Retrieval of total suspended matter concentration in Hangzhou Bay based on simulated HICO from in situ hyperspectral data

doi:10.6046/gtzyyg.2018.04.25

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(4559 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In this study, field data such as the concentration of total suspended matter (TSM) in Hangzhou Bay and its adjacent areas in Hangzhou’s coastal waters were observed, meanwhile, hyperspectral remote snesing data were measured with SVC GER1500 spectrometer during four cruises carried out on 20th, 22nd, 23rd and 24th July 2010. The coastal water-leaving refectance of HICO was simulated from in situ hyperspectral remote sensing spectra. The normalized peak area of remote sensing reflectance in the near-infrared region was applied to retrieving TSM after the spectra of simulated HICO were analyzed, as well as the application of single band model and band ratio model. The result indicated that the band ratio algorithm of Rrs(724.84)/Rrs(461.36) of HICO could be used to retrieve TSM in Hangzhou Bay. This study is helpful to retrieving TSM in coastal waters using HICO.

Keywords HICO total suspended matter remote sensing Hangzhou Bay

X87

Corresponding Authors: Wandong MA E-mail: mawdcn@163.com

Issue Date: 07 December 2018

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Dingfeng YU
	Yan ZHOU
	Wandong MA
	Zhigang GAI
	Enxiao LIU

Cite this article:

Dingfeng YU,Yan ZHOU,Wandong MA, et al. Retrieval of total suspended matter concentration in Hangzhou Bay based on simulated HICO from in situ hyperspectral data[J]. Remote Sensing for Land & Resources, 2018, 30(4): 171-175.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.25 OR https://www.gtzyyg.com/EN/Y2018/V30/I4/171

Fig.1 Sampling sites in Hangzhou Bay

Fig.2 TSM concentration of 19 sampling sites

Tab.1 Main techno-parameters of HICO

Fig.3 Spectral cuves of simulated HICO at 19 stations in Hangzhou Bay

Fig.4 Correlation coefficients between TSM concentration and single bands of simulated HICO

Fig.5 Relationship between the different remote sensing reflectance and TSM concentration

