Prerequisite Condition of Diffuse Attenuation Coefficient K<sub>d</sub>(490) for Detecting Seafloor from ICESat-2 Geolocated Photons During Shallow Water Bathymetry (2023)

Prerequisite Condition of Diffuse Attenuation Coefficient Kd(490) for Detecting Seafloor from ICESat-2 Geolocated Photons During Shallow Water Bathymetry

Bathymetry refers to the depth measurement of the topographic seafloor surface and is essential geophysical data for understanding the land-ocean interplay. Recently, researchers have taken advantage of photon penetration of the green laser of NASA ICESat-2 to profile the seafloor as a part of the bathymetric mapping of shallow nearshore coastal waters. Prerequisite conditions for using the ICESat-2 geolocated photons for reconstructing the bathymetric profiles include a preference for using nighttime acquisitions followed by applying refraction correction to the water column returned photons to correct the apparent depths due to the change in the speed of light that occurs at the air-water interface. The success of detecting the seafloor from the bathymetric profiles from ICESat-2 photons will depend on the optical clarity of the water. The diffuse attenuation coefficient for downwelling irradiance, Kd(490), measures how light dissipates with depth in water and indicates how strongly light intensity at 490 nm of wavelength is attenuated in the water column, providing a hint about the water clarity. In this research, we have explored ICESat-2's photon-based bathymetric mapping potential in relation to the Kd(490). ICESat-2 photon data and Kd(490) data from level-2 OLCI of Sentinel-3 A/B mission were acquired with overlapping dates to investigate the possible depth penetration of ICESat-2 photons in the shallow waters during clear water conditions and sediment load periods. Two nearshore study sites were chosen that are located at the head of the Bay of Bengal. This research proves that the ICESat-2 photons can successfully reflect from the seafloor in shallow waters while the optical water condition is clear, during which the Kd(490) is less than 0.12 m-1. On the contrary, during the periods of sediment load in the water, where the Kd(490) is above 0.15 m-1, the ray tracing mechanism of ICESat-2 photons has been impacted due to absorption and scattering caused by the sediments load in the water column; thus, seafloor detection by ICESat-2 photons will not be successful in sediment loaded waters. The results from this research suggest the necessity of Kd(490) to be complementary data with ICESat-2 photons for successful bathymetric applications.

Bathymetry, ICESat-2 Geolocated Photons, Diffuse Attenuation Coefficient, Sentinel-3 OLCI, Kd(490)

Dandabathula Giribabu, Rohit Hari, Jayant Sharma, Aryan Sharma, Koushik Ghosh, Apurba Kumar Bera, Sushil Kumar Srivastav. Prerequisite Condition of Diffuse Attenuation Coefficient Kd(490) for Detecting Seafloor from ICESat-2 Geolocated Photons During Shallow Water Bathymetry, Hydrology. Volume 11, Issue 1, March 2023 , pp. 11-22. doi: 10.11648/j.hyd.20231101.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Boggs, S. A. Jr. (2012). Principles of stratigraphy and sedimentology (5th edition). Prentice Hall, Upper Saddle River, NJ.


Vogt, P. R., & Tucholke, B. E. (1986). Imaging of the ocean floor. In: Vogt, P. R., & Tucholke, B. E. (Eds.). The western north Atlantic region, Geological Society of America, Boulder. pp. 19-44.


Lyzenga, D. R. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. Int. J. Remote Sens., 6 (1), 115-125. doi: 10.1080/01431168508948428.


Vyas, N. K, & Andharia, H. I. (1988). Coastal bathymetric studies from space imagery. Mar. Geod., 12 (3), 177-187.


Abdallah, H., Bailly, J. S., Baghdadi, N. N., Saint-Geours, N., & Fabre, F. (2012). Potential of space-borne LiDAR sensors for global bathymetry in coastal and inland waters. IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens., 6 (1), 202-216. doi: 10.1109/JSTARS.2012.2209864.


Salameh, E., Frappart, F., Almar, R., Baptista, P., Heygster, G., Lubac, B.,... & Laignel, B. (2019). Monitoring beach topography and nearshore bathymetry using spaceborne remote sensing: A review. Remote Sens., 11 (19), 2212. doi: 10.3390/rs11192212.


Ashphaq, M., Srivastava, P. K., & Mitra, D. (2021). Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research. J. Ocean Eng. Sci., 6 (4), 340-59. doi: 10.1016/j.joes.2021.02.006.


Cesbron, G., Melet, A., Almar, R., Lifermann, A., Tullot, D., & Crosnier, L. (2021). Pan-European Satellite-Derived Coastal Bathymetry—Review, User Needs and Future Services. Front. Mar. Sci., 8, 740830. doi: 10.3389/fmars.2021.740830.


