KaydedilenlerDerginizi ekleyin

Dergiler / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 2

Landsat-8 uydu verileri ve derin öğrenme teknikleri ile klorofil-a parametresinin mekansal modellenmesi: Mogan Gölü örneği

Spatial modeling of chlorophyll-a parameter by Landsat-8 satellite data and deep learning techniques: The case of Lake Mogan

Osman Karakoç(Çevre, Şehircilik ve İklim Değişikliği Bakanlığı)0000-0003-0351-7075
İlkay Buğdaycı(NECMETTIN ERBAKAN UNIVERSITY)0000-0001-8361-1306
Sayfa
615–629
Yayın Tarihi
2025-04-15
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bu çalışma, yüksek doğruluklu sonuçlar üretme yeteneği ile gelecekteki stratejik izleme çalışmaları için literatüre etkili bir araç sunmaktadır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Su, yaşamın sürdürülebilirliği ve ekosistemlerin sağlıklı işleyişi için kritik öneme sahiptir.
📊
Temel Bulgular & Veri:Artan çevresel kirlilik, dünyadaki su kütlelerine yönelik ciddi tehditler oluşturmakta olup, su kalitesinin izlenmesi ve korunmasını stratejik bir zorunluluk haline getirmiştir.
💡
Sonuç & Katkı:Bu çalışma, yüksek doğruluklu sonuçlar üretme yeteneği ile gelecekteki stratejik izleme çalışmaları için literatüre etkili bir araç sunmaktadır.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:2018-2024 Verileri
⚙️Değişkenler:Landsat-8 · Uzaktan algılama · Derin öğrenme

Özet

Su, yaşamın sürdürülebilirliği ve ekosistemlerin sağlıklı işleyişi için kritik öneme sahiptir. Artan çevresel kirlilik, dünyadaki su kütlelerine yönelik ciddi tehditler oluşturmakta olup, su kalitesinin izlenmesi ve korunmasını stratejik bir zorunluluk haline getirmiştir. Klorofil-a, su ekosistemlerinin yaşam kaynağı olan fotosentetik aktivenin ve fitoplankton yoğunluğunun bir göstergesi olarak, su kalitesini ve ekosistem sağlığını şekillendiren en kritik göstergelerden biridir. Uzaktan algılama tabanlı veri setleri, göl ekosistemlerindeki klorofil-a değişimlerini analiz etmek için eşsiz bir fırsat sunmaktadır. Bu çalışmada, Mogan Gölü'nün 2018-2024 yılları arasındaki klorofil-a ölçümleri, Landsat-8 yüzey yansıma değerleri ve spektral indeksleri kullanılarak klorofil-a konsantrasyonu makine ve derin öğrenme teknikleri ile modellenmiştir. RF, ANN ve CNN modelleri sırasıyla 0.84, 0.85 ve 0.92 R2 değerlerine ulaşmıştır. Spektral ilişkileri öğrenme kapasitesi, karmaşık veri setlerindeki desenleri tanımlama becerisi ve uzaktan algılama görüntülerini işleme konusundaki üstün yetenekleriyle, araştırmada en iyi performansı gösteren CNN modeli kullanılarak tematik haritalar üretilmiştir. Çalışma sonuçları, klorofil-a parametresinin izlenmesinde uzaktan algılama tabanlı derin öğrenme yaklaşımlarının potansiyelini ortaya koymaktadır. Bu çalışma, yüksek doğruluklu sonuçlar üretme yeteneği ile gelecekteki stratejik izleme çalışmaları için literatüre etkili bir araç sunmaktadır.

