Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Nikolaos Nagkoulis, Giorgos Vasiloudis, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
{"title":"Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks","authors":"Nikolaos Nagkoulis, Giorgos Vasiloudis, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris","doi":"10.1029/2024wr037138","DOIUrl":null,"url":null,"abstract":"Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll-<span data-altimg=\"/cms/asset/56d9a941-e73e-41ed-9d83-e30ef0d09f2c/wrcr27456-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"118\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0001\" display=\"inline\" location=\"graphic/wrcr27456-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> (Chl-<span data-altimg=\"/cms/asset/400bae36-5108-47c6-9861-aebbf2fd9567/wrcr27456-math-0002.png\"></span><mjx-container ctxtmenu_counter=\"119\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0002.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0002\" display=\"inline\" location=\"graphic/wrcr27456-math-0002.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container>) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl-<span data-altimg=\"/cms/asset/82a2909b-be9e-4112-9864-6d6f58e3c390/wrcr27456-math-0003.png\"></span><mjx-container ctxtmenu_counter=\"120\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0003.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0003\" display=\"inline\" location=\"graphic/wrcr27456-math-0003.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel-2 images to get Chl-<span data-altimg=\"/cms/asset/6822565d-3634-4c2c-980f-23edf866c262/wrcr27456-math-0004.png\"></span><mjx-container ctxtmenu_counter=\"121\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0004.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0004\" display=\"inline\" location=\"graphic/wrcr27456-math-0004.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set (<span data-altimg=\"/cms/asset/b768599c-b07f-4709-8c0a-4f35a9a8bb43/wrcr27456-math-0005.png\"></span><mjx-container ctxtmenu_counter=\"122\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0005.png\"><mjx-semantics><mjx-mrow><mjx-mo data-semantic- data-semantic-role=\"equality\" data-semantic-speech=\"tilde\" data-semantic-type=\"relation\"><mjx-c></mjx-c></mjx-mo></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0005\" display=\"inline\" location=\"graphic/wrcr27456-math-0005.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mo data-semantic-=\"\" data-semantic-role=\"equality\" data-semantic-speech=\"tilde\" data-semantic-type=\"relation\">∼</mo></mrow>${\\sim} $</annotation></semantics></math></mjx-assistive-mml></mjx-container>1,000 Sentinel-2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl-<span data-altimg=\"/cms/asset/8c64a785-16c3-49b9-9052-6cc5a9f04ef0/wrcr27456-math-0006.png\"></span><mjx-container ctxtmenu_counter=\"123\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0006.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0006\" display=\"inline\" location=\"graphic/wrcr27456-math-0006.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> maps to future Chl-<span data-altimg=\"/cms/asset/2f8f0758-1901-4aaf-ac2c-9dd207001e7b/wrcr27456-math-0007.png\"></span><mjx-container ctxtmenu_counter=\"124\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0007.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0007\" display=\"inline\" location=\"graphic/wrcr27456-math-0007.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> maps. This model has been applied to 3 water bodies around Europe that are not included in the 15-lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl-<span data-altimg=\"/cms/asset/c6005afe-c349-4b01-8d61-65d6a73d5860/wrcr27456-math-0008.png\"></span><mjx-container ctxtmenu_counter=\"125\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27456-math-0008.png\"><mjx-semantics><mjx-mrow><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic- data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27456:wrcr27456-math-0008\" display=\"inline\" location=\"graphic/wrcr27456-math-0008.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"italic\" data-semantic-role=\"greekletter\" data-semantic-speech=\"alpha\" data-semantic-type=\"identifier\">α</mi></mrow>$\\alpha $</annotation></semantics></math></mjx-assistive-mml></mjx-container> maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2024wr037138","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll-α$\alpha $ (Chl-α$\alpha $) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl-α$\alpha $ increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel-2 images to get Chl-α$\alpha $ maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set (${\sim} $1,000 Sentinel-2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl-α$\alpha $ maps to future Chl-α$\alpha $ maps. This model has been applied to 3 water bodies around Europe that are not included in the 15-lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl-α$\alpha $ maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body.
利用卫星图像和生成式对抗网络预测湖泊叶绿素浓度
卫星数据被广泛用于水质监测,与现场数据采样相比,成本大大降低。由于涉及到复杂的自然现象,利用过去的测量结果来预测未来的情况仍然是一项具有挑战性的任务,但在水资源管理方面却有着巨大的潜力。本文提出了一个可用于预测水体中叶绿素-α$\α$(Chl-α$\α$)值的模型,叶绿素-α$\α$是一种常见的水质指标。该模型的运行依赖于 Chl-α$\alpha $ 的周期性增减这一事实。首先,我们将常用的大气校正算法 C2RCC 应用于哨兵-2 图像,得到欧洲 15 个湖泊连续 12 个月的 Chl-α$\alpha $ 地图。然后,我们使用该数据集(∼${\sim} 1,000 张 Sentinel-2 图像)来训练生成对抗网络(GAN),以识别时空模式。为完成这一任务,采用了 pix2pix 算法,将过去和当前连续的 Chl-α$\alpha $ 地图与未来的 Chl-α$\alpha $ 地图进行匹配。该模型已被应用于欧洲的 3 个水体,这些水体不包括在 15 个湖泊的训练数据集中,结果表明该模型表现准确,实现了较高的皮尔逊和斯皮尔曼相关性以及较低的 RMSE 值。总之,该模型可用于预测 Chl-α$alpha 地图,计算成本低,无需使用任何现场数据,也不需要对每个水体进行训练。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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