Please wait a minute...
 
Remote Sensing for Land & Resources    2019, Vol. 31 Issue (4) : 182-189     DOI: 10.6046/gtzyyg.2019.04.24
|
Synergistic inversion method of chlorophyll a concentration in GF-1 and Landsat8 imagery: A case study of the Taihu Lake
Honge FENG1,2, Jiaguo LI2(), Yunfang ZHU2, Qijin HAN3, Ning ZHANG4, Shufang TIAN1
1. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
3. China Resources Satellite Application Center, Beijing 100094, China
4. Urban and Rural Planning Management Center of the Ministry of Housing and Urban Rural Development of the People’s Republic of China, Beijing 100835, China
Download: PDF(2085 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Different spatial resolutions, spectral resolutions and radiation resolutions influence the accurate estimation of remotely sensed chlorophyll a concentration of water. In this study, GF-1 WFV and Landsat8 OLI imagery was used as objects, and the cooperative methods of single-band substitution, single-band fusion and three-band fusion were respectively used to analyze dominant characteristics of spatial resolution and spectral resolution for improving the precision of chlorophyll a concentration inversion in multi-source remote sensing data. On such a basis, the optimal combination of GF-1 WFV and Landsat8 OLI data was further explored so as to improve the inversion accuracy of chlorophyll a concentration and promote the application of domestic high-resolution satellite GF-1 imagery. The results show that, in the GF-1 WFV and Landsat8 OLI cooperative inversion process, the spectral resolution and radiation resolution of near infrared band dominate the characteristics, and the influence of the near infrared band spectrum resolution enhancement is more favorable for improving the inversion accuracy of chlorophyll a concentration, whereas in the blue and red bands, the higher the spatial resolution, the higher the accuracy of chlorophyll a concentration inversion. The combination factors of GF-1 WFV and Landsat8 OLI optimal chlorophyll a concentration synergistic inversion spectral index are as follows: Landsat8 OLI near infrared band, GF-1 WFV and Landsat8 OLI fused red band, GF-1 WFV and Landsat8 OLI fused blue band. The GF-1 WFV and Landsat8 OLI separate inversion accuracy with average relative errors of 41.93% and 38.37%, respectively. After optimization, the average relative error of synergistic inversion is reduced to 17.35%. This study preliminarily explored the spectral resolution and spatial resolution of GF-1 WFV and Landsat8 OLI imagery of water chlorophyll a concentration cooperative inversion dominant characteristics and the optimal coordinated way. The authors are in the hope of providing reference for the channel design of the following domestic satellites and the cooperative inversion of multi-source satellites.
Keywords Taihu      chlorophyll a      dominant trait      cooperative inversion      band combination     
:  TP79  
Corresponding Authors: Jiaguo LI     E-mail: jacoli@126.com
Issue Date: 03 December 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Honge FENG
Jiaguo LI
Yunfang ZHU
Qijin HAN
Ning ZHANG
Shufang TIAN
Cite this article:   
Honge FENG,Jiaguo LI,Yunfang ZHU, et al. Synergistic inversion method of chlorophyll a concentration in GF-1 and Landsat8 imagery: A case study of the Taihu Lake[J]. Remote Sensing for Land & Resources, 2019, 31(4): 182-189.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.24     OR     https://www.gtzyyg.com/EN/Y2019/V31/I4/182
Fig.1  Geographic location of study area
卫星传感器 波段 光谱范
围/μm		 空间分
辨率/m		 重访周
期/d
GF-1 WFV B1 0.45~0.52 16 2
B2 0.52~0.59
B3 0.63~0.69
B4 0.77~0.89
Landsat8 OLI B1 0.43~0.45 30 16
B2 0.45~0.52
B3 0.53~0.60
B4 0.63~0.68
B5 0.85~0.89
B6 1.56~1.66
B7 2.10~2.30
B8 0.50~0.68 15
B9 1.36~1.39 30
Tab.1  Comparison between GF-1 WFV and Landsat8 OLI sensor parameters
卫星传感器 波段 最小值 最大值 全距 平均值
