailing ships, is vital to a successful voyage.


3

Wave height is a major factor that affects ship performance in open waters. The

development of waves in any region of ocean mostly depends on wind speed and

direction, water depth and presence and distribution of ice in that region. Wave action

causes increased drag and ship motions which reduces propeller thrust. The

relationship of wave direction and height with ship performance is also not simple. Head

waves reduce ship speed, while following waves increase ship speed slightly to a

certain point, beyond which they start reducing it. In very high wave seas, prediction of

performance may be difficult because of the adjustments to course and speed for ship

handling. In a study carried out by Haltiner et al, 1962, inspection of the basic

differential equations and the empirical relation between ship's speed and the state of

the sea was carried out which indicated that in cases of moderate wave heights, the

dependence on wave direction may be omitted as a good approximation. Usually, the

effect of sea waves and swells is much greater for large size ships than that of wind

speed and direction. However, it is difficult to separate the effects caused by these two

in ship routing.

Ocean currents don’t change frequently over a region of ocean and may be considered

relatively constant over a short span of voyage duration. Direction and speed of ocean

currents are more predictable than wind and sea waves. Hence they do not present a

significant problem in ship routing; although they can be a determining factor in route

selection and diversion. Ocean currents can get disrupted for short duration by very

intense weather systems such as hurricanes and by global phenomena such as El Nino.

While sailing to Antarctica, ships need to cross Antarctic Circumpolar Current (ACC)

which is considered the strongest ocean current on this planet.

Sea ice has remained a challenge for ship navigation in Polar regions. It causes

retardation in ship speed due to friction. It varies in lateral extent and thickness which

depends mainly on geographical position and host of whether parameters. The greater


4

the extent and thickness of sea ice, bigger shall be the duration and force of friction

which adversely affects the ship’s speed and fuel consumption. At times, with the sea

ice thickness being greater than the ship’s capability to break or cut through results in

ships getting stuck and being crushed under the formidable pressure that sea ice can

exert because of the ocean currents or volumetric expansion due to cooling

temperatures. Recent trends on climate change suggests contrasting rather

contradicting effects on sea ice. The Arctic sea ice extent is declining at a rate of 0.53 ×

106 km2 per decade, whereas Antarctica exhibits a positive trend at the rate of 0.167 ×

106 km2 per decade (Teleti and Luis, 2013).

3. Traditional Methods of Data Collection and Ship Routing

The sea state, characterised by statistics, including the wave height, period, and power

spectrum was traditionally assessed by the experienced observers (e.g. trained

mariners) or through instruments like weather buoys. Based on Douglas Sea Scale,

World Meteorological Organization (WMO) has assigned 10 sea codes for describing

sea sate condition. While sea state code 0 corresponds to calm (glassy) sea surface

with almost 0 meter wave height; sea state 9 represents phenomenal rough sea surface

with wave height of more than 14 meters. Ocean currents at local scales may be

measured by arrays of current meter moorings. Radars located at shores can map

coastal currents, Sea-Surface Currents, swell-wave parameters etc (Gurgel et al., 2003;

Bathgate et al., 2006; Braun et al, 2008) up to certain range inside ocean. Circulation

patterns over larger areas may be obtained by tracking ocean drifters, but it takes long

time to accomplish the task. Similarly, forecast of wind condition in any region was

conventionally based on the few available data points which were collected by buy

network and ships sailing in that region. The inaccurate wind condition would represent

inaccurate atmospheric cyclonic features resulting into inaccurate swells during wave

forecast.

–

All said such information was available in parts of oceans where high level of marine

activities take place like North West Shelf in Europe is an example wherein, countries

like Norway, Denmark, Netherlands and UK have deployed buoy Such network of buoys

measure both winds and wave heights with the spatial and temporal sampling. These


5

dedicated buoy networks have high running costs of over a million pounds per annum

for each 10 buoy network (https://earth.esa.int/web/guest/-/wind-and-wave-forecasts-

for-offshore-operations-and-ship-routing-4024).

