Abstract
This study investigates the spatial and temporal distribution of FUI and its changes in Japan and its coastal seas from 2013 to 2023 using the Google Earth Engine platform, analyzes the reasons for these changes and provides corresponding recommendations. The results indicate that during this decade, the FUI values in Japan and its coastal seas mainly range from 10 to 15, showing a spatial distribution of relatively high FUI values in the south and relatively low values in the north. Additionally, FUI exhibits obvious seasonality, with values in autumn and winter significantly higher than those in spring and summer, in the order of autumn > winter > spring > summer. From 2013 to 2023, the sea surface temperature has shown an increasing trend, characterized by a similar spatial distribution: high in the south and low in the north, akin to the distribution of FUI. Compared to the period from 2013 to 2018, the changes in FUI from 2018 to 2023 are more positive, indicating a decline in the quality of water bodies in Japan and its coastal seas.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
No datasets were generated or analysed during the current study.
References
Akada H, Kodama T, Yamaguchi T (2023) Eutrophication trends in the coastal region of the Great Tokyo area based on long-term trends of Secchi depth. PeerJ 11:e15764. https://doi.org/10.7717/peerj.15764
Arévalo P, Olofsson P, Woodcock CE (2020) Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: a test methodology for REDD + reporting. Remote Sens Environ 238:111051. https://doi.org/10.1016/j.rse.2019.01.013
Azuma K, Kagi N, Kim H, Hayashi M (2020) Impact of climate and ambient air pollution on the epidemic growth during COVID-19 outbreak in Japan. Environ Res 190:110042. https://doi.org/10.1016/j.envres.2020.110042
Baek SH, Lee M, Park BS, Lim YK (2020) Variation in Phytoplankton Community Due to an Autumn Typhoon and Winter Water Turbulence in Southern Korean Coastal Waters. Sustainability 12
Bhatnagar S, Gill L, Regan S et al (2020) Mapping Vegetation communities inside wetlands using Sentinel-2 imagery in Ireland. Int J Appl Earth Obs Geoinf 88:102083. https://doi.org/10.1016/j.jag.2020.102083
Chang Y-C, Zhao X, Jian A, Tan Y (2024) Frontier issues in international ocean governance: Japan’s discharge of nuclear contaminated water into the sea. Mar Pollut Bull 198:115853. https://doi.org/10.1016/j.marpolbul.2023.115853
Chen Q, Huang M, Tang X (2020) Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using landsat 8-derived Forel-Ule index: a six-year (2013–2018) observation. Sci Total Environ 745:135392. https://doi.org/10.1016/j.scitotenv.2019.135392
Chen S, Woodcock CE, Bullock EL et al (2021a) Monitoring temperate forest degradation on Google Earth Engine using landsat time series analysis. Remote Sens Environ 265. https://doi.org/10.1016/j.rse.2021.112648
Chen X, Liu L, Zhang X et al (2021b) An Assessment of Water Color for Inland Water in China using a landsat 8-Derived forel–Ule Index and the Google Earth Engine platform. IEEE J Sel Top Appl Earth Obs Remote Sens 14:5773–5785. https://doi.org/10.1109/JSTARS.2021.3085411
Chiwa M (2021) Long-term changes in atmospheric nitrogen deposition and stream water nitrate leaching from forested watersheds in western Japan. Environ Pollut 287:117634. https://doi.org/10.1016/j.envpol.2021.117634
Dong Z, Shi X, Zou J et al (2021) Paleoceanographic insights on meridional ventilation variations in the Japan Sea since the last glacial Maximum: a radiolarian assemblage perspective. Glob Planet Change 200:103456. https://doi.org/10.1016/j.gloplacha.2021.103456
Ehalt MacEdo H, Lehner B, Nicell J et al (2022) Distribution and characteristics of wastewater treatment plants within the global river network. Earth Syst Sci Data 14:559–577. https://doi.org/10.5194/essd-14-559-2022