Evagorou, E., Argyriou, A., Papadopoulos, N., Mettas, C., Alexandrakis, G., & Hadjimitsis, D. (2022). Evaluation of Satellite-Derived Bathymetry from High and Medium-Resolution Sensors Using Empirical Methods. Remote Sens., 14 (3), 772. doi: 10.3390/rs14030772.


Duplančić Leder, T., Baučić, M., Leder, N., & Gilić, F. (2023). Optical Satellite-Derived Bathymetry: An Overview and WoS and Scopus Bibliometric Analysis. Remote Sens., 15 (5), 1294. doi: 10.3390/rs15051294.


Lu, X., Hu, Y., Omar, A., Yang, Y., Vaughan, M., Rodier, S., Garnier, A., Ryan, R., Getzewich, B., & Trepte, C. (2022). Nearshore bathymetry and seafloor property studies from Space lidars: CALIPSO and ICESat-2. Opt. Express, 30 (20): 36509-36525. doi: 10.1364/OE.471444.


Santos, D., Fernández-Fernández, S., Abreu, T., Silva, P. A., & Baptista, P. (2022). Retrieval of nearshore bathymetry from Sentinel-1 SAR data in high energetic wave coasts: The Portuguese case study. Remote Sen. Appl.: Soc. Environ., 25, 100674. doi: 10.1016/j.rsase.2021.100674.


Wiehle, S., Pleskachevsky, A., & Gebhardt, C. (2019). Automatic bathymetry retrieval from SAR images. CEAS Space J., 11 (1), 105-14. doi: 10.1007/s12567-018-0234-4.


Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., & Jasinski M. (2017). The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sens. Environ., 190, 260-73. doi: 10.1016/j.rse.2016.12.029.


Neumann, T. A., Martino, A. J., Markus, T., Bae, S., Bock, M. R., Brenner, A. C., Brunt, K. M., Cavanaugh, J., Fernandes, S. T., Hancock, D. W., & Harbeck K. (2019). The Ice, Cloud, and Land Elevation Satellite–2 Mission: A global geolocated photon product derived from the advanced topographic laser altimeter system. Remote Sens. Environ., 233, 111325. doi: 10.1016/j.rse.2019.111325.


NSIDC, (National Snow and Ice Data Center). (2023).


Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K., Gibbons, A., Lee, J., Luthcke, S. B., & Rebold, T. (2021). ATLAS/ICESat-2 L2A global geolocated photon data, version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: 10.5067/ATLAS/ATL03.005.


Magruder, L., Neumann, T., & Kurtz, N. (2021). ICESat-2 Early Mission Synopsis and Observatory Performance. Earth Space Sci., 8 (5), e2020EA001555. doi: 10.1029/2020EA001555.


Martino, A. J., Neumann, T. A., Kurtz, N. T, & McLennan, D. (2019). ICESat-2 mission overview and early performance. In: Proceedings of SPIE 11151, Sensors, Systems, and Next-Generation Satellites - XXIII, 111510C, Bellingham. doi: 10.1117/12.2534938.


Brown, M. E., Arias, S. D., & Chesnes, M. (2022). Review of ICESat and ICESat-2 literature to enhance applications discovery. Remote Sens. Appl.: Soc. Environ., 100874. doi: 10.1016/j.rsase.2022.100874.


Neuenschwander, A. L., & Magruder, L. A. (2019). Canopy and terrain height retrievals with ICESat-2: A first look. Remote Sens., 11 (14), 1721. doi: 10.3390/rs11141721.


Jasinski, M. F., Stoll, J. D., Cook, W. B., Ondrusek, M., Stengel, E., & Brunt, K. (2016). Inland and near-shore water profiles derived from the high-altitude Multiple Altimeter Beam Experimental Lidar (MABEL). J. Coast Res., 76 (sp1), 44-55. doi: 10.2112/si76-005.


Dandabathula, G., Bera, A. K., Sitiraju, S. R., & Jha, C. S. (2021). Inferring Lake Ice Status Using ICESat-2 Photon Data. Remote Sens. Earth Syst. Sci., 4 (4), 264-79. doi: 10.1007/s41976-022-00067-4.


Parrish, C. E., Magruder, L. A., Neuenschwander, A. L., Forfinski-Sarkozi, N., Alonzo, M., & Jasinski, M. (2019). Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance. Remote Sens., 11 (14), 1634. doi: 10.3390/rs11141634.


Ma, Y., Xu, N., Liu, Z., Yang, B., Yang, F., Wang, X. H., & Li, S. (2020). Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets. Remote Sens. Environ., 250, 112047. doi: 10.1016/j.rse.2020.112047.


Parrish, C. E., Magruder, L., Herzfeld, U., Thomas, N., Markel, J., Jasinski, M., Imahori, G., Herrmann, J., Trantow, T., Borsa, A., & Stumpf, R. (2022). ICESat-2 Bathymetry: Advances in Methods and Science. In: OCEANS 2022, Hampton Roads: IEEE, 1-6. doi: 10.1109/OCEANS47191.2022.9977206.


