Document Type : Research Paper


1 MSc. Student of Remote Sensing and Geographical Information System, Faculty of Planning and Environmental Sciences, University of Tabriz

2 Assoc. Prof. Department of Remote Sensing and Geographical Information System, Faculty of Planning and Environmental Sciences, University of Tabriz

3 Assis. Prof. Department of Plant Biology, Faculty of Natural Sciences , University of Tabriz

4 MSc. of Hydrography, Faculty of Surveying and Geospatial Engineering, College of Engineering, University of Tehran


Extended Abstract
The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems.Ingenerally,global waters are classified into two types of water.In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However second type waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter andtotalsuspended matter.Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves.Chlorophyll-A considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale.The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll-A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-A concentration.
Materials & Methods
In this study, Landsat-8, Sentinel-2 satellite time series data and field data collected from the study area,were used.First, the satellite images used in ENVI 5.3.1 softwarewereconverted to Surface Reflectance and Top of Atmosphere Reflectance.Then, MATLAB 2018a software was used for image processing and coding.To estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results,two statistical parameters R2 and RMSE were used.
Results & Discussion
Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere ReflectanceSentinel-2 and Landsat-8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat-8 and Sentinel-2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat-8 and Sentinel-2 Surface Reflectance data, respectively, while Landsat-8 and Sentinel-2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat-8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-A concentrations using the surface reflectance data (Sentinel-2) during the seasons estimated the chlorophyll-A concentration to be uniformly and downward.The reason for this poor performance in the Sentinel-2 is the lack of sufficient field data for calibration.
In this study, we tried to evaluate and compare the reflectancealgorithms (Landsat-8 and Sentinel-2) in the OC2 algorithm.Preliminary results indicate that the type of satellite data used (Surface ReflectanceandTop Atmospherereflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data.In general, the results showed that 10 field data is enough to calibrate with Landsat-8 data, but for Sentinel-2 data, more than 10 numbers field data must be calibrated to obtain a good result.


1- فاطمی، محمدرضا، 1375؛ خورهای آب­ های جنوبی ایران. آبزیان، دوره 7، شماره 12، صفحات 15-12.
2- مرادی، نسرین (1395)، بررسی و مدل­ سازی رنگ اقیانوس با استفاده از تصاویر ماهواره ­ای با توان تفکیک مکانی بالا، حسنلو، مهدی، دانشگاه تهران، گروه مهندسی و نقشه ­برداری و اطلاعات مکانی.
3- موسوی ده­موردی، بنایی؛ لاله، مهدی؛ 1397؛ تخمین و مدل­ سازی کلروفیل- آ با استفاده از ماهواره لندست8 در آب­ های ساحلی دیلم. مجله علمی پژوهشی زیست­ شناسی دریا دانشگاه آزاد اسلامی واحد اهواز، دوره 10، شماره 38، صفحات 29-21.
4- مهدوی­ فرد، مصطفی (1399)، تخمین غلظت کلروفیل- آ با استفاده از روش­ های میدانی و پردازش تصاویر ماهواره ­ای (مطالعه موردی: خورتیاب)، ولیزاده کامران، خلیل، دانشگاه تبریز، گروه سنجش ­ازدور و سیستم اطلاعات جغرافیایی.
5- ولیزاده کامران، مهدوی­ فرد؛ خلیل، مصطفی، 1398؛ مبانی سنجش ­ازدور کاربردی. انتشارات ماهواره، چاپ اول، تهران، 270 صفحه.
6- Bernstein, L. S., S. M. Adler-Golden, R. L. Sundberg, et al, 2005. Validation of the QuickAtmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery. SPIE Proceedings, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI. Vol. 5806, pp. 668-678.
7- Flaash, U. S. G. (2009). Atmospheric Correction Module: QUAC and Flaash User Guide v. 4.7. ITT Visual Information Solutions Inc.: Boulder, CO, USA.
8- 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), 290.
9- Han, L., & Jordan, K. J. (2005). Estimating and mapping chlorophyll‐a concentration in Pensacola Bay, Florida using Landsat ETM+ data. International Journal of Remote Sensing, 26(23), 5245-5254
10- Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R. R., & Flynn, L. (1997). The MODIS 2.1 µm channel-correlation with visible reflectance for use in remote sensing of aerosol, IEEE T. Geosci. Remote., 35, 1286–1298.
11- Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.
12- Matsushita, B., Yang, W., Chang, P., Yang, F., & Fukushima, T. (2012). A simple method for distinguishing global Case-1 and Case-2 waters using SeaWiFS measurements. ISPRS journal of photogrammetry and remote sensing, 69, 74-87.
13- McLeroy-Etheridge, S. L., &Roesler, C. S. (1998). Are the inherent optical properties of phytoplankton responsible for the distinct ocean colors observed during harmful algal blooms. Ocean Opt, 14, 109-116.
14- Miller, R. L., Del Castillo, C. E., & McKee, B. A. (Eds.). (2005). Remote sensing of coastal aquatic environments (Vol. 511). Dordrecht, The Netherlands: Springer.
15- Mobley, C. D., Stramski, D., Paul Bissett, W., & Boss, E. (2004). Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?. Oceanography, 17(SPL. ISS. 2), 60.
16- Mollaee, S. (2018). Estimation of phytoplankton chlorophyll-a concentration in the western basin of Lake Erie using Sentinel-2 and Sentinel-3 data (Master’s thesis, University of Waterloo).
17- Nusch, E. A. (1980). Comparison of different methods for chlorophyll and phaeopigment determination. Arch HydrobiolBeihErgebnLimnol, 14, 14-36.
18- O’Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., ... & McClain, C. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research: Oceans, 103(C11), 24937-24953.
19- Poddar, S., Chacko, N., & Swain, D. (2019). Estimation of Chlorophyll-a in northern coastal Bay of Bengal using Landsat-8 OLI and Sentinel-2 MSI sensors. Frontiers in Marine Science, 6, 598.
20- Schofield, O., Grzymski, J., Bissett, W. P., Kirkpatrick, G. J., Millie, D. F., Moline, M., &Roesler, C. S. (1999). Optical monitoring and forecasting systems for harmful algal blooms: possibility or pipe dream?. Journal of Phycology, 35(6), 1477-1496.
21- Soomets, T., Uudeberg, K., Jakovels, D., Brauns, A., Zagars, M., &Kutser, T. (2020). Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data. Sensors, 20(3), 742.
22- Sun, D., Hu, C., Qiu, Z., Cannizzaro, J. P., & Barnes, B. B. (2014). Influence of a red bandbased water classification approach on chlorophyll algorithms for optically complex estuaries. Remote sensing of environment, 155, 289-302.
23- Yati, E. (2016). Retrrieval of Chlorophyll-a and Suspended Sediment Concentration Using Landsat 8 OLI In Lampung Bay, Indonesia (Master’s thesis, University of India).