Satellite estimation of suspended particle types using a backscattering efficiency-based model in the marginal seas

被引:3
|
作者
Wang, Shengqiang [1 ,2 ,3 ]
Li, Xiaofan [1 ,4 ]
Sun, Deyong [1 ,3 ]
He, Xianqiang [2 ]
Zhang, Hailong [1 ,3 ]
Zhao, Wenyuan [1 ]
He, Yijun [1 ,3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China
[2] Minist Nat Resources, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China
[3] Minist Nat Resources, Key Lab Space Ocean Remote Sensing & Applicat, Beijing 100081, Peoples R China
[4] Shenzhen Ecol & Environm Monitoring Ctr Guangdong, Shenzhen 518049, Peoples R China
基金
中国国家自然科学基金;
关键词
INHERENT OPTICAL-PROPERTIES; OCEAN COLOR DATA; BOHAI SEA; YELLOW SEA; PARTICULATE MATTER; BEAM ATTENUATION; MARINE PARTICLES; VARIABILITY; REMOTE; SIZE;
D O I
10.1364/OE.476192
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The particle composition of suspended matter provides crucial information for a deeper understanding of marine biogeochemical processes and environmental changes. Particulate backscattering efficiency (Q(bbe)(lambda)) is critical to understand particle composition, and a Q(bbe)(lambda)based model for classifying particle types was proposed. In this study, we evaluated the applicability of the Q(bbe)(lambda)-based model to satellite observations in the shallow marginal Bohai and Yellow Seas. Spatiotemporal variations of the particle types and their potential driving factors were studied. The results showed that the Q(bbe)(lambda) products generated from Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua agreed well with the in situ measured values, with determination coefficient, root mean square error, bias, and mean absolute percentage error of 0.76, 0.007, 16.5%, and 31.0%, respectively. This result verifies the satellite applicability of the Qbbe(.)-based model. Based on long-term MODIS data, we observed evident spatiotemporal variations of the Q(bbe)(lambda), from which distinct particle types were identified. Coastal waters were often dominated by minerals, with high Q(bbe)(lambda) values, though their temporal changes were also observed. In contrast, waters in the offshore regions showed clear changes in particle types, which shifted from organic-dominated with low Q(bbe)(lambda) levels in summer to mineral-dominated with high Q(bbe)(lambda) values in winter. We also observed long-term increasing and decreasing trends in Q(bbe)(lambda) in some regions, indicating a relative increase in the proportions of mineral and organic particles in the past decades, respectively. These spatiotemporal variations of Q(bbe)(lambda) and particle types were probably attributed to sediment re-suspension related to water mixing driven by wind and tidal forcing, and to sediment load associated with river discharge. Overall, the findings of this study may provide valuable proxies for better studying marine biogeochemical processes, material exchanges, and sediment flux.
引用
收藏
页码:890 / 906
页数:17
相关论文
共 50 条
  • [21] Estimation of particulate matter concentrations in Turkiye using a random forest model based on satellite AOD retrievals
    Tuygun, Gizem Tuna
    Elbir, Tolga
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2023, 37 (09) : 3469 - 3491
  • [22] Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model
    Du, Xin
    Li, Qiangzi
    Dong, Taifeng
    Jia, Kun
    GEOCARTO INTERNATIONAL, 2015, 30 (03) : 258 - 269
  • [23] A Regression-Based Prediction Model of Suspended Sediment Yield in the Cuyahoga River in Ohio Using Historical Satellite Images and Precipitation Data
    Ampomah, Richard
    Hosseiny, Hossein
    Zhang, Lan
    Smith, Virginia
    Sample-Lord, Kristin
    WATER, 2020, 12 (03)
  • [24] Li-ion Battery SOC Estimation Using Particle Filter Based on an Equivalent Circuit Model
    Du Jiani
    Wang Youyi
    Wen Changyun
    2013 10TH IEEE INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2013, : 580 - 585
  • [25] Spectrum Sensing of NOMA Signals Using Particle Swarm Optimization Based Channel Estimation With a GMM Model
    Zhou, Heng
    Jin, Ming
    Guo, Qinghua
    Yuan, Chang
    Tian, Ye
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2023, 12 (11) : 1856 - 1860
  • [26] ESTIMATION AND ANALYSIS OF USER IPP DELAYS USING BILINEAR MODEL FOR SATELLITE-BASED AUGMENTED NAVIGATION SYSTEMS
    Ratnam, D. Venkata
    AVIATION, 2013, 17 (02) : 65 - 69
  • [27] A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery
    Baek, You-Hyun
    Moon, Il-Ju
    Im, Jungho
    Lee, Juhyun
    REMOTE SENSING, 2022, 14 (02)
  • [28] Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model
    Li, Xiaolong
    Yang, Yi
    Ishizaka, Joji
    Li, Xiaofeng
    REMOTE SENSING OF ENVIRONMENT, 2023, 294
  • [29] Estimation of particulate matter concentrations in Türkiye using a random forest model based on satellite AOD retrievals
    Gizem Tuna Tuygun
    Tolga Elbir
    Stochastic Environmental Research and Risk Assessment, 2023, 37 : 3469 - 3491
  • [30] Emulation of Channel Model and Estimation for MIMO based Satellite Land Mobile System using Software Defined Radio
    Pandey, Amitesh
    Gandhiraj, R.
    Kirthiga, S.
    Jayakumar, M.
    Devi, Nirmala M.
    Bera, Subash Chandra
    8TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2018), 2018, 143 : 868 - 875