Rs Essay Final

THE UNIVERSITY OF NEW SOUTH WALES

SCHOOL OF BILOGICAL, EARTH & ENVIRONMENTAL SCIENCES

GEOS9012

Remote Sensing Applications Major Essay: Application of Remote sensing on Estimation of Sea Surface Temperature Lecturer: Dr. Ray Merton

By Bimal KC (z32740

Table of Contents: 1. Introduction 2. Method: 3. Application (Techniques) of Satellite Remote Sensing: 3.1 Hyper-Spectral Remote Sensing: 3.2 Thermal Infrared Remote (TIR) Sensing: 4. Satellites: 4.1. Geostationary Operational Environmental Satellite(GOES): 4.2. Polar Orbiting Environmental Satellite (POES): 5. Instruments:

10

5.1 Advanced Very High Resolution Radiometer (AVHRR): 5.2 Thermal Infrared Sensor: 6. Sea Surface Temperature Estimation: 6.1. Sea Surface Temperature (SST) Estimation from Split-Window Method:

2

6.2 Sea Surface Temperature Measurement from Thermal Wave length: 14

Infrared

6.3. Sea Surface Temperature Estimation using Visible and Infrared Scanner (VIRS):

15

7. Result and Discussion: 7.1 Problems in Sea Surface Temperature (SST) Estimation. 7.2 Accuracy of the Estimated SST data from satellites: 7.3 Application of Sea Surface Temperature: 8. Satellite Imagery Analysis:

19

9. Conclusion:

38

10. References

39

List of Figures: Figure 1: 224 Band

AVIRIS Datacube of Jasper Ridge (California)

Figure 2: Atmospheric Windows Figure 3: Geostationary and Polar Orbiting Satellite Figure 4: Global View of Meteorological Phenomenon Figure 5: Global SST (18th July, 2005) Figure 7: Global Maps of SST derived from the AVHRR Pathfinder data sets (Dec 1993 & 1997) Figure 8: Tropical SST by TRMM (Tropical Rainfall Measuring Mission) Microwave Imager (MI) ( 22nd Dec, 1999) Figure 9: MW SST image from AVHRR Pathfinder SST, (1st, Oct 2005 Figure 10: Global Sea Surface Temperature ( 22nd Jun 2000)

3

Figure 11: Sea Surface Temperature Anomaly, (3rd Sept 2009) Figure 12: Global SST Anomaly (8th Oct 2008) Figure 13: Normal Condition (Mean & Anomaly in Dec 1993), El Nino Condition (Mean & Anomaly in Dec 1997) and La Nina Condition (Mean & Anomaly in Dec 1998) Figure 14: SST (26th April, 2008) Figure 15: SST (2nd May 2005) Figure 16: SST (Jan 2009) Figure 17: SST(8th May 2009) Figure 18: Regional SST around the NSW Cost (7th May 2009) Figure 19: Regional SST around the Queensland/ Coral Sea (7th May,2009) Figure 20: Regional SST around South Australia / Great Australian Bight (7th May 2009) Figure 21: Regional SST around South West Australia (7th May, 2009) Figure 22: Regional SST around Northern Territory (7th Ma, 2009)

List of Tables:

Table 1: GOES-10 Characteristics Table 2: NOAA-15 Characteristics Table 3: AVHRR/ 3 Channel Characteristic

List of Equation:

4

Equation 1: Split Window Equation Equation 2: Grey Body Spectral Radiance Equation 3 : Black Body Spectral Radiance Equation 4 : Multichannel Split Window Equation

5

Abstract:

Sea surface temperature (SST) is a significant in the study of both the ocean and the atmosphere as it is directly related to heat exchange, momentum and gases between the ocean and the atmosphere. The Impacts of sear surface temperature is vital for Terrestrial Environment analysis as it affects the regional and global weather patterns, as a long term effects on Global Climate Change. The main objective of this essay is to delineate the different techniques and applications of Satellite remote sensing used for estimation of Sea surface temperature(SST).NOAA series of meteorological satellites: Geostationary Operational Environmental Satellite (GEOS) and Polar Orbiting Environmental Satellite (POES) with Advanced Very High Resolution Radiometer (AVHRR) and the Thermal Infrared Sensor (TIRS) in wavelength of 8-14 μm are used to estimate the surface skin temperature of about 20 μm thick at the ocean surface. This essay focuses the hyperspectral and Thermal Infrared remote sensing techniques along with

split -window method

to

estimate the sea surface temperature. Furthermore, Satellite Imagery and their hyperspectral signature in multi-spatial and temporal scales are analysed to distinguish the sea surface temperature. However, the estimated SST is associated with errors due to the atmospheric forcing, systematic and instrumental errors, so estimated SST data

are

recommended to validate and correlated with surface data from ships, drifting buoys and other source to avoid the errors. From the different research, it is found that the accuracy of satellite derived SST are within the range of ±1°C. Due to the changing global weather pattern, the old SST data does not represent the current ocean structure, so it is better to use as quickly as possible to get information form the SST images. Furthermore, SST data are significant for weather predication, climate research, commercial fishery and sea sports

