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

résumé

Avec le réchauffement climatique et l'intensification de l'activité humaine, les écosystèmes lacustres subissent des changements significatifs dans les aspects hydrologiques, thermodynamiques et l'équilibre écologique, présentant des modèles de réponse spatio-temporelle complexes. Le développement rapide de technologies d'intelligence artificielle telles que l'apprentissage en profondeur a considérablement favorisé l'utilisation des données de télédétection à grande échelle dans la révélation des changements spatiaux et temporels des lacs. Dans cet article, nous avons systématiquement passé en revue les derniers progrès de la recherche dans le domaine de la télédétection des lacs à l'ère de l'IA, et constaté que ce domaine accorde de plus en plus d'attention à la réponse intégrée des lacs aux conditions de réchauffement climatique (en particulier l'augmentation des conditions météorologiques extrêmes, telles que les vagues de chaleur). Pour faire face aux défis des changements rapides des écosystèmes lacustres à différentes échelles et dans le temps et l'espace, les moyens d'observation par télédétection passent progressivement à un mode d'observation intégré multi-source constellation virtuelle + intégration ciel-terre qui améliore considérablement la précision de la surveillance dynamique des lacs à différentes échelles et dimensions. En même temps, les modèles de télédétection évoluent progressivement des modèles empiriques/mécaniques traditionnels vers un mécanisme de double entraînement de modèle mécanique explicite + modèle d'apprentissage automatique caché, améliorant progressivement la capacité de modélisation et de prévision des processus hydrologiques, thermiques et biologiques. Cet article examine également le potentiel d'intégration des données massives de télédétection et des algorithmes d'IA dans la surveillance à long terme et les prévisions de scénarios futurs, offrant de nouvelles possibilités de compréhension de la dynamique des lacs sous des pressions environnementales complexes et les tendances futures.

mots-clés

lake remote sensing;AI;climate change;aglorithm

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