ioe 184 - the basics of satellite oceanography. 3. remote sensing of the sea

IoE 184 - The Basics of Satellite Oceanography. 3. Remote Sensing of the Sea

Lecture 3Remote Sensing of the Sea

Remote sensing of the sea includes:

1. Sensor calibration

2. Atmospheric correction

3. Positional registration

4. Oceanographic sampling for "sea truth"

5. Image processing


6. Oceanographic applications of satellite remote sensing

Compare satellite remote sensing and the traditional sources of oceanographic information:


Remote sensing is better than traditional methods:

1. Synoptic view, because satellites collect huge amount of information much exceeding the data collected by contact oceanographic observations;

2. Satellite observations cover wide areas of the World Ocean hardly accessible for field observations.

Problems of remote sensing:

1. The parameters measured by the satellites cannot be directly attributed to traditionally measured oceanographic characteristics;

2. Some satellite observations (ocean color and infrared) are more sensitive to unfavorable meteorological conditions than traditional oceanographic methods.



The sensors look at the ocean surface through another medium, the atmosphere. The atmosphere is opaque to electromagnetic radiation at many wavelengths, and there are only certain wavelengths through which radiation may be fully or partly transmitted.



The following compounds of the atmosphere change its transmittance:

• Gas molecules themselves • Water vapor • Aerosols • Suspended particles of dust • Water droplets in the form

of clouds



~40% of sunlight is reflected by clouds~20% of sunlight is absorbed by the atmosphere~40% of sunlight is absorbed by Earth’s surface



Atmospheric pathways of electromagnetic radiation between the sea and the satellite sensor.

Ray 1 - the useful signal;Ray 2 - the radiation leaving the sea which is absorbed by the atmosphere;Ray 3 - the radiation, which is scattered by the atmosphere out of the sensor field of vision.Ray 4 - the energy emitted by the constituents of the atmosphere;Ray 5 - the energy reflected by scattering into the field of vision of the sensor;Ray 6 - the energy which previously left the sea surface but from outside the field of view.




The ocean area within the IFOV emits rays 1+2+3

Rays 4, 5, and 6 reach the sensor without having left the sea surface in the field of view, and therefore constitute extraneous "noise" on top of the signal.

The sensor receives rays 1+4+5+6

The complete atmospheric correction should result in the sum of rays 1+2+3.




In the case of optical sensors Ray 2 is absent.

On the contrast, in thermal IR sensors Ray 2 is important: the cool atmosphere absorbs radiation (Ray 2) and re-emits it with lower temperature characteristics (Ray 4).



Increased atmospheric pathlength resulting from oblique viewing. The oblique view results in looking through longer path length of atmosphere than for nadir viewing. This feature is used in atmospheric correction. By viewing the same piece of sea twice, through different lengths of atmosphere, an objective estimate of atmospheric effect can be made.



- Cloud detection

Cloud cover is a main obstacle for satellite imagery in visible and infrared spectral bands. Clouds are transient atmospheric features that consist of small ice and liquid water particles with dimensions from under a micrometer to a few millimeters, resulting from water condensation and freezing.

Cloud properties vary with height. In the visible and infrared part of spectrum, the liquid water and ice crystals contained in the clouds scatter and absorb radiation, so that thick clouds make it impossible to view the surface. At any time, clouds cover almost two-thirds of the globe.



- Cloud detection

In both ocean color and SST, the first step of the procedure of atmospheric correction is to determine if every oceanic pixel in the image under investigation is cloud-free.

The SeaWiFS (8 optical channels) cloud detection is most primitive.

It is assumed, that the water-leaving radiance of near-infrared wavelength is near zero. As such, the pixels with a reflectance greater than a preset threshold are classified as clouds.



- Cloud detection

In AVHRR and MODIS the cloud detection is based on two factors:

1) the clouds are colder and more reflective than the ocean surface;2) for spatial scales of order 100 km, the ocean surface, in contrast

to clouds, is nearly uniform in temperature and reflectance.

