Recent progress in chromaticity remote sensing of inland and nearshore water bodies

LI Kailin; LIAO Kuo; DANG Haofei

doi:10.6046/zrzyyg.2022009

2023 Vol. 35, No. 1

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LI Kailin, LIAO Kuo, DANG Haofei. 2023. Recent progress in chromaticity remote sensing of inland and nearshore water bodies. Remote Sensing for Natural Resources, 35(1): 15-26. doi: 10.6046/zrzyyg.2022009

Citation:

LI Kailin, LIAO Kuo, DANG Haofei. 2023. Recent progress in chromaticity remote sensing of inland and nearshore water bodies. Remote Sensing for Natural Resources, 35(1): 15-26. doi: 10.6046/zrzyyg.2022009

Recent progress in chromaticity remote sensing of inland and nearshore water bodies

Fujian Meteorological Institute, Fuzhou 350007, China

Received Date: 12 January 2022

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

Water color represents the most intuitive visible perception of the color of water bodies that is jointly affected by substances such as suspended particulate matter, chlorophyll, and soluble organic matter. Water color is a water environmental parameter with a long history and plays a critical role in research on the ecosystem of inland and nearshore water bodies. With the progress made in colorimetric research, as well as hyperspectral imaging and satellite remote sensing techniques, the colorimetric method of water color has developed. This study systematically reviewed the colorimetric research progress of inland and nearshore water bodies and elaborated on the theories and practical applications of the colorimetric method from the angles of apparent optical properties (AOP) and inherent optical properties (IOP). Moreover, it presented the colorimetric processing method of satellite remote sensing data. The colorimetric method is a technical method for the quantitative expression of water color. It is also an important branch of water color research and an extension and supplement to the study of water color components, with a broad application prospect. To further improve the application of the colorimetric methods in inland and nearshore water bodies, it is necessary to enhance the construction of bio-optical datasets of water bodies in the future. Moreover, colorimetric studies should be conducted in two dimensions, namely AOP and IOP, and it is necessary to intensify research on domestic satellite-based colorimetric methods and increase the types of relevant water color products.
- chromaticity /
- FU /
- water color component /
- satellite remote sensing

