Recent advances in lake remote sensing in the AI era

  • role: First author第一作者
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

  • Email:htduan@niglas.ac.cn
  • Introduction:E-mailhtduan@niglas.ac.cn
DUAN Hongtao12,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

SHEN Ming12,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

LUO Juhua12,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

SUN Zhe12,  
  • Affiliation:

    Shaanxi Key Laboratory of Earth Surface System and Environment Carrying Capacity, Northwest University, Xi'an 710127, China

    College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China

CHEN Panpan34,  
  • Affiliation:

    Shaanxi Key Laboratory of Earth Surface System and Environment Carrying Capacity, Northwest University, Xi'an 710127, China

    College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China

QIU Zhiqiang34,  
  • Affiliation:

    Shaanxi Key Laboratory of Earth Surface System and Environment Carrying Capacity, Northwest University, Xi'an 710127, China

    College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China

ZHANG Kaili34,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

SI Yunrui12,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

YANG Chen12,  
  • Affiliation:

    Key Laboratory of Lake and Watershed Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China

    University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing 211135, China

QI Tianci12

Resümee

Mit dem Klimawandel und der Intensivierung der menschlichen Aktivitäten verändern sich die Ökosysteme der Seen signifikant in hydrologischer, thermodynamischer und ökologischer Balance und zeigen komplexe zeitlich-räumliche Reaktionsmuster. Die rasante Entwicklung von KI-Technologien wie Deep Learning hat die Rolle von großflächigen Fernerkundungsdaten bei der Aufdeckung von zeitlich-räumlichen Veränderungen in Seen erheblich gefördert. In diesem Artikel haben wir die neuesten Forschungsfortschritte im Bereich der Seenfernerkundung im KI-Zeitalter systematisch untersucht und festgestellt, dass diesem Bereich zunehmend die ganzheitliche Reaktion der Seen auf Klimaveränderungen (insbesondere die Zunahme extremer Wetterbedingungen wie Hitzewellen) Beachtung geschenkt wird. Um den schnellen, räumlich-zeitlichen Veränderungen in den Seenökosystemen auf verschiedenen Maßstäben entgegenzuwirken, entwickeln sich die Fernerkundungsmittel allmählich zu einem integrierten Multi-Source-Beobachtungsmodell Virtuelle Konstellation + Himmels-Erde-Integration, welches die Genauigkeit der dynamischen Seenüberwachung in verschiedenen Maßstäben und Dimensionen signifikant verbessert. Gleichzeitig entwickeln sich die Fernerkundungsmodelle allmählich von traditionellen empirischen/mechanischen Modellen zu einem doppelten Antriebsmechanismus explizites mechanisches Modell + verstecktes maschinelles Lernen, welcher die Modellierung und Vorhersage hydrologischer, thermischer und biologischer Prozesse schrittweise verbessert. Dieser Artikel untersucht auch das Potenzial der Integration von großen Fernerkundungsdaten und KI-Algorithmen in der Langzeitüberwachung und zukünftigen Szenarienvorhersage und bietet so neue Möglichkeiten, die Dynamik der Seen unter komplexen Umweltbedingungen und zukünftigen Trends zu verstehen.

Schlüsselwort

lake remote sensing;AI;climate change;aglorithm

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