GF-1 WFV B1 279 600 321 131
B2 216 606 390 297
B3 132 510 378 398
B4 57 699 642 415
Landsat8 OLI B2 9 300 12 500 3 200 10 409
B3 8 037 11 844 3 807 9 820
B4 7 152 12 048 4 896 8 701
B5 6 208 24 638 18 430 7 180
Tab.2  Characteristics statistics of GF-1 WFV and Landsat8 OLI data radiation resolution in water area
Fig.2  Comparison of spectral bands of GF-1 WFV and Landsat8 OLI and reflectance of chlorophyll a
卫星传感器 波段 中心波长 有效波宽
GF-1 WFV B1 485 48
B2 555 71
B3 660 65
B4 830 119
Landsat8 OLI B2 485 60
B3 565 57
B4 655 38
B5 870 28
Tab.3  Comparison of GF-1 WFV and Landsat8 OLI central wavelength and effective wave width(nm)
卫星传感器 均值 方差 同质性 对比度 非相似性 熵 角二阶矩 相关性
GF-1 WFV 55.8 505.7 1.0 939.4 26.0 2.2 1.0 2.0
Landsat8 OLI 50.1 238.4 1.0 466.9 18.8 2.2 1.0 2.0
Tab.4  Comparison of GF-1 WFV and Landsat8 OLI texture statistical characteristics
反演模型 反演模型公式 R2
单独反演 CF-1 WFV y = -4 088.8x2 + 1 601x + 60.778 0.366 4
Landsat8 OLI y = 7 388.3x2 + 4 044.7x + 179.38 0.439 0
单波段替代协同反演 近红外波段替代 y = 33 245x2 + 5 391.7x + 239.25 0.859 6
红光波段替代 y = -4 752.9x2 + 1 564.6x + 31.428 0.177 2
蓝光波段替代 y = -4 364.4x2 + 1 616.2x + 59.771 0.352 9
单波段融合协同反演 近红外波段融合 y = -30.245x2 + 142.52x + 1.696 8 0.127 8
红光波段融合 y = -4 416.2x2 + 1 645.9x + 63.088 0.373 2
蓝光波段融合 y = -4 195.5x2 + 1 621.2x + 61.174 0.370 1
三波段融合协同反演 y = -21.137x2 + 133.65x + 1.596 2 0.128 5
Tab.5  Chlorophyll a concentration inversion model formula
Fig.3  Inversion results of chlorophyll a verification samples
指标 单独反演 单波段替代协同反演 单波段融合协同反演 三波段融合
协同反演
GF-1 WFV Landsat8 OLI 近红外波段 红光波段 蓝光波段 近红外波段 红光波段 蓝光波段
RMSE/(mg·m-3) 107.87 107.91 36.23 128.98 109.59 132.95 107.32 107.63 133.20
e/% 41.93 38.37 17.76 61.31 43.23 59.50 41.09 41.82 56.89
Tab.6  Comparison of accuracy of the different inversion methods
Fig.4  Optimal cooperative inversion model fitting
Fig.5  Results of chlorophyll a optimal co-inversion
[1] 马荣华, 戴锦芳 . 结合Landsat ETM与实测光谱估测太湖叶绿素及悬浮物含量[J]. 湖泊科学, 2005,17(2):97-103.
[1] Ma R H, Dai J F . Quantitative estimation of chlorophyll-a and total suspended matter concentration with Landsat ETM based on field spectral features of Lake Taihu[J]. Journal of Lake Sciences, 2005,17(2):97-103.
[2] Zimba P V, Gitelson A . Remote estimation of chlorophyll concentration in hyper-eutrophic aquatic systems:Model tuning and accuracy optimization[J]. Aquaculture, 2006,256(1-4):272-286.
[3] Dall'Olmo G, Gitelson A A . Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters:Experimental results[J]. Applied Optics, 2005,44(3):412-422.
[4] 徐雯佳, 杨斌, 田力 , 等. 应用MODIS数据反演河北省海域叶绿素a浓度[J]. 国土资源遥感, 2012,24(4):152-156.doi: 10.6046/gtzyyg.2012.04.25.
doi: 10.6046/gtzyyg.2012.04.25
[4] Xu W J, Yang B, Tian L , et al. Retrieval of chlorophyll-a concentration by using MODIS data in Hebei Sea Area[J]. Remote Sensing for Land and Resources, 2012,24(4):152-156.doi: 10.6046/gtzyyg.2012.04.25.
[5] Elalem A, Chokmani K, Laurion I . Comparative analysis of four models to estimate chlorophyll-a concentration in Case-2 waters using moderate resolution imaging spectroradiometer (MODIS) imagery[J]. Remote Sensing, 2012,4(8):2373-2400.
[6] 张明慧, 苏华, 季博文 . MODIS时序影像的福建近岸叶绿素a浓度反演[J]. 环境科学学报, 2018,38(12):4831-4839.
[6] Zhang M H, Su H, Ji B W . Retrieving nearshore chlorophyll-a concentration using MODIS time-series images in the Fujian Province(China)[J]. Acta Scientiae Circumstantiae, 2018,38(12):4831-4839.
[7] 旷达, 韩秀珍, 刘翔 . 基于环境一号卫星的太湖叶绿素a浓度提取[J]. 中国环境科学, 2010,30(9):1268-1273.
doi:
[7] Kuang D, Han X Z, Liu X . Quantitative estimation of Taihu chlorophyll-a concentration using HJ-1A and 1B CCD imagery[J]. China Environmental Science, 2010,30(9):1268-1273.
[8] 唐军武, 田国良, 汪小勇 , 等. 水体光谱测量与分析Ⅰ:水面以上测量法[J]. 遥感学报, 2004,8(1):37-44.
doi: 10.3321/j.issn:1007-4619.2004.01.006 url: http://d.wanfangdata.com.cn/Periodical/ygxb200401006
[8] Tang J W, Tian G L, Wang X Y , et al. The methods of water spectra measurement and analysis Ⅰ:Above-water method[J]. Journal of Remote Sensing, 2004,8(1):37-44.
