Evaluation of Landsat 8 imagery capability to estimate chlorophyll-a concentrations using spatially and temporally different data


  • Devy Risky Panji Wijaya Water Resources Engineering Department, Universitas BrawijayaMinistry of Villages, Development of Disadvantaged Regions, and Transmigration
  • Riyanto Haribowo
  • James E. Ball




water quality, chlorophyll-a, remote sensing, Landsat 8


Water quality has been one of the major issues in water resources. A water quality monitoring program should be performed regularly. However, this program requires numerous resources and efforts, especially using a direct measurement method. An alternative should be carried out to minimise the issues. Landsat 8 (L8) can be the alternative. Water clarity is one of the essential water parameters affecting sunlight’s ability to penetrate water and engage photosynthesis. Algae is vital in photosynthesis and usually indicated as chlorophyll-a (chl-a). Several studies present that L8 is adequate to estimate chl-a concentrations as it provides high-accuracy results. This paper will generate a new model using data from different places and compare it with other chl-a models from previous studies by their capabilities to estimate chl-a concentrations. The results indicate that the generated model cannot provide consistent and precise estimations in different places and times, although it has a “good” R2 value at 0.7245 from the regression analysis for model generation. The same results arise from other models that cannot reasonably estimate chl-a concentrations.


Asian Development Bank, Indonesia Country Water Assessment. Manila: Asian Development Bank, 2016.

M. Gholizadeh, A. Melesse, and L. Reddi, ‘A comprehensive review on water quality parameters estimation using remote sensing techniques’, Sensors, vol. 16, no. 8, p. 1298, Aug. 2016, doi: 10.3390/s16081298.

S. N. Topp, T. M. Pavelsky, D. Jensen, M. Simard, and M. R. V. Ross, ‘Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications’, Water, vol. 12, no. 1, p. 169, Jan. 2020, doi: 10.3390/w12010169.

H. Yang, J. Kong, H. Hu, Y. Du, M. Gao, and F. Chen, ‘A review of remote sensing for water quality retrieval: progress and challenges’, Remote Sens., vol. 14, no. 8, p. 1770, Apr. 2022, doi: 10.3390/rs14081770.

Q. Chen, M. Huang, K. Bai, and X. Li, ‘An optimal two bands ratio model to monitor chlorophyll-a in urban lake using Landsat 8 data’, E3S Web Conf., vol. 143, p. 02003, 2020, doi: 10.1051/e3sconf/202014302003.

S. Dong, H. He, B. Fu, D. Fan, and T. Wang, ‘Remote sensing retrieval of chlorophyll-a concentration in the coastal waters of Hong Kong based on Landsat-8 OLI and Sentinel-2 MSI sensors’, IOP Conf. Ser. Earth Environ. Sci., vol. 671, no. 1, p. 012033, Feb. 2021, doi: 10.1088/1755-1315/671/1/012033.

J. Lim and M. Choi, ‘Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea’, Environ. Monit. Assess., vol. 187, no. 6, p. 384, Jun. 2015, doi: 10.1007/s10661-015-4616-1.

L. G. Olmanson, P. L. Brezonik, J. C. Finlay, and M. E. Bauer, ‘Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes’, Remote Sens. Environ., vol. 185, pp. 119–128, Nov. 2016, doi: 10.1016/j.rse.2016.01.007.

Y. O. Ouma, K. Noor, and K. Herbert, ‘Modelling reservoir chlorophyll-a, TSS, and turbidity using Sentinel-2A MSI and Landsat-8 OLI satellite sensors with empirical multivariate regression’, J. Sens., vol. 2020, pp. 1–21, Sep. 2020, doi: 10.1155/2020/8858408.

J. A. Urbanski et al., ‘Application of Landsat 8 imagery to regional-scale assessment of lake water quality’, Int. J. Appl. Earth Obs. Geoinformation, vol. 51, pp. 28–36, Sep. 2016, doi: 10.1016/j.jag.2016.04.004.

A. P. Estigade et al., ‘Physical waters suitability for floating net cages cultivation mapping using Landsat 8 OLI and Worldview-2 imageries in part of Hurun Bay, Lampung Province, Indonesia’, IOP Conf. Ser. Earth Environ. Sci., vol. 169, p. 012070, Jul. 2018, doi: 10.1088/1755-1315/169/1/012070.

R. C. Trinh et al., ‘Application of Landsat 8 for monitoring impacts of wastewater discharge on coastal water quality’, Front. Mar. Sci., vol. 4, p. 329, Oct. 2017, doi: 10.3389/fmars.2017.00329.

