A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery

Nguyen Thien Phuong Thao, Nguyen Thi Thu Ha, Pham Quang Vinh, Tran Thi Hien, Dinh Xuan Thanh
Author affiliations

Authors

  • Nguyen Thien Phuong Thao Faculty of Geology, VNU University of Science, Vietnam National University, Hanoi, Vietnam
  • Nguyen Thi Thu Ha Faculty of Geology, VNU University of Science, Vietnam National University, Hanoi, Vietnam
  • Pham Quang Vinh Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Tran Thi Hien Faculty of Geology, VNU University of Science, Vietnam National University, Hanoi, Vietnam
  • Dinh Xuan Thanh Faculty of Geology, VNU University of Science, Vietnam National University, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/20714

Keywords:

Empirical model, Sen2Cor, Sentinel-2B images, reservoirs, trophic state

Abstract

Monitoring chlorophyll-a concentration (Chla) in inland waters is vital for environmental assessment. This study develops an empirical multivariate linear regression (MLR) model to directly estimate Chla in Quan Son Reservoir using Sentinel-2B (S2B) Level 2A images. Regression analysis of a 68-point in-situ Chla dataset measured in Quan Son Reservoir between 2021 and 2023, in conjunction with the corresponding S2B reflectance data, reveals a significant correlation between Chla and a combination of the blue (B2), green (B3), and red (B4) bands (coefficient of determination, = 0.95). The Chla estimation model is validated using a 30-point in-situ dataset collected on various dates ( = 0.87; the root-mean-squared error RMSE < 5%). Subsequently, the model is applied to ten S2B images acquired from 2021 to 2023, revealing Chla's spatio-temporal distribution across the reservoir. Two key trends emerge: (1) Chla is lower during winter (November and December) than in summer and early autumn (July and September), and (2) The distribution of Chla undergoes noticeable spatial changes, particularly in July, with elevated levels observed in areas characterized by tourist hotspots. This approach shows promise for monitoring Chla in similar inland waters.

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References

Adams H., Ye J., Persaud B., Slowinski S., Kheyrollah Pour H., Van Cappellen P., 2021. Chlorophyll-a growth rates and related environmental variables in global temperate and cold-temperate lakes. Earth System Science Data Discussions, 1–30.

Al-Kharusi E.S., Tenenbaum D.E., Abdi A.M., Kutser T., Karlsson J., Bergström A.K., Berggren M., 2020. Large-scale retrieval of coloured dissolved organic matter in northern lakes using Sentinel-2 data. Remote Sensing, 12(1), 157.

APHA, 1998. WPCF, 1998. Standard methods for the examination of water and wastewater, 20, 39–42.

Augusto-Silva P.B., Ogashawara I., Barbosa C.C., De Carvalho L.A., Jorge D.S., Fornari C.I., Stech J.L., 2014. Analysis of MERIS reflectance algorithms for estimating chlorophyll-a concentration in a Brazilian reservoir. Remote Sensing, 6(12), 11689–11707.

Barraza-Moraga F., Alcayaga H., Pizarro A., Félez-Bernal J., Urrutia R., 2022. Estimation of chlorophyll-a concentrations in Lanalhue Lake using Sentinel-2 MSI satellite images. Remote Sensing, 14(22), 5647.

Barsi J.A., Lee K., Kvaran G., Markham B.L., Pedelty J.A., 2014. The spectral response of the Landsat-8 operational land imager. Remote sensing, 6(10), 10232–10251.

Bennett A., Bogorad L., 1973. Complementary chromatic adaptation in a filamentous blue-green alga. The Journal of cell biology, 58(2), 419–435.

Berk A., Anderson G.P., Acharya P.K., Bernstein L.S., Muratov L., Lee J., Shettle E.P., 2006. MODTRAN5: 2006 Update, Defense and Security Symposium. Proceedings of the SPIE, Orlando, FL, USA, 6233.

Brezonik P., Menken K.D., Bauer M., 2005. Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake and Reservoir Management, 21(4), 373–382.

Brown C.D., Canfield Jr D.E., Bachmann R.W., Hoyer M.V., 1998. Seasonal patterns of chlorophyll, nutrient concentrations and Secchi disk transparency in Florida lakes. Lake and Reservoir Management, 14(1), 60–76.