1. Introduction

Remote Sensing is the science and art of acquiring information (spectral, spatial, and temporal) about material objects, area, or phenomenon, without coming into physical contact with the objects, or area, or phenomenon under investigation. Without direct contact, some means of transferring information through space must be utilised which is based on sensing and recording reflected or emitted energy and processing, analysing, and applying that information in different application. Remote sensing techniques are most widely used in various applications such as atmospheric science, environment, geology, meteorology, forestry, hydrology, agriculture, mineralogy, military, glaciology, and bathymetry Remotely sensed data of ocean is applicable for different purposes, as ocean occupies 70% of earth’s surface, and it controls global climate acting as heat reservoir for storing, distributing and releasing solar energy.. High spatial resolution seasonal Sea surface temperature data sets which represents fundamental properties such as the minimum and maximum temperatures, dates of these extremes, and rates of change in temperature provides important information for understanding

coastal processes. Sear surface

temperature data is vital for terrestrial as well as ocean environment analysis as it affects the regional and global weather patterns, as a long term effects on Global Climate Change. The study of the sea surface temperature is significant for the prediction and analysis of El Nino and La Nina, and Climate research. The main objective of this essay is to discuss the different techniques and application of remote sensing used for the sea surface temperature estimation and analysis of satellite imagery. The methodology of this essay will based on different researches and studies that has been carried on surface temperature analysis and estimation. Different spatial signature of hyperspectral remote sensing, instruments, sensors and the methods that are used in the estimation of the sea surface temperature will be discussed. Furthermore, this essay will attempt to outline the different applications and techniques of satellite remote sensing used on the estimation of sea surface temperature and, it will also address the problems related to the estimation of sea surface temperature using satellite imagery. 2. Method:

NOAA series of meteorological satellites are most commonly used for estimating the sea surface temperature since 1979. The instrument used for sea surface estimation is the Advanced Very High Resolution Radiometer (AVHRR).Thermal infrared sensors in the wavelength of 8-14 μm are normally used for sea surface temperature. Remote sensing is used to measure the surface skin temperature about 20 μm thick at the ocean surface. The sensors records the thermal radiation emitted by the water. Hyper-Spectral and Thermal Infrared remote sensing

techniques are used for the analysis of sea surface temperature.

Hyperspectral imagery provides a spatial, spectral and temporal snapshot of any specified area.

3. Application (Techniques) of Satellite Remote Sensing: This section describes the Hyperspectral and Thermal Infrared Remote Sensing as a application of satellite remote sensing used to measure SST.

3.1 Hyper-Spectral Remote Sensing: Recent advances in remote sensing and geographic information has led the way for the development of hyperspectral sensors. Hyperspectral imaging is the simultaneous acquisition of images in many, narrow, contiguous, spectral bands. The distinction between hyperspectral and multispectral is usually defined as the number of spectral bands. Hyperspectral data sets are generally composed of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral remote sensing involves analysis of image data obtained from airborne sensors (CASI-2 and HyMap) and space-borne sensors (Hyperion). Hyperspectral data offers a more detailed examination of a scene than other type of remote sensing data, which is collected in broad, widely separated bands. Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Spectral resolution can be defined by the limits of the continuous wavelengths (or frequencies) that can be detected in the spectrum.

Hyperspectral remote sensing, also known as imaging spectroscopy, is a relatively new technology that is currently being investigated by researchers and scientists with regard to the detection and identification of minerals, terrestrial vegetation, and man-made materials and backgrounds However, the primary application of this new technique has currently shifted towards the observation of the Earth with hyperspectral sensors, such as AVIRIS (Airborne Visible Infrared Imaging Spectrometer). The interaction of electromagnetic radiation with materials on a macroscopic level including refraction, diffraction, and scattering effects, formed the basis of traditional remote sensing theory. Furthermore, Terms referring to high spectral resolution systems are hyperspectral remote sensing, hyperspectral spectroscopy, imaging spectroscopy and narrow-band imaging. Hyperspectral sensors collect information as a set of 'images'. These 'images' are then combined and form a three dimensional hyperspectral cube for processing and analysis. Hyperspectral cubes are generated from airborne sensors like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), or from satellites like NASA’s Hyperion.(NASA/ NOOA) Finally, Hyperspectral Remote sensing techniques are most widely used in various applications such as atmospheric science, environment, geology, meteorology, forestry, hydrology, agriculture, mineralogy, military, glaciology, and bathymetry

Figure 1: 224 Band AVIRIS Datacube of Jasper Ridge (California) Note: x and y axes represent spatial data (1024 x 614) as a 3 band colour composite image (R = band 43, G = 17, B = 10). The z axis represents spectral data as 224 contiguous bands from 0.4mm (foreground) to 2.5mm (background) in pseudocolour (rainbow). Dataset: two combined 30 April 1994 ATREM calibrated reflectance cubes (281Mb) centred on Jasper Ridge, prior to spatial subsetting to the standard 512 x 614 pixels. (Source: http://www.cossa.csiro.au/hswww/Overview.htm)

