地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (3): 546-557.doi: 10.12082/dqxxkx.2022.210322

• 遥感科学与应用技术 • 上一篇    下一篇

高分六号影像在内陆水体叶绿素a反演中的应用潜力分析

曹引1,*(), 冶运涛1, 赵红莉1, 蒋云钟1, 董甲平1, 严登明2   

  1. 1.中国水利水电科学研究院 水资源研究所,北京 100038
    2.黄河勘测规划设计研究院有限公司,郑州 450000
  • 收稿日期:2021-06-08 修回日期:2021-07-09 出版日期:2022-03-25 发布日期:2022-05-25
  • 通讯作者: *曹 引(1991— ),男,安徽滁州人,博士,工程师,主要从事水资源遥感研究。E-mail: caoyin@iwhr.com
  • 基金资助:
    高分辨率对地观测系统重大专项(08-Y30F02-9001-20/22);国家重点研发计划课题(2018YFC0407705);中国工程科技知识中心水利专业知识服务系统(CKCEST-2020-2-9)

Application Potential Analysis on Chlorophyll-a Retrieval for Inland Water based on a Gaofen-6 WFV Imagery

CAO Yin1,*(), YE Yuntao1, ZHAO Hongli1, JIANG Yunzhong1, DONG Jiaping1, Yan Dengming2   

  1. 1. Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    2. Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450000, China
  • Received:2021-06-08 Revised:2021-07-09 Online:2022-03-25 Published:2022-05-25
  • Supported by:
    China High-resolution Earth Observation System(08-Y30F02-9001-20/22);National Key Research and Development Program of China(2018YFC0407705);Water Related Knowledge Service System of China Knowledge Centre for Sciences and Technology(CKCEST-2020-2-9)

摘要:

GF-6 WFV影像具有宽覆盖、高时空分辨率、高光谱分辨率等特点,目前在农业和林业遥感领域都有一定应用,但是在水质遥感中的应用潜力还缺乏系统的评估。本文以潘家口和大黑汀水库为研究区,采用2019年9月24—25日获取的潘家口和大黑汀水库叶绿素a浓度、实测遥感反射率和准同步GF-6 WFV影像,构建了潘家口和大黑汀水库叶绿素a浓度经验反演模型,探索GF-6 WFV在内陆水体叶绿素a浓度遥感监测中的应用潜力。研究结果表明,基于GF-6 WFV模拟光谱构建的潘家口和大黑汀水库叶绿素a浓度经验模型决定系数均在0.90以上,GF-6 WFV影像在水体叶绿素a遥感监测中具有应用潜力,尤其是新增的黄波段和红边波段1,有助于提高GF-6 WFV影像叶绿素a浓度遥感监测能力;GF-6 WFV影像大气校正误差降低了叶绿素a浓度遥感监测精度,GF-6 WFV影像水体大气校正精度有待改进,以提升GF-6 WFV影像水质遥感监测能力。

关键词: 高分六号, 内陆水体, 潘家口和大黑汀水库, 叶绿素a, 遥感, 大气校正, 经验模型, 应用潜力

Abstract:

Gaofen-6 wide field of view (GF-6 WFV) imagery, with wide coverage, high temporal, spatial, and spectral resolution, has been applied in the fields of remote sensing of agriculture and forestry. However, the application potential of GF-6 imagery in the field of remote sensing of water quality lacks a systematic assessment. In this study, four empirical models of single-band model, band-ratio model, partial least squares model, and support vector machine model were developed to retrieve Chlorophyll-a (Chl-a) in Panjiakou and Daheiting reservoirs. The retrieval was based on measured Chl-a concentration and in situ remote sensing reflectance of 37 samples acquired in September 24 and 25, 2019, as well as a quasi-synchronous GF-6 WFV imagery. The application potential of GF-6 imagery in the field of remote sensing of water quality was evaluated according to the performance of four empirical models for Chl-a retrieval in Panjiakou and Daheiting reservoirs. The determination coefficients and comprehensive errors of four empirical models based on GF-6 WFV reflectance simulated by in situ reflectance were above 0.9 and less than 15% for Chl-a retrieval in Panjiakou and Daheiting reservoirs, respectively. The partial least squares model had the highest accuracy among the four empirical models, with a determination coefficient of 0.96 and a comprehensive error of 13.22%. Finally, the partial least squares model was applied to retrieve the spatial distribution of Chl-a concentration in Panjiakou and Daheiting reservoirs based on the GF-6 WFV imagery acquired on September 26, 2019. The Chl-a retrieval result indicated that Chl-a concentration was less than 10 µg/L in Panjiakou reservoir but more than 10 µg/L in Daheiting reservoir. The trophic states of Panjiakou reservoir and Daheiting reservoir were respectively mesotrophic and eutrophic according to trophic level index calculated by Chl-a concentration. GF-6 WFV imagery, with eight bands in visible and near-infrared, has application potential in remote sensing of Chl-a concentration in inland water. In particular, the newly added yellow band and red-edge band 1 in GF-6 WFV imagery contribute to improving the performance of Chl-a retrieval. The band reflectance of the GF-6 WFV imagery, derived from atmospheric correction, has obvious systematic deviations and correction errors, especially for band 4 (Near Infrared, NIR) and band 6 (Red-edge 2). Atmospheric correction error reduces the performance of the GF-6 WFV imagery in Chl-a retrieval in Panjiakou and Daheiting reservoirs. In order to improve the capability of GF-6 WFV imagery in remote sensing of water quality in inland water, the atmospheric correction accuracy of GF-6 WFV imagery needs to be further improved.

Key words: GF-6, inland water, Panjiakou and Daheiting reservoirs, chlorophyll-a, remote sensing, atmospheric correction, empirical model, application potential