Xuerong Sun , Robert J.W. Brewin , Shubha Sathyendranath , Giorgio Dall’Olmo , David Antoine , Ray Barlow , Astrid Bracher , Malika Kheireddine , Mengyu Li , Dionysios E. Raitsos , Fang Shen , Gavin H. Tilstone , Vincenzo Vellucci
{"title":"将生态概念与海洋颜色模型相结合:参数化和前瞻性建模","authors":"Xuerong Sun , Robert J.W. Brewin , Shubha Sathyendranath , Giorgio Dall’Olmo , David Antoine , Ray Barlow , Astrid Bracher , Malika Kheireddine , Mengyu Li , Dionysios E. Raitsos , Fang Shen , Gavin H. Tilstone , Vincenzo Vellucci","doi":"10.1016/j.rse.2024.114487","DOIUrl":null,"url":null,"abstract":"<div><div>In the first part of this paper series (<span><span>Sun et al., 2023</span></span>), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114487"},"PeriodicalIF":11.1000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling\",\"authors\":\"Xuerong Sun , Robert J.W. Brewin , Shubha Sathyendranath , Giorgio Dall’Olmo , David Antoine , Ray Barlow , Astrid Bracher , Malika Kheireddine , Mengyu Li , Dionysios E. Raitsos , Fang Shen , Gavin H. Tilstone , Vincenzo Vellucci\",\"doi\":\"10.1016/j.rse.2024.114487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the first part of this paper series (<span><span>Sun et al., 2023</span></span>), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. 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Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling
In the first part of this paper series (Sun et al., 2023), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.