3.2 Thermal Infrared Remote (TIR) Sensing: The basic concept of satellite remote sensing based on the laws of radiation, and terrestrial absorption. All the objects that have a temperature above absolute zero (0 K) emit electromagnetic energy. Therefore, all the feature as we encounter in the earth on a typical day such as vegetation, soil, rock, water and even people emit thermal infrared electromagnetic radiation in the 3.0-12 micro meter portion of the spectrum. The electromagnetic radiation exiting an object is called radiant flux (Φ) and is measured in Watts. The concentration of the amount of radiant flux emitted from an object is its radiant temperature (Trad). For most real world objects, there is usually a high positive correlation between the true kinetic temperature of the object (Tkin ) and the amount of radiant flux radiated from the object (Trad). Therefore, the radiometers placed some distance from the

object to measure its radiant temperature which correlates well with the object’s true kinetic temperature. This is the basis of Thermal Infrared Remote Sensing. Beyond the visible region of the electromagnetic spectrum, there is reflective infrared region from 0.73 micro meter and the thermal infrared region from 3-14 micro meters. The remote sensing instruments can be use to detect the infrared energy in these region because the atmosphere allows a portion of the infrared energy to be transmitted from the terrain to the detectors. This region which passes the energy is called Atmospheric Window and the region of the spectrum where the atmosphere absorbs most of the infrared energy present is called Absorption bands (John, R. Jenson 200)

4. Figure 2: Atmospheric Windows (Source: NASA)

4. Satellites:

This section discusses the types of satellites used for the SST measurement and analysis. NOAA Series of Meteorological Satellites are generally used. Meteorological remote sensing is primarily directed towards making observations of the atmospheric profile. Satellites provide a great deal of the remote sensing imagery commonly used today. Satellites have several unique characteristics which makes them particularly useful for remote sensing of the Earth's surface. NOAA's operational weather satellite system is composed of two types of satellites: geostationary operational environmental satellites (GOES) for short-range warning and polar-orbiting satellites for longer-term forecasting. Both types of satellite are necessary for providing a complete global weather monitoring system. A new series of GOES and polarorbiting satellites has been developed for NOAA by the National Aeronautics and Space Administration (NASA).

4.1 Geostationary Operational Environmental Satellite(GOES): GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They circle the Earth in a geosynchronous orbit, which means they orbit the equatorial plane of the Earth at a speed matching the Earth's rotation. This allows them to hover continuously over one position on the surface. The geosynchronous plane is about 35,800 km (22,300 miles) above the Earth, high enough to allow the satellites a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hail storms, and hurricanes. When these conditions develop the GOES satellites are able to monitor storm development and track their movements. NASA launched the first GOES for NOAA in 1975 and followed it with another in 1977. Currently, the United States is operating GOES-10 and GOES-12. (NOAA, Satellite and Information Service)

Figure 3: Geostationary and Polar orbiting satellite( Source:NOAA/ NASA) Table 1: GOES-10 Characteristics

Main body: Solar array: Weight at liftoff: Launch vehicle: Launch date: Orbital information:

GOES-10 Characteristics 2.0m (6.6 ft) by 2.1m (6.9 ft) by 2.3m (7.5 ft) 4.8m (15.8 ft) by 2.7m (8.9 feet) 2105 kg (4641 pounds) Atlas I April 25, 1997 Cape Canaveral Air Station, FL Type: Geosynchronous Altitude: 35, 786 km (22, 236 statute miles) Period: 1,436 minutes

Sensors:

Inclination: 0.41 degrees Imager Sounder Space Environment Monitor (SEM) Data Collection System (DCS) Search and Rescue (SAR) Transponder

(Source: NOAA Satellite and Information Service)

4.2 Polar Orbiting Environmental Satellite (POES): Complementing the geostationary satellites are two polar-orbiting satellites known as Advanced Television Infrared Observation Satellite (TIROS-N or ATN), constantly circling the Earth in an almost north-south orbit, passing close to both poles. The orbits are circular, with an altitude between 830 (morning orbit) and 870 (afternoon orbit) km, and are sun synchronous. A suite of instruments is able to measure many parameters of the Earth's atmosphere, its surface, cloud cover, incoming solar protons, positive ions, electron-flux density, and the energy spectrum at the satellite altitude. The satellites can receive process and retransmit data from Search and Rescue beacon transmitters, and automatic data

collection platforms on land, ocean buoys, or aboard free-floating balloons. The primary instrument aboard the satellite is the Advanced Very High Resolution Radiometer or AVHRR. Data from all the satellite sensors is transmitted to the ground via a broadcast called the High Resolution Picture Transmission (HRPT). A second data transmission consists of only image data from two of the AVHRR channels, called Automatic Picture Transmission (APT).The polar orbiters are able to monitor the entire Earth, tracking atmospheric variables and providing atmospheric data and cloud images. The satellites provide visible and infrared radiometer data that are used for imaging purposes, radiation measurements, and temperature profiles. These satellites send more than 16,000 global measurements daily via NOAA's CDA station to NOAA computers, adding valuable information for forecasting models, especially for remote ocean areas, where conventional data are lacking. A new series in this generation began with the launch of TIROS-N on October 13, 1978. This satellite was the first in that series to carry the AVHRR, along with the first sounder, TIROS Operational Vertical Sounder (TOVS), designed to profile temperature and water vapor. Even-numbered NOAA Meteorological Satellites have north to South equatorial daytime crossing times near 7:30 A.M. and have orbital repeat periods (approximately the same paths) of four to five days. Odd-numbered NOAA Meteorological Satellites cross the equator from North to South at night 2:30 A.M. and have eight-nine day repeat periods Currently, NOAA is operating five polar orbiters. A new series of polar orbiters, with improved sensors, began with the launch of NOAA-15 in May 1998 and NOAA-16 on September 21, 2000. The newest, NOAA-17, was launched June 24, 2002. NOAA-12, NOAA-14 and NOAA-15 all continue transmitting data as stand-by satellites. NOAA-16 and NOAA-17 are classified as the "operational" satellites.

