评估欧空局气候变化倡议数据,以监测深湖浮游植物的丰度和物候:日内瓦湖调查

IF 2.4 3区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
{"title":"评估欧空局气候变化倡议数据,以监测深湖浮游植物的丰度和物候:日内瓦湖调查","authors":"","doi":"10.1016/j.jglr.2024.102372","DOIUrl":null,"url":null,"abstract":"<div><p>Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-<em>a</em> concentrations, based on medium resolution satellite data, on a global scale. Chl-<em>a</em> concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved <em>in-situ</em> data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-<em>a</em> estimates provided by the remote sensing algorithms. Strong correlation (R-value &gt; 0.89) and acceptable discrepancies (<em>rmse</em> ∼ 1.4 mg.m<sup>−3</sup>) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and <em>in-situ</em> data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of <em>in-situ</em> and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology.</p></div>","PeriodicalId":54818,"journal":{"name":"Journal of Great Lakes Research","volume":"50 4","pages":"Article 102372"},"PeriodicalIF":2.4000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0380133024001229/pdfft?md5=8551f33bcff095c2e5432b9f0f11a66f&pid=1-s2.0-S0380133024001229-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Assessing ESA Climate Change Initiative data for the monitoring of phytoplankton abundance and phenology in deep lakes: Investigation on Lake Geneva\",\"authors\":\"\",\"doi\":\"10.1016/j.jglr.2024.102372\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-<em>a</em> concentrations, based on medium resolution satellite data, on a global scale. Chl-<em>a</em> concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved <em>in-situ</em> data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-<em>a</em> estimates provided by the remote sensing algorithms. Strong correlation (R-value &gt; 0.89) and acceptable discrepancies (<em>rmse</em> ∼ 1.4 mg.m<sup>−3</sup>) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and <em>in-situ</em> data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of <em>in-situ</em> and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology.</p></div>\",\"PeriodicalId\":54818,\"journal\":{\"name\":\"Journal of Great Lakes Research\",\"volume\":\"50 4\",\"pages\":\"Article 102372\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0380133024001229/pdfft?md5=8551f33bcff095c2e5432b9f0f11a66f&pid=1-s2.0-S0380133024001229-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Great Lakes Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0380133024001229\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Great Lakes Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0380133024001229","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

湖泊水质评估需要对浮游植物的丰度进行量化。通过光学卫星图像,我们可以绘制出整个湖区的相关信息。欧空局气候变化倡议(ESA-CCI)根据中等分辨率卫星数据估算了全球范围内的 Chl-a 浓度。我们对 ESA-CCI 联盟提供的 Chl-a 浓度进行了分析,以评估其在日内瓦湖水质监测和后续物候研究中的代表性。根据垂直分辨的原位数据,通过匹配比较对这些数据集进行了评估。由于基础算法没有考虑浮游植物的垂直分布,因此进行了一项具体分析,以评估遥感估算中的任何潜在偏差,以及对观测到的物候趋势的影响。采用不同的数据平均方法来重建遥感算法提供的 Chl-a 估计值。在 ESA-CCI 数据中观察到了较强的相关性(R 值为 0.89)和可接受的差异(rmse ∼ 1.4 mg.m-3)。这种方法允许对日内瓦湖的欧空局 CCI 数据进行重新校准。最后,合并卫星和原位数据为长期分析浮游植物物候及其自 2002 年以来的年际变化提供了一致的时间序列。原位数据和卫星数据的结合提高了时间序列的时间分辨率,从而能够更准确地确定浮游植物物候学特征的特定春季事件的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing ESA Climate Change Initiative data for the monitoring of phytoplankton abundance and phenology in deep lakes: Investigation on Lake Geneva

Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-a concentrations, based on medium resolution satellite data, on a global scale. Chl-a concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved in-situ data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-a estimates provided by the remote sensing algorithms. Strong correlation (R-value > 0.89) and acceptable discrepancies (rmse ∼ 1.4 mg.m−3) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and in-situ data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of in-situ and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology.

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
Journal of Great Lakes Research
Journal of Great Lakes Research 生物-海洋与淡水生物学
CiteScore
5.10
自引率
13.60%
发文量
178
审稿时长
6 months
期刊介绍: Published six times per year, the Journal of Great Lakes Research is multidisciplinary in its coverage, publishing manuscripts on a wide range of theoretical and applied topics in the natural science fields of biology, chemistry, physics, geology, as well as social sciences of the large lakes of the world and their watersheds. Large lakes generally are considered as those lakes which have a mean surface area of >500 km2 (see Herdendorf, C.E. 1982. Large lakes of the world. J. Great Lakes Res. 8:379-412, for examples), although smaller lakes may be considered, especially if they are very deep. We also welcome contributions on saline lakes and research on estuarine waters where the results have application to large lakes.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信