Three kinds of tests are used:1) “Threshold” tests eliminate pixels that are more reflective or

colder than the ocean surface. 2) “Uniformity” tests examine the variance of temperature or

reflectance in a rectangular array of pixels.3) The retrieved SSTs are compared with climatology and with SSTs

retrieved using alternative algorithms; e.g., according to the “unreasonableness” test, SST must be within the range from -2ºC to +35ºC.



Positional registration means the identification on a map of the place to which a remote-sensed measurement refers. The problem of knowing where the satellite was when a measurement was made depends on type of sensor, first of all its spatial resolution.

An approximate estimation of the satellite position can be obtained from the time of observation. However, the precision of this estimation is within few kilometers.

AVHRR radiometer on NOAA satellite



Often the "ground control points" are used. However, the ground control points can be used mostly in the coastal zones. The problem of distortion of the image results from oblique viewing of the spherical earth surface. During processing each pixel of the image should be attributed to geographical coordinates.



In recent satellites more precise estimation of the position is obtained using the signals of GPS (Global Positioning System) satellites.



DORIS system determines the position of TOPEX/Poseidon satellite orbit to within a few centimetres.

The technique used (known as orbit determination), consists of locating a satellite in relation to about fifty ground control points on the Earth's surface.



The strategy of collecting of samples is very important. The samples must span as wide range of data values as possible. Typically, transects across the gradients are used.



Spatial resolution of the sensor is important as compared with spatial variability of the measured parameter, because the value measured within a point may not be representative of the average parameter within the whole pixel measured by the satellite.

MODIS nLw(551) (W /m 2/µm/sr) 04/13/2001

33.7

33.8

33.9

34.0

34.1

34.2

34.3

34.4

34.5

-119.8 -119.6 -119.4 -119.2 -119.0 -118.8

0

1

2

3

4

5

6

7

8

9

10



Comparing satellite observations and “sea truth” data we should keep in mind that the data collected by contact methods can be not more precise than remotely-sensed data.

In practice, the satellite data and “sea truth” data are nothing but two data arrays collected by different methods.

5. Image processing


Level of data processing

Level 0 -

Level 1 -

Level 2 -

Level 3 -

Level 4 -

Raw data received from satellite, in standard binary form;

Image data in sensor coordinates, containing individual calibrated channels;

Derived oceanic variable, atmospherically corrected and geolocated, but presented in sensor coordinates;

Composite images of derived ocean variable resampled onto standard map base and averaged over a certain time period (may contain gaps);

Image representing an ocean variable averaged within each grid cell as a result of data analysis, e.g., modeling.

5. Image processing


The “raw” information measured by the sensor onboard the satellite (raw radiance counts from all bands as well as spacecraft and instrument telemetry) is transmitted by radio-signal and received by the ground station.

These data are called “Level 0” data.

Level-1 Data Products

Level-1 products contain all the Level-0 data, appended calibration and navigation data, and instrument and selected spacecraft telemetry that are reformatted and also appended.

5. Image processing


The radiances are measured at different wavebands, called “channels”. Different channels provides information on different properties of the Earth’ surface. One method of analysis is when the images observed at different wavebands can be combined to result in a “true color image”.

5. Image processing


At this MODIS image of the Mississippi River delta you can see clouds, coastline, river, the zones of phytoplankton bloom and pollution in the coastal ocean, etc.

5. Image processing


True color images are an important source of information about natural disasters like these wildfires in California in autumn 2003.

5. Image processing



Each pixel of Level-2 data contains geophysical values (e.g., sea surface temperature, surface chlorophyll concentration, etc.) estimated from the radiances measured by the satellite

Each Level-2 product is generated from a corresponding Level-1 product.

Level-2 data are derived from the Level-1 raw radiance counts by applying the sensor calibration, atmospheric corrections, and the algorithms specific for each kind of geophysical value (e.g., bio-optical algorithms for water color data, etc.).

