Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 22, 2025
Номер 1
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 5, pp. 50-68

Analysis and verification of turbidity and suspended solids concentration determination algorithms implemented in the ACOLITE software package

P.D. Zhadanova 1 , K.R. Nazirova 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 14.10.2023
DOI: 10.21046/2070-7401-2023-20-5-50-68
Obtaining quantitative information on physical and hydro-optical parameters of the marine environment based on remotely sensed data is one of the urgent tasks of satellite oceanology. The paper presents a detailed review of modern Nechad and Dogliotti algorithms after application of ACOLITE DSF atmospheric correction. The methodology of calculations of seawater optical parameters using the above mentioned algorithms is detailed and the experience of using various standard satellite algorithms in the coastal zones of the Caspian and Black seas is described. Examples of calculations of suspended solids concentration and turbidity of seawater in test estuaries are given. The aim of the work was to verify the algorithms for calculating suspended sediment concentration and seawater turbidity in test areas using the results of quasi-synchronous in situ measurements. It was found that the use of the standard Nechad algorithm for calculating seawater turbidity gives high correlation with the results of sub-satellite measurements in the Mzymta river mouth areas at turbidity values <50 NTU. The possibility of using the Dogliotti algorithm in different geographical areas under the condition of high turbidity of coastal waters ≥100 NTU is shown. The results obtained can serve as a methodological recommendation for the use of the algorithms considered in this paper in the coastal areas of the Black and Caspian seas.
Keywords: water turbidity, suspended sediment concentration, in situ measurements, ACOLITE, Sentinel 2 MSI, Landsat-8, -9 OLI/TIRS, Caspian Sea, Terek, Sulak, Black Sea, Mzymta
Full text

References:

  1. Dzhaoshvili S., Rivers of the Black Sea, Technical Report No. 71, European Environment Agency, 2002, 58 p. (in Russian).
  2. Drozhzhina K. V., Features of the climatic conditions of the Mzymta river basin for recreational activities, Young Scientist, 2013, Vol. 5, pp. 196–198 (in Russian).
  3. Zav’yalov P. O., Makkaveev P. N., Konovalov B. V. et al., Hydrophysical and hydrochemical characteristics of the sea areas adjacent to the estuaries of small rivers of the Russian coast of the Black Sea, Oceanology, 2014, Vol. 54, No. 3, pp. 265–280, DOI: 10.1134/S0001437014030151.
  4. Lavrova O. Yu., Mityagina M. I., Kostianoy A. G., Sputnikovye metody vyyavleniya i monitoringa zon ekologicheskogo riska morskikh akvatorii (Satellite Methods for Detecting and Monitoring Marine Zones of Ecological Risk), Moscow: IKI RAN, 2016, 334 p. (in Russian).
  5. Lavrova O. Yu., Nazirova K. R., Alferyeva Ya. O. et al, Comparison of plume parameters of the Sulak and Terek rivers based on satellite data and in situ measurements, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 264–283 (in Russian), DOI: 10.21046/2070-7401-2022-19-5-264-283.
  6. Nazirova K. R., Lavrova O. Yu., Krayushkin E. V. et al., Features of river plume parameter determination by in situ and remote sensing methods, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 227–243 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-227-243.
  7. Barreneche J. M., Guigou B., Gallego F. et al., Monitoring Uruguay’s freshwaters from space: An assessment of different satellite image processing schemes for chlorophyll-a estimation, Remote Sensing Applications: Society and Environment, 2023, Vol. 29, Article 100891, https://doi.org/10.1016/j.rsase.2022.100891.
  8. Dogliotti A. I., Ruddick K. G., Nechad B. et al., A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters, Remote Sensing of Environment, 2015, Vol. 156, pp. 157–168, https://doi.org/10.1016/j.rse.2014.09.020.
  9. Doxaran D., Froidefond J. M., Castaing P., Babin M., Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data, Estuarine, Coastal and Shelf Science, 2009, Vol. 81, No. 3, pp. 321–332, DOI: 10.1016/j.ecss.2008.11.013.
  10. Kopelevich O. V., Sheberstov S. V., Burenkov V. I. et al., Assessment of underwater irradiance and absorption of solar radiation at water column from satellite data, Current Research on Remote Sensing, Laser Probing, and Imagery in Natural Waters: Proc. SPIE, 2007, Vol. 6615, pp. 56–66, https://doi.org/10.1117/12.740441.
  11. Kuhn C., de Matos Valerio A., Ward N. et al., Performance of Landsat-8 and Sentinel 2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity, Remote Sensing of Environment, 2019, Vol. 224, pp. 104–118, https://doi.org/10.1016/j.rse.2019.01.023.
  12. Maciel F. P., Pedocchi F., Evaluation of ACOLITE atmospheric correction methods for Landsat-8 and Sentinel 2 in the Río de la Plata turbid coastal waters, Intern. J. Remote Sensing, 2021, Vol. 43, pp. 215–240, https://doi.org/10.1080/01431161.2021.2009149.
  13. Nazirova K., Alferyeva Y., Lavrova O. et al., Comparison of in situ and remote-sensing methods to determine turbidity and concentration of suspended matter in the estuary zone of the Mzymta river, Black Sea, Remote Sensing, 2021, Vol. 13, No. 1, Article 143, https://doi.org/10.3390/rs13010143.
  14. Nechad B., Ruddick K. G., Neukermans G., Calibration and validation of a generic multisensor algorithm for mapping of turbidity in coastal waters, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions, 2009, Vol. 7473, pp. 161–171, https://doi.org/10.1117/12.830700.
  15. Nechad B., Ruddick K. G., Park Y., Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters, Remote Sensing of Environment, 2010, Vol. 114(4), pp. 854–866, https://doi.org/10.1016/j.rse.2009.11.022.
  16. Nechad B., Ruddick K., Schroeder T. et al., Coastcolour round robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters, Earth System Science Data, 2015, Vol. 7, No. 7, pp. 319–348, https://doi.org/10.5194/essd-7-319-2015.
  17. Vanhellemont Q. (2019a), Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel 2 archives, Remote Sensing of Environment, 2019, Vol. 225, pp. 75–192, https://doi.org/10.1016/j.rse.2019.03.010.
  18. Vanhellemont Q. (2019b), Daily metre-scale mapping of water turbidity using CubeSat imagery, Optics Express, 2019, Vol. 27, pp. A1372–A1399, https://doi.org/10.1364/OE.27.0A1372.
  19. Vanhellemont Q., Sensitivity analysis of the dark spectrum fitting atmospheric correction for metre- and decametre-scale satellite imagery using autonomous hyperspectral radiometry, Optics Express, 2020, Vol. 28, pp. 29948–29965, https://doi.org/10.1364/OE.397456.
  20. Vanhellemont Q., Ruddick K., Turbid wakes associated with offshore wind turbines observed with Landsat 8, Remote Sensing of Environment, 2014, Vol. 145, pp. 105–115, https://doi.org/10.1016/j.rse.2014.01.009.
  21. Vanhellemont Q., Ruddick K., Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8, Remote Sensing of Environment, 2015, Vol. 161, pp. 89–106, https://doi.org/10.1016/j.rse.2015.02.007.
  22. Vanhellemont Q., Ruddick K., Acolite for Sentinel 2: Aquatic applications of MSI imagery, Proc. 2016 ESA Living Planet Symp., Prague, Czech Republic, 2016, pp. 9–13.
  23. Vanhellemont Q., Ruddick K., Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications, Remote Sensing of Environment, 2018, Vol. 216, pp. 586–597, https://doi.org/10.1016/j.rse.2018.07.015.
  24. Vanhellemont Q., Ruddick K., Atmospheric correction of Sentinel 3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters, Remote Sensing of Environment, 2021, Vol. 256, Article 112284, https://doi.org/10.1016/j.rse.2021.112284.
  25. Wang J., Lee Z., Wang D. et al., Atmospheric correction over coastal waters with aerosol properties constrained by multi-pixel observations, Remote Sensing of Environment, 2021, Vol. 265, https://doi.org/10.1016/j.rse.2021.112633.