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References

[1]	Wernand M R, Novoa S, van der Woerd H, et al. A centuries-long history of participatory science in optical oceanography:From observation to interpretation of natural water colouring[J]. History of Oceanography Yearbook, 2014, 19(20):61-90. Google Scholar
[2]	Wernand M R. Poseidon’s paintbox:Historical archives of ocean colour in global-change perspective[D]. Utrecht: Utrecht University, 2011. Google Scholar
[3]	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(10014S):1-7. Google Scholar
[4]	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. Google Scholar
[5]	Wernand M R, Woerd H J, Gieskes W C. Trends in ocean colour and chlorophyll concentration from 1889 to present[J]. PLOS ONE, 2013, 8(6):1-20. Google Scholar
[6]	Arthur D B. A critical review of the development of the CIE1931 RGB color-matching functions[J]. Color Research and Application, 2004, 29(4),267-272. Google Scholar
[7]	中国计量科学研究院. GB/T3977—2008.颜色的表示方法[S]. 北京: 中国标准出版社, 2008. Google Scholar
[8]	National Institute of Metrology. GB/T3977—2008[S]. Beijing: China Standards Publishing House, 2008. Google Scholar
[9]	贾婉丽. Photoshop中的色彩空间转换[D]. 西安: 西安理工大学, 2002. Google Scholar
[10]	Jia W L. Color conversions in Photoshop[D]. Xi’an: Xi’an University of Technology, 2022. Google Scholar
[11]	唐军武, 陈清莲, 谭世祥, 等. 海洋光谱测量与数据分析处理方法[J]. 海洋通报, 1998, 17(1):71-79. Google Scholar
[12]	Tang J W, Chen Q L, Tang S X, et al. Method of oceanic spectral data measurement and analysis[J]. Marine Science Bulletin, 1998, 17(1):71-79. Google Scholar
[13]	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. Google Scholar
[14]	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. Google Scholar
[15]	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. Google Scholar
[16]	Woerd H J, Wernand M R. Hue-angle product for low to medium spatial resolution optical satellite sensors[J]. Remote Sensing, 2018, 10(2):180-198. Google Scholar
[17]	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(13057):1-8. Google Scholar
[18]	Pitarch J, Bellacicco M, Marullo S, et al. Global maps of Forel-Ule index,hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data[J]. Earth System Science Data Discussions, 2020(13):1-17. Google Scholar
[19]	Stomp M, Huisman J, Stal L J, et al. Colorful niches of phototrophic microorganisms shaped by vibrations of the water molecule[J]. The ISME Journal, 2007, 1(4):271-282. Google Scholar
[20]	Holtrop T, Huisman J, Stomp M, et al. Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans[J]. Nature Ecology and Evolution, 2021, 5(1):55-66. Google Scholar
[21]	Haverkamp T H A. Shades of red and green:The colorful diversity and ecology of picocyanobacteria in the Baltic Sea[D]. Amsterdam: Royal Netherlands Academy of Arts and Sciences, 2008. Google Scholar
[22]	Rueffler C, van Dooren T J M, Leimar O, et al. Disruptive selection and then what?[J]. Trends in Ecology and Evolution, 2006, 21(5):238-245. Google Scholar
[23]	Smith R C, Goldman T. Optical properties and color of Lake Tahoe and crater lake[J]. Limnology and Oceanography, 1973, 18(2):189-199. Google Scholar
[24]	Alfoldi T T, Munday J C. Water quality analysis by digital chromaticity mapping of Landsat data[J]. Canadian Journal of Remote Sensing, 1978, 4(2):108-126. Google Scholar
[25]	Jaquet J, Zand B. Colour analysis of inland waters using Landsat TM data[J]. European Space Agency Monographs, 1989, 1102(11):57-67 Google Scholar
[26]	Sovdat B, Kadunc M, Batic M, et al. Natural color representation of Sentinel-2 data[J]. Remote Sensing of Environment, 2019(255):392-402. Google Scholar
[27]	Jolliff J K, Lewis M D, Ladner S, et al. Observing the ocean submesoscale with enhanced-color GOES-ABI visible band data[J]. Sensors, 2019, 19(3900):1-23. Google Scholar
[28]	Novoa S, Wernand M, van der Woerd H J. WACODI:A generic algorithm to derive the intrinsic color of natural waters from digital images[J]. Limnology and Oceanography:Methods, 2015, 13(12):697-711. Google Scholar
