Satellite-Based Chlorophyll-a Analysis of River Tapi: An Effective Water Quality Management tool with Landsat-8 OLI and Acolite Software

Document Type : Original Research Paper


Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, P.O. Box 395007, Surat, India


Most pollutants found in rivers come from the discharge of raw sewage from both point and nonpoint sources. So, monitoring the pollution levels in surface water sources is essential. River pollution monitoring is a real challenge. Using remote sensing, precise outcomes can be achieved with the help of the selection of the right combination of satellite images and algorithms. Generally, established available algorithms are site-specific, indicating that they may not work at all areas on Earth's surface due to differences in altitude, cloud cover, and sun glint. The present work determined Chlorophyll-a concentrations in the Tapi River at various locations using Landsat-8 satellite images and Acolite software from 2017 to 2021 Period. The outcomes reveal that applying the dark spectrum fitting with sun glint correction when processing Landsat-8 satellite images is needed. In the present study, water quality results were obtained very precisely for the months of January, February, November, and December after processing and analysing satellite images. Due to factors such as sun glare, cloud cover, cloud shadow, and haze, the desired effect could not be achieved in the remaining months of the study period. This research provides a solid foundation for estimating the impact of eutrophication in the water body by estimating chlorophyll-a concentration from satellite images.


Main Subjects

Aswathy, T. S., Achu, A. L., Francis, S., Gopinath, G., Joseph, S., Surendran, U., & Sunil, P. S. (2021). Assessment of water quality in a tropical ramsar wetland of southern India in the wake of COVID-19. Remote Sensing Applications: Society and Environment, 23(August), 100604.
Bennett, M. G., Lee, S. S., Schofield, K. A., Ridley, C. E., Washington, B. J., & Gibbs, D. A. (2021). Response of chlorophyll a to total nitrogen and total phosphorus concentrations in lotic ecosystems: a systematic review. Environmental Evidence, 10(1), 1–25.
Bernardo, N., Watanabe, F., Rodrigues, T., & Alcântara, E. (2017). Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image. Advances in Space Research, 59(9), 2335–2348.
Bhattacharjee, R., Gupta, A., Das, N., Agnihotri, A. K., Ohri, A., & Gaur, S. (2022). Analysis of algal bloom intensification in mid-Ganga river, India, using satellite data and neural network techniques. Environmental Monitoring and Assessment, 194(8), 547.
Boucher, J., Weathers, K. C., Norouzi, H., & Steele, B. (2018). Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring. Ecological Applications, 28(4), 1044–1054.
Bowes, M. J., Read, D. S., Joshi, H., Sinha, R., Ansari, A., Hazra, M., Simon, M., Vishwakarma, R., Armstrong, L. K., Nicholls, D. J. E., Wickham, H. D., Ward, J., Carvalho, L. R., & Rees, H. G. (2020). Nutrient and microbial water quality of the upper Ganga River, India: identification of pollution sources. Environmental Monitoring and Assessment, 192(8).
Bresciani, M., Cazzaniga, I., Austoni, M., Sforzi, T., Buzzi, F., Morabito, G., & Giardino, C. (2018). Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8. Hydrobiologia, 824(1), 197–214.
Bu, J., Cai, L., Yan, X., Xu, H., Hu, H., & Jiang, J. (2022). Monitoring the Chl-a Distribution Details in the Yangtze River Mouth Using Satellite Remote Sensing. Water (Switzerland), 14(8).
Chander, S., Pompapathi, V., Gujrati, A., Singh, R. P., Chaplot, N., & Patel, U. D. (2018). Growth of invasive aquatic macrophytes over Tapi river. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(5), 829–833.
Cheng, C., Wei, Y., Lv, G., & Yuan, Z. (2013). Remote estimation of chlorophyll-a concentration in turbid water using a spectral index: a case study in Taihu Lake, China. Journal of Applied Remote Sensing, 7(1), 073465.
De Keukelaere, L., Sterckx, S., Adriaensen, S., Knaeps, E., Reusen, I., Giardino, C., Bresciani, M., Hunter, P., Neil, C., Van der Zande, D., & Vaiciute, D. (2018). Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters. European Journal of Remote Sensing, 51(1), 525–542.
Filstrup, C. T., & Downing, J. A. (2017). Relationship of chlorophyll to phosphorus and nitrogen in nutrient-rich lakes. Inland Waters, 7(4), 385–400.
Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. In Sensors (Switzerland) (Vol. 16, Issue 8). MDPI AG.
Guo, Q., Wu, X., Bing, Q., Pan, Y., Wang, Z., Fu, Y., Wang, D., & Liu, J. (2016). Study on retrieval of chlorophyll-a concentration based on Landsat OLI Imagery in the Haihe River, China. Sustainability (Switzerland), 8(8).