Abstract

Water is essential for the sustainability of life and the healthy functioning of ecosystems. Increasing pollution poses a serious threat to the world's waters, making the monitoring and protection of water quality a strategic imperative. Chlorophyll-a is one of the most important indicators of water quality and ecosystem health, as it is a measure of photosynthetic activity and phytoplankton density, the lifeblood of aquatic ecosystems. Remote sensed data provide a unique opportunity to analyse chlorophyll-a changes in lake ecosystems. In this study, chlorophyll-a concentration was modelled by machine and deep learning techniques using chlorophyll-a measurements, Landsat-8 surface reflectance values and spectral indices of Lake Mogan between 2018 and 2024. The RF, ANN, and CNN models achieved R² values of 0.84, 0.85, and 0.92, respectively. With its ability to learn spectral relationships, identify patterns in complex datasets, and its superior ability to process remote sensing imagery, thematic maps were generated using the CNN model, which performed best in the study. The results of the study demonstrate the potential of remote sensing-based deep learning approaches for monitoring chlorophyll-a. With its ability to produce highly accurate results, this study provides the literature with an effective tool for future strategic monitoring studies.

🔖

Bu Makaleye Atıf Yap

Karakoç, O., Buğdaycı, İ. (2025). Landsat-8 uydu verileri ve derin öğrenme teknikleri ile klorofil-a parametresinin mekansal modellenmesi: Mogan Gölü örneği. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 2, 615–629. https://doi.org/10.28948/ngumuh.1603421

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (68)

