基于Landsat8影像的Himawari-8叶绿素a空间降尺度研究

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2018 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The chlorophyll-a products of the new geostationary meteorological satellite Himawari-8 are difficult to meet the requirements of water quality monitoring in near-shore waters with high spatial heterogeneity due to their low spatial resolution. To overcome this limitation, a non-linear random forest algorithm was used to improve the spatial resolution of the chlorophyll a data from Himawari-8 by constructing a downscaling model using the band reflectance data from Landsat 8and the chlorophyll-a products from Himawari-8. The results showed that the coefficients of determination (R²) of the two autumn models and two winter models reached 0.6, 0.72, 0.71 and 0.85, respectively, and the root mean square errors (RMSE) were 1.47, 1.05, 1.89, 0.76mg/m³, respectively. The comparative analysis of the measured site data showed that the chlorophyll-a data generated by the downscaled model had a high consistency with the chlorophyll-a data of Himawari-8, and the R² reached 0.81 The spatial variation of chlorophyll-a concentration in the near-shore sea area is well reflected by the spatial variation of chlorophyll a data.

Key words： downscale chlorophyll-a random forests himawari-8 landsat 8

Received: 08 April 2022

PACS:

X171

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XIONG Yuan-kang
	FAN Dong-lin
	HE Hong-chang
	SHI Jin-ke
	ZHANG Jie
	XIAO Bin
	FU Bo-lin

Cite this article:

XIONG Yuan-kang,FAN Dong-lin,HE Hong-chang等. Spatial downscaling of chlorophyll A in Himawari-8 based on Landsat 8 images[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(11): 5341-5350.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I11/5341