Fronkova L, Greenwood N, Martinez R, et al (2022) Can Forel–Ule Index Act as a Proxy of Water Quality in Temperate Waters? Application of Plume Mapping in Liverpool Bay, UK. Remote Sens. 14
Harifidy RZ, Hiroshi I (2022) Analysis of River Basin Management in Madagascar and lessons learned from Japan. Water 14:449. https://doi.org/10.3390/w14030449
Hori M, Shozugawa K, Sugimori K, Watanabe Y (2021) A survey of monitoring tap water hardness in Japan and its distribution patterns. Sci Rep 11:13546. https://doi.org/10.1038/s41598-021-92949-8
Horie Y, Takahashi C (2021) Development of an in vivo acute bioassay using the marine medaka Oryzias Melastigma. Environ Monit Assess 193:725. https://doi.org/10.1007/s10661-021-09527-8
Ilechukwu I, Das RR, Jamodiong EA et al (2024) Abundance and distribution of marine litter on the beaches of Okinawa Island, Japan. Mar Pollut Bull 200:116036. https://doi.org/10.1016/j.marpolbul.2024.116036
Jin S, Liu Y, Fagherazzi S et al (2021) River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method. Remote Sens Environ 255. https://doi.org/10.1016/j.rse.2021.112297
Karube Z, Inuzuka Y, Tanaka A et al (2016) Radiostrontium monitoring of bivalves from the Pacific coast of eastern Japan. Environ Sci Pollut Res 23:17095–17104. https://doi.org/10.1007/s11356-016-6878-8
Kosczor E, Forkel M, Hernández J et al (2022) Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int J Appl Earth Obs Geoinf 112. https://doi.org/10.1016/j.jag.2022.102862
Koudryashova Y, Chizhova T, Inoue M et al (2022) Deep Water PAH Cycling in the Japan Basin (the Sea of Japan). J Mar Sci Eng 10:2015. https://doi.org/10.3390/jmse10122015
Kovács GM, Horion S, Fensholt R (2022) Characterizing ecosystem change in wetlands using dense earth observation time series. Remote Sens Environ 281:113267. https://doi.org/10.1016/j.rse.2022.113267
Kuraji K, Saito H (2022) Long-term changes in relationship between Water Level and Precipitation in Lake Yamanaka. Water 14:2232. https://doi.org/10.3390/w14142232
Kurihara Y (2020) GCOM-C/SGLI Sea Surface Temperature (SST) ATBD. 1–7
Lauvset SK, Key RM, Olsen A et al (2016) A new global interior ocean mapped climatology: the 1° × 1° GLODAP version 2. Earth Syst Sci Data 8:325–340. https://doi.org/10.5194/essd-8-325-2016
Ma C, Zhao J, Ai B, Sun S (2021) Two-decade variability of Sea Surface temperature and Chlorophyll-a in the Northern South China Sea as revealed by reconstructed cloud-free Satellite Data. IEEE Trans Geosci Remote Sens 59:9033–9046. https://doi.org/10.1109/TGRS.2021.3051025
Matsui K, Shirai H, Kageyama Y, Yokoyama H (2021) Improving the resolution of UAV-based remote sensing data of water quality of Lake Hachiroko, Japan by neural networks. Ecol Inf 62:101276. https://doi.org/10.1016/j.ecoinf.2021.101276
Matsunaka T, Nagao S, Inoue M et al (2022) Seasonal variations in marine polycyclic aromatic hydrocarbons off Oki Island, Sea of Japan, during 2015–2019. Mar Pollut Bull 180:113749. https://doi.org/10.1016/j.marpolbul.2022.113749
Nakagawa K, Amano H, Yu Z-Q, Berndtsson R (2022) Groundwater Quality and Potential Pollution in the Southern Shimabara Peninsula, Japan. Water 14
Narita K, Matsui Y, Matsushita T, Shirasaki N (2020) Selection of priority pesticides in Japanese drinking water quality regulation: validity, limitations, and evolution of a risk prediction method. Sci Total Environ 751:141636. https://doi.org/10.1016/j.scitotenv.2020.141636
Nguyen LH, Joshi DR, Clay DE, Henebry GM (2020) Characterizing land cover/land use from multiple years of Landsat and MODIS time series: a novel approach using land surface phenology modeling and random forest classifier. Remote Sens Environ 238:111017. https://doi.org/10.1016/j.rse.2018.12.016
Nie Y, Guo J, Sun B, Lv X (2020) An evaluation of apparent color of seawater based on the in-situ and satellite-derived Forel-Ule color scale. Estuar Coast Shelf Sci 246:107032. https://doi.org/10.1016/j.ecss.2020.107032