Table 2: NOAA-15 Characteristics

Main body:

NOAA-15 Characteristics 4.2m (13.75 ft) long, 1.88m (6.2 ft) diameter

Solar array: Weight at liftoff: Launch vehicle: Launch date: Orbital information:

2.73m (8.96 ft) by 6.14m (20.16 ft) 2231.7 kg (4920 pounds) including 756.7 kg of expendable fuel Lockheed Martin Titan II May 13, 1998 Vandenburg Air Force Base, CA Type: sun synchronous Altitude: 833 km Period: 101.2 minutes

Sensors:

Inclination: 98.70 degrees Advanced Very High Resolution Radiometer (AVHRR/3) Advanced Microwave Sounding Unit-A (AMSU-A) Advanced Microwave Sounding Unit-B (AMSU-B) High Resolution Infrared Radiation Sounder (HIRS/3) Space Environment Monitor (SEM/2) Search and Rescue (SAR) Repeater and Processor

Data Collection System (DCS/2) (Source: NOAA Satellite and Information Service) Since, the new GOES-I through M series provide higher spatial and temporal resolution images and full-time operational soundings (vertical temperature and moisture profiles of the atmosphere). Instruments on board the NOAA spacecraft measure a range of atmospheric and terrestrial parameters. The newest polar-orbiting meteorological satellites (that began with NOAA-K in 1998) provide improved atmospheric temperature and moisture data in all weather situations. These NOAA series of Meteorological satellites are generally used to sea surface temperature analysis and estimation .(NOAA, Satellite and Information Services)

5. Instruments: This section deals with the different instrument and sensor carried by satellites to records the reflected and emitted energy from the earth surface (e.g. Water Surface)

5.1 Advanced Very High Resolution Radiometer (AVHRR):

The Sun-synchronous polar-orbiting environmental satellites (POES) carry the Advanced Very High Resolution Radiometer (AVHRR). The Advanced Very High Resolution Radiometer (AVHRR) provides four to six band multispectral data from the NOAA polarorbiting satellite series. The AVHRR instrument consists of an array of small sensors that records the amount of visible and infrared radiation reflected and (or) emitted from the Earth's surface. This provides images of the Earth's surface showing elements that cannot normally be viewed with the human eye. The Advanced Very High Resolution Radiometer data set is comprised of data collected by the AVHRR sensor. The latest instrument version is AVHRR/3, with 6 channels, first carried on NOAA-15 launched in May 1998.There is fairly continuous global coverage since June 1979, with morning and afternoon acquisitions available. They provide twice-daily (Temporal Resolution) global coverage, and ensure that data for any region of the earth are no more than six hours old. The AVHRR is a radiation-detection imager that can be used for remotely determining cloud cover and the surface temperature. The AVHRR sensor is a broad-band, 4- or 5-channel scanning radiometer, sensing in the visible, near-infrared, and thermal infrared portions of the electromagnetic spectrum The objective of the AVHRR instrument is to provide radiance data for investigation of clouds, land-water boundaries, snow and ice extent, ice or snow melt inception, day and night cloud distribution, temperatures of radiating surfaces, and sea surface temperature. Hence, AVHRR instrument is used for a wide range of applications in polar and climate research. AVHRR data provide a global, long-term, consistent time series with high spectral and spatial resolution suitable for albedo and sea surface temperature measurements. Such measurements are necessary for studies involving climate, sea ice distribution and movement, and ice sheet coastal configuration. Infrared AVHRR imagery has been proven very useful for the estimation of sea surface temperature (SST). Furthermore, the instruments used for the SST estimation is AVHRR with

spatial

resolution( 1.1 km(LAC), 4 km(GLC)), radiometric resolution (10 bit), instrument resolution (0.1o C) and temporal resolution( 2/per day)(NOAA, Satellite and Information Service) Table 3: AVHRR/ 3 Channel Characteristics

Channel Resolution

AVHRR/3 Channel Characteristics at Wavelength Typical Use

Number 1 2 3A 3B

Nadir 1.09 km 1.09 km 1.09 km 1.09 km

(um) 0.58 – 0.68 0.725 - 1.00 1.58 – 1.64 3.55 – 3.93

Daytime cloud and surface mapping Land-water boundaries Snow and ice detection Night cloud mapping, sea surface

4

1.09 km

10.30 – 11.30

temperature Night cloud mapping, sea surface

temperature 5 1.09 km 11.50 – 12.50 Sea surface temperature (Source: NOAA Satellite and Information Service)

5.2 Thermal Infrared Sensor: In 1980, NASA and Jet Propulsion Laboratory developed the six-channel Thermal Infrared Multispectral