Ha, N. T. T., Koike, K., Nhuan, M. T., Canh, B. D., Thao, N. T. P., & Parsons, M. (2017). Landsat 8/OLI Two bands ratio algorithm for chlorophyll-a concentration mapping in hypertrophic waters: An application to west lake in Hanoi (Vietnam). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(11), 4919–4929.
Ha, N. T. T., Thao, N. T. P., Koike, K., & Nhuan, M. T. (2017). Selecting the best band ratio to estimate chlorophyll-a concentration in a tropical freshwater lake using sentinel 2A images from a case study of Lake Ba Be (Northern Vietnam). ISPRS International Journal of Geo-Information, 6(9).
Ilori, C. O., Pahlevan, N., & Knudby, A. (2019). Analyzing performances of different atmospheric correction techniques for Landsat 8: Application for coastal remote sensing. In Remote Sensing (Vol. 11, Issue 4).
Jang, M. T. G., Alcântara, E., Rodrigues, T., Park, E., Ogashawara, I., & Marengo, J. A. (2022). Increased chlorophyll-a concentration in Barra Bonita reservoir during extreme drought periods. The Science of the Total Environment, 843(June), 157106.
Jeong, K. S., Kim, D. K., Shin, H. S., Kim, H. W., Cao, H., Jang, M. H., & Joo, G. J. (2010). Flow regulation for water quality (chlorophyll a) improvement. International Journal of Environmental Research, 4(4), 713–724.
Kärcher, O., Filstrup, C. T., Brauns, M., Tasevska, O., Patceva, S., Hellwig, N., Walz, A., Frank, K., & Markovic, D. (2020). Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions. Inland Waters, 10(1), 29–41.
Lantzanakis, G., Mitraka, Z., & Chrysoulakis, N. (2016). Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 9688(August), 96880A.
Le, C., Hu, C., Cannizzaro, J., English, D., Muller-Karger, F., & Lee, Z. (2013). Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. Remote Sensing of Environment, 129, 75–89.
Liu, S., Glamore, W., Tamburic, B., Morrow, A., & Johnson, F. (2022). Remote sensing to detect harmful algal blooms in inland waterbodies. Science of the Total Environment, 851(May), 158096.
Maciel, F. P., & Pedocchi, F. (2022). Evaluation of ACOLITE atmospheric correction methods for Landsat-8 and Sentinel-2 in the Río de la Plata turbid coastal waters. In International Journal of Remote Sensing (Vol. 43, Issue 1, pp. 215–240).
Mishra, S., & Mishra, D. R. (2012). Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment, 117, 394–406.
Moses, W. J., Gitelson, A. A., Berdnikov, S., & Povazhnyy, V. (2009). Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data - Successes and challenges. Environmental Research Letters, 4(4).
Naderahmadian, A., Eftekhari-Sis, B., Jafari, H., Zirak, M., Padervand, M., Mahmoudi, G., & Samadi, M. (2023). Cellulose nanofibers decorated with SiO2 nanoparticles: Green adsorbents for removal of cationic and anionic dyes; kinetics, isotherms, and thermodynamic studies. International Journal of Biological Macromolecules, 247(July), 125753.
Padervand, M., Ghasemi, S., Hajiahmadi, S., & Wang, C. (2021). K4Nb6O17/Fe3N/α-Fe2O3/C3N4 as an enhanced visible light-driven quaternary photocatalyst for acetamiprid photodegradation, CO2 reduction, and cancer cells treatment. Applied Surface Science, 544(December 2020).
Padervand, M., Lichtfouse, E., Robert, D., & Wang, C. (2020). Removal of microplastics from the environment. A review. Environmental Chemistry Letters, 18(3), 807–828.
Padervand, M., Rhimi, B., & Wang, C. (2021). One-pot synthesis of novel ternary Fe3N/Fe2O3/C3N4 photocatalyst for efficient removal of rhodamine B and CO2 reduction. Journal of Alloys and Compounds, 852.
Prasad, S., Saluja, R., & Garg, J. K. (2020). Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River, India. International Journal of Remote Sensing, 41(7), 2439–2456.
Premkumar, R., Venkatachalapathy, R., & Visweswaran, S. (2021). Materials Today : Proceedings Bio-optical studies on chlorophyll-a concentration in Hooghly River , India. Materials Today: Proceedings, 47, 488–492.
Stow, C. A., & Cha, Y. (2013). Are chlorophyll a -total phosphorus correlations useful for inference and prediction? Environmental Science and Technology, 47(8), 3768–3773.
Timbadiya, P. V, Patel, P. L., & Porey, P. D. (2011). Calibration of HEC-RAS Model on Prediction of Flood for Lower Tapi River , India. 2011(November), 805–811.
Vanhellemont, Q. (2019). Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives. Remote Sensing of Environment, 225(October 2018), 175–192.
Vanhellemont, Q. (2020). Sensitivity analysis of the dark spectrum fitting atmospheric correction for metre- and decametre-scale satellite imagery using autonomous hyperspectral radiometry. Optics Express, 28(20), 29948.
Vanhellemont, Q., & Ruddick, K. (2018). Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications. Remote Sensing of Environment, 216(July), 586–597.
Waghwala, R. K., & Agnihotri, P. G. (2021). Impact assessment of urbanization on flood risk and integrated flood management approach: a case study of Surat city and its surrounding region. ISH Journal of Hydraulic Engineering, 27(S1), 577–587.