On the contrary oceans in polar region especially surrounding Antarctica did not have

such networks of buoys and radars etc because of very little traffic. And the navigation

was mainly depended on experienced eyes of mariners with little support in for from of

historical weather data tables for ship routing. The climatic data available from such

resources include wind speed and direction, wave height, ocean currents, visibility,

barometric pressure, sea surface temperatures and ice limits, for the major ocean

basins of the world for each month of the year. There are a few online service that

provide some information on ship routing based on scanty data available through ships

around the area, (http://www.bocmetocean.com/index.php;

https://www.marinetraffic.com/en/; http://www.meteogroup.com/en/gb/sectors/ marine/

shipping/routeguard.html).

For the Indian Antarctic Expedition vessel the routing decisions are made by the

Captain/ Ice Navigator in consultation with the Leader of the expedition keeping in view

the safety of the ship and the mission objectives based on the limited field of view, aided

by the scanty information available through online services providing weather

information and not necessarily taking fuel economics into account. The experience of

the ship navigators plays an important role in optimum ship routing.

The ship navigators usually take help of Atlas of Pilot Charts

(http://www.offshoreblue.com/navigation/pilot-charts.php), the Sailing Directions both

Planning Guides and En-route (http://www.offshoreblue.com/navigation/ sailings.php)

and other climatological data.

Prior to satellite era, availability of sea ice records was poor. Limited information was

available through the whalers or other ships that visited the polar sea ice regions. Now-

a-days sea ice records prepared based on satellite data include sea ice extent, area,

concentration, thickness and the age of the sea ice. Sea ice in Antarctic region is very

different that in Arctic region as far as its age, thickness, growth/melting patterns, snow

http://www.meteogroup.com/en/gb/sectors/%20marine/�


6

coverage etc are concerned. Based on analysis of 10 years data, Massom et al, 2001 found large regional and seasonal differences in snow properties and thicknesses and

thicker snow and thinner ice in the Antarctic relative to the Arctic.

4. Earth Observation Data to Support Ship Routing

Traditional methods of data collection over ocean have certain limitations. Insufficient

data and time consumption are two of the major concerns. Moreover, in many cases

incompatibility problems prevent neighbouring countries from sharing the data. The

availability of useful data in Southern Hemisphere is further poor in comparison to

Northern Hemisphere. With the increasing availability of remotely sensed data this

situation is gradually improving. Weather data and other environmental information

obtained from satellites are contributing greatly to an improvement in southern

hemisphere forecasts.

Wind speed and direction, sea surface temperature, and significant wave height are

some of the parameters that are derived from satellite data analysis. Data available

from microwave radiometers as well as scatterometer can be used for estimating wind

speed and wind direction over ocean surface.

Ocean surface wind vector can be modelled using angular sigma-0 measurements by

scatterometer at different azimuth angles. Sigma-0 over the ocean depends on

backscattering from wind-generated capillary-gravity waves, which are generally in

equilibrium with the near-surface wind vector. Viewed from different azimuth angles, the

observed backscatter from these waves varies. Relating wind with backscattering

sigma-0 using a geophysical model function enables estimation of wind vector using

scatterometer data.

–


7

Gohil and Pandey, 1985 presented an algorithm for retrieval of oceanic wind vectors

from the simulated SASS (Seasat A satellite scatterometer) normalized radar cross-

section measurements. Wentz, 1992 investigated the retrieval of wind speed and

direction over ocean using microwave radiometer measurements. Bentamy et al, 1999

compared the wind estimates derived from scatterometer (ERS-1), altimeter (ERS-1)

and radiometer (SSM/I) and established the usefulness of merging of these estimates to

generate wind fields. Data available from Advanced Scatterometer (ASCAT) onboard

EUMETSAT MetOp-A and MetOp-B satellites is successfully used for ocean wind

vector generation (http://www.remss.com/missions/ascat). Wind vector has been

generated using polarimetric microwave radiometry from WindSat, a satellite-based

multifrequency polarimetric microwave radiometer (Gaiser et al, 2004). An algorithm

was developed by Gohil et al, 2006, for retrieving wind vector from scatterometer with a

solution ranking criteria of minimum normalized standard deviation of wind speeds

derived using backscatter measurements through a geophysical model function.