5. Image processing


Example of Level 2 data: MODIS Sea Surface Temperature, 2000 December 6, 17:05

5. Image processing


Example of Level 2 data: MODIS Surface Chlorophyll Concentration, 2000 December 6, 17:05

5. Image processing


Example of Level 2 data: MODIS Total Suspended Solids , 2000 December 6, 17:05

5. Image processing



Level-3 means geophysical parameters observed during a certain period and interpolated on a global grid. For The SeaWiFS the periods of Level 3 data are: - one day, - 8 days, - a calendar month, or - a calendar year. For other satellites these periods can be different.

The spatial resolution of the global grid can be:1 degree (360 x 180 grid);18 km (2048 x 1024 grid) - MC SST;9 km (4096 x 2048 grid) - SeaWiFS, Pathfinder SST v.1-4;4.5 km (8192 x 4086 grid) - MODIS, Pathfinder SST v.5.

5. Image processing


SeaWiFS Level 3 chlorophyll image, 1997 December 8 (daily)

5. Image processing


SeaWiFS Level 3 chlorophyll image, 1997 December 11 – 18 (8-day)

5. Image processing


SeaWiFS Level 3 chlorophyll image, 1997 December 1 – 31 (monthly)

5. Image processing


The satellite data are disseminated via internet.

Users select the images in online databases and either download or order data files. Typical satellite images are very big (e.g., one MODIS image is about 250-300 Mb).

To enable the users to have a brief look at each image before selecting it low-resolution “browse” images are often produced. If the area of interest is free from clouds, the user orders the data file, downloads it, and works with it using appropriate software.

5. Image processing


Data format.

Many types of satellite information are stores in Hierarchical Data Format (HDF).

HDF is a cross-platform file format for storing a wide variety of scientific data. This public-domain open standard was created by the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign (NCSA).

A typical HDF file might contain a dataset, data table, descriptions of data, images produced from the data and other related information. It can be processed using special software, such as Noesys, MATLAB, IDL, etc.

5. Image processing


All good software packages are commercial.

To understand the basic features of HDF files you can use free program HDFExplorer from the Internet site

http://www.space-research.org/

1. Select <<Download>>

2. Fill out the form with your name, etc.

3. Click <<Submit>>

4. Store the HDFExplorerSetup.exe file on the hard drive of your computer

5. Double-click HDFExplorerSetup.exe to install HDFExplorer.

5. Image processing


Download from the site

www.obee.ucla.edu/test/faculty/nezlin

From the section Lecture 3 “Remote Sensing of the Sea”

two example files:

MO36MWN2.sst4.zip and

C1978341012416.L2_BRS.hdf.zip

Uncompress these files using WinZip and open them in HDFExplorer. On the left you see the content of the file. Clicking “+” expand the structures.

http://www.obee.ucla.edu/test/faculty/nezlin

5. Image processing


MO36MWN2.sst4.zip contains data on sea surface temperature (SST4) collected by MODIS Terra satellite.

The dataset sst4_mean contains the data array. Double-click it to see the content.

Let us analyze the content of the dataset at the example of one grid node (x=1; y=150).

The grid node with column = 1 and row = 150 contains the value 31706. To understand its meaning double-click on the records Scale_type, Slope and Intercept.

5. Image processing


You see the following:

Scale_type =Y=Slope * x + Intercept;

Slope = 0.01

Intercept = -300

31706 * 0.01 – 300 = 17.06

Double-click Units and Name.

Now you see that it is the temperature of the ocean surface measured in Degrees C.

5. Image processing


Double-click Start Year, Start Day, End Year, End Day.

You see that the data were collected from 2000, Julian Day 336 (December 1) to 2000 Julian Day 344 (December 9).

Double-click Northernmost LatitudeSouthernmost LatitudeWesternmost LongitudeEasternmost Longitude.

You can see that the data array covers the entire Earth surface from 90S (i.e., -90) to 90N and from 180W (i.e., -180) to 180E.

Number of Columns and Number of Lines indicate the grid size: 1024 x 512.

5. Image processing


HDFExplorer cannot transform 2-byte arrays into images, but other software can help you to make a graphical representation of the HDF file content.

5. Image processing


Open the file C1978341012416.L2_BRS.hdf and expand the structures clicking “+”.