[9] 陈宇炜, 陈开宁, 胡耀辉 . 浮游植物叶绿素a测定的“热乙醇法”及其测定误差的探讨[J]. 湖泊科学, 2006,18(5):550-552.
[9] Chen Y W, Chen K N, Hu Y H . Discussion on possible error for phytoplankton chlorophyll-a concentration analysis using hot-ethanol extraction method[J]. Journal of Lake Sciences, 2006,18(5):550-552.
[10] Lathrop R G, Lillesand T M, Yandell B S . Testing the utility of simple multi-data Thematic Mapper calibration algorithms for monitoring turbid inland water[J]. International Journal of Remote Sensing, 1991,12(10):2045-2063.
[11] Kahru M, Michell B G, Diaz A , et al. MODIS detects a devastating algal bloom in Paracas Bay,Peru[J]. EOS Transactions American Geophysical Union, 2004,85(45):465-472.
[12] 姜晓晨, 邓正栋, 武国瑛 , 等. Landsat 8 OLI多光谱与全色影像融合算法的比较[J]. 信息技术与网络安全, 2018,37(8):31-35.
[12] Jiang X C, Deng Z D, Wu G Y , et al. Comparison on fusion algorithms of Landsat 8 OLI multi-spectral and panchromatic images[J]. Information Technology and Network Security, 2018,37(8):31-35.
[13] Antoine D, Morel A . A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument):Principle and implementation for atmospheres carrying various aerosols including absorbing ones[J]. International Journal of Remote Sensing, 1999,20(9):1875-1916.
[14] Gitelson. A . The peak near 700 nm on radiance spectral of algae and water:Relationships of its magnitude and position with chlorophyll concentration[J]. International Journal of Remote Sensing, 1992,13(17):3367-3373.
[15] Gitelson A A, Merzlyak M N . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. Journal of Plant Physiology, 2003,160(3):271-282.
[16] Gons H J . Optical teledetection of chlorophyll a in turbid inland waters[J]. Environmental Science and Technology, 1999,33(7):1127-1132.
[17] 疏小舟, 尹球, 匡定波 . 内陆水体藻类叶绿素浓度与反射光谱特征的关系[J]. 遥感学报, 2000,4(l):41-45.
[17] Shu X Z, Ying Q, Kuang D B . Relationship between algal chlorophyll concentration and spectral reflectance of inland water[J]. Journal of Remote Sensing, 2000,4(l):41-45.
[18] 王珊珊, 李云梅, 王永波 . 太湖水体叶绿素浓度反演模型适宜性分析[J]. 湖泊科学, 2015,27(1):150-162.
[18] Wang S S, Li Y M, Wang Y B . Suitability of the retrieval models for estimating chlorophyll concentration in Lake Taihu[J]. Journal of Lake Sciences, 2015,27(1):150-162.
[1] WEI Haohan, XU Renjie, YANG Qiang, ZHOU Quanping. Variation and effect analysis of the water level of the Taihu Lake based on multi-source satellite altimetry data[J]. Remote Sensing for Natural Resources, 2021, 33(3): 130-137.
[2] MA Shibin, YANG Wenfang, ZHANG Kun. Study of key technology of SPOT6 satellite image processing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 30-35.
[3] ZHANG Yue, XIAO Yu, CHANG Jingjing, LIU Jian, WANG Yaqiong, HE Chunyan, HE Bing. Effects of atmospheric correction on extracting cyanobacteria bloom information based on remote sensing indices[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 7-12.
[4] CHEN Jun, QUAN Wenting. Scale properties of the apparent reflectance of false dark pixel: A case study of the images of AWiFS and LISS sensors[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(2): 33-36.
[5] XIA Shuang, RUAN Renzong, ZHANG Yue, YAN Meichun. Effects of aerosol optical thickness on extracting cyanbacteria bloom[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 33-38.
[6] CHEN Jun, LU Kai, WANG Bao-jun. The Uncertainty Model for Water Qualities Retrieval Results: A Case Study of Chlorophyll-a Concentration[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(4): 83-86.
[7] LI Xu-Wen, LI Gang, MIU Bei-Bei. THE APPLICATION OF Google Earth AND ArcGIS 9.2 SOFTWARE TO THE VISUALIZATION OF WATER POLLUTION AND CYANOPHYTA  ALGAE MONITORING DATA FROM THE TAIHU LAKE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(1): 97-99.
[8] DENG Shu-Bin, WU Hong-Gan, JIANG Tao. AN APPROACH TO THE EXTRACTION OF REMOTELY-SENSED INFORMATION OF
FOREST COVERAGE BASED ON PCA/NDVI[J]. REMOTE SENSING FOR LAND & RESOURCES, 2007, 19(2): 82-85.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech