Comparison of Landsat-8 and Sentinel-2 Satellite Images to Estimate the Amount of Chlorophyll-a in Zaribar Lake

Document Type : Original Article

Authors

1 Department of Water Engineering, Faculty of Agriculture, University of Bu-Ali Sina, Hamedan, Iran

2 Department of Remote Sensing and GIS, Faculty of Earth Sciences, University of Shahid Beheshti, Tehran, Iran

3 Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Technology, Isfahan, Iran

4 Department of Water Engineering, Faculty of Agriculture, University of Kurdestan, Sanandaj, Iran

Abstract

Introduction: Population growth and pollution caused by the discharge of all types of municipal, industrial, agricultural sewage, and waste disposal valves have caused the spread of pollution and the limitation of water resources. Surface water sources such as seas, lakes, rivers, and reservoirs of dams are more exposed to pollution than underground water sources. This pollution leads to the increase of nutrients and the blooming of algae and their consequences, such as the increase of chlorophyll-a, change in dissolved oxygen, and ultimately the reduction of water quality. Considering the close relationship between water quality and environmental health and quality of life, it is necessary to monitor the quality of surface water. By monitoring the changes in water quality, it is possible to observe, evaluate, and correct the long-term trends of water quality reduction and also predict its quality changes for the future. Due to the fact that the traditional methods of water quality evaluation are time-consuming, risky, and expensive, experts use remote sensing images to control water quality.
Material and Methods: In this research, the chlorophyll-a of Zaribar Lake was investigated from Landsat-8 and Sentinel-2 satellite images in 2019 using the Google Earth Engine platform. For this purpose, the water body of ​​the lake was separated from the non-water body using the NDWI index. Then, four spectral indices 2DBA, 3DBA, NDCI, and FLH-Violet were applied on the separated water body from satellite images. Finally, the predicted amount of chlorophyll-a was compared with the actual amount of chlorophyll-a on the ground in order to select the most suitable spectral index and satellite image to estimate the concentration of chlorophyll-a.
Results and Discussion: The results obtained from the comparison of spectral indices showed that 2DBA and NDCI indices are more accurate than 3DBA and FLH-Violet indices in both satellite images and were able to predict the chlorophyll-a concentration well. Therefore, 2DBA and NDCI indices were considered the most efficient indices to evaluate the chlorophyll-a concentration. Also, the amount of R2 obtained from 2BDA and NDCI indices in Landsat-8 and Sentinel-2 satellite images were compared to determine which satellite image is able to estimate the concentration of chlorophyll-a with higher accuracy. The results indicated that the amount of R2 in Sentinel-2 images was 2DBA=0.799 and NDCI=0.794 and in Landsat-8 was 2DBA=0.156 and NDCI=0.125. Therefore, Sentinel-2 was able to predict the concentration of chlorophyll-a more accurately than Landsat-8. This is due to the larger size of Landsat-8 cells compared to Sentinel-2, which can make the detection of chlorophyll-a a challenge in small areas. In addition, there was a one-day time interval between ground sampling and the date of Landsat-8 image collection, when the movement of chlorophyll-a concentration had occurred temporally and spatially, on the surface and in the depth of the lake. However, the ground sampling and the taking of Sentinil-2 images were simultaneous and within the same day.
Conclusion: Based on the obtained results, it can be concluded that the use of 2BDA and NDCI indices compared to other indices for small areas in Sentinel-2 images provided higher accuracy than Landsat-8 images. The most important reason is the smaller size of cells in Sentinel-2 images. In order to more accurately evaluate the concentration of chlorophyll-a, the lake must be monitored in a time series and different seasons, because a large volume of water flows from the bottom of the lake through rivers and boiling springs every day, on which the concentration of chlorophyll-a depends; Therefore, the concentration of chlorophyll-a in the lake should be evaluated in low water and high water conditions in order to determine its polluting sources, which unfortunately was not addressed in this research due to the lack of sampling.