5. Image processing


This file contains CZCS chlorophyll image; the snapshot was obtained in the western Pacific.

8-bit Raster Image 1 contains the image of surface chlorophyll concentration.

8-bit Raster Image 2 indicates the snapshot location.

Point the mouse cursor at the Raster Image 1, right-click and select “View”. You see the values from 0 to 255, i.e. bytes.

Point the mouse cursor at the Raster Image 1, right-click and select “Image View”. You see the graphic representation of the data array made using the Image Palette.

5. Image processing


The records Start Year, Start Day, End Year, End Day indicate that the image was obtained in 1978, Julian Day 341 (December 7).

Northernmost Latitude, Southernmost Latitude, Westernmost Longitude and Easternmost Longitude indicate the approximate location of the image.

Latitude and Longitude structures indicate the exact geographic locations of selected pixels of the image. These arrays can be used by the software like MATLAB Mapping Toolbox to produce the image in any geographical projection you wish.

5. Image processing


Double-click the records Scaling Equation,Base,Slope, andIntercept.

You see:Base = 10;Slope = 0.012;Intercept = -1.4;

Scaling Equation == “Base**((Slope*brs_data) + Intercept) = chlorophyll a” We can check some values within the range 0-255 and see that the brs_data = 100 results in 0.630957…, the brs_data = 200 results in 10.00000…, etc.

Double-click Parameter and Units. You see the description of the data“chlorophyll a concentration” and “mgm^-3”.

Oceanographic applications of satellite remote sensing include:

1. Visible wavelength "ocean color" sensors

2. Sea surface temperature from infrared scanning radiometers

3. Passive microwave radiometers

4. Satellite altimetry of sea surface topography

5. Active microwave sensing of sea-surface roughness



These sensors operate in the visible part of the electromagnetic spectrum, measuring electromagnetic radiation emitted by the sun and reflected by land and ocean surface.



The color of the Earth’ surface, especially the color of the ocean, results primarily from biological processes.

Measuring the absorption and backscattering characteristics of ocean surface, we can estimate the concentrations of different kinds of matter suspended in seawater, including phytoplankton cells.


2. Sea surface temperature from infrared radiometers

Infrared sensors measure electromagnetic radiation within the band 1-30 µm, emitted by the ocean surface and resulting from the temperature of the upper sea layer.


2. Sea surface temperature from infrared radiometers

The near-infrared and infrared radiation is processed to sea surface temperature (SST). The most important SST sensors are Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites, MODIS, GOES geostationary satellites, and some others.


3. Passive microwave radiometers

Passive microwave radiometers operate at electromagnetic wavelengths 1.5–300 mm (i. e., the frequency 1–200 GHz).

Their advantage is the comparatively long wavelength, which is not sensitive to scattering by the atmosphere or aerosols, haze, dust, or small water particles in clouds. So, the microwave sensors are all-weather devices. This principle advantage is countered by the fact that thermal emission is very weak at these longer wavelengths. To overcome noise levels a large field of view must be received; that results in low spatial resolution (25–150 km). So, these observations are used for studies of heat balance of the ocean.

The emissivity of the sea at microwave frequencies varies with the dielectric properties of sea water (including salinity) and the surface roughness. Hence, the development of this technique in future can enable the measurements of surface salinity.


4. Satellite altimetry of sea surface topography

Satellite altimeters are radars, which transmit short pulses toward the earth beneath them. The return time of the pulse after reflection at the earth's surface is measured, and this yields the height of the satellite. The most important are ERS-1, ERS-2, TOPEX/Poseidon, and Jason-1 satellites.


5. Active microwave sensing of sea-surface roughnessSynthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is based on the comprehensive analysis of contribution from individual points to the signal received when the sensor is at a particular point. The result is very high resolution.


5. Active microwave sensing of sea-surface roughnessSynthetic Aperture Radar (SAR)

SAR images enable the analysis of small-scale and mesoscale eddies, river plumes, oil slicks, ice packs, etc.


ioe 184 - the basics of satellite oceanography. 3. remote sensing of the sea

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