Bui Q.T., Jamet C., Vantrepotte V., Mériaux X., Cauvin A., Mograne M.A., 2022. Evaluation of sentinel-2/MSI atmospheric correction algorithms over two contrasted French coastal waters. Remote Sensing, 14(5), 1099.

Cao Z., Ma R., Duan H., Pahlevan N., Melack J., Shen M., Xue K., 2020. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248, 111974.

Carlson R.E., 1977. A trophic state index for lakes 1. Limnology and oceanography, 22(2), 361-369.

Carlson R.E., 1996. A coordinator's guide to volunteer lake monitoring methods. North American Lake Management Society, 96, 305.

Chavez Jr P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote sensing of environment, 24(3), 459–479.

Chen J., Zhu W., Tian Y.Q., Yu Q., Zheng Y., Huang L., 2017. Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements. Journal of Applied Remote Sensing, 11(3), 036007–036007.

Chen Z., Curran P.J., Hansom J.D., 1992. Derivative reflectance spectroscopy to estimate suspended sediment concentration. Remote Sensing of Environment, 40(1), 67–77.

Cho K.H., Kang J.H., Ki S.J., Park Y., Cha S.M., Kim J.H., 2009. Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: A case study of the Yeongsan Reservoir, Korea. Science of the total environment, 407(8), 2536–2545.

Chu H.J., He Y.C., Chusnah W.N.U., Jaelani L.M., Chang C.H., 2021. Multi-reservoir water quality mapping from remote sensing using spatial regression. Sustainability, 13(11), 6416.

Cloern J.E., Foster S.Q., Kleckner A.E., 2014. Phytoplankton primary production in the world's estuarine-coastal ecosystems. Biogeosciences, 11(9), 2477–2501.

Conkright M.E., Gregg W.W., 2003. Comparison of global chlorophyll climatologies: In situ, CZCS, Blended in situ-CZCS and SeaWiFS. International Journal of Remote Sensing, 24(5), 969–991.

Dall’Olmo G., Gitelson A.A., 2005. Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results. Applied optics, 44(3), 412–422.

De Keukelaere L., Sterckx S., Adriaensen S., Knaeps E., Reusen I., Giardino C., 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.

Franklin J.B., Sathish T., Vinithkumar N.V., Kirubagaran R., 2020. A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores. Marine pollution bulletin, 152, 110902.

Fujiki T., Taguchi S., 2002. Variability in chlorophyll a specific absorption coefficient in marine phytoplankton as a function of cell size and irradiance. Journal of Plankton Research, 24(9), 859–874.

Gholizadeh M.H., Melesse A.M., Reddi L., 2016. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16(8), 1298.

Gitelson A., 1992. The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13(17), 3367–3373.

Gitelson A.A., Gao B.C., Li R.R., Berdnikov S., Saprygin V., 2011. Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean the Azov Sea case study. Environmental Research Letters, 6(2), 024023.

Grendaitė D., Stonevičius E., 2022. Uncertainty of atmospheric correction algorithms for chlorophyll α concentration retrieval in lakes from Sentinel-2 data. Geocarto International, 37(23), 6867–6891.

Guanter L., 2023. New Algorithms for Atmospheric Correction and Retrieval of Biophysical Parameters in Earth Observation Application to ENVISAT/MERIS Data. In Departament de Física de la Terra i Termodinàmica; Universitat de Valéncia: Valéncia, Spain, 2007; Available online: https://www.tesisenred.net/handle/10803/9877;jsessionid=402C46 F935666E5FF2CEF46F057954A7.tdx2#page=1 (accessed on August 6 2023).

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.

Ha Tay Provincal Committee, 2008. Decision No. 462/QD-UBND approving detailed planning for construction at 1/2000 scale of Quan Son Reservoir urban eco-tourism area, My Duc district.

Hunter P.D., Tyler A.N., Présing M., Kovács A.W., Preston T., 2008. Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution. Remote Sensing of Environment, 112(4), 1527–1544.

Jiang D., Matsushita B., Setiawan F., Vundo A., 2019. An improved algorithm for estimating the Secchi disk depth from remote sensing data based on the new underwater visibility theory. ISPRS journal of photogrammetry and remote sensing, 152, 13–23.