[29]	Novoa S, Wernand M R, van der Woerd H J. The modern Forel-Ule scale:A “Do-it-yourself” colour comparator for water monitoring[J]. Journal of the European Optical Society-Rapid Publications, 2014, 9(14025):1-10. Google Scholar
[30]	Busch J A, Price I, Jeansou E, et al. Citizens and satellites:Assessment of phytoplankton dynamics in a NW Mediterranean aquaculture zone[J]. International Journal of Applied Earth Observation and Geoinformation, 2016(47):40-49. Google Scholar
[31]	Busch J A, Bardaji R, Ceccaroni L, et al. Citizen bio-optical observations from coast-and ocean and their compatibility with ocean colour satellite measurements[J]. Remote Sensing, 2016, 8(11):879. Google Scholar
[32]	Malthus T J, Ohmsen R, Woerd H J. An evaluation of citizen science smartphone APPs for inland water quality assessment[J]. Remote Sensing, 2020, 12(1578):1-20. Google Scholar
[33]	段洪涛, 罗菊花, 曹志刚, 等. 流域水环境遥感研究进展与思考[J]. 地理科学进展, 2019, 38(8):1182-1195. Google Scholar
[34]	Duan H T, Luo J H, Cao Z G, et al. Progress in remote sensing of aquatic environments at the watershed scale[J]. Progress in Geography, 2019, 38(8):1182-1195. Google Scholar
[35]	段洪涛, 曹志刚, 沈明, 等. 湖泊遥感研究进展与展望[J]. 遥感学报, 26(1):3-18. Google Scholar
[36]	Duan H T, Cao Z G, Shen M, et al. Review of lake remote sensing research[J]. National Remote Sensing Bulletin, 2019, 26(1):3-18. Google Scholar
[37]	Garaba S P, Friedrichs A, Vo? D, et al. Classifying natural waters with the Forel-Ule colour index system:Results,applications,correlations and crowdsourcing[J]. International Journal of Environmental Research and Public Health, 2015, 12(12):16096-16109. Google Scholar
[38]	Garaba S P, Vo? D, Zielinski O. Physical,bio-optical state and correlations in North-Western European Shelf Seas[J]. Remote Sensing, 2014, 6(6):5042-5066. Google Scholar
[39]	Woerd H J, Wernand M R, Peters M et al. True color analysis of natural waters with SeaWiFS,MODIS,MERIS and OLCI by SNAP[C]// Ocean Optics Conference, 2016. Google Scholar
[40]	Pitarch J, van der Woerd H J, Brewin R J W, et al. Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations[J]. Remote Sensing of Environment, 2019(231):1-16. Google Scholar
[41]	Petus C, Waterhouse J, Lewis S, et al. A flood of information:Using Sentinel-3 water colour products to assure continuity in the monitoring of water quality trends in the Great Barrier Reef (Australia)[J]. Journal of Environmental Management, 2019(248):1-20. Google Scholar
[42]	Nie Y, Guo J, Sun B, et al. An evaluation of apparent color of seawater based on the in-situ and satellite-derived Forel-Ule color scale[J]. Estuarine,Coastal and Shelf Science, 2020(246):1-10. Google Scholar
[43]	Sung T, Kim Y J, Choi H, et al. Spatial downscaling of ocean colour-climate change initiative (OC-CCI) Forel-Ule index using GOCI satellite image and machine learning technique[J]. Korean Journal of Remote Sensing, 2021, 37(5-1):959-974. Google Scholar
[44]	Zhan J, Zhang D J, Zhou G Q, et al. MODIS-based research on Secchi disk depth using an improved Semianalytical algorithm in the Yellow Sea[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021(14):5964-5972. Google Scholar
[45]	Li M J, Sun Y H, Li X J et al. An improved eutrophication assessment algorithm of estuaries and coastal waters in Liaodong Bay[J]. Remote Sensing, 2021, 13(19):3866-3884. Google Scholar
[46]	Wang S, Li J, Shen Q, et al. MODIS-based radiometric color extraction and classification of inland water with the Forel-Ule scale:A case study of Lake Taihu[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 8(2):907-918. Google Scholar
[47]	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. Google Scholar
[48]	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. Google Scholar
[49]	王胜蕾. 基于水色指数的大范围长时序湖库水质遥感监测研究[D]. 北京: 中国科学院大学, 2018. Google Scholar
[50]	Wang S L. Large-scale and long-time water quality remote sensing monitoring over lakes based on water color index[D]. Beijing: University of Chinese Academy of Sciences, 2018. Google Scholar
[51]	Lehmann M K, Nguyen U, Allan M, et al. Colour classification of 1 486 lakes across a wide range of optical water types[J]. Remote Sensing, 2018, 10(8):1273. Google Scholar
[52]	Jafar S M, Bowers D G, Griffiths J W. Remote sensing observations of ocean colour using the traditional Forel-Ule scale[J]. Estuarine,Coastal and Shelf Science, 2018(215):52-58. Google Scholar
[53]	Wang S, Li J, Zhang B, et al. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS[J]. Remote Sensing of Environment, 2020(247):1-17. Google Scholar
[54]	Chen Q, Huang M, Tang X. Eutrophication assessment of seasonal urban lakes in China Yangtze River basin using Landsat8-derived Forel-Ule index:A six-year (2013—2018) observation[J]. Science of the Total Environment, 2020(745):135392-135392. Google Scholar
[55]	许杨. 基于Landsat的长江中下游流域湖泊水体颜色长时序变化研究[D]. 武汉: 武汉大学, 2020. Google Scholar
[56]	Xu Y. Study on the long-term change of lacustrine water color in the middle and lower basins of the Yangtze river based on Landsat datasets[D]. Wuhan: Wuhan University, 2020. Google Scholar
[57]	许杨, 王野, 陆建忠, 等. 基于FUI模型的柬埔寨洞里萨湖水体颜色研究[J]. 华中师范大学学报(自然科学版), 2020, 54(3):454-462. Google Scholar
[58]	Xu Y, Wang Y, Lu J Z, et al. Study on water color of Tonle Sap Lake in Cambodia based on FUI model[J]. Journal of Central China Normal University(Natural Science), 2020, 54(3):454-462. Google Scholar
[59]	王野. 基于多源遥感数据的洞里萨湖水环境长时序动态过程研究[D]. 哈尔滨: 哈尔滨工业大学, 2020. Google Scholar
[60]	Wang Y. Research on long-time dynamic process of Tonle Sap Lake water environment based on multi-source remote sensing data[D]. Harbin: Harbin Institute of Technology, 2020. Google Scholar
[61]	曹畅, 王胜蕾, 李俊生, 等. 基于MODIS数据的全国144个重点湖库营养状态监测:以2018年夏季为例[J]. 湖泊科学, 2018, 33(2):405-413. Google Scholar
[62]	Cao C, Wang S L, Li J S, et al. MODIS-based monitoring of spatial distribution of trophic status in 144 key lakes and reservoirs of China in summer of 2018[J]. Journal of Lake Sciences, 2018, 33(2):405-413. Google Scholar
[63]	姜倩. 卫星遥感在湖库水质监测中的有效性评价方法研究——以GF-1号卫星为例[D]. 兰州: 兰州交通大学, 2020. Google Scholar
[64]	Jiang Q. Study on the effectiveness evaluation method of satellite remote sensing in the monitoring of lake and reservoir water quality:Take GF-1 satellite as an example[D]. Lanzhou: Lanzhou Jiaotong University, 2020. Google Scholar
[65]	温爽, 王桥, 李云梅, 等. 基于高分影像的城市黑臭水体遥感识别:以南京为例[J]. 环境科学, 2018, 39(1):57-67. Google Scholar
[66]	Wen S, Wang J, Li Y M, et al. Remote sensing identification of urban black-odor water bodies based on high-resolution images:A case study in Nanjing[J]. Environmental Science, 2018, 39(1):57-67. Google Scholar
[67]	杨子谦, 刘怀庆, 吕恒, 等. 基于高分影像的城市水体遥感综合分级方法[J]. 环境科学, 2021, 42(5):2213-2222. Google Scholar
[68]	Yang Z Q, Liu H Q, Lyu H, et al. A comprehensive classification method of urban water by remote sensing based on high-resolution images[J]. Environmental Science, 2021, 42(5):2213-2222. Google Scholar
[69]	Zhao Y, Shen Q, Wang Q, et al. Recognition of water colour anomaly by using hue angle and Sentinel 2 image[J]. Remote Sensing, 2020, 12(4):716-737. Google Scholar
[70]	Sathyendranath S, Brewin B, Mueller D, et al. Ocean colour climate change initiative:Approach and initial results[C]// 2012 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2012:2024-2027. Google Scholar
[71]	Jackson T, Chuprin A, Sathyendranath S, et al. Ocean colour climate change initiative (OC_CCI)-interim phase[R]. Plymouth: Plymouth Marine Laboratory, 2020. Google Scholar
[72]	张兵, 李俊生, 申茜, 等. 长时序大范围内陆水体光学遥感研究进展[J]. 遥感学报, 2021, 25(1):37-52. Google Scholar
[73]	Zhang B, Li J S, Shen Q, et al. Recent research progress on long time series and large scale optical remote sensing of inland water[J]. National Remote Sensing Bulletin, 2021, 25(1):37-52. Google Scholar
[74]	Wang S, Li J, Zhang W, et al. A dataset of remote-sensed Forel-Ule index for global inland waters during 2000—2018[J]. Scientific Data, 2021, 8(1):1-10. Google Scholar
[75]	Boyce D G, Lewis M, Worm B. Integrating global chlorophyll data from 1890 to 2010[J]. Limnology and Oceanography:Methods, 2012(10):840-852. Google Scholar
[76]	Dutkiewicz S, Hickman A E, Jahn O, et al. Ocean colour signature of climate change[J]. Nature Communications, 2019, 10(1):1-13. Google Scholar