Novoa S, Wernand M, Woerd H (2013) The Forel-Ule scale revisited spectrally: Preparation protocol, transmission measurements and chromaticity. J Eur Opt Soc Rapid Publ 8:13057. https://doi.org/10.2971/jeos.2013.13057
Oda T, Maksyutov S (2011) A very high-resolution (1km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys 11:543–556. https://doi.org/10.5194/acp-11-543-2011
Olsen A, Key RM, van Heuven S et al (2016) Earth Syst Sci Data 8:297–323. https://doi.org/10.5194/essd-8-297-2016. The Global Ocean Data Analysis Project version 2 (GLODAPv2) – an internally consistent data product for the world ocean
Ouma Y, Waga J, Okech M et al (2018) Estimation of Reservoir Bio-optical Water Quality parameters using smartphone sensor apps and Landsat ETM+: review and comparative experimental results. J Sens 2018:1–32. https://doi.org/10.1155/2018/3490757
Petukhov V, Petrova E, Kiryanov A et al (2023) Assessment of contamination of marine sediments and their potential toxicity in the Uglovoy Bay, Peter the Great Gulf, Sea of Japan/East Sea. Environ Sci Pollut Res 30:77798–77806. https://doi.org/10.1007/s11356-023-28021-x
Pitarch J, van der Woerd HJ, Brewin RJW, Zielinski O (2019) Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations. Remote Sens Environ 231:111249. https://doi.org/10.1016/j.rse.2019.111249
Pitarch J, Bellacicco M, Marullo S, Woerd H (2020) Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty. -one years of monthly ESA-OC-CCI data
Pitarch J, Bellacicco M, Marullo S, Van Der Woerd HJ (2021) Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from 21 years of monthly ESA Ocean Colour Climate Change Initiative data. Earth Syst Sci Data 13:481–490. https://doi.org/10.5194/essd-13-481-2021
Rendenieks Z, Nita MD, Nikodemus O, Radeloff VC (2020) Half a century of forest cover change along the latvian-russian border captured by object-based image analysis of Corona and Landsat TM/OLI data. https://doi.org/10.1016/j.rse.2020.112010. Remote Sens Environ 249:
Smirnov SV, Yaroshchuk IO, Shvyrev AN et al (2021) Resonant oscillations in the western part of the Peter the Great Gulf in the Sea of Japan. Nat Hazards 106:1729–1745. https://doi.org/10.1007/s11069-021-04561-8
Takata H, Wada T, Aono T et al (2022) Factors controlling dissolved 137Cs activities in coastal waters on the eastern and western sides of Honshu, Japan. Sci Total Environ 806:151216. https://doi.org/10.1016/j.scitotenv.2021.151216
Uhl JH, Leyk S (2020) Towards a novel backdating strategy for creating built-up land time series data using contemporary spatial constraints. Remote Sens Environ 238:111197. https://doi.org/10.1016/j.rse.2019.05.016
van der Woerd HJ, Wernand MR (2015) True Colour classification of Natural Waters with Medium-Spectral Resolution satellites: SeaWiFS, MODIS, MERIS and OLCI. Sensors 15:25663–25680
Van der Woerd HJ, Wernand MR (2018) Hue-Angle product for low to medium spatial resolution Optical Satellite Sensors. Remote Sens 10:180
Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ 185:46–56. https://doi.org/10.1016/j.rse.2016.04.008
von Keyserlingk J, de Hoop M, Mayor AG et al (2021) Resilience of vegetation to drought: studying the effect of grazing in a Mediterranean rangeland using satellite time series. Remote Sens Environ 255:112270. https://doi.org/10.1016/j.rse.2020.112270
Wang S, Li J, Zhang W et al (2021a) A dataset of remote-sensed Forel-Ule Index for global inland waters during 2000–2018. Sci Data 8:26. https://doi.org/10.1038/s41597-021-00807-z
Wang Y, Gong Z, Zhang Y, Su S (2021b) Extraction and application of Forel-Ule index based on images from multiple sensors. Remote Sens Nat Resour 33:262–271
Wang L, Meng Q, Wang X et al (2023) Forel-Ule index extraction and spatiotemporal variation from MODIS imagery in the Bohai Sea of China. Opt Express 31:17861–17877. https://doi.org/10.1364/OE.487312