W. G. Buma and S. I. Lee, ‘Evaluation of Sentinel-2 and Landsat 8 Images for estimating chlorophyll-a concentrations in Lake Chad, Africa’, Remote Sens., vol. 12, no. 15, p. 2437, Jul. 2020, doi: 10.3390/rs12152437.

A. S. Mansaray, A. R. Dzialowski, M. E. Martin, K. L. Wagner, H. Gholizadeh, and S. H. Stoodley, ‘Comparing PlanetScope to Landsat-8 and Sentinel-2 for sensing water quality in reservoirs in agricultural watersheds’, Remote Sens., vol. 13, no. 9, p. 1847, May 2021, doi: 10.3390/rs13091847.

A. H. Baktiar, A. P. Wijaya, and A. Sukmono, ‘Analsis kesuburan dan pencemaran air berdasarkan kandungan klorofil-a dan konsentrasi Total Suspended Solid secara multitemporal di Muara Banjir Kanal Timur [Multitemporal water fertility and pollution based on chlorophyll-a and Total Suspended Solid concentration in Muara Banjir Kanal Timur]’, J. Geod. Undip, vol. 5, no. 4, Art. no. 4, Nov. 2016.

R. Dewi, M. Zainuri, S. Anggoro, T. Winanto, and H. Endrawati, ‘Spatio-temporal distribution of chlorophyll-a using multitemporal Landsat image and ground check in Segara Anakan Lagoon’, E3S Web Conf., vol. 47, p. 03007, 2018, doi: 10.1051/e3sconf/20184703007.

B. Hamuna and L. Dimara, ‘Pendugaan konsentrasi klorofil-a dari citra satelit Landsat 8 di perairan kota Jayapura [Estimation of chlorophyll-a from Landsat 8 images in the waterbody around Jayapura city]’, Maspari J., vol. 9, no. 2, pp. 139–148, Jul. 2017.

T. Hariyanto, T. C. Krisna, K. Khomsin, C. B. Pribadi, and N. Anwar, ‘Developing of total suspended sediment model Using Landsat-8 satellite image and in-situ data at the Surabaya Coast, East Java, Indonesia’, Indones. J. Geogr., vol. 49, no. 1, p. 73, Jul. 2017, doi: 10.22146/ijg.12010.

J. Chen, W.-N. Zhu, Y. Q. Tian, and Q. Yu, ‘Estimation of colored dissolved organic matter from Landsat-8 imagery for complex inland water: case study of Lake Huron’, IEEE Trans. Geosci. Remote Sens., vol. 55, no. 4, pp. 2201–2212, Apr. 2017, doi: 10.1109/TGRS.2016.2638828.

L. A. Karondia and L. M. Jaelani, ‘Validasi algoritma estimasi Total Suspended Solid dan Chl-a pada citra satelit Aqua MODIS dan Terra MODIS dengan data in situ (Studi kasus : Laut Utara Pulau Jawa) [Validation of the estimation algorithm of Total Suspended Solid and Chl-a on Aqua MODIS and Terra MODIS satellite images with in situ data (Case study: North Sea of Java Island)]’, Geoid, vol. 11, no. 1, p. 46, Aug. 2015, doi: 10.12962/j24423998.v11i1.1095.

L. Kristianingsih, A. P. Wijaya, and A. Sukmono, ‘Analisis pengaruh koreksi atmosfer terhadap estimasi kandungan klorofil-a menggunakan citra Landsat 8 [Analysis of the effect of atmospheric correction on the estimation of chlorophyll-a content using Landsat 8 imagery]’, J. Geod. Undip, vol. 5, no. 4, Art. no. 4, Nov. 2016.

S. Subiyanto, Z. Ramadhanis, and A. Hafidh Baktiar, ‘Integration of remote sensing technology using Sentinel-2A satellite images for fertilization and water pollution analysis in estuaries inlet of Semarang Eastern Flood Canal’, E3S Web Conf., vol. 31, p. 12008, 2018, doi: 10.1051/e3sconf/20183112008.

H. Wibisana, S. Zainab, and A. Dara K., ‘Optimalisation of remote sensing algorithm in mapping of chlorophyl-a concentration at Pasuruan coastal based on surface reflectance images of Aqua Modis’, J. Phys. Conf. Ser., vol. 953, p. 012224, Jan. 2018, doi: 10.1088/1742-6596/953/1/012224.

M. Azharuddin, I. Usman, and Nurgiantoro, ‘Studi perbandingan pemodelan algoritma Chl-a menggunakan data Citra L8 Dan S2b [A comparison study algorithmic modelling using L8 and S2b images]’, JAGAT J. Geogr. Apl. Dan Teknol., vol. 4, no. 1, pp. 25–38, Apr. 2020, doi: 10.5281/ZENODO.3871265.