Sharma et al (2007a and 2007b) developed and applied new technique based on

genetic algoritm for predicting wind field in the Bay of Bengal using satellite

scatterometer observations. They found that the predictions made by the proposed

technique up to 5 days in advance were superior to the forecast by persistence method.

Reul et al, 2012 demonstrated that surface wind speeds estimated from SMOS (Soil

Moisture and Ocean Salinity) brightness temperature images agreed well with the

observed and modelled surface wind speed features. Recently, Tang et al, 2014 have

presented and validated a method of reconstructing high-resolution sea surface wind

fields from multi-sensor satellite data.

Satellite remote sensing data can be used to determine currents synoptically over

extensive ocean. It may be used to monitor dynamic ocean conditions, including surface

currents, local wind speed, significant wave height etc (Klemas, 2011). For many years,

active satellite remote sensing data, from Synthetic Aperture Radar (SAR) and

altimeter, have been used to determine ocean wave height. Now these estimation

techniques are imbedded on many meteorological and oceanographic forecasting


8

systems. Satellite data has been used for sea surface topography measurement since

the first radar altimeter was tested on NASA's Skylab space station in 1973 (Mourad et

al, 1976). Barrick and Fedor, 1978 presented some of the first wave height plots

prepared using the satellite based radar data. Rufenach, 1978 measured ocean wave

height using altimeter signals transmitted from the low-orbiting satellite Geos 3.

Mognard and Lago, 1979 processed radar altimeter data from more than 100 Geos 3

passes and found that the wave height and wind speed estimates were in favourable

agreement with the meteorological information obtained from weather maps.

Techniques of observing ocean variability using satellite altimetry and the challenges

faced during 1980s have been discussed by Fu et al, 1988. The ocean wave heights

products are prepared from the shape and intensity of the satellite based altimeter radar

echo. Romeiser et al., 2005 presented a study on ocean current retrievals from

interferometric Synthetic Aperture Radar (InSAR) data acquired during the Shuttle

Radar Topography Mission (SRTM) in February 2000.

Sea ice condition derived from earth observation data is becoming available for ship

routing in polar regions. Sea ice detection, classification and extent mapping using

remote sensing data available from different sources have been demonstrated by

various researchers (Riggs et al., 1999; Remund and Long, 1999; Haarpaintner et al.,

2004; Oza et al., 2010; Rivas and Stoffelen, 2011; Belmonte et al., 2012). Sea ice

extent mapping attempt was made by Laxon (1994a, 1994b) using pulse-peakiness

parameter derived from altimeter data. Kim et al., 2001 studied distribution of sea ice in

the Weddell Sea, Antarctic region using radar altimeter data from Topex/Poseidon and

ERS-1. Cavalieri et al., 1984 presented an algorithm to determine sea ice concentration

from SSMR (Scanning Multichannel Microwave Radiometer) data. Long term sea ice

concentration data are available at NSIDC (National Snow and Ice Data Center at

University of Colorado, Boulder) site. These sea ice concentrations products are

generated from brightness temperature data derived from the Nimbus-7 SMMR, the

Defense Meteorological Satellite Program (DMSP) SSM/I i.e. Special Sensor Microwave

Imager sensors, and the DMSP SSMIS i.e. Special Sensor Microwave Imager/Sounder

(Cavalieri et al., 1995; Cavalieri et al., 1996). Near Real Time (NRT) daily gridded sea

ice concentration products available at NSIDC site are based on an algorithm


9

developed by Maslanik and Stroeve, 1999. There are a number of studies that discuss

the problem of sea ice freeboard and thickness determination using remotely sensed

data (Kwok and Cunningham, 2008; Zwally et al., 2008; Singh et al., 2011; Kurtz et al.,

2012; Laxon et al., 2012; Kurtz et al., 2013; Kurtz et al., 2014). Kwok and Rothrock,

2009 examined sea ice thickness records from submarines (1958 to 2000) and ICESat

observations (2003 to 2008). They found that mean Arctic sea ice thickness declined

from 3.64 meters in 1980 to 1.89 meters in 2008—a decline of 1.75 meters. Zwally et al,

2008 derived sea ice freeboard heights in the Weddell Sea of Antarctica from ICESat

(Ice, Cloud, and Land Elevation Satellite) laser altimeter measurements. Singh et al.,

2011 presented an algorithm for estimating sea ice thickness using passive microwave

data over thin ice. The overall sea ice thickness is much higher in Arctic than in

Antarctic. Moreover, high inter annual variability of sea ice in Arctic (Laxon et al., 2003)

poses a greater challenge for ship routing experts. Recently, Kern et al., 2014

discussed about uncertainties involved in retrieving sea ice thickness using satellite

based altimeter data.