  1. S. Prashant, M. Ramandeep, P. Prem, A. Akash, S. Prachi, P. Manish and G. Ayushi, Revisiting hyperspectral remote sensing: Origin, processing, applications and way forward. Hyperspectral Remote Sensing, Elsevier, 3-21, 2020. https://doi.org/10.1016/B978-0-08-102894-0.00001-2.
  2. M. I. H. Zaidi Farouk, Z. Jamil, M.F. Abdul Latip, Towards online surface water quality monitoring technology: A review. Environmental research, 238, 117147, 2023. https://doi.org/10.1016/j.envres.2023.117147.
  3. S. G. Virdis, W. Xue, E. Winijkul, V. Nitivattananon and P. Punpukdee, Remote sensing of tropical riverine water quality using sentinel-2 MSI and field observations. Ecological Indicators, 144, 109472, 2022. https://doi.org/10.1016/j.ecolind.2022.109472.
  4. A. Kulkarni, Water quality retrieval from Landsat TM imagery. Procedia Computer Science, 6, 475-480, 2011. https://doi.org/10.1016/j.procs.2011.08.088.
  5. J. Kravitz, M. Matthews, S. Bernard and D. Griffith, Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges. Remote Sensing of Environment, 237, 111562, 2020. https://doi.org/10.1016/j.rse.2019.111562.
  6. H. Huang, W. Wang, J. Lv, Q. Liu, X. Liu, S. Xie, F. Wang and J. Feng, Relationship between chlorophyll a and environmental factors in lakes based on the random forest algorithm. Water, 14, 19, 3128, 2022. https://doi.org/10.3390/w14193128.
  7. S.T.P. Phu, Research on the correlation between chlorophyll-a and organic matter BOD, COD, phosphorus, and total nitrogen in Stagnant Lake Basins. In: Kaneko, Sustainable Living with Environmental Risks, Springer, Tokyo, 2014. https://doi.org/10.1007/978-4-431-54804-1_15.
  8. A. Sudradjat, B. S. Muntalif, N. Marasabessy, F. Mulyadi and M. I. Firdaus, Relationship between chlorophyll-a, rainfall, and climate phenomena in tropical archipelagic estuarine waters, Heliyon, 10, 4, 2024. https://doi.org/10.1016/j.heliyon.2024.e25812.
  9. C. Kislik, I. Dronova, T. E. Grantham and M. Kelly, Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine, Ecological Indicators, 140, 109041, 2022. https://doi.org/10.1016/j.ecolind.2022.109041.
  10. J. Wang and X. Chen, A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data. Science of The Total Environment, 906, 167631, 0048-9697, 2024. https://doi.org/10.1016/j.scitotenv.2023.167631.
  11. K. Dörnhöfer, P.Klinger, T. Heege and N. Oppelt, Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. Science of The Total Environment, 612, 1200-1214, 2018. https://doi.org/10.1016/j.scitotenv.2017.08.219.
  12. M. W. Matthews, A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. International Journal of Remote Sensing, 32, 21, 6855-6899, 2011. https://doi.org/10.1080/01431161.2010.512947
  13. U. Tarı and N. Olğun Kıyak, Uydu verisi ve CBS ile Van Gölü klorofil-a dinamiklerinin izlenmesi [English Translation: Monitoring the chlorophyll-a dynamics of Van Lake with satellite data and GIS]. Journal of Advanced Research in Natural and Applied Sciences, 10, 1, 60-79, 2024. https://doi.org/10.28979/jarnas.1317247.
  14. B. Nas, H. Karabork and S. Ekercin, Mapping chlorophyll-a through in-situ measurements and Terra ASTER satellite data. Environ Monit Assess, 157, 375–382, 2009. https://doi.org/10.1007/s10661-008-0542-9.
  15. H. Cen, J. Jiang, G. Han, X. Lin, Y. Liu, X. Jia, Q. Ji and B. Li, Applying deep learning in the prediction of chlorophyll-a in the east china sea. Remote Sensing. 14, 21, 5461, 2022. https://doi.org/10.3390/rs14215461
  16. J. G. N. Paulino, G. G. Esperanza, R. A. F. José and D. M. Cristina, Forecast of chlorophyll-a concentration as an indicator of phytoplankton biomass in El Val reservoir by utilizing various machine learning techniques: A case study in Ebro river basin, Spain. Journal of Hydrology, 639, 131639,0022-1694, https://doi.org/10.1016/j.jhydrol.2024.131639.