Yang, J., Ma, Y., Zheng, H., Xu, N., Zhu, K., Wang, X. H., & Li, S. (2022). Derived Depths in Opaque Waters Using ICESat-2 Photon-Counting Lidar. Geophy. Res. Lett., 49 (22), e2022GL100509. doi: 10.1029/2022GL100509.


Xu, N., Ma, Y., Zhou, H., Zhang, W., Zhang, Z., & Wang, X. H. (2020). A method to derive bathymetry for dynamic water bodies using ICESat-2 and GSWD data sets. IEEE Geosci. Remote Sens. Lett., 19, 1-5. doi: 10.1109/LGRS.2020.3019396.


Hsu, H. J., Huang, C. Y., Jasinski, M., Li, Y., Gao, H., Yamanokuchi, T., Wang, C. G., Chang, T. M., Ren, H., Kuo, C. Y., & Tseng, K. H. (2021). A semi-empirical scheme for bathymetric mapping in shallow water by ICESat-2 and Sentinel-2: A case study in the South China Sea. ISPRS J. Photogramm. Remote Sens., 178, 1-9. doi: 10.1016/j.isprsjprs.2021.05.012.


Guo, X., Jin, X., & Jin, S. (2022). Shallow Water Bathymetry Mapping from ICESat-2 and Sentinel-2 Based on BP Neural Network Model. Water, 14 (23), 3862. doi: 10.3390/w14233862.


Austin, R. W., & Petzold, T. J. (1981). The determination of the diffuse attenuation coefficient of sea water using the Coastal Zone Color Scanner. In: Gower, J. F. R. (ed). Oceanography from space. Marine Science, Vol 13. Boston: Springer. pp. 239-56. doi: 10.1007/978-1-4613-3315-9_29.


Jamet, C., Loisel, H., & Dessailly, D. (2012) Retrieval of the spectral diffuse attenuation coefficient Kd (λ) in open and coastal ocean waters using a neural network inversion. J. Geophys. Res.: Oceans, 117 (C10). doi: 10.1029/2012JC008076.


Lee, Z. P., Darecki, M., Carder, K. L., Davis, C. O., Stramski, D., & Rhea, W. J. (2005). Diffuse attenuation coefficient of downwelling irradiance: An evaluation of remote sensing methods. J. Geophys. Res.: Oceans, 110 (C2). doi: 10.1029/2004JC002573.


Mueller, J. L. (2000). SeaWiFS algorithm for the diffuse attenuation coefficient, K (490), using water-leaving radiances at 490 and 555 nm. In: Hooker, S. B., (Ed.). SeaWiFS postlaunch calibration and validation analyses, Part 3. Greenbelt; NASA Goddard Space Flight Centre. pp. 24-7.


Morel, A., Huot, Y., Gentili, B., Werdell, P. J., Hooker, S. B., & Franz, B. A. (2007). Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ., 111 (1), 69-88. doi: 10.1016/j.rse.2007.03.012.


Sentinel-3 Mission. (2023). Sentinel Online.


Sentinel-3 User Handbook. (2017).


Mangin, A., Bourg, L., & d’Andon, O. F. (2010). OLCI level 2 algorithm theoretical basis document: Transparency products.


Kyryliuk, D., & Kratzer, S. (2019). Evaluation of Sentinel-3A OLCI products derived using the Case-2 Regional CoastColour processor over the Baltic Sea. Sensors, 19 (16), 3609. doi: 10.3390/s19163609.


Glukhovets, D., Kopelevich, O., Yushmanova, A., Vazyulya, S., Sheberstov, S., Karalli, P., & Sahling, I. (2020). Evaluation of the CDOM Absorption Coefficient in the Arctic Seas Based on Sentinel-3 OLCI Data. Remote Sens., 12 (19), 3210. doi: 10.3390/rs12193210.


Valdiya, K. S. (2015). The making of India: geodynamic evolution. (2nd edition). Springer. p. 924. doi: 10.1007/978-3-319-25029-8.


Murty, T. S., Flather, R. A., & Henry, R. F. (1986). The storm surge problem in the Bay of Bengal. Prog. Oceanogr., 16 (4), 195-233.


Bhaskaran, P. K., Gayathri, R., Murty, P. L., Bonthu, S., & Sen, D. (2014). A numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal. Coast Eng., 83, 108-18. doi: 10.1016/j.coastaleng.2013.10.005.


Mascarenhas, A. (2004). Oceanographic validity of buffer zones for the east coast of India: A hydrometeorological perspective. Curr. Sci., 86 (3), 399-406.