Scanner (TIMS) that acquires thermal infrared energy in six bands at

wavelength intervals of <= 0.1 micro meter.(Quattrochi,Ridd,1994).The successful studies using resulted in the development of the 15-channel Air Borne Terrestrial Application Sensor (ATLAS) (Lo et al., 1997). NOAA Geostationary Operational Environmental Satellite (GEOS) collects thermal infrared data at a spatial resolution of 8 × 8 km for weather prediction. Also, NOAA Advanced Very High Resolution Radiometer (AHHRR) collects thermal infrared local area coverage (LAC) data at 1.1× 1.1 km and global area coverage (GAC) 4 × 4 km.(NOAA, NASA) Weather Satellites equipped with scanning radiometers produce thermal or infrared images which can then enable a trained analyst to determine cloud heights and types, to calculate land and surface water temperatures, and to locate ocean surface features. The scanning is typically in the range 10.3-12.5 µm (IR4 and IR5 channels). High, cold ice cloud such as Cirrus or Cumulonimbus show up bright white, lower warmer cloud such as Stratus or Stratocumulus show up as grey with intermediate clouds shaded accordingly. Hot land surfaces will show up as dark grey or black. One disadvantage of infrared imagery is that low cloud such as stratus or fog can be a similar temperature to the surrounding land or sea surface and does not show up. However, using the difference in brightness of the IR4 channel (10.3-11.5 µm) and the near-infrared channel (1.58-1.64 µm), low cloud can be

distinguished, producing a fog satellite picture. The main advantage of infrared is that images can be produced at night, allowing a continuous sequence of weather to be studied. With the use of the thermal infrared imagery the El Nino and La Nina phenomenon can also be spotted. Hoever, Thermal infrared sensors in the 8 - 14μm are normally used for measuring sea surface temperature these sensors record the thermal radiation emitted by the water. Although this region is least affected by the atmosphere, clouds and water vapour in the atmosphere have an adverse effect on thermal measurements.(NOAA/NASA)

6. Sea Surface Temperature Estimation: Sea surface temperature measurement plays a vital role in numbers of oceanographic application. This section attempts to outline the different techniques and process used for sea surface temperature estimation in Remote sensing and also, discussed the problems associated with the estimation of sea surface temperature from satellite data.

6.1. Sea Surface Temperature (SST) Estimation from Split-Window Method: NOAA Series of the meteorological satellites are most commonly used to estimates the SST(Sea Surface Temperature) Since 1979 which carry Advanced Very High Resolution Radiometer (AVHRR), and Thermal Infrared Sensor (8 - 14μm) records the thermal radiation emitted by sea surface. Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the multichannel (MCSST). Absolute measurement of SST from the satellites requires the correction of the attenuation introduced by the intervening atmosphere. Assuming that cloudy radiances can be effectively removed, atmospheric absorption and emission are mainly caused by water vapour in the 10.5-12.5 μm window regions. Typical values of water vapour transmittance vary from about 95% for dry atmosphere to 30 % for humid atmospheres, the reason for this substantial variation being the large variability of the total

column water vapour and the strong dependence o humidity of the continuum absorption mechanism, which is the dominant in this part of the spectrum. The split-window method is most useful atmospheric correction method for SST. It used different channels inside the 10.5-12.5 μm windows, as AVHRR-NOAA channel 4 and 5, which are effected by different amount of absorption due to the wavelength dependence of water vapour continuum. The differential absorption has been used to correct for water vapour attenuation in simple, linear algorithm. The general form of the split- wind equation for the SST can be written as: T=T4 +A (T4 -T5) +B

1

Equation 1: Split Window Equation (Gudmandsen, Preben, 1997) Where, T is estimated SST, T4 and T5 are the brightness temperature in channel 4 and 5, and A and B, are coefficients (Constants) dependent on the atmospheric transmittances in channel 4 and 5.( Gudmandsen, Preben 1997, pp 369). The best estimates of these constants have to be obtained by correlating satellite data with surface data from ships, drifting buoys and other sources. Brightness temperatures do not give the exact temperature of the viewed object because some radiation is absorbed by water vapour in the atmosphere. Hence, the amount of absorption must be estimated & the temperature estimates should be corrected.(Gudmandsen,1997)

6.2 Sea Surface Temperature Measurement from Thermal Infrared Wavelength: The estimation of water surface temperature by spatially scanning space-borne systems involves the simultaneous radiometric measurements in two wavelength intervals in the thermal infrared atmospheric-window spectral regions. The spectral radiance emitted by a greybody at wavelength

is given by ( D. Anding ,R. Kauth, 1970): 2

Equation 2: Grey Body Spectral Radiance( D. Anding , R. Kauth, 1970)

Where

is the spectral emissivity of the greybody, and

is the radiance emitted

by a blackbody, which is represented by 3

Equation 3 : Black Body Spectral Radiance(D. Anding ,R. Kauth, 1970) Where Tis the temperature of the blackbody, c is the velocity of light, h is Planck's constant, k is Boltzmann's constant, and

is the wavelength. If emissivity is known, the

greybody temperature can be determined by measuring the emitted spectral radiance and using it in equation . Hence, the temperature determined from a measurement of the radiance at the surface is the temperature of a greybody whose emissivity is equal to the emissivity of the water surface which gives an equivalent value of radiance i.e equivalent greybody temperature. It is different from the actual surface temperature, the degree of difference depending on the magnitude of the reflected radiation and the temperature gradient near the surface. This analysis deals with the effect of the atmosphere on the equivalent greybody temperature derived from a radiometric measurement performed at satellite altitudes. However, the water surface temperature represents the equivalent greybody temperature that can be derived from a radiance measurement at the surface.( D. Anding and R. Kauth, 1970)