Fig.6 Relationship of TSM concentration and band ration

Fig.7 Comparison of the retrieved and the measured value

Fig.8 Illustration of normalized peak area in near-infrared region

Fig.9 Relationship between normalized peak area and TSM concentration

[1]	Gordon H R, Mccluney W R . Estimation of the depth of sunlight penetration in the sea for remote sensing[J]. Applied Optics, 1975,14(2):413-416. doi: 10.1364/AO.14.000413 pmid: 20134900 url: https://www.osapublishing.org/abstract.cfm?URI=ao-14-2-413
[2]	光洁, 韦玉春, 黄家柱 , 等. 分季节的太湖悬浮物遥感估测模型研究[J]. 湖泊科学, 2007,19(3):241-249. doi: 10.3321/j.issn:1003-5427.2007.03.003 url: http://www.cqvip.com/Main/Detail.aspx?id=24564564
[2]	Guang J, Wei Y C, Huang J Z , et al. Seasonal suspended sediment models in Lake Taihu using remote sensing data[J]. Journal of Lake Science, 2007,19(3):241-249.
[3]	Mao Z H, Chen J Y, Pan D L , et al. A regional remote sensing algorithm for total suspended matter in the East China Sea[J]. Remote Sensing of Environment, 2012,124:819-831. doi: 10.1016/j.rse.2012.06.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425712002507
[4]	He X Q, Bai Y, Pan D L , et al. Using geostationary satellite ocean color data to map the diurnal dynamics of suspended particulate matter in coastal waters[J]. Remote Sensing of Environment, 2013,133:225-239. doi: 10.1016/j.rse.2013.01.023 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425713000503
[5]	Mao Z H, Chen J Y, Pan D L , et al. A regional remote sensing algorithm for total suspended matter in the East China Sea[J]. Remote Sensing of Environment, 2012,124:819-831. doi: 10.1016/j.rse.2012.06.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425712002507
[6]	Binding C E, Bowers D G, Mitchelson-Jacob E G . Estimating suspended sediment concentrations from ocean colour measurements in moderately turbid waters;the impact of variable particle scattering properties[J]. Remote sensing of Environment, 2005,94(3):373-383. doi: 10.1016/j.rse.2004.11.002 url: http://linkinghub.elsevier.com/retrieve/pii/S003442570400344X
[7]	Zhang M W, Tang J W, Dong Q , et al. Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery[J]. Remote Sensing of Environment, 2010,114(2):392-403. doi: 10.1016/j.rse.2009.09.016 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425709002880
[8]	Ahn Y H, Moon J E, Gallegos S . Development of suspended particulate matter algorithms for ocean color remote sensing[J]. Korean Journal of Remote Sensing, 2001,17(4):285-295.
[9]	宋庆君, 马荣华, 唐军武 , 等. 秋季太湖悬浮物高光谱估算模型[J]. 湖泊科学, 2008,20(2):196-202.
[9]	Song Q J, Ma R H, Tang J W , et al. Models of estimated total suspend matter concentration base on hyper-spectrum in Lake Taihu,in autumn[J]. Journal of Lake Science, 2008,20(2):196-202.
[10]	Ma W D, Xing Q G, Chen C Q , et al. Using the normalized peak area of remote sensing reflectance in the near-infrared region to estimate total suspended matter[J]. International Journal of Remote Sensing, 2011,32(22):7479-7486. doi: 10.1080/01431161.2010.524673 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2010.524673
[11]	Gitelson A A, Gao B C, Li R R , et al. Estimation of chlorophyll-a concentration in productive turbid waters using a hyperspectral imager for the coastal ocean? The Azov Sea case study[J]. Environmental Research Letters, 2011,6(2):024023. doi: 10.1088/1748-9326/6/2/024023 url: http://stacks.iop.org/1748-9326/6/i=2/a=024023?key=crossref.533b7023d5901ca90fe4a659f3f4334f
[12]	Moses W J, Gitelson A A, Berdnikov S , et al. HICO-based NIR-red models for estimating chlorophyll-concentration in productive coastal waters[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(6):1111-1115. doi: 10.1109/LGRS.2013.2287458 url: http://ieeexplore.ieee.org/document/6670037/
[13]	Mishra D R, Schaeffer B A, Keith D . Performance evaluation of normalized difference chlorophyll index in northern Gulf of Mexico estuaries using the hyperspectral imager for the coastal ocean[J]. GIScience and Remote Sensing, 2014,51(2):175-198. doi: 10.1080/15481603.2014.895581 url: http://www.tandfonline.com/doi/abs/10.1080/15481603.2014.895581
[14]	Braga F, Giardino C, Bassani C , et al. Assessing water quality in the northern Adriatic Sea from HICO ^TM data [J]. Remote Sensing Letters, 2013,4(10):1028-1037. doi: 10.1080/2150704X.2013.830203 url: http://www.tandfonline.com/doi/abs/10.1080/2150704X.2013.830203
[15]	Keith D J, Schaeffer B A, Lunetta R S , et al. Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor[J]. International Journal of Remote Sensing, 2014,35(9):2927-2962. doi: 10.1080/01431161.2014.894663 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2014.894663
[16]	Garcia R A , Fearns P R C S,Mckinna L I W.Detecting trend and seasonal changes in bathymetry derived from HICO imagery:A case study of Shark Bay,Western Australia[J]. Remote Sensing of Environment, 2014,147:186-205. doi: 10.1016/j.rse.2014.03.010 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425714000819
[17]	Sathyendranath S . Remote Sensing of Ocean Colour in Coastal,and Other Optically-complex,Waters[R].Dartmouth:the International Ocean- Colour Coordinating Group, 2000.
[18]	Smith R C, Baker K S . Optical properties of the clearest natural waters(200-800 nm)[J]. Applied Optics, 1981,20(2):177-184. doi: 10.1364/AO.20.000177 pmid: 20309088 url: https://www.osapublishing.org/abstract.cfm?URI=ao-20-2-177
[19]	Pope R M, Fry E S . Absorption spectrum (380-700 nm) of pure water.II.Integrating cavity measurements[J]. Applied Optics, 1997,36(33):8710-8723. doi: 10.1364/AO.36.008710 url: https://www.osapublishing.org/abstract.cfm?URI=ao-36-33-8710
[20]	Koponen S, Pulliainen J, Kallio K , et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J]. Remote Sensing of Environment, 2002,79(1):51-59. doi: 10.1016/S0034-4257(01)00238-3 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425701002383

[1]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4]	ZHENG Xiucheng, ZHOU Bin, LEI Hui, HUANG Qiyu, YE Haolin. Extraction and spatio-temporal change analysis of the tidal flat in Cixi section of Hangzhou Bay based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 18-26.
[5]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[6]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[7]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[8]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[9]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[10]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[11]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[12]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[13]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[14]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[15]	LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.

Viewed

Full text

Abstract

Cited

Shared

Discussed