[1]	Bierman P, Lewis M, Ostendor B, et al. A review of methods for analysing spatial and temporal patterns in coastal water quality[J]. Ecological Indicators, 2011,11(1):103-114.
[2]	Goetz S J, Gardiner N, Viers J H. Monitoring freshwater, estuarine and near-shore benthic ecosystems with multi-sensor remote sensing:An introduction to the special issue[J]. Remote Sensing of Environment, 2008,112(11):3993-3995.
[3]	于博文.基于深度学习的VIIRS卫星全球海洋叶绿素a浓度反演[D]. 北京:中国地质大学, 2019. Yu B W. Global ocean chlorophyll-a concentration inversion based on deep learning viirs satellite[D]. Beijing:China University of Geosciences, 2019.
[4]	Tang D L, Ni I H, Kester D R, et al. Remote sensing observations of winter phytoplankton blooms southwest of the Luzon Strait in the South China Sea[J]. Marine Ecology Progress, 1999,191:43-51.
[5]	Kim H H, Ko B C, Nam J Y. Predicting chlorophyll-a using Landsat 8OLI sensor data and the non-linear RANSAC method-a case study of Nakdong River, South Korea[J]. International Journal of Remote Sensing, 2016,37(14):3255-3271.
[6]	Frouin R J, Shenoi S C, Rao K H, et al. Ocean color estimation by Himawari-8/AHI[C]//Spie Asia-pacific Remote Sensing. Remote Sensing of the Oceans and Inland Waters:Techniques, Applications, and Challenges, 2016:987810.
[7]	Nazeer M, Nichol J E. Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal waters of Hong Kong[J]. Journal of Hydrology, 2016, 532:80-89.
[8]	Atkinson, Peter M. Downscaling in remote sensing[J]. International Journal of Applied Earth Observation & Geoinformation, 2013,22(Complete):106-114.
[9]	Fu Y, Xu S, Zhang C, et al. Spatial downscaling of MODIS Chlorophyll-a using Landsat 8images for complex coastal water monitoring[J]. Estuarine Coastal & Shelf Science, 2018,209:149-159.
[10]	Guo S, Sun B, Zhang H K, et al. MODIS ocean color product downscaling via spatio-temporal fusion and regression:The case of chlorophyll-a in coastal waters[J]. International journal of applied earth observation and geoinformation, 2018,73:340-361.
[11]	Mohebzadeh H, Yeom J, Lee T. Spatial downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea[J]. Remote Sensing, 2020,12(9):1412.DOI:10.3390/rs12091412.
[12]	杨明悦,毛献忠.基于特征重要性评分-随机森林的溶解氧预测模型及其在深圳湾的应用[J]. 中国环境科学, 2022,42(8):3876-3881. Yang M Y, Mao X Z. Dissolved oxygen prediction model based on characteristic importance score-random forest and its application in Shenzhen Bay[J]. China Environmental Science, 2022,42(8):3876-3881.
[13]	Ghosh A, Joshi P K. Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes[J]. ISPRS journal of photogrammetry and remote sensing, 2014, 96:76-93.
[14]	Hutengs C, Vohland M. Downscaling land surface temperatures at regional scales with random forest regression[J]. Remote Sensing of Environment, 2016,178:127-141.
[15]	徐轶肖,陶晓娉,刘成辉,等.广西北海半岛夏季营养盐及水质状况分析[J]. 海洋科学, 2021,45(6):107-117. Xu Y X, Tao X L, Liu C H, et al. Analysis of summer nutrient salts and water quality in Guangxi Beihai Peninsula[J]. Marine Science, 2021, 45(6):107-117.
[16]	刘子琳,宁修仁,蔡昱明.北部湾浮游植物粒径分级叶绿素a和初级生产力的分布特征[J]. 海洋学报, 1998,20(1):50-57. Liu Z L, Ning X R, Cai Y M. Distribution characteristics of phytoplankton grain size graded chlorophyll a and primary productivity in Beibu Gulf[J]. Acta Oceanic Sinica, 1998,20(1):50-57.
[17]	刘大召,晁俏利.北部湾海域叶绿素a质量浓度时空分布研究[J]. 海洋学研究, 2019,37(2):95-102. Liu D Z, Chao Q L. Spatial and temporal distribution of chlorophyll-a mass concentration in Beibu Gulf[J]. Ocean Research, 2019,37(2):95-102.
[18]	杨斌,钟秋平,张晨晓,等.钦州湾叶绿素a和初级生产力时空变化及其影响因素[J]. 环境科学学报, 2015,35(5):1333-1340. Yang B, Zhong Q P, Zhang C X, et al. Temporal and spatial variation of chlorophyll a and primary productivity in Qinzhou Bay and its influencing factors[J]. Journal of Environmental Science, 2015,35(5):1333-1340.
[19]	HK EPD. Marine water quality in Hong Kong in 2012[Z]. 2013
[20]	游介文,邹滨,赵秀阁,等.基于随机森林模型的中国近地面NO2浓度估算[J]. 中国环境科学, 2019,39(3):969-979. You J W, Zou B, Zhao X G, et al. Estimation of near-surface NO2 concentrations in China based on random forest model[J]. China Environmental Science, 2019,39(3):969-979.
[21]	Belgiu, Dragut. Random forest in remote sensing:A review of applications and future directions[J]. Isprs. J. Photogramm, 2016,2016, 114(-):24-31.
[22]	Hutengs C, Vohland M. Downscaling land surface temperatures at regional scales with random forest regression[J]. Remote Sensing of Environment, 2016,178:127-141.
[23]	李光一,李海萍,万华伟,等.随机森林算法在新疆物种丰富度影响因素研究中的应用[J]. 中国环境科学, 2021,41(2):941-950. Li G Y, Li H P, Wan H W, et al. Application of Stochastic Forest Algorithm in The Study of Influencing Factors of Species Richness in Xinjiang[J]. China Environmental Science,2021, 41(2):941-950.
[24]	Maung S H T Ohara S, Matsuoka K, et al. Seasonal dynamics influencing coastal primary production and phytoplankton communities along the southern Myanmar coast[J]. Journal of Oceanography, 2017,73:345-364.
[25]	潘俊.春夏季南黄海水文环境季节变化及其生态效应[D]. 青岛:中国科学院大学(中国科学院海洋研究所), 2020. Pan J. Seasonal changes and ecological effects of the South Yellow Sea in spring and summer[D]. Qingdao:University of Chinese Academy of Sciences (Institute of Oceanology, Chinese Academy of Sciences), 2020.
[26]	孙宏亮,何宏昌,付波霖,等.香港近海海域叶绿素a定量反演及时空变化分析[J]. 中国环境科学, 2020,40(5):2222-2229. Sun H L, He H C, Fu B L, et al. Analysis of chlorophyll a quantitative inversion and space-to-space change in hong Kong coastal waters[J]. Chinese Journal of Environmental Science, 2020,40(5):2222-2229.
[27]	Lee L, Srivastava P K, Petropoulos G P. Overview of sensitivity analysis methods in earth observation modeling-Science direct[J]. Sensitivity Analysis in Earth Observation Modelling, 2017:3-24.