5. Satellite Based Sea Ice Advisory to Indian Antarctic Expeditions:

Selection of optimum ship route is a problem of multiple criteria decision making

(MCDM). Based on the multiple objectives, multiple criteria decision analysis (MCDA) is

carried out to support decision ship navigator during planning of ship route as well as

during the real time navigation. A number of software tools are available which perform

MCDA and help ship navigators. Such software tools need real time forecast of data for

the transit to be travelled by ship. The current and past status of sea ice features and

other parameters can be derived using remotely sensed data. Combining satellite

derived information with numerical modelling can be a strong base for sea ice advisory

for ship navigation. Following two sections present brief details on the satellite based

sea ice advisory provided to 32nd and 33rd Antarctic expeditions.

–


10

a.

Ships travelling to Antarctica need information of sea ice condition before nearing its

coast. The historical data on sea ice extent and sea ice thickness helps in selecting the

general ship route. The actual route to be followed by ship can be determined only

based on near real time sea ice condition. The first satellite based sea ice advisory was

requested by Voyage Leader of 32nd ISEA in December 2013. The expedition ship was

sailing from Bharati to Maitri while it got struck and could not make any headway for the

next two days. The ice-breaker, which was part of the expedition, was supposed to

create a wide track for voyage ship to move out of fast ice. The track created by ice-

breaker was so narrow and zigzag shaped that the voyage ship could not sail properly.

After struggling for two days, the expedition ship could come out of fast ice. But then

there was thick fast ice near Maitri that the expedition ship was going to encounter. That

is the time when first satellite based sea ice advisory prepared at SAC was made

available to 32nd ISEA through NCAOR. The advisory was mainly based on analysis of

SIC data and MODIS LANCE data.

First Satellite Based Sea Ice Advisory to 32nd ISEA

b.

33rd ISEA was the first Indian Antarctic Expedition for which comprehensive satellite

based sea ice advisory was made available. Following sections provide brief summary

of sea ice advisory provided during second leg of 33rd ISEA –

A Comprehensive Sea Ice Advisory to 33rd ISEA

M/V Ivan Papanin, a Russian Ice Class ship hired by India started its journey of second

leg of ISEA from Cape Town (South Africa) toward Bharati (an Indian Research Station

in Antarctica) on February 14, 2014. The ship Master and Voyage Leader had apriori

information from 1st leg of 33rd ISEA that they will encounter thick sea ice south of 66o

latitude and ship will not be able to break pack ice with almost 100% fraction. To

support ship navigation satellite data analysis was carried out at Space Applications

Centre (SAC), Ahmedabad and sea ice fraction, sea ice deformations and sea ice


11

thickness were analysed and the advisory regarding most suitable entry locations were

suggested. The advisory was sent to ship navigator on February 20, 2014 through

National centre for Antarctic & Ocean Research (NCAOR), Goa. After completing the

expedition objectives at Bharati the ship had to sail from Bharati toward Maitri (another

Indian Research Station in Antarctica). Again, based on satellite data analysis sea ice

advisory suggesting the most suitable exit locations was sent on February 26, 2014.

While ship nearing India Bay toward Maitri coast, it encountered densely packed drifting

sea ice with very high concentration. Multi-date satellite data analysis was carried out at

SAC and sea ice drifting pattern was monitored and sea ice advisory suggesting

possible ship route was sent on March 7, 2014. When ship reached 69o 35’ S, 10o 25’ E,

it got trapped within thick pack ice. Based on the sea ice concentration and the

deformation patterns visible on satellite imagery another sea ice advisory was sent on

March 14, 2014. Finally on March 29, 2014 a sea ice advisory was sent that suggested

three exit routes from India Bay to sail toward Cape Town.

6. Satellite Data Analysis for Sea Ice Advisory During 33rd ISEA 2013-14

6.1 Study Area –

–

The study area comprises of two sectors in southern ocean. One sector covers Prydz

Bay in southern ocean extending from 65o South latitude to Ingrid Christensen Coast,

Larsemann Hills between 60o to 90o East longitudes. Indian research station, Bharati is

situated in this section. In Prydz Bay, the ocean circulation is characterized by a closed

cyclonic gyre adjacent to the Amery Ice Shelf. Circumpolar Deep Water (0° to 2°C;

34.50 to 34.75 salinity), a large mass of cold, moderately saline water along with colder

and more saline Antarctic Bottom Water (0°C; 34.60 to 34.72 salinity), are the major

bodies of deep water masses carried by the Antarctic Circumpolar Currents (Smith et

al., 1984). Wong, 1998 postulated the possibility of high salinity bottom water formation

episodically on the continental slope of the Prydz Bay. Yabuki et al., 2006 confirmed,

through their observations, the formation of saltier Low Salinity Shelf Water (LSSW) in

the Prydz Bay region which provides evidence for the idea that saltier LSSW originating


12

in the Amery Basin mixed with unmodified Circumpolar Deep Water in continental slope

resulted in the formation of the Antarctic Bottom Water.

The second sector of study area covers India Bay in Southern Ocean. It covers parts of

King Haakon VII Sea from 65o South latitude to Princess Astrid Coast, Antarctica.

Princess Astrid Coast is a portion of the coast of Queen Maud Land lying between 5°

and 20° E longitudes. Indian research station, Maitri is situated at Schirmacher Oasis on

central Queen Maud Land. Its aerial distance from India Bay coast is around 80 km. The

Indian voyage ships arrive in India Bay, unloads cargo, and decant fuel on an ice shelf.

Expedition members disembark from ship on ice shelf and reach Maitri by helicopter.

The study area in shown in Figure 1.

Fig 1: Sea Ice Advisory Study area: Ocean regions near Bharati & Maitri Stations

.

.

Maitri

Bharati


13

6.2 Satellite Data, Products and Analysis –

Following are the major remotely sensed data used to derive sea ice condition

parameters –

1. SAR data from RISAT-1 (Obtained from NRSC/ISRO)

2. AltiKa data from SARAL (From MOSDAC/ISRO local database site)

3. Scatterometer data from Oceansat-2 (OSCAT-2 from MOSDAC/ISRO local

database site)

4. MODIS LANCE mosaic (From lance-modis.eosdis.nasa site)

5. Sea Ice Concentration products at 3.125 km resolution (From ftp://ftp-

projects.zmaw.de/seaice/AMSR2 site)

6. Initial Sea Ice Extent (Rajak et al., 2014)

Further details on datasets used in this study are provided in a table given below -

Data Name Satellite Sensor Reso-

lution Derived Information Data Source

A-priori SIE

Multiple Multiple 25 km Sea Ice presence Rajak et al., 2014

RISAT-1 SAR

RISAT-1 SAR 36 m Ice features NRSC

OSCAT Oceansat-2 Scatterometer

12.5 km

Sea Ice Concentration (overall)

MOSDAC Local database (SAC)

SARAL AltiKa

SARAL Altimeter 175 m Sea Ice Thickness MOSDAC Local database (SAC)

Sea Ice Concen-tration

GCOM-W AMSR2 3.125 km

Sea Ice Concentration & Sea ice Trend Analysis

ftp://ftp-projects.zmaw.de/ seaice/AMSR2

MODIS LANCE Mosaic

EOS AM/ EOS PM

MODIS (Terra/ Aqua)

250 m Sea Ice Deformation and Sea Ice drift Analysis

http://lance-modis.eosdis.nasa.gov/imagery/subsets/?mosaic=Antarctica

Satellite data acquired over two segments of study area during the expedition period

were used to derive the information needed for sea ice advisory. Coarse Resolution

ftp://ftp-projects.zmaw.de/seaice/AMSR2�

ftp://ftp-projects.zmaw.de/seaice/AMSR2�


14

scanSAR (CRS) and Moderate Resolution ScanSAR (MRS) SAR data at C-band (5.35

GHz) available from Radar Satellite-1 (RISAT-1) for two dates were used for polar ice

feature identification. CRS data with 50 m spatial resolution (pixel size of 36 m) over

India Bay was acquired on March 08, 2014 and MRS data with 25 m spatial resolution

(pixel size of 18m) was acquired on March 26, 2014. Oceansat-2 Scatterometer data

[OSCAT-2, enhanced resolution, 10-day running composite prepared at MOSDAC,

SAC, Ahmedabad0 was used to derive sea ice extent and sea ice concentration over

Antarctic region for ship voyage duration. SARAL AltiKa was used for sea ice thickness.

Cloud free mosaic MODIS (Moderate Resolution Imaging Spectro-radiometer) LANCE

(Land Atmosphere Near real-time Capability for EOS) data available from “http://lance-

modis.eosdis.nasa.gov/imagery/subsets/?mosaic=Antarctica” for voyage duration were

used for sea ice deformation analysis and sea ice drift analysis. The 3.125 km sea ice

concentration data available from ftp://ftp-projects.zmaw.de/ seaice/AMSR2 site was

used at selected locations for finer details.

The satellite data and products available from different sources were analysed to derive

polar ice and sea ice parameters like sea ice extent, sea ice concentration, sea ice

thickness, wind speed and direction, sea ice deformations, sea ice growth trend, sea ice

drift analysis etc. A schematic diagram showing major elements of RS data analysis for

arriving at a safer ship route are shown in Figure 2.

6.2.1 Polar Ice Features Identification using RISAT-1 SAR Data –

Synthetic Aperture Radar (SAR) data acquired by Indian satellite RISAT-1 were used

for identification of various polar ice features like ice shelf region near Maitri coast, fast

ice, ice rise, ice bergs, sea ice floe etc. This is a high resolution data with relatively

narrow swath, hence spatial coverage is limited. Accurate geo-graphic locations

(longitude and latitude) of the ship birthing site and finer details around it are derived

from these data. An example of a RISAT-1 SAR scene (36m) acquired on March 8,

2014 near Maitri coast is shown in Figure 3.


15

6.2.2 Sea Ice Extent & Concentration Analysis –

Sea ice occurrence probability data generated by Rajak et al., 2014 is primarily used for

arriving at first guess of voyage ship encountering sea ice. Current season sea ice

concentration images prepared from OSCAT and ASCAT data were used for overall

assessment of sea ice condition in the Antarctic regions. For assessing the sea ice

status at finer level sea ice concentration products available at ftp://ftp-

projects.zmaw.de/ seaice/AMSR2 site were used. The shortest path of ship from its

current location to next destination is plotted and overlaid on SIC image. SIC values

throughout the transect are plotted and analysed alongwith sea ice thickness values.

Fig 2: A schematic diagram showing major elements of RS data analysis. SIE, SIC,

SID, SIT are Sea Ice Extent, Concentration, Deformation and Thickness, respectively;

SITr and SIDr are Sea Ice Trend and Drift analysis, respectively.

Current Location & Next Destination of Ship

SIE

SIC

SIT

SID

SIDr

SITr

Ship Entering Sea Ice ?

Next Shorter Route with Lowest SIC & Lowest SIT

SIC=100% & High SIT ?

Polynya Or Deformation?

Refreezing ?

DriftHindrance ?

No Sea Ice Advisory

Likely Safe Route

Yes

No

No

No

No

No

Wait ?

Yes

Yes

YesYes


16

6.2.3 Sea Ice Thickness Estimation –

Deriving sea ice thickness information using remotely sensed data is a challenging task.

Sea ice freeboard/thickness images were prepared using SARAL/AltiKa IGDR (Interim-

Geophysical Data Records) data. These images with scattered points with estimated

SIT were used for determining the possibility of ship encountering thick sea ice on its

route. A-priori knowledge of sea ice thickness along with relative thickness/thinness

derived from current season altimeter data were taken into consideration. An example

of SARAL/AltiKa data and estimated sea ice thickness over Antarctic region is shown in

Figure 4.

Fig 3: A RISAT-1 SAR scene (March 8, 2014) showing different polar ice features.

6.2.4 Sea Ice Deformation Analysis –

Detection and analysis of Sea ice deformation using remotely sensed data in an important component of sea ice advisory. In case of pack ice, the regions with high sea ice deformation density are usually preferred over the regions with lover deformation density. MODIS data obtained from LANCE-MODIS site were used for sea ice deformation analysis. An example of MODIS data (250m) showing sea ice deformations is shown in Figure 5.

Ice-Shelf

Polynya

Sea-Ice

Indicates Maitri CoastFor Fuel Decanting

Indicates the Location whereShip is expected to reach

Indicates locations of some Of the ice-bergs in India Bay

Ice-RiseIce-Sheet


17

Fig 4: An example of SARAL/AltiKa Data and estimated SIT

Fig 5: Sea Ice Deformation Patterns visible on MODIS scene (Near Maitri coast)

Sea Ice Thickness

Estimated using SARAL

Altika Data

Red: Thick Sea Ice

Green: Medium Thick Sea Ice

Blue: Thin Sea Ice

Sea Ice Deformation Pattern as seen from 250 m MODIS Data

OCEAN

The Sea Ice: RED

Open Water / Polynya: BLACK

A N T A R C T I C AMaitri

A

B


18

6.2.5 Sea Ice Drift Analysis –

The knowledge of sea ice drift rate and direction are vital parameters before suggesting

the ship route though sea ice ocean region. In this study cloud free MODIS LANCE

mosaics over study area at 250 m were used for tracking big sea ice floe and then to

determine the direction and speed of sea ice. Figure 6 shows 5 images (MODIS

LANCE) from February 23 to March 15, 2014. The track followed by a sea ice floe from

February 23 to March 15, 2014 is depicted by yellow coloured line. The location of the

floe under study at different dates is marked by magenta coloured circles on different

images. Sea ice drift of 284.4 km in 20 days (February 23 to March 15, 2014) was

monitored using 12 dates MODIS cloud free mosaic images.

Fig 6: Sea Ice Drift monitored using MODIS data (Feb 23 to March 15, 2014)


19

15- 40

40-70

>70

< 15

Ice Concentration (%)

India Bay

Ship Position on Dec 26

6.2.6 Sea Ice Advisory Workout –

The information needed for sea ice advisory was extracted from different sources as

mentioned above. While the sea ice advisory made available to 32nd ISEA was based

on basic analysis of SIC data (See Figures 7 and 8), during 33rd ISEA comprehensive

data analysis was taken up to provide near real time advisory. The last known location

of 33rd ISEA voyage ship was plotted on current sea ice concentration, sea ice

deformation, RISAT-1 SAR (whenever available) data.

Fig 7: Image showing sea ice concentration near Bharati and Maitri research stations: Part of sea ice advisory sent to 32nd ISEA in December 2013.


20

Fig 8: Image showing sea ice concentration trend analysis (RED: Increasing trend; GREEN: Decreasing trend) near Maitri research stations: Part of sea ice advisory sent to 32nd ISEA in December 2013.

A transect between the current location of ship to next scheduled location of ship

destination was drawn. The status of sea ice condition was assessed considering

various sea ice parameters. The technique presented in Figure 2 was adopted to arrive

at a likely safe shipping route for voyage. Predicted atmospheric air temperature, wind

vectors, air pressure values available from different web-sites were analysed along with

satellite based sea ice growth/melt trend and sea ice drift analysis.The advisory was

provided to NCAOR in the form of text and images. NCAOR used to send it to 33rd ISEA

voyage leader and Ship Captain. The feedback from voyage used to reach SAC via

NCAOR. Suggested Entry/Exit likely safe ship routes to/from Bharati and Maitri coasts

are shown in Figure 9 to Figure 12.


21

The sea ice advisory was found to be very useful for ship navigation and the voyage

was completed successfully.