Kasprzak P., Padisák J., Koschel R., Krienitz L., Gervais F., 2008. Chlorophyll a concentration across a trophic gradient of lakes: An estimator of phytoplankton biomass?. Limnologica, 38(3–4), 327–338.

Kim H.H., Ko B.C., Nam J.Y., 2016b. Predicting chlorophyll-a using Landsat 8 OLI sensor data and the non-linear RANSAC method a case study of Nakdong River, South Korea. International Journal of Remote Sensing, 37(14), 3255–3271.

Kim W., Moon J.E., Park Y.J., Ishizaka J., 2016a. Evaluation of chlorophyll retrievals from Geostationary Ocean color imager (GOCI) for the north-east Asian region. Remote Sensing of Environment, 184, 482–495.

Kim Y.W., Kim T., Shin J., Lee D.S., Park Y.S., Kim Y., Cha Y., 2022. Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters. Ecological Indicators, 137, 108737.

Kravitz J., Matthews M., Bernard S., Griffith D., 2020. Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sensing of Environment, 237, 111562.

Kuhn C., de Matos Valerio A., Ward N., Loken L., Sawakuchi H.O., Kampel M., Richey J., Stadler P., Crawford J., Striegl R., Vermote E., Pahlevan N., Butman D., 2019. Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity. Remote Sensing of Environment, 224, 104–118.

Latwal A., Rehana S., Rajan K.S., 2023. Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies. Environmental Monitoring and Assessment, 195(11), 1304.

Le C., Hu C., Cannizzaro J., Duan H., 2013a. Long-term distribution patterns of remotely sensed water quality parameters in Chesapeake Bay. Estuarine, Coastal and Shelf Science, 128, 93–103.

Le C., Hu C., Cannizzaro J., English D., Muller-Karger F., Lee Z., 2013b. Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. Remote Sensing of Environment, 129, 75–89.

Lehmann M.K., Gurlin D., Pahlevan N., Alikas K., Conroy T., Anstee J., Balasubramanian S.V., Barbosa C.C.F., Binding C., Bracher A., Bresciani M., Burtner A., Cao Z., Dekker A.G., Vittorio C.D., Drayson N., Errera R.M., Fernandez V., Ficek D., Fichot C.G., Gege P., Giardino C., Gitelson A.A., Greb S.R., Henderson H., Higa H., Rahaghi A.I., Jamet C., Jiang D., Jordan T., Kangro K., Kravitz J.A., Kristoffersen A.S., Kudela R., Li L., Ligi M., Loisel H., Lohrenz S., Ma R., Maciel D.A., Malthus T.J., Matsushita B., Matthews M., Minaudo C., Mishra D.R., Mishra S., Moore T., Moses W.J., Hà N., Novo E.M.L.M, Novoa S., Odermatt D., O’Donnell D.M., Olmanson L.G., Ondrusek M., Oppelt N., Ouillon S., Filho W.P., Plattner S., Verdú A.R., Salem S.I., Schalles J.F., Simis S.G.H., Siswanto E., Smith B., Somlai-Schweiger I., Soppa M.A., Spyrakos E., Tessin E., van der Woerd H.J., Woude A.V., Vandermeulen R.A., Vantrepotte V., Wernand M.R., Werther M., Young K., Yue L., 2023. GLORIA-A globally representative hyperspectral in situ dataset for optical sensing of water quality. Scientific Data, 10(1), 100.

León J.G., Beamud S.G., Temporetti P.F., Atencio A.G., Diaz M.M., Pedrozo F.L., 2016. Stratification and residence time as factors controlling the seasonal variation and the vertical distribution of chlorophyll‐a in a subtropical irrigation reservoir. International Review of Hydrobiology, 101(1–2), 36–47.

Li J., Gao M., Feng L., Zhao H., Shen Q., Zhang F., Wang S., Zhang B., 2019. Estimation of chlorophyll-a concentrations in a highly turbid eutrophic lake using a classification-based MODIS land-band algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3769–3783.

Lim J., Choi M., 2015. Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environmental monitoring and assessment, 187, 1–17.