6.3. Sea Surface Temperature Estimation using Visible and Infrared Scanner (VIRS):

This article presents the estimation of the Sea Surface Temperature using the Visible and Infrared Scanner (VIRS). Satellites simultaneously observe SST over a wide area, their data are useful for analysing the spatial distribution of SST and supplementing coverage of ground observation data SST is estimated from brightness temperatures (BTs) of channels 3, 4 and 5 using the multichannel SST (MCSST) method (McMillan and Crosby, 1984). BTs are calculated

from the thermal infrared radiation of Level 1B data using the reverse Plank formula at each centre wavelength and Multichannel Sea Surface temperature (MCSST) coefficients are determined by making a regression of BTs to ground observation data. The global BT data are used in this regression and derived day and night time coefficients separately for the SST estimation formula. The formula for estimating SST is given below: (McMillan , Crosby, 1984). SST[K]=a0+a1*BT4+a2*(BT4-BT5)+a3*(BT4-BT5)*m+a4*(BT3-BT4)+a5*(BT3BT4)*m

4

m=sec(θ )-1, Equation 4 : Multichannel Split Window Equation(McMillan , Crosby, 1984) Where θ is satellite zenith angle. BT3, BT4, BT5 are brightness temperatures of channel 3 (3.75 μm), 4 (10.8 μm), 5 (12.0 μm). The Coefficients for VIRS SST estimation a0 a1 a2 a3 a4 a5 at daytime are 10.4585, 0.9650, 2.3996, 0.73560 and night time are 14.4559, 0.9502, 0.0936, 0.3958, 1.3712, and 0.2430 respectively. As TRMM carries both TMI (Tropical Microwave Imager) and VIRS, it is possible to estimate SST using microwave and infrared radiation simultaneously. Due to the difference of their wavelengths, responses to the atmospheric and ocean surface conditions, and observed depths on the ocean surface (2mm to 3mm by TMI and 2 μm to 3 μm by VIRS) differ between the sensors. However, study showed several problems arise because the satellites observe the Earth’s surface through the atmosphere and infrared sensors observe a very thin layer (2mm to 3mm) in the ocean (Hiroshi,1999 )

7. Result and Discussion: This section discusses about the accuracy, errors and problems associated with estimation of the SST and, also outlines the global application of the temperature.

the

sea surface

7.1 Problems in Sea Surface Temperature (SST) Estimation: Remote sensing is used to measure the surface “skin” temperature (about 20μm thick) at the ocean surface atmosphere interface, so, it only covers temperature of the

certain

thickness (Depth) of the water layers. Which is not the representation of average sea surface temperature for that particular area specified by the satellite. Another major problem in estimation of SST with AVHRR data is cloud. Clouds absorb all infra-red radiation coming up to them from below and emit their own infra-red radiation from their tops. SST can only be estimated accurately for areas of ocean that are not cloud covered when the spacecraft goes overhead. Cloud affected pixels are minimized by making composite images from a cloud-number of satellite passes over an area. So, the estimated SST date should be validated with surface data from ships, drifting buoys and other sources. However, these estimated SST data needs to be corrected to avoid the systemic, instrumental and atmospheric forcing errors. Similarly, the differences between SST retrieved from the two sensors are significant, since the sensors effectively respond to the temperature at slightly different depths in the water column, and their dependencies on atmospheric forcing such as wind speed, cloud, water vapour, aerosols and atmospheric stability. Other problem with estimating temperature from satellite data is that the sensor measure radiation from the surface of the ocean, from which the temperature of the sea surface and the ocean structure is inferred. During the calm afternoons solar heating can create a stable layer of warm water on the surface of ocean which misleading if interpreted as indicative of the can be deeper structure. It is therefore preferable either to use night-time images during summer or to identify such "skin" effects use a sequence of images to effects. After SST is estimated by processing the AVHRR data, it is essential to related the on the points of the Earth surface. Due to the curvature of the earth, the AVHRR images show distorted view of the earth near the edge. So, the geometry of the instrument and details of the spacecraft orbit are used to locate the ground location of each pixel in the image, and the known features in the image such as coastlines are often used to improve the geometric correction. Furthermore, Due to the changing global weather pattern, the old SST data does

not represent the current ocean structure, so it is better to use as quickly as possible to get information from the SST images.

7.2 Accuracy of the Estimated SST data from satellites:

This section discusses the results of different study and researches that has been carried out to analysis the accuracy of SST estimation derived from satellite, where satellite derived data are validated and correlated with situ SST data. A new high-resolution, global satellite-derived sea surface temperature (SST) data set called "Pathfinder," from the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA Polar Orbiters, is now available from the Jet Propulsion Laboratory Physical

Oceanography

Distributed

Active

Archive

Centre

(JPL

PO.DAAC).

(NOAA/NASA Pathfinder Program) According to the Pathfinder program, the multichannel algorithms for estimating SST from the AVHRR( MCSST, NLSST and Pathfinder NLSST), do not indicates any the difference between the ocean's skin temperature and underlying mixed layer SST, which is the bulk temperature. The bulk temperature is generally measured by most buoys. The uppermost millimetre of the ocean, or skin, can be as much as 0.7°C cooler than the water just below due to evaporative or radiative cooling(Ewing, McAlister, 1960). Correlating the satellitederived SST estimates using buoys in the Pathfinder SST algorithms, the SST more closely represents the bulk SST estimate, not a skin estimate (Schluessel et al., 1990).So, biases in satellite-derived SST estimates due to the difference between the ocean's skin and bulk temperatures are being closely examined in the Pathfinder SST data sets.(S. Elizabeth , 1996) According to another research, most of the differences between the satellite-derived SST and in-situ SST are distributing within a ±1°C range. All techniques (Dual and Triple window algorithm in MCSST/ CPSST) examined in this research give RMS errors smaller than 0.6°C. (F. Sakaida , H. Kawamura, 1992)

