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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 262-271     DOI: 10.6046/zrzyyg.2020324
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Extraction and application of Forel-Ule index based on images from multiple sensors
WANG Yifei1,2,3,4,5(), GONG Zhaoning1,2,3,4(), ZHANG Yuan1,2,3,4, SU Shuo1,2,3,4
1. College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China
2. Key Laboratory of 3D Information Acquisition and Application of Ministry, Beijing 100048, China
3. Beijing Key Laboratory of Resources Environment and GIS, Beijing 100048, China
4. Beijing Laboratory of Water Resources Security, Beijing 100048, China
5. Ministry of Ecology and Environment Center for Satellite Application onEcology and Environment, Beijing 100094, China
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Abstract  

The quantitative characterization of water body color can provide important reference data for the comprehensive water quality assessment of inland lakes and reservoirs. The Guanting Reservoir is a large inland lake in North China. Based on FUI inversion using the seasonal-scale Sentinel-2 and Landsat 8 OLI reflectance data during 2016—2020, this study quantitatively analyzed the heterogeneous characteristics of Forel-Ule Index (FUI) of the Guanting Reservoir on the spatial, intra-annual, and inter-annual scales. To explore the coupling relationship between the FUI and the nutrient status of the water body, models were built using both hue angle α and FUI and the trophic status index (TSI). Moreover, this study demonstrated the comparability of FUI among different sensors and its application potential. The results are as follows. ① On the spatial scale, the FUI value was low at the center but high on the edge of the reservoir. ② On the seasonal scale within a year, the FUI value showed a trend of reaching the highest in winter, slightly decreasing in spring, reaching the lowest in summer, and rising again in autumn. ③ On the interannual scale, the FUI value in the latest three years was lower than that in the first two years during 2016—2020 and the water color changed accordingly from yellowish brown to yellowish green. These may be attributable to the effective governance of the Guanting Reservoir by the Beijing Municipal Government. ④ The Pearson correlation coefficient between TSI and α and that between TSI and FUI were -0.85 and 0.80, respectively, indicating a strong correlation between FUI and TSI. ⑤ The FUI values obtained through the inversion based on the Sentinel-2 and Landsat 8 OLI images of the same day were very approximate and were 13.04 and 13.16, respectively. This indicates that FUI is comparable between the images from different sensors. Therefore, the inversion of FUI can be achieved using the long time-series remote sensing data from multiple sensors. Meanwhile, FUI possesses notable application potential and advantages in the assessment of water quality and trophic status.
Keywords Guanting Reservoir      hue angle      FUI      trophic status assessment     
ZTFLH:  TP79  
Corresponding Authors: GONG Zhaoning     E-mail: cyanodee@foxmail.com;gongzhn@163.com
Issue Date: 24 September 2021
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Yifei WANG
Zhaoning GONG
Yuan ZHANG
Shuo SU
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Yifei WANG,Zhaoning GONG,Yuan ZHANG, et al. Extraction and application of Forel-Ule index based on images from multiple sensors[J]. Remote Sensing for Natural Resources, 2021, 33(3): 262-271.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020324     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/262
Fig.1  Map of the study area
Fig.2  Guanting Reservoir in-situ measured points
Fig.3  Water extraction method
Fig.4  Schematic diagram of 21 chromaticity coordinates of CIE-xy chromaticity diagram and FUI water color index
i αi FUI i αi FUI
1 227.168 1 12 62.186 12
2 220.977 2 13 56.435 13
3 209.994 3 14 50.665 14
4 190.779 4 15 45.129 15
5 163.084 5 16 39.769 16
6 132.999 6 17 34.906 17
7 109.054 7 18 30.439 18
8 94.037 8 19 26.337 19
9 83.346 9 20 22.741 20
10 74.572 10 21 19.000 21
11 67.957 11
Tab.1  FUI Index lookup table
传感器 a5 a4 a3 a2 a 常数项
Sentinel-2 -161.23 1 117.08 -2 950.14 3 612.17 -1 943.57 364.28
Landsat8 OLI -52.16 373.81 -981.83 1 134.19 -533.61 76.72
Tab.2  Sentinel-2, Landsat8 OLI sensor chromaticity angle deviation correction polynomial coefficients based on linear interpolation of the band (a=αM/100)
Fig.5  The fitting of TSI trophic state index with chromaticity angle α and FUI based on measured chlorophyll a concentration
Fig.6  Relationships between FUI and TSI from the IOCCG simulated dataset (N=500)
Fig.7  Seasonal variation of the FUI for Guanting Reservoir
Fig.8  The average FUI value of Guanting Reservoir in each quarter
Fig.9  Sentinel-2 and Landsat8 OLI inversion FUI index results comparison
[1] Allan J D, McIntyre P B, Smith S D D, et al. Joint analysis of stressors and ecosystem services to enhance restoration effectiveness[J]. Proceedings of the National Academy of Sciences, 2013, 110(1):372-377.
doi: 10.1073/pnas.1213841110 url: http://www.pnas.org/cgi/doi/10.1073/pnas.1213841110
[2] Guan Q, Feng L, Hou X, et al. Eutrophication changes in fifty large lakes on the Yangtze Plain of China derived from MERIS and OLCI observations[J]. Remote Sensing of Environment, 2020, 246:111890.
doi: 10.1016/j.rse.2020.111890 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425720302601
[3] Feng L, Hou X, Zheng Y. Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations[J]. Remote Sensing of Environment, 2019, 221:675-686.
doi: 10.1016/j.rse.2018.12.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425718305601
[4] Bukata R P. Retrospection and introspection on remote sensing of inland water quality:“Like Déjà Vu All Over Again”[J]. Journal of Great Lakes Research, 2013(39):2-5.
[5] 宫鹏. 拓展与深化中国全境的环境变化遥感应用[J]. 科学通报, 2012, 57(16):1379-1387.
[5] Gong P. Expanding and deepening the application of remote sensing to environmental change in China[J]. Chinese Science Bulletin, 2012, 57(16):1379-1387.
[6] 王明翠, 刘雪芹, 张建辉. 湖泊富营养化评价方法及分级标准[J]. 中国环境监测, 2002(5):47-49.
[6] Wang M C, Liu X Q, Zhang J H. Evaluate method and classification standard on lake eutrophication[J]. Environmental Monitoring in China, 2002(5):47-49.
[7] Carlson R E. A trophic state index for lakes 1[J]. Limnology and Oceanography, 1977, 22(2):361-369.
doi: 10.4319/lo.1977.22.2.0361 url: http://doi.wiley.com/10.4319/lo.1977.22.2.0361
[8] Carlson R E. Expanding the trophic state concept to identify non-nutrient limited lakes and reservoirs[J]. Enhancing the States’s Lake Management Programs, 1991:59-71.
[9] Sheela A M, Letha J, Joseph S, et al. Trophic state index of a lake system using IRS (P6-LISS III) satellite imagery[J]. Environmental Monitoring and Assessment, 2011, 177(1):575-592.
doi: 10.1007/s10661-010-1658-2 url: http://link.springer.com/10.1007/s10661-010-1658-2
[10] Joniak T, Nagengast B, Kuczyńska-Kippen N. Can popular systems of trophic classification be used for small water bodies?[J]. Oceanological and Hydrobiological Studies, 2009, 38(4):145-151.
doi: 10.2478/v10009-009-0053-1 url: https://www.sciendo.com/article/10.2478/v10009-009-0053-1
[11] Sathyendranath S. Reports of the international ocean-colour coordinating group[R]. IOCCG, 2000.
[12] Novoa S, Wernand M R, Van der Woerd H J. The Forel-Ule scale revisited spectrally:Preparation protocol,transmission measurements and chromaticity[J]. Journal of the European Optical Society-Rapid publications, 2013, 8.
[13] Wernand M R, Van der Woerd H J. Spectral analysis of the Forel-Ule Ocean colour comparator scale[J]. Journal of the European Optical Society-Rapid Publications, 2010, 5.
[14] Wernand M R, Van der Woerd H J, Gieskes W W C. Trends in ocean colour and chlorophyll concentration from 1889 to 2000,worldwide[J]. PLOS One, 2013, 8(6):e63766.
doi: 10.1371/journal.pone.0063766 url: https://dx.plos.org/10.1371/journal.pone.0063766
[15] Wernand M R, Hommersom A, Van der Woerd H J. MERIS-based ocean colour classification with the discrete Forel-Ule scale[J]. Ocean Science, 2013, 9(3):477-487.
doi: 10.5194/os-9-477-2013 url: https://os.copernicus.org/articles/9/477/2013/
[16] Li J, Wang S, Wu Y, et al. MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012[J]. International Journal of Digital Earth, 2016, 9(8):788-805.
doi: 10.1080/17538947.2016.1139637 url: http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1139637
[17] Chen Q, Huang M, Tang X. Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index:A six-year (2013—2018) observation[J]. Science of The Total Environment, 2019: 135392.
[18] 汪星, 宫兆宁, 井然, 等. 基于连续统去除法的水生植物提取及其时空变化分析——以官厅水库库区为例[J]. 植物生态学报, 2018, 42(6):640-652.
doi: 10.17521/cjpe.2017.0240
[18] Wang X, Gong Z N, Jing R, et al. Extraction of aquatic plants based on continuous removal method and analysis of its temporal and spatial changes:A case study of Guanting Reservoir[J]. Chinese Journal of Plant Ecology, 2018, 42(6):640-652.
doi: 10.17521/cjpe.2017.0240 url: http://www.plant-ecology.com/EN/10.17521/cjpe.2017.0240
[19] 吴世民, 王芸. 北京水史[M]. 北京: 中国水利水电出版社, 2013.
[19] Wu S M, Wang Y. Beijing water history[M]. Beijing: China Water & Power Press, 2013.
[20] 于宗绪, 马东春. 新形势下水库水质治理对策——以官厅水库为例[J]. 水利发展研究, 2019, 19(10):57-61,79.
[20] Yu Z X, Ma D C. Countermeasures for reservoir water quality control under new situation:A case of Guanting Reservoir[J]. Water Resources Development Research, 2019, 19(10):57-61,79.
[21] 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(1):37-44.
[21] 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(1):37-44.
[22] Mobley C D. Estimation of the remote-sensing reflectance from above-surface measurements[J]. Applied Optics, 1999, 38(36):7442-7455.
pmid: 18324298
[23] 苏伟, 张明政, 蒋坤萍, 等. Sentinel-2卫星影像的大气校正方法[J]. 光学学报, 2018, 38(1):322-331.
[23] Su W, Zhang M Z, Jiang K P, et al. Atmospheric correction method for Sentinel-2 satellite imagery[J]. Acta Optica Sinica, 2018, 38(1):322-331.
[24] Cooley T, Anderson G P, Felde G W, et al. FLAASH,a MODTRAN4-based atmospheric correction algorithm,its application and validation[C]// IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2002: 7528709.
[25] 袁金国, 牛铮, 王锡平. 基于FLAASH的Hyperion高光谱影像大气校正[J]. 光谱学与光谱分析, 2009, 29(5):1181-1185.
[25] Yuan J G, Niu Z, Wang X P. Atmospheric correction of Hyperion hyperspectral image based on FLAASH[J]. Spectroscopy and Spectral Analysis, 2009, 29(5):1181-1185.
[26] Mcfeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features[J]. International Journal of Remote Sensing, 1996, 17(7):1425-1432.
doi: 10.1080/01431169608948714 url: https://www.tandfonline.com/doi/full/10.1080/01431169608948714
[27] Smith T, Guild J. The CIE colorimetric standards and their use[J]. Transactions of the Optical Society, 1931, 33(3):73.
doi: 10.1088/1475-4878/33/3/301 url: https://iopscience.iop.org/article/10.1088/1475-4878/33/3/301
[28] Woerd H J, Wernand M R. True colour classification of natural waters with medium-spectral resolution satellites:SeaWiFS,MODIS,MERIS and OLCI[J]. Sensors, 2015, 15(10):25663-25680.
doi: 10.3390/s151025663 url: http://www.mdpi.com/1424-8220/15/10/25663
[29] Van der Woerd H J, Wernand M R. Hue-angle product for low to medium spatial resolution optical satellite sensors[J]. Remote Sensing, 2018, 10(2):180.
doi: 10.3390/rs10020180 url: http://www.mdpi.com/2072-4292/10/2/180
[30] Benesty J, Chen J, Huang Y, et al. Pearson correlation coefficient[M]// Noise Reduction in Speech Processing.Springer,Berlin,Heidelberg, 2009:1-4.
[31] Lee Z P. Remote sensing of inherent optical properties:Fundamentals,tests of algorithms,and applications[R]. IOCCG, 2006.
[32] Wang S, Li J, Zhang B, et al. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index[J]. Remote Sensing of Environment, 2018, 217:444-460.
doi: 10.1016/j.rse.2018.08.026 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425718304012
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