I'm surprised by all of the preceeding answers.  Yes, all the suggestions are good software for GIS/remote sensing work, but you will find it difficult to obtain the data necessary for building bathymetric terrain models.  As far as I know, that is not possible with typical satellite imagery (e.g., Landsat or SPOT).  Lidar is the most appropriate data source, but you will find that most lidar sensors operate in the NIR range, which is not good for underwater data collection.  There are a few lidar systems that have been deployed for bathymetry that operate in green spectrum and will penetrate clear/shallow water with good results.....but not always, I know this firsthand.  I'm not aware of any green/hybrid lidar acquisitions in India, but I haven't looked.  If the water is very clear, it may be possible to build topography using classic photogrammetric methods.   That would require a stereo plotter (analog or digital, depending on your situation).  Of course, if you are interested in terrain that is fully exposed at low tide, a lidar acquisition could easily provide what you need; photogrammetry would work well too.  If you're interested in underwater terrain that's subtidal-offshore, and the water is deep enough, its possible to have a sonar survey done.  This is the most common historical source of bathymetry.  I have found this to be infeasible for nearshore acquisitions due to shallow water and risk of "shipwreck" on rocky intertidal substrates.  A sonar survey would include data processing and you would receive GIS-ready products.  Probably not inexpensive. 

The coastal margin is a tricky area to work in.  Good luck.  I suspect you will find a creative solution.

I think ERDAS software works for such purposes.

Grass also is a great opensource software

SAGA also is a great open source software.There are a lot of tools for surface analysis

http://www.saga-gis.org/en/index.html

You can use GRASS for that I think, You can  use r.mapcalc (http://grass.osgeo.org/grass64/manuals/r.mapcalc.html) for your algorithm to run. However, you have to collect some of the field data to verify ur result.

If you are not afraid of the command line, Python has great libraries in matplotlib and basemap and R has some good libraries too.

I think the best set of software is: QGIS, GRASS and SAGA packages.

R... if you use raster files, raster library, and for shapefiles, maptools, among others

Any recent GIS software will probably do this task

If you want to try a variety of open source software packages, OSGeo provides a bootable Linux distribution geared toward GIS: http://live.osgeo.org/en/index.html

I'm surprised by all of the preceeding answers.  Yes, all the suggestions are good software for GIS/remote sensing work, but you will find it difficult to obtain the data necessary for building bathymetric terrain models.  As far as I know, that is not possible with typical satellite imagery (e.g., Landsat or SPOT).  Lidar is the most appropriate data source, but you will find that most lidar sensors operate in the NIR range, which is not good for underwater data collection.  There are a few lidar systems that have been deployed for bathymetry that operate in green spectrum and will penetrate clear/shallow water with good results.....but not always, I know this firsthand.  I'm not aware of any green/hybrid lidar acquisitions in India, but I haven't looked.  If the water is very clear, it may be possible to build topography using classic photogrammetric methods.   That would require a stereo plotter (analog or digital, depending on your situation).  Of course, if you are interested in terrain that is fully exposed at low tide, a lidar acquisition could easily provide what you need; photogrammetry would work well too.  If you're interested in underwater terrain that's subtidal-offshore, and the water is deep enough, its possible to have a sonar survey done.  This is the most common historical source of bathymetry.  I have found this to be infeasible for nearshore acquisitions due to shallow water and risk of "shipwreck" on rocky intertidal substrates.  A sonar survey would include data processing and you would receive GIS-ready products.  Probably not inexpensive. 

The coastal margin is a tricky area to work in.  Good luck.  I suspect you will find a creative solution.

I have to agree with everything said by JEFF. First you have to define and find out the most suitable data for bathimetric studies on shallow, deep, .... environmentsThe software is not the biggest problem. We have ILWIS, GRASS, SPRING, QGIS, ....

Can i use landsat-8 band  blue( 0.45-0.51) for bathymetric mapping. Is that will be  right selection for bathymetric  mapping. And i know that worldview,

quickbird will give good image. Is  landsat will give me right  results.Thanks for all answer, and  there are some literature available for bathymetric mapping by using

Landsat image.

Studying bathymetry,  using Landsat data is almost impossible. Directly try to find and use bathymetrical data from navy or any performed side scanner sonar data if you can

Absorptive methods, like the ones referenced by Waqas Qazi above, are not straightforward and I don't know of any published algorithms (code or software) for this.  For those with an interest, I think the following is a good presentation of the absorptive methods I think you're looking for:

http://www.ssec.wisc.edu/meetings/ciw/Workshop_Presentations/Wednesday_6_20_2012/2_Algorithm_Approaches/ignite_algorithm_application/5_Liu_Bathymetry.pdf

These methods are tricky, and influenced by turbidity and reflectance angle (underwater topography).  You may need to use function fitting software (I use R statistical software) and try the bands you feel appropriate.  The WorldView "coastal blue" band being of particular interest -- perhaps the landsat blue will suffice?  Note that these methods are not considered terribly reliable (from image to image, and time to time) and a model that fits well with good results (with your chosen bands) in one image might not fit so well in the next.  I'll say again, absorptive methods appear quite dependent on relatively low suspended materials (turbidity, plankton/algae, etc.).  Maybe the Hawaiian coast satisfies this, but I know firsthand that it is not the case in many other places.  You will probably want to work close to the nadir of your imagery to reduce reflectance angle effects.  Perhaps contact GRAS and see if they would provide hints? (doesn't seem likely).

Sonar surveys currently remain the best source for deeper water.  Green/hybrid lidar seems hopeful for relatively clear shallow water.  Everything else is a challenge.  Good luck and please let us know if you have success with the spectral approach.

Landsat 8 Operational Land Imager (OLI) provides Band 2 range from 0.45-0.51,and has resolution of 30m, which can be used for Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation. By this i concluded myself that Landsat 8 (i.e) Band Blue will be appropriate for bathymetric mapping.And my question is why cant we use band blue of landsat 8 which is free of cost.

Take a look at this paper:

Lee, Z., C. Hu, B. Casey, S. Shang, H. Dierssen, and R. Arnone (2010). Global shallow-water bathymetry from satellite ocean color data. Eos. AGU Trans. 91(46):429-430.

HI all Thanks for your suggestion, and i done bathymetric mapping using landsat image, we got good results and at same time Landsat data is not sufficient to read    in-depth of ocean.

Search for this

Bathymetry Mapping Using Landsat 8 Satellite Imagery

Jagalingam P1, Akshaya B J1 and Arkal Vittal Hegde2

I'm actually in what was your situation, but I can't use ArcGis 'cause I got no the money jajaj

Hey Wait! You are the paper writer! jajaja

Hi, Please try my module currently available as GRASS GIS addon and let me know the feed backs. https://grass.osgeo.org/grass72/manuals/addons/i.image.bathymetry.html)

Thanks

QGIS and GRASS

SAGA

R

@ Tea Duplančić Leder and @ Jason Daniel Tilley

if you kindly share your contact details o my email ([email protected]) then it will very helpful. I want to discuss on R