  17. K. Doyun, L. KyoungJin, J. SeungMyeong, S. MinSeok, K. ByeoungJun, P. Jungsu and H. Tae-Young, Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data. Environmental Research, 262, 119823, 0013-9351, 2024. https://doi.org/10.1016/j.envres.2024.119823.
  18. F. H. Villota-González, B. Sulbarán-Rangel, F. Zurita-Martínez, K. J. Gurubel-Tun and V. Zúñiga-Grajeda, Assessment of machine learning models for remote sensing of water quality in Lakes Cajititlán and Zapotlán. Jalisco-Mexico, Remote Sensing. 15, 23, 5505, 2023. https://doi.org/10.3390/rs15235505
  19. L. Rodríguez-López, L. Bravo Alvarez, I. Duran-Llacer, D. E. Ruíz-Guirola, S. Montejo-Sánchez, R. Martínez-Retureta, E. López-Morales, L. Bourrel, F. Frappart and R. Urrutia, Leveraging machine learning and remote sensing for water quality analysis in lake ranco, Southern Chile. Remote Sensing, 16, 18, 3401, 2024. https://doi.org/10.3390/rs16183401
  20. F. Pu, C. Ding, Z. Chao, Y. Yu and X. Xu, Water-quality classification of inland lakes using Landsat8 images by convolutional neural networks. Remote Sensing, 11, 14, 1674, 2019. https://doi.org/10.3390/rs11141674
  21. H. Yang, Y. Du, H. Zhao and F. Chen, Water quality chl-a inversion based on spatio-temporal fusion and convolutional neural network. Remote Sensing. 14, 5, 1267, 2022. https://doi.org/10.3390/rs14051267
  22. C. Karul, S. Soyupak, A. F. Çilesiz, N. Akbay and E. Germen, Case studies on the use of neural networks in eutrophication modeling. Ecological Modelling, 134, 145-152, 2000. https://doi.org/10.1016/S0304-3800(00)00360-4.
  23. Anonim, Gölbaşı özel çevre koruma bölgesi yönetim planı, Çevre, Şehircilik ve İklim Değişikliği Bakanlığı Tabiat Varlıklarını Koruma Genel Müdürlüğü, 213, Ankara, 2015.
  24. A. Velioğlu and M. Kırkağaç, Seasonal variation of zooplankton in Lake Mogan. Aquatic Sciences and Engineering, 32, 3, 146-153, 2017.
  25. Özel Çevre Koruma Kurumu Başkanlığı, Ö.Ç.K.K.B. Yayınları, Ankara, 1994.
  26. E. Kırtıloğlu and H. Karabörk, Evaluating the performance of algorithms in estimating the Chl-a concentration of Lake Bafa. Turkish Journal of Geosciences, 3, 1, 30-38, 2022. https://doi.org/10.48053/turkgeo.1118373.
  27. M. Kavurmacı, S. Ekercin, L. Altaş and Y. Kurmaç, Hirfanlı baraj gölü su kalitesinin cbs ve uzaktan algılama teknikleri kullanılarak değerlendirilmesi [English Translation: Evaluation of water quality of Hirfanlı Dam Lake using gis and remote sensing techniques]. 65. Türkiye Jeoloji Kurultayı, 2-6 Nisan, 2012.
  28. M. Polatgil, Veri ölçekleme ve eksik veri tamamlama yöntemlerinin makine öğrenmesi yöntemlerinin başarısına etkisinin incelenmesi. Duzce University Journal of Science and Technology, 11, 1, 78-88, 2023 https://doi.org/10.29130/dubited.948564.
  29. M. E. Döş and M. Uysal, Uzaktan algılama verilerinin derin öğrenme algoritmaları ile sınıflandırılması. Türkiye Uzaktan Algılama Dergisi, 1, 28-34, 2019.
  30. T. Perivolioti, A. Mouratidis, D. Bobori, G. Doxani and D. Terzopoulos, Monitoring water quality parameters of Lake Koronia by means of long time-series multispectral satellite images. Acta Universitatis Carolinae, Geographica, Univerzita Karlova, 52, 2017. https://doi.org/10.14712/23361980.2017.14.
  31. C. Qi, S. Huang and X. Wang, Monitoring water quality parameters of Taihu Lake based on remote sensing images and LSTM-RNN. IEEE Access, 8, 188068-188081, 2020. https://doi.org/10.1109/ACCESS.2020.3030878
  32. B. Nas, S. Ekercin, H. Karabörk, A. Berktay and D. Mulla, An application of Landsat-5TM image data for water quality mapping in Lake Beysehir, Turkey. Water Air Soil Pollution 212, 183-197, 2010. https://doi.org/10.1007/s11270-010-0331-2.
  33. W. Ahmed, S. Mohammed, A. El-Shazly and S. Morsy, Tigris River water surface quality monitoring using remote sensing data and GIS techniques. The Egyptian Journal of Remote Sensing and Space Science. 26, 816-825, 2023. https://doi.org/10.1016/j.ejrs.2023.09.001.
