利用卫星图像和生成式对抗网络预测湖泊叶绿素浓度

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES Water Resources Research Pub Date : 2024-10-15 DOI:10.1029/2024wr037138
Nikolaos Nagkoulis, Giorgos Vasiloudis, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
{"title":"利用卫星图像和生成式对抗网络预测湖泊叶绿素浓度","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":"{\"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}","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

摘要

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$alpha 地图,计算成本低,无需使用任何现场数据,也不需要对每个水体进行训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks
Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll-α$\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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Efficient Model Calibration Using Submodels Unsteady Secondary Flow Structure at a Large River Confluence Can Satellite or Reanalysis Precipitation Products Depict the Location and Intensity of Rainfall at Flash Flood Scale Over the Eastern Mountainous Area of the Tibetan Plateau? Estimation of Recovery Efficiency in High-Temperature Aquifer Thermal Energy Storage Considering Buoyancy Flow A Spatially-Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1