Fig 9: Likely safe ship entry points to reach Bharati (Part of sea ice advisory sent to NCAOR on February 20, 2014.

7. Conclusion

–

Advance information about the sea ice within ship transit is very important for

planning of optimum ship routing during Antarctic Expeditions. The information on

sea ice condition has become available after the advent of satellite data. However,

this information is not readily available for ship routing. The information on sea ice

concentration, deformation of pack ice, sea ice drift, sea ice thickness etc were

derived from satellite data analysis. This knowledge along with the other a-priori

information was used to provide sea ice advisory for ship routing durin 33rd ISEA.

The routes suggested during these advisories were followed by the voyage ship and

the expedition could be completed successfully.

SIC: > 99%

SIC: 90-99%

SIC: < 90%

White

Arrow

Heads:

Likely

Safer

Ship

Entry

Points.

Bharati


22

Fig 10: Likely safe ship points points to leave Bharati (Part of sea ice advisory sent to NCAOR).

Fig 11: Likely safe ship entry points to reach Maitri coast (Part of sea ice advisory sent to NCAOR on March 7, 2014.

OCEAN

A N T A R C T I C AMaitri

A

B

L E G E N DSIC > 95%SIC: 90 to 95%SIC < 90%


23

Fig 12: Likely safe ship exit points to leave Maitri coast (Part of sea ice advisory sent to NCAOR on March 29, 2014.

The ship routes actual followed by the ship were compared with those suggested in the advisory. It was found that the ship fairly followed the advisory routes.

8. Future Plan and Scope

The sea ice advisory made available to 32nd and 33rd ISEA was based on satellite data

analysis with extensive involvement of image analysts. It warrants a lot of time and

efforts from subject experts. Also, there are more chances of subjectivity creeping into

the advisory. Efforts are being made toward automation of different modules of data

analysis for sea ice advisory. Hence, the sea ice advisory in future should be more

objective and should be available to Voyage Leader and Ship Master in near real time

through NCAOR.

–

Colour Legend

SIC > 95%

SIC: 90 to 95%

SIC < 90%

BA

Maitri

EW1 & EW2

Are Likely

Safe ship

Exit

Ways.

A & B are

On Indian

Bay.

EW1

EW2


24

India’s satellite Resorcesat-2 frequently visits Antarctica and Arctic regions. It has got a

very good combination of sensors onboard. It can acquire data at 56 m resolution

(Advanced Wide Field Sensor i.e. AWiFS) with very high revisit capability along with

collecting data at 24 m (Linear Imagine Scanning Sensor 3 i.e. LISS-III) and 6 m (LISS-

IV) resolutions with relatively lower revisit capability. It is planned to acquire wide swath

data over sea ice regions during study period through NRSC along with LISS-III and

LISS-IV data over selected locations. The AWiFS data mosaics will be prepared and

used for sea ice deformation, sea ice drift and other polar feature identification.

Also, there are many polar ice / ocean parameters (wind direction, wind speed, wave

characteristics, currents speed and direction etc) that are useful for safer shipping

navigation, may be derived using remotely sensed data. Inclusion of such parameters

will improve the quality of sea ice advisory. The safe ship routing warrants predictive

data related to various polar parameters. The predicted parameters based on sea ice

modelling may be used to expand the scope of advisory and to make it more

comprehensive.

Acknowlegments –

The authors sincerely acknowledge the encouragement provided by Director SAC. We

thank Deputy Director EPSA for his guidance and support at various stages of the

study. The keen interest taken by Director – NCAOR in this study is also dully

acknowledged. We herewith acknowledge that the sea ice concentration (3.125 km)

data and MODIS LANCE mosaic (250 m) used in this study were downloaded from

ftp://ftp-projects.zmaw.de/seaice/AMSR2 and http://lance-

modis.eosdis.nasa.gov/imagery/subsets/?mosaic=Antarctica sites and OSCAT and

SARAL/AltiKa data were downloaded from MOSDAC/ISRO site.

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Documents

Satellite Based Sea Ice Advisory for Antarctic Expedition · The technique of multi-sensor remote sensing data analysis to derive sea ice characteristics has been explained. The actual