Sarma, V. V., Krishna, M. S., & Srinivas, T. N. (2020). Sources of organic matter and tracing of nutrient pollution in the coastal Bay of Bengal. Mar. Pollut. Bull., 159: 111477. doi: 10.1016/j.marpolbul.2020.111477.


GEBCO Compilation Group (2022). GEBCO_2022 Grid. doi: 10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c.


Copernicus Data Space Ecosystem. (2023).


STEP, Science Toolbox Exploitation Platform. (2022).


Hanagan, C., & Mershon, B. (2020). Geoid Height Calculator. UNAVCO: Boulder, CO, USA.


Ranndal, H., Sigaard Christiansen, P., Kliving, P., Baltazar Andersen, O., & Nielsen, K. (2021). Evaluation of a statistical approach for extracting shallow water bathymetry signals from ICESat-2 ATL03 photon data. Remote Sens., 13 (17), 3548. doi: 10.3390/rs13173548.


Gattuso, J. P., Frankignoulle, M., & Wollast, R. (1998). Carbon and carbonate metabolism in coastal aquatic ecosystems. Annu. Rev. Ecol. Syst., 29 (1), 405-434.


Wetzel, R. G. (2001). Limnology: Lake and River Ecosystems. (3rd edition). Academic Press, San Diego, p. 1006.


Gallegos, C., & Moore, K. A. (2000). Factors contributing to water-column light attenuation, In: Batiukand, R. A., et al. [Eds.], Chesapeake Bay submerged aquatic vegetation water quality and habitat-based requirements and restoration targets: A second technical synthesis. U.S. Environmental Protection Agency, Chesapeake Bay Program, Annapolis, Maryland. pp. 35-54.


Kheireddine, M., Ouhssain, M., Organelli, E., Bricaud, A., & Jones, B. H. (2018). Light absorption by suspended particles in the Red Sea: effect of phytoplankton community size structure and pigment composition. J. Geophy. Res.: Oceans, 123 (2), 902-921. doi: 10.1002/2017JC013279.


Davies-Colley, R. J., & Nagels, J. W. (2008). Predicting light penetration into river waters. J. Geophy. Res.: Biogeosci., 113 (G3).


Ackleson, S. G. (1997). Diffuse attenuation in optically-shallow water: effects of bottom reflectance. In: Ocean Optics-XIII (Vol. 2963), pp. 326-330. SPIE.


Mishra, D. R., Narumalani, S., Rundquist, D., & Lawson, M. (2005). Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data. ISPRS J. Photogramm. Remote Sens., 60 (1), 48-64. doi: 10.1016/j.isprsjprs.2005.09.003.


Cao, B., Fang, Y., Gao, L., Hu, H., Jiang, Z., Sun, B., & Lou, L. (2021). An active-passive fusion strategy and accuracy evaluation for shallow water bathymetry based on ICESat-2 ATLAS laser point cloud and satellite remote sensing imagery. Int. J. Remote Sens., 42 (8), 2783-2806. doi: 10.1080/01431161.2020.1862441.


Thomas, N., Pertiwi, A. P., Traganos, D., Lagomasino, D., Poursanidis, D., Moreno, S., & Fatoyinbo, L. (2021). Space-borne cloud-native satellite-derived Bathymetry (SDB) models using ICESat-2 and sentinel-2. Geophy. Res. Lett., 48 (6), e2020GL092170. doi: 10.1029/2020GL092170.


Xie, C., Chen, P., Pan, D., Zhong, C., & Zhang, Z. (2021). Improved filtering of ICESat-2 lidar data for nearshore bathymetry estimation using sentinel-2 imagery. Remote Sens., 13 (21), 4303. doi: 10.3390/rs13214303.


Li, S., Wang, X. H., Ma, Y., & Yang, F. (2023). Satellite-Derived Bathymetry with Sediment Classification Using ICESat-2 and Multispectral Imagery: Case Studies in the South China Sea and Australia. Remote Sens., 15 (4), 1026. doi: 10.3390/rs15041026.


Eidam, E., Walker, C., Bisson, K., Paris, M., & Cooper, L., (2022). Novel application of ICESat-2 ATLAS data to determine coastal light attenuation as a proxy for suspended particulate matter. In: OCEANS 2022. Hampton Roads, VA, USA. pp. 1-7, doi: 10.1109/OCEANS47191.2022.9977084.


Zhang, X., Ma, Y., Li, Z., & Zhang, J. (2022). Satellite derived bathymetry based on ICESat-2 diffuse attenuation signal without prior information. Int. J. App. Earth Obs. Geoinform., 113, 102993. doi: 10.1016/j.jag.2022.102993.


Xie, H., Sun, Y., Liu, X., Xu, Q., Guo, Y., Liu, S.,... & Tong, X. (2021). Shore Zone Classification from ICESat-2 Data over Saint Lawrence Island. Mar. Geod., 44 (5), 454-466. doi: 10.1080/01490419.2021.1898498.

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