[77]	邢小罡, 赵冬至, 刘玉光, 等. 叶绿素a荧光遥感研究进展[J]. 遥感学报, 2007, 11(1):137-144. Google Scholar
[78]	Xing X G, Zhao D Z, Liu Y G, et al. Process in fluorescence remote sensing of chlorophy-a[J]. Journal of Remote Sensing, 2007, 11(1):137-144. Google Scholar
[79]	Lee Z P. Remote sensing of inherent optical properties:Fundamentals,tests of algorithms,and applications[R]. Dartmouth: International Ocean-Colour Coordinating Group, 2006. Google Scholar
[80]	Friedrichs A, Busch J A, van der Woerd H J, et al. SmartFluo:A method and affordable adapter to measure chlorophyll a fluorescence with smartphones[J]. Sensors, 2017, 17(4):678. Google Scholar
[81]	Pozdnyakov D V, Kondratyev K Y. Numerical modelling of natural water colour:Implications for remote sensing and limnological studies[J]. International Journal of Remote Sensing, 1998, 19(10):1913-1932. Google Scholar
[82]	Wo?niak S B, Meler J. Modelling water colour characteristics in an optically complex nearshore environment in the Baltic Sea:Quantitative interpretation of the Forel-Ule scale and algorithms for the remote estimation of seawater composition[J]. Remote Sensing, 2020,(12):2851-2885. Google Scholar
[83]	Bukata R P, Jerome J H, Kondratyev K Y, et al. IEEE Conference on Computer Vision and Pattern Recognition.[J]. Journal of Great Lakes Research, 1997, 23(3):254-269. Google Scholar
[84]	Leech D M, Pollard A I, Labou S G, et al. Fewer blue lakes and more murky lakes across the continental US:Implications for planktonic food webs[J]. Limnology and Oceanography, 2018, 63(6):2661-2680. Google Scholar
[85]	Ting C S, Rocap G, King J, et al. Cyanobacterial photosynthesis in the oceans:The origins and significance of divergent light-harvesting strategies[J]. Trends in Microbiology, 2002, 10(3):134-142. Google Scholar
[86]	Croce R, van Amerongen H. Natural strategies for photosynthetic light harvesting[J]. Nature Chemical Biology, 2014, 10(7):492-501. Google Scholar
[87]	Monteith D T, Stoddard J L, Evans C D, et al. Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry[J]. Nature, 2007, 450(7169):537-540. Google Scholar
[88]	Weyhenmeyer G A, Müller R A, Norman M, et al. Sensitivity of freshwaters to browning in response to future climate change[J]. Climatic Change, 2016, 134(1-2):225-239. Google Scholar
[89]	Kritzberg E S. Centennial-long trends of lake browning show major effect of afforestation[J]. Limnology and Oceanography Letters, 2017, 2(4):105-112. Google Scholar
[90]	Urrutia C P, Ekvall M K, Ratcovich J, et al. Phytoplankton diversity loss along a gradient of future warming and brownification in freshwater mesocosms[J]. Freshwater Biology, 2017, 62(11):1869-1878. Google Scholar
[91]	Wilken S, Soares M, Pablo U C, et al. Primary producers or consumers? Increasing phytoplankton bacterivory along a gradient of lake warming and browning[J]. Limnology and Ceanography, 2018(63):S142-S155. Google Scholar
[92]	FeuchtmayrH, Pottinger T G, Moore A, et al. Effects of brownification and warming on algal blooms,metabolism and higher trophic levels in productive shallow lake mesocosms[J]. Science of the Total Environment, 2019, 678:227-238. Google Scholar
[93]	Deininger A, Faithfull C L, Bergstr?m A K. Phytoplankton response to whole lake inorganic N fertilization along a gradient in dissolved organic carbon[J]. Ecology, 2017, 98(4):982-994. Google Scholar
[94]	Tan X, Zhang D, Duan Z, et al. Effects of light color on interspecific competition between microcystis aeruginosa and chlorella pyrenoidosa in batch experiment[J]. Environmental Science and Pollution Research, 2020, 27(1):344-352. Google Scholar
[95]	Luimstra V M, Verspagen J M H, Xu T, et al. Changes in water color shift competition between phytoplankton species with contrasting light-harvesting strategies[J]. Ecology, 2020, 101(3):1-17. Google Scholar
[96]	李云梅, 赵焕, 毕顺, 等. 基于水体光学分类的二类水体水环境参数遥感监测进展[J]. 遥感学报, 2022, 26(1):19-31. Google Scholar
[97]	Li Y M, Zhao H, Bi S, et al. Research progress of remote sensing monitoring of case II water environmental parameters based on water optical classification[J]. National Remote Sensing Bulletin, 2022, 26(1): 19-31. Google Scholar