  34. L. Rodríguez-López, I. Duran-Llacer, L. Bravo Alvarez, A. Lami, R. Urrutia, Recovery of water quality and detection of algal blooms in Lake Villarrica through Landsat satellite images and monitoring data. Remote Sensing, 15, 7, 1929, 2023. https://doi.org/10.3390/rs15071929
  35. C. Martin, J. Junchang, G. M. Jeffrey, L. D. Jennifer, F. V. Eric, R. Jean-Claude, V. S. Sergii and J. Christopher, The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161, 2018. https://doi.org/10.1016/j.rse.2018.09.002.
  36. H. Yıldız, A. Mermer, E. Ünal and F. Akbaş, Türkiye bitki örtüsünün NDVI verileri ile zamansal ve mekansal analizi [English Translation: Spatial and Temporal analysis of Turkey vegetation with NDVI images]. Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, 21, 2, 50-56, 2012.
  37. M. Akgün, Uzaktan algılama ile klorofil-a izlenmesi üzerine bir çalışma [English Translation: A study on monitoring of chlorophyll-a level by remote sensing]. Jsat, 1,41–47, 2023. https://doi.org/10.5281/zenodo.8074879.
  38. B. Datt, Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+ b, and total carotenoid content in eucalyptus leaves. Remote sensing of environment, 66, 2, 111-121, 1998. https://doi.org/10.1016/S0034-4257(98)00046-7.
  39. Ü. H. Atasever and H. H. Abbas, Drought monitoring in Burdur Lake using Sentinel-2 images. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 13, 3, 882-891, 2024. https://doi.org/10.28948/ngumuh.1411803
  40. H. Xu, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 14, 3025-3033, 2006. https://doi.org/10.1080/01431160600589179
  41. C. Praeger, M. J. Vucko, L. I. McKinna, R. D. Nys and A. J. Cole, Estimating the biomass density of macroalgae in land-based cultivation systems using spectral reflectance imagery. Algal Research-Biomass Biofuels and Bioproducts, 50, 102009, 2020. https://doi.org/10.1016/j.algal.2020.102009.
  42. Y. Choo, G. Kang, D. Kim and S. Lee, A study on the evaluation of water-bloom using image processing. Environmental science and pollution research international, 25, 36, 36775–36780, 2018. https://doi.org/10.1007/s11356-018-3578-6
  43. G. L. Feyisa, H. Meilby, R. Fensholt and S. R. Proud, Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23-35, 2014. https://doi.org/10.1016/j.rse.2013.08.029.
  44. Y. Dokuz, A Bozdağ and B. Gökçek, Hava kalitesi parametrelerinin tahmini ve mekansal dağılımı için makine öğrenmesi yöntemlerinin kullanılması [English Translation:Use of machine learning methods for estimation and spatial distribution of air quality parameters]. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9, 1, 37-47. https://doi.org/10.28948/ngumuh.654092
  45. L. Breiman, Random forests. Machine Learning, 45, 1, 5-32, 2001. https://doi.org/10.1023/A:1010933404324
  46. R. Qamar and B. Zardari, Artificial neural networks: an overview. Mesopotamian Journal of Computer Science. 130-139, 2023 https://doi.org/10.58496/MJCSC/2023/015.
  47. P. P. Biswas, W. H. Chen, S. S. Lam, Y. K. Park, J. S. Chang and A. T. Hoang, A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment. Journal of hazardous materials, 465, 133154, 2024. https://doi.org/10.1016/j.jhazmat.2023.133154
  48. F. Bre, J. Gimenez and V. Fachinotti, Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energy and Buildings, 158, 2017. https://doi.org/10.1016/j.enbuild.2017.11.045.
  49. M. M. Taye, Theoretical understanding of convolutional neural network: concepts, architectures, applications, Future Directions. Computation, 11, 3, 52, 2023. https://doi.org/10.3390/computation11030052
  50. X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci and M. Parmar, A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57, 99, https://doi.org/10.1007/s10462-024-10721-6
  51. N. Pravati, R. D. Shitya, K. M. Ranjan, M. Sairam, A. Ahmed, M. Alsharef and A. Flah, 2D-convolutional neural network based fault detection and classification of transmission lines using scalogram images. Heliyon, 10, 19, 2405-8440, 2024. https://doi.org/10.1016/j.heliyon.2024.e38947.