7.3 Application of Sea Surface Temperature: Sea Surface Temperature is one of the most useful date sets offered by the remote sensing of ocean. Infrared sensors on the environmental satellite are used to measure the temperature across larges expanses of the ocean surface. This data has many important application which allows us to observe the ocean currents, monitor the changes on ocean temperature which in turns a plays a significant roles in weather and Climate change research. SST data is very important for the prediction of El Nino and La Nino and its effects. Similarly it is important to forecast areas where the development of the cyclone is possible. It plays significant roles in Weather prediction. SST data are generally used in commercial fishing to find out the areas where significant catch may be found. SST map provides the identification of the areas where changes of temperature occur and where fish population is likely to be greater. So, SST date is useful in commercial fishery

8. Satellite Imagery Analysis: This section discuss the analysis of different satellite images

around the world to

distinguish SST and its significance for the detection of associated phenomenon such as El Nino and La Nino. It also analyses the satellite images to discuss the SST around the

Australian costal region. Spectral signature of the satellite imagery in their multi temporal and spatial scale will be analysed. Figure 4: Global View of Meteorological Phenomenon (Source: National Atlas, US)

Figure 5: Global SST (Source: MODIS/ NASA, July 18) Note: The red pixels show warmer surface temperatures, while yellows and greens are intermediate values, and blue pixels show cold water.

Figure 6: Global SST (Source: NOAA/AVHHR, June 20-24 1997) Note: NOAA recognized that one of the environmental variables easiest to compute from the sensors on the AVHRR was sea surface temperature (SST). Subsequently, infrared AVHRR imagery has proven very useful in mapping mesoscale ocean features, such as currents, in terms of their SST signatures. The Gulf Stream and other major ocean currents are readily visible in SST data because their temperature is different from the surrounding water.

Figure 7: Global Maps of SST Derived from the AVHRR Pathfinder data sets (Source: RSMAS, NOAA/ NASA, Dec 1993 & 1997) Note: These are monthly composites of cloud-free pixels and show the normal situation in the tropical Pacific Ocean (above) and the perturbed state during an El Nino event (below). The tropical Pacific SST field in the normal situation (December 1993) is shown in the upper panel, while the lower panel shows the anomalous field during the El Nino event of 1997& 1998.

Figure 8: Tropical SST by TRMM (Tropical Rainfall Measuring Mission) Microwave Imager (MI) (Source: NOAA/ NASA, Dec 22 1999)

Note: The image shows the cold tongue of surface water along the Equator in the Pacific Ocean and cold water off the Pacific coast of South America, indicating a non-El Nino situation

Sea Surface Temperature (°C) Figure 9: MW SST image from AVHRR Pathfinder SST, 1st Oct 2005 (Source: MODIS/ NOOA) Note: Ice-covered are masked with dark orange whereas equatorial region shows high temperature and both polar region show relatively low temperature.

Figure 10: Global Sea Surface Temperature (Source: NASA/ PMEL/ ERRET, 22 Jun 2000) Note: Image shows the SS temperature around the subtropical region is relatively higher than the sub polar region. SST around the northern east and west coastal part of the Australia seems higher than the southern east and west part of the Australia.

SST Anomaly (°C) Figure 11: Sea Surface Temperature Anomaly, 3 Sept 2009 (Source: NOAA/ NESDIS) Note: SST Anomaly is produced by subtracting the long-term mean SST for particular location in particular time of year from the current value. A positive anomaly means that the current sea surface temperature is warmer than average, and a negative anomaly means it is cooler than average. The colour range of temperature anomalies displayed on the SST

anomaly charts is -5.0 to +5.0 °C . Areas with SST anomaly values less than -5.0 °C are displayed as -5.0 °C, and areas with values greater than +5.0 °C are displayed as +5.0 °C. Figure depicts that the yellow colour area are the positive anomaly and blue colour area are negative anomaly.

Global SST Anomaly (°C) Figure 12: Global SST Anomaly (Source: NOAA/ NESDIS, 8th Oct 2008) Note: Red areas shows high Temperature (Positive anomaly), yellow areas shows intermediate temperature whereas blue area shows relatively low temperature (Negative Anomaly)

Figure 13: Normal Condition (Mean & Anomaly in Dec 1993), El Nino Condition (Mean & Anomaly in Dec 1997) and La Nina Condition (Mean & Anomaly in Dec 1998) (Source: PMEL/NOAA) Note: The sea surface temperatures and the winds were near normal, with warm water in the Western Pacific Ocean showing red on the top panel of December 1993 plot and cool water, called "cold tongue" in the Eastern Pacific Ocean sowing green on the top panel of the December 1993 plot) .On

the bottom panel of the December 1993 plot shows

anomalies where the sea surface temperature and wind differs from a normal December, So the anomalies are very small (yellow/green) which indicates a normal December. During, December 1997, the warm water indicating red in the top panel of the December 1997 plot, has spread from the western Pacific Ocean towards the east, in the direction of South America and cold tongue indicting green colour in the top panel of the December 1997 plot, has weakened, and the weak western Pacific winds are blowing strongly towards the east, pushing the warm water eastward. The anomalies show clearly that the water in the centre of Pacific Ocean is much warmer indicating red than in a normal December. So, December 1997 was near the peak of a strong El Niño year. The cold tongue indicating blue is cooler than usual by about 3° Centigrade. The cold La Niña events sometimes but not always follow El Niño events. So, December 1998was a strong La Niña (cold) event.

SST Anomaly (°C) Figure 14: SST (Source: NOAA/CIRES, 26 April, 2008) Note: North East and South East costal parts of the Australia seems in Negative SST Anomaly which means the recent (26 the 2008) temperature is cooler than the average.

Sea Surface Temperature (°C)

Figure 15: SST (2 May 2005) (Source: Source: NOAA/ AVHRR, CSIRO Marine Laboratories Remote Sensing Facility) Note: Figure shows the sea surface temperature satellite image indicating strong cold water upwelling along the Bonney Coast, and cool water near Eyre Peninsula, Kangaroo Island, and eastern Victoria.

Sea Surface Temperature (°C) Figure 16: Sea Surface Temperature in 1st Jan 2009 (Source: NOAA/ AVHRR, Australian Bureau of Meteorology, Jan 2009) Note: Figure shows higher SST in Northern, Northern West and East costal region of Australia whereas Southern East and West costal region shows relatively low Temperature (Summer Season)

Sea Surface Temperature (°C) Figure 17: SST in 8 May 2009 (Source: NOAA/ AVHRR, Australian Bureau of Meteorology, 8 May 2009)

Note: Figure depicts higher SST around the costal region of Northern West and East part of Australia indicating red . In contrast, costal region of the Southern West and East part of Australia shows relatively low SST (Autumn Season) indicting green. In comparison to the SST on Summer Season (Previous Figure 16, 1st MAY 2008), the Higher SST is seems to shift towards to Equator and Lower SST is seems to shift towards to Pole.

Sea Surface Temperature (°C) Figure 18: Regional SST around the NSW Cost (7 May 2009) (Source: NOOA/AVHRR, NMOC, Australia 2009) Note: Figure shows the regional SST analysis (Isotherms (10-30°C)) on NSW costal region indicating

higher temperature in red , intermediate temperature in yellow and lower

temperature in light green to Blue colour.

Sea Surface Temperature (°C) Figure 19: Regional SST around the Queensland/ Coral Sea (7 May 2009).(Source: NOOA/AVHRR, NMOC, Australia 2009) Note: Figure shows the regional SST analysis (Isotherms (16-32 °C)) on Queensland costal region indicating higher temperature in red, intermediate temperature in yellow and lower temperature in light green to Blue colour.

Sea Surface Temperature (°C) Figure 20: Regional SST around South Australia / Great Australian Bight (7 May 2009) (Source: NOOA/AVHRR, NMOC, Australia 2009) Note: Figure shows the regional SST analysis (Isotherms (8-24°C) on South Australia/ Great Australia Bight indicating higher temperature in red, intermediate temperature in yellow and lower temperature in light green to Blue colour.

Sea Surface Temperature (°C) Figure 21: Regional SST around South West Australia (7th May 2009) (Source: NOOA/AVHRR, NMOC, Australia 2009)

Note: Figure shows the regional SST analysis (Isotherms(10-24°C)) on South Australia indicating higher temperature in red, intermediate temperature in yellow and lower temperature in light green to Blue colour.

Sea Surface Temperature (°C) Figure 22: Regional SST around Northern Territory(7th May 2009) (Source: NOOA/AVHRR, NMOC, Australia, 7 May 2009) Note: Figure shows the regional SST analysis (Isotherms(16-32°C)) on Northern Territory indicating higher temperature in red and lower Temperature in yellow.

9. Conclusion: This Research Essay has attempted to outline the different techniques and methods of Satellite Remote Sensing used for the Estimation of Sea Surface Temperature. NOAA series of Meteorological Satellite namely: Geostationary Operational Environmental

satellites

(GOES)

for

short-range

warning

and

Polar-Orbiting

Environmental Satellites (POES) for longer-term forecasting are generally used for SST data. Advanced Very High Resolution Radiometer (AVHRR) and Thermal Infrared (TIR) sensors in the wavelength of 8-14 μm are normally used to measure the surface skin temperature about 20 μm thick at the ocean surface. Hyperspectral and Thermal Infrared (TIR) Remote sensing approach (Techniques) are generally used in SST estimation. Different methods of SST estimation are discussed. The Hyperspectral signature of different satellite imagery in their multi-spatial and temporal scales are also analysed to distinguish the SST and detect associated phenomenon such as El Nino and La Nino Furthermore, it also discussed the problems associated with the satellite SST estimation. The estimated SST is limited to the certain thickness (20μm) of water depth which does not represent the average SST for that particular area specified by the satellite. As Clouds absorb all infra-red radiation coming up to them from below and emit their own infra-red radiation from their tops, becoming major problems in SST estimation. Since, estimated SST contains some errors due to the atmospheric forcing (absorption), Systematic and Instrumental errors. So, the estimated SST data must be validated and correlated with surface SST data from ships, drifting buoys and other sources. Different research showed that the differences between the satellite-derived SST and situ SST data are distributing within a ±1°C range indicating RMS errors smaller than 0.6°C. Hence, it is assumed that accuracy of satellite derived SST is ±1°C The skin temperatures are also found as 0.7 degree cooler than the water just below due to the radiative or evaporative cooling. Finally, The SST date are significant to Weather Prediction and climate change study, where as it is also commercially useful for fishery industries.

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