K. C. Paramita and R. H. Jatmiko, ‘Aplikasi citra Landsat-8 OLI/TIRS untuk identifikasi status trofik di Rawapening, Kabupaten Semarang, Propinsi Jawa Tengah [Application of Landsat-8 OLI/TIRS imagery to identify trophic status in Rawapening, Semarang Regency, Central Java Province]’, J. Bumi Indones., vol. 3, no. 4, 2014.

D. Helder et al., ‘Observations and recommendations for the calibration of Landsat 8 OLI and Sentinel 2 MSI for improved data interoperability’, Remote Sens., vol. 10, no. 9, p. 1340, Aug. 2018, doi: 10.3390/rs10091340.

E. Knight and G. Kvaran, ‘Landsat-8 operational land imager design, characterization and performance’, Remote Sens., vol. 6, no. 11, pp. 10286–10305, Oct. 2014, doi: 10.3390/rs61110286.

U.S. Geological Survey, Landsat 8 (L8) Data Users Handbook Version 5.0. South Dakota: Department of the Interior, U.S. Geological Survey, 2019.

P. Sedgwick, ‘A comparison of parametric and non-parametric statistical tests’, BMJ, vol. 350, no. apr17 1, pp. h2053–h2053, Apr. 2015, doi: 10.1136/bmj.h2053.

D. Sheskin, Handbook of parametric and nonparametric statistical procedures, 3rd ed. Boca Raton: Chapman & Hall/CRC, 2004.

E. Van Buren and A. H. Herring, ‘To be parametric or non‐parametric, that is the question: Parametric and non‐parametric statistical tests’, BJOG Int. J. Obstet. Gynaecol., vol. 127, no. 5, pp. 549–550, Apr. 2020, doi: 10.1111/1471-0528.15545.

Z. Y. Avdan, G. Kaplan, S. Goncu, and U. Avdan, ‘Monitoring the water quality of small water bodies using high-resolution remote sensing data’, ISPRS Int. J. Geo-Inf., vol. 8, no. 12, p. 553, Dec. 2019, doi: 10.3390/ijgi8120553.

M. Modiegi, I. T. Rampedi, and S. G. Tesfamichael, ‘Comparison of multi-source satellite data for quantifying water quality parameters in a mining environment’, J. Hydrol., vol. 591, p. 125322, Dec. 2020, doi: 10.1016/j.jhydrol.2020.125322.

K. Mosimanegape, ‘Integration of physicochemical assessment of water quality with remote sensing techniques for the Dikgathong Dam in Botswana’, Master’s thesis, University of Zimbabwe, Harare, 2016.

J. Schmuller, Statistical analysis with R for dummies. Hoboken, NJ: John Wiley & Sons, Inc, 2017.

N. J. Horton and K. Kleinman, Using R and RStudio for data management, statistical analysis, and graphics, Second edition. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

P. Schober, C. Boer, and L. A. Schwarte, ‘Correlation coefficients: Appropriate use and interpretation’, Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, May 2018, doi: 10.1213/ANE.0000000000002864.

N. Torbick, S. Hession, S. Hagen, N. Wiangwang, B. Becker, and J. Qi, ‘Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery’, Int. J. Remote Sens., vol. 34, no. 21, pp. 7607–7624, Nov. 2013, doi: 10.1080/01431161.2013.822602.

M. Nuzapril, S. B. Susilo, and J. P. Panjaitan, ‘Estimasi produktivitas primer perairan berdasarkan konsentrasi klorofil-a yang diekstrak dari citra satelit Landsat-8 di perairan Kepulauan Karimun Jawa [Estimated primary productivity of waters based on the concentration of chlorophyll-a extracted from Landsat-8 satellite imagery in the waters of the Karimun Jawa Islands]’, J. Penginderaan Jauh Dan Pengolah. Data Citra Digit., vol. 14, no. 1, Sep. 2017, doi: 10.30536/j.pjpdcd.2017.v14.a2548.

D. N. Moriasi, M. W. Gitau, N. Pai, and P. Daggupati, ‘Hydrologic and water quality models: performance measures and evaluation criteria’, Am. Soc. Agric. Biol. Eng., vol. 58, no. 6, pp. 1763–1785, Dec. 2015, doi: 10.13031/trans.58.10715.

S. Chatterjee and A. S. Hadi, Regression analysis by example, 5th ed. Hoboken, New Jersey: Wiley, 2012.




How to Cite

D. R. P. Wijaya, R. Haribowo, and J. E. Ball, “Evaluation of Landsat 8 imagery capability to estimate chlorophyll-a concentrations using spatially and temporally different data”, CIVENSE, vol. 6, no. 1, pp. 15–23, Apr. 2023.