Lins R.C., Martinez J.M., Motta Marques D.D., Cirilo J.A., Medeiros P.R.P., Fragoso Júnior C.R., 2018. A multivariate analysis framework to detect key environmental factors affecting spatiotemporal variability of chlorophyll-a in a tropical productive estuarine-lagoon system. Remote Sensing, 10(6), 853.

Liu X., Steele C., Simis S., Warren M., Tyler A., Spyrakos E., Selmes N., Hunter P., 2021. Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs. Remote Sensing of Environment, 267, 112710.

Liu Y., Xiao C., Li J., Zhang F., Wang S., 2020. Secchi disk depth estimation from China's new generation of GF-5 hyperspectral observations using a semi-analytical scheme. Remote Sensing, 12(11), 1849.

Loisel H., Vantrepotte V., Ouillon S., Ngoc D.D., Herrmann M., Tran V., Mériaux X., Dessailly D., Jamet C., Duhaut T., Nguyen H.H., Van Nguyen T., 2017. Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002–2012). Remote sensing of environment, 190, 217–232.

Louis J., Debaecker V., Pflug B., Main-Knorn M., Bieniarz J., Mueller-Wilm U., Cadau E., Gascon F. Sentinel-2 Sen2Cor: L2A processor for users. In Proceedings living planet symposium 2016. Spacebooks Online. 9–13 May 2016, Prague, Czech Republic, 1–8.

Ma R., Ma X., Dai J., 2007. Hyperspectral feature analysis of chlorophyll a and suspended solids using field measurements from Taihu Lake, eastern China. Hydrological Sciences Journal, 52(4), 808–824.

Ma R.H., Dai J.F., 2005. Chlorophyll-a concentration estimation with field spectra of waterbody near Meiliang Bayou in Taihu Lake. Journal of remote sensing, 9(1), 78–86.

Martins V.S., Barbosa C.C.F., De Carvalho L.A.S., Jorge D.S.F., Lobo F.D.L., Novo E.M.L.D.M., 2017. Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sensing, 9(4), 322.

Matus-Hernández M.Á., Hernández-Saavedra N.Y., Martínez-Rincón R.O., 2018. Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery. Plos One, 13(10), e0205682.

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.

Mitchell B.G., Kiefer D.A., 1988. Chlorophyll α specific absorption and fluorescence excitation spectra for light-limited phytoplankton. Deep Sea Research Part A. Oceanographic Research Papers, 35(5), 639–663.

Mobley C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied optics, 38(36), 7442–7455.

Moore T.S., Dowell M.D., Bradt S., Verdu A.R., 2014. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote sensing of Environment, 143, 97–111.

Moses W.J., Gitelson A.A., Berdnikov S., Saprygin V., Povazhnyi V., 2012. Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters the Azov Sea case study. Remote Sensing of Environment, 121, 118–124.

Mueller J.L., Morel A., Frouin R., Davis C., Arnone R., Carder K., Lee Z.P., 2003. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume III: Radiometric Measurements and Data Analysis Protocols. Goddard Space Flight Center: Greenbelt, USA.

Nguyen X.H., Dao T.N., Nguyen T.N., 2010. The fish species composition in the area of Quan Son reservoir in My Duc district, Ha Noi. Journal of Science, Natural Sciences and Technology, VNU, Hanoi, 26, 531–536.

Niroumand-Jadidi M., Bovolo F., Bruzzone L., Gege P., 2021. Inter-comparison of methods for chlorophyll-a retrieval: Sentinel-2 time-series analysis in Italian lakes. Remote Sensing, 13(12), 2381.

Ogashawara I., Alcântara E.H., Curtarelli M.P., Adami M., Nascimento R.F., Souza A.F., Stech J.L., Kampel M., 2014. Performance analysis of MODIS 500-m spatial resolution products for estimating chlorophyll-a concentrations in oligo-to meso-trophic waters case study: Itumbiara Reservoir, Brazil. Remote Sensing, 6(2), 1634–1653.

Ogashawara I., Kiel C., Jechow A., Kohnert K., Ruhtz T., Grossart H.P., Hölker F., Nejstgaard J.C., Berger S.A., Wollrab S., 2021. The use of Sentinel-2 for chlorophyll-a spatial dynamics assessment: A comparative study on different lakes in northern Germany. Remote Sensing, 13(8), 1542.

Ouma Y.O., Noor K., Herbert K., 2020. Modelling reservoir chlorophyll-a, TSS, and turbidity using Sentinel-2A MSI and Landsat-8 OLI satellite sensors with empirical multivariate regression. Journal of Sensors, 1–21.

Pahlevan N., Mangin A., Balasubramanian S.V., Smith B., Alikas K., Arai K., Barbosa C., Bélanger S., Binding C., Bresciani M., Giardino C., Gurlin D., Fan Y., Harmel T., Hunter P., Ishikaza J., Kratzer S., Lehmann M.K., Ligi M., Ma R., Martin-Lauzer F.R., Olmanson L., Oppelt N., Pan Y., Peters S., Reynaud N., Sander de Carvalho L.A., Simis S., Spyrakos E., Steinmetz F., Stelzer K., Sterckx S., Tormos T., Tyler A., Vanhellemont Q., Warren M., 2021. ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters. Remote Sensing of Environment, 258, 112366.

Pahlevan N., Sarkar S., Franz B.A., Balasubramanian S.V., He J., 2017. Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations. Remote sensing of environment, 201, 47–56.

Palmer S.C., Kutser T., Hunter P.D., 2015. Remote sensing of inland waters: Challenges, progress and future directions. Remote sensing of Environment, 157, 1–8.

Patra P.P., Dubey S.K., Trivedi R.K., Sahu S.K., Rout S.K., 2017. Estimation of chlorophyll‐a concentration and trophic states in Nalban Lake of East Kolkata Wetland, India from Landsat 8 OLI data. Spatial Information Research, 25, 75–87.

Pereira-Sandoval M., Ruescas A., Urrego P., Ruiz-Verdú A., Delegido J., Tenjo C., Soria-Perpinyà X., Vicente E., Soria J., Moreno J., 2019. Evaluation of atmospheric correction algorithms over Spanish inland waters for Sentinel-2 multi spectral imagery data. Remote Sensing, 11(12), 1469.

Perrone M., Scalici M., Conti L., Moravec D., Kropáček J., Sighicelli M., Lecce F., Malavasi M., 2021. Water mixing conditions influence Sentinel-2 monitoring of chlorophyll content in monomictic lakes. Remote Sensing, 13(14), 2699.

Pflug B., Louis J., Debaecker V., Mueller-Wilm U., Quang C., Gascon F., Boccia V., 2020. Next updates of atmospheric correction processor Sen2Cor. In Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI. SPIE Remote Sensing, 20 September 2020, 1153304.

Pu F., Ding C., Chao Z., Yu Y., Xu X., 2019. Water-quality classification of inland lakes using Landsat8 images by convolutional neural networks. Remote Sensing, 11(14), 1674.

Quan Sơn Fisheries and Tourism Joint Stock Company, 2013. Environmental Protection Project Report.

Rundquist D.C., Han L., Schalles J.F., Peake J.S., 1996. Remote measurement of algal chlorophyll in surface waters: the case for the first derivative of reflectance near 690 nm. Photogrammetric Engineering and Remote Sensing, 62(2), 195–200.

Schalles J.F., 2006. Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. In Remote sensing of aquatic coastal ecosystem processes. Dordrecht: Springer Netherlands, 27, 27–79.

Schalles J.F., Sheil A.T., Tycast J.F., Alberts J.J., Yacobi Y.Z., 1998. Detection of chlorophyll, seston, and dissolved organic matter in the estuarine mixing zone of Georgia coastal plain rivers. In 5th International Conference on Remote Sensing for Marine and Coastal Environments, 2, 315–324.

Sent G., Biguino B., Favareto L., Cruz J., Sá C., Dogliotti A.I., Palma C., Brotas V., Brito A.C., 2021. Deriving water quality parameters using sentinel-2 imagery: A case study in the Sado Estuary, Portugal. Remote sensing, 13(5), 1043.

Smith G., 2015. Essential statistics, regression, and econometrics. Academic Press, 386p.

Sterckx S., Knaeps S., Kratzer S., Ruddick K., 2015. SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters. Remote Sensing of Environment, 157, 96–110.

Tang F., Ohto T., Sun S., Rouxel J.R., Imoto S., Backus E.H., Mukamel S., Bonn M., Nagata Y., 2020. Molecular structure and modeling of water-air and ice-air interfaces monitored by sum-frequency generation. Chemical reviews, 120(8), 3633–3667.

Tehrani N.A., Janalipour M., Babaei H., 2021. Estimating water surface chlorophyll-a concentration by big remote sensing data in the Persian Gulf, Bushehr. Remote Sensing in Earth Systems Sciences, 4(1-2), 87–95.

Thao N.T.P., Thang P.D., Hien T.T., Ha N.T.T., Vinh P.Q., 2023. Assessing and modelling the trophic state of Quan Son Reservoir in space and time. Vietnam Journal of Hydro-Meteorology, 748, 32–41.

USEPA, 2009. Environmental Protection Agency (USEPA). National Lakes Assessment: A Collaborative Survey of the Nation's Lakes; EPA 841-R-09-001; U.S. Environmental Protection Agency, Office of Water and Office of Research and Development: Washington, DC, USA, 2009; 103p. Available online: https://www.epa.gov/sites/production/files/2013-11/documents/nla_newlowres_fullrpt.pdf (assessed on July 4 2023).

UNEP, 2014. Review of Existing Water Quality Guidelines for Freshwater Ecosystems and Application of Water Quality Guidelines on Basin Level to Protect Ecosystems. In Proceedings of the First International Environment Forum for Basin Organizations towards Sustainable Freshwater Governance, Nairobi, Kenya, 26–28 November 2014; Available online: https://wedocs.unep.org/rest/bitstreams/35090/retrieve (assessed on July 4 2023).

Vilas L.G., Spyrakos E., Palenzuela J.M.T., 2011. Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain). Remote Sensing of Environment, 115(2), 524–535.

Vinh P.Q., Ha N.T.T., Binh N.T., Thang N.N., Oanh L.T., Thao N.T.P., 2019. Developing algorithm for estimating chlorophyll-a concentration in the Thac Ba Reservoir surface water using Landsat 8 Imagery. Vietnam J. Earth Sci., 41(1), 10–20. https://doi.org/10.15625/0866-7187/41/1/13542.

Warren M.A., Simis S.G., Martinez-Vicente V., Poser K., Bresciani M., Alikas K., Spyrakos E., Giardino C., Ansper A., 2019. Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sensing of Environment, 225, 267–289.

Werther M., Spyrakos E., Simis S.G., Odermatt D., Stelzer K., Krawczyk H., Berlage O., Hunter P., Tyler A., 2021. Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters. ISPRS Journal of Photogrammetry and Remote Sensing, 176, 109–126.

Werther M., Odermatt D., Simis S.G., Gurlin D., Lehmann M.K., Kutser T., Gupana R., Varley A., Hunter P., Tyler A., Spyrakos E., 2022. A Bayesian approach for remote sensing of chlorophyll-a and associated retrieval uncertainty in oligotrophic and mesotrophic lakes. Remote Sensing of Environment, 283, 113295.

Wu M., Zhao Y., Sun L.E., Huang J., Wang X., Ma Y., 2020. Remote sensing of spatial-temporal variation of chlorophyll-a in the Jiaozhou bay using 32 years Landsat data. Journal of Coastal Research, 102(SI), 271–279.

Ylöstalo P., Kallio K., Seppälä J., 2014. Absorption properties of in-water constituents and their variation among various lake types in the boreal region. Remote sensing of Environment, 148, 190–205.

Zhang S., Liu N., Luo M., Jiang T., Chan T.O., Yau C.S.T., Sun Y, 2023. Downscaling Sentinel-3 Chlorophyll-a Concentration for Inland Lakes based on Multivariate Analysis and Gradient Boosting Decision Trees Regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 7850–7865.

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03-05-2024

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Nguyen Thien Phuong, T., Nguyen Thi Thu, H., Pham Quang, V., Tran Thi, H., & Dinh Xuan, T. (2024). A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery. Vietnam Journal of Earth Sciences, 46(3), 360–380. https://doi.org/10.15625/2615-9783/20714

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