  52. K. Simonyan, A. and Zisserman, Very deep convolutional networks for large-scale image recognition. The 3rd International Conference on Learning Representations (ICLR2015), 2015. https:://doi.org/10.48550/arXiv.1409.1556.
  53. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9, 2015. https://doi.org/10.1109/CVPR.2015.7298594.
  54. D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization. CoRR, 2014. https:://doi.org/10.48550/arXiv.1412.6980
  55. P. D. Devi and G. Mamatha, Machine learning approach to predict the turbidity of Saki Lake, Telangana, India, using remote sensing data, Measurement: Sensors, 33, 101139, 2665-9174, 2024. https://doi.org/10.1016/j.measen.2024.101139.
  56. H. Zhang, B. Xue, G. Wang, X. Zhang and Q. Zhang. Deep learning-based water quality retrieval in an impounded lake using Landsat 8 imagery: an application in Dongping Lake. Remote Sensing, 14, 18, 4505, 2022. https://doi.org/10.3390/rs14184505
  57. A. Ali, G. Zhou, F. Pablo, L. F. Antezana, C. Xu, G. Jing and Y. Tan, Deep learning for water quality multivariate assessment in inland water across China. International Journal of Applied Earth Observation and Geoinformation. 133, 104078, 2024. https://doi.org/10.1016/j.jag.2024.104078.
  58. C. Peng, W. Biao, W. Yanlan, W. Qijun, H. Zuoji and W. Chunlin, Urban river water quality monitoring based on self-optimizing machine learning method using multi-source remote sensing data. Ecological Indicators, 146, 109750, 2023. https://doi.org/10.1016/j.ecolind.2022.109750.
  59. D. Chicco, M. J. Warrens and G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science 7:e623 2021. https://doi.org/10.7717/peerj-cs.623
  60. U. Tarı, N. Olğun Kıyak, Uydu verisi ve CBS ile Van Gölü klorofil-a dinamiklerinin izlenmesi [English Translation: Monitoring the chlorophyll-a dynamics of Van Lake with satellite data and GIS]. Journal of Advanced Research in Natural and Applied Sciences, 10, 1, 60-79. 2024. https://doi.org/10.28979/jarnas.1317247
  61. W. G. Buma, S. I. Lee, Evaluation of Sentinel-2 and Landsat 8 images for estimating chlorophyll-a concentrations in Lake Chad, Africa. Remote Sensing, 12, 15, 2437, 2020. https://doi.org/10.3390/rs12152437
  62. Mandanici, E., & Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sensing, 8(12), 1014. https://doi.org/10.3390/rs8121014.
  63. Seleem, T., Bafi, D., Karantzia, M. et al. Water Quality Monitoring Using Landsat 8 and Sentinel-2 Satellite Data (2014–2020) in Timsah Lake, Ismailia, Suez Canal Region (Egypt). J Indian Soc Remote Sens 50, 2411–2428 (2022). https://doi.org/10.1007/s12524-022-01613-9
  64. K. G Vivek, K. G. Piyush, P. Murugan, M. Annadurai, Assessment of surface water dynamicsin bangalore Using WRI, NDWI, MNDWI, supervised classification and K-T transformation,Aquatic Procedia, 4, 739-746, 2214-241X, 2015. https://doi.org/10.1016/j.aqpro.2015.02.095
  65. D. Dewi, A. Wei, L. Lin and C. Heng, Water quality prediction using random forest algorithm and optimization. Journal of Applied Data Sciences, 5, 3, 1354-1362, 2024. https://doi.org/10.47738/jads.v5i3.348
  66. S. Ming, L. Juhua, C. Zhigang, X. Kun, Q. Tianci, M. Jinge, L. Dong, S. Kaishan, F. Lian, D. Hongtao, Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties. Journal of Hydrology; 615, Part A, 2022. https://doi.org/10.1016/j.jhydrol.2022.128685
  67. M. A Syariz and C. H. Lin, M.V. Nguyen, L. M. Jaelani and A. C. Blanco, WaterNet: A convolutional neural network for chlorophyll-a concentration retrieval. Remote Sensing, 12, 12, 1966, 2020. https://doi.org/10.3390/rs12121966
  68. X. Zhang and M. Zhou, A general convolutional neural network to reconstruct remotely sensed chlorophyll-a concentration. Journal of Marine Science and Engineering, 11, 4, 810, 2023. https://doi.org/10.3390/jmse11040810