Watanabe K, Ji J, Harada H et al (2022) Recent Characteristics of Fog Water Chemistry at Mt. Tateyama, Central Japan: recovery from high sulfate and acidity. Water Air Soil Pollut 233. https://doi.org/10.1007/s11270-022-05778-4
Yamamoto M, Takeshige A, Yamaguchi A et al (2022) Investigating terrestrial and oceanic environmental conditions to identify possible factors influencing seaweed bed distribution in Tsushima Islands, Japan. Cont Shelf Res 245:104792. https://doi.org/10.1016/j.csr.2022.104792
Ye M, Sun Y (2022) Review of the forel–Ule Index based on in situ and remote sensing methods and application in water quality assessment. Environ Sci Pollut Res 29:13024–13041. https://doi.org/10.1007/s11356-021-18083-0
You N, Dong J (2020) Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS J Photogramm Remote Sens 161:109–123. https://doi.org/10.1016/j.isprsjprs.2020.01.001
Zhang B, Guo J, Rong Z, Lv X (2023) Variations of remote-sensed Forel-Ule Index in the Bohai and Yellow seas during 1997–2019. Remote Sens 15: https://doi.org/10.3390/rs15143487
Zhao Y, Wang S, Zhang F et al (2021) Remote sensing-based analysis of spatial and temporal water colour variations in baiyangdian lake after the establishment of the xiong’an new area. Remote Sens 13. https://doi.org/10.3390/rs13091729
Zhu L, Xing H, Hou D (2022) Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China. Sci Rep 12:1–12. https://doi.org/10.1038/s41598-022-12080-0
Zhu L, Guo Z, Xing H, Sun W (2023a) A coupled temporal-spectral-spatial multidimensional information change detection framework method: a case of the 1990–2020 Tianjin, China. IEEE J Sel Top Appl Earth Obs Remote Sens 16:5741–5758. https://doi.org/10.1109/JSTARS.2023.3288218
Zhu L, Jiang X, Zhao L et al (2023b) A temporal-spectral value and shape change detection method integrating thematic index information and spectral band information. Environ Sci Pollut Res 30:47408–47421. https://doi.org/10.1007/s11356-023-25685-3
Zhu L, Sun W, Zhang Q et al (2023c) Fine-grained agricultural and pastoral information extraction using Sentinel-1 and Sentinel-2 intra-year time series in Jingyang District, Deyang City. Adv Sp Res 72:4031–4047. https://doi.org/10.1016/j.asr.2023.07.061
Zhu L, Xing H, Zhao L et al (2023d) A change type determination method based on knowledge of spectral changes in land cover types. Earth Sci Inf 16:1265–1279
Zhu L, Sun W, Fan D et al (2024a) Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images. Pattern Recognit 149:110237. https://doi.org/10.1016/j.patcog.2023.110237
Zhu L, Sun W, Xing H et al (2024b) A phenological knowledge transfer-based fine grained land cover change sample collection method: a case study of coastal wetlands. Int J Digit Earth 1–19. https://doi.org/10.1080/17538947.2024.2310090
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the quality of the manuscript.
Funding
This work was supported by the Fundamental Research Funds for the Central Universities (Ph.D. Top Innovative Talents Fund of CUMTB), the key program of the National Natural Science Foundation of China (grant number 41930650), and the general program of the National Natural Science Foundation of China (grant number 42271435).
Author information
Authors and Affiliations
Contributions
All authors were involved in the conception and design of the study. Material preparation, data collection, analysis and methodology were carried out by Linye Zhu, Xiaoyi Jiang, Longfei Zhao, Hui Qu, Wenbin Sun, and Haibo Ban. The first draft of the manuscript was written by Linye Zhu and Wenbin Sun, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Communicated by Hassan Babaie.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, L., Jiang, X., Zhao, L. et al. Changes in remotely sensed Forel-Ule Index for the coastal seas of Japan, 2013–2023. Earth Sci Inform 18, 57 (2025). https://doi.org/10.1007/s12145-024-01507-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12145-024-01507-z