Keywords

References

Arheimer, B., Torstensson, G. and Wittgren, H.B., 2004. Landscape planning to reduce coastal eutrophication: agricultural practices and constructed wetlands. Landscape and Urban Planning. 67(1-4), 205-215. https://doi.org/10.1016/S0169-2046(03)00040-9.
Asaeda, T., Trung, V.K., Manatunge, J. and Van Bon, T., 2001. Modelling macrophyte–nutrient–phytoplankton interactions in shallow eutrophic lakes and the evaluation of environmental impacts. Ecological Engineering. 16(3), 341-357. https://doi.org/10.1016/S0925-8574(00)00120-8.
Beck, R., Shengan Zhan, Hongxing Liu, Susanna Tong, Bo Yang, Min Xu, Zhaoxia Ye, Yan Huang, Song Shu, Qiusheng Wu, Shujie Wang, Kevin Berling, Andrew Murray, Erich Emery, Molly Reif, Joseph Harwood, Jade Young, Christopher Nietch, Dana Macke, Mark Martin, Garrett Stillings, Richard Stump, Haibin Su., 2016. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations. Remote Sensing of Environment. 178, 15-30. https://doi.org/10.1016/j.rse.2016.03.002.
Buma, W. G., and Lee, S. I., 2020. Evaluation of sentinel-2 and landsat 8 images for estimating chlorophyll-a concentrations in lake Chad, Africa. Remote Sensing, 12(15), 2437. 10.3390/rs12152437.
Dodds, W. K., 2003. Misuse of inorganic N and soluble reactive P concentrations to indicate nutrient status of surface waters. Journal of the North American Benthological Society, 22(2), 171-181. https://doi.org/10.2307/1467990.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031.
Karimi, B., Hashemi, S. H., and Aghighi, H. 2022., Performance of Sentinel-2 and Landsat-8 satellites in estimating Chlorophyll-a concentration in a shallow freshwater lake. https://doi.org/10.21203/rs.3.rs-1968542/v1.
Kenarkoohi, M., Ahmadi Nadoushan, M., and Abolhasani, M. H., 2020. Estimation of Chlorophyll-A Concentration in Choghakhor wetland using remote sensing and in-situ measurements. Journal of Animal Environment, 12(3), 475-486. https://www.magiran.com/p2214207.
Khan, F. A., and Ansari, A. A. 2005., Eutrophication: an ecological vision. The botanical review, 71(4), 449-482. http://dx.doi.org/10.1663/0006-8101(2005)071[0449:EAEV]2.0.CO;2.
Koelmans, A.A., Van der Heijde, A., Knijff, L.M., Aalderink, RH., 2001. Integrated Modelling of Eutrophication and Organic Contaminant Fate & Effects in Aquatic Ecosystems, A Review. 10.1016/s0043-1354(01)00095-1.
Nyenje, P.M., Foppen, J.W., Uhlenbrook, S., Kulabako, R, Muwanga, A., 2010. utrophication and nutrient release in urban areas of sub-Saharan Africa - A review. Science of the Total Environment. 408: 447-455. 10.1016/j.scitotenv.2009.10.020.
Padisak, J., Borics, G.A., Feh'er, G., Grigorszky, I.A., Oldal, I., Schmidt, A., Zambone-Doma, Z., 2003. Dominant species, functional assemblages and frequency of equilibrium phases in late summer
phytoplankton assemblages in Hungarian small shallow lakes. Hydrobiologia. 502: 157-168. 10.1023/B:HYDR.0000004278.10887.40.
Samadi M.T, Saghi M.H, Rahmani A.R, Torabzadeh H., 2009. Zoning of Water Quality of Hamadan Darreh-Morad Beyg River Based on NSFWQI Index Using Geographic Information System. Journal of Hamadan University of Medical Sciences; 16(3): 8- 43. http://sjh.umsha.ac.ir/article-1-309-fa.html.
Simeonov V, J.A. Stratis, C. Samara, G. Zachariadis, D. Voutsa, A. Anthemidis, M. Sofoniou, Th. Kouimtzis., 2003. Assessment of the surface water quality in Northern Greece,Water Res; 37: 4119–4124. https://doi.org/10.1016/S0043-1354(03)00398-1.
Suthers, I., Rissik, D., and Richardson, A. (Eds.)., 2019. Plankton: A guide to their ecology and monitoring for water quality. CSIRO publishing. https://doi.org/10.1093/plankt/fbp102.
Schippers, P., Van de Weerd, H., De Klein, J., De Jong, B., and Scheffer, M., 2006. Impacts of agricultural phosphorus use in catchments on shallow lake water quality: about buffers, time delays and equilibria. Science of the Total Environment, 369(1-3), 280-294. https://doi.org/10.1016/j.scitotenv.2006.04.028.
Taheri, A., Serajian, M. R., Ghashghaie, M., and Weysi, K. 2018. Estimation of Chlorophyll-a Concentration Using Remote Sensing Images. Iranian Journal of Soil and Water Research, 49(1), 39-50. doi: 10.22059/ijswr.2018.208586.667472.
Valizadeh Kamran, Khalil & Atazadeh, Ehsan and Mahdavifard, Mostafa,. 2020. Estimation of chlorophyll-a concentration using ground data and Sentinel-2 and Landsat-8 Satellite images processing (Case study: Tiab Estuary). Journal of RS and GIS for Natural Resources,1,72-83. 10.30495/girs.2020.672377.
Wang, X.1., Lu, Y.1., He, G.Z., Han, J.Y., Wang, T.Y. 2007. Exploration of relationships between phytoplankton biomass and related environmental variables using multivariate statistic analysis in a eutrophic shallow lake: A 5-year study. Journal of Environmental Sciences. 19: 920-927. https://doi.org/10.1016/S1001-0742(07)60152-1.
Environmental Sciences
Volume 22, Issue 4
2024
Pages 571-584

Files

Share

How to cite

Statistics