{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T08:22:45Z","timestamp":1781079765801,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen science and technology program","award":["Grant No. KQTD20180410161218820"],"award-info":[{"award-number":["Grant No. KQTD20180410161218820"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 42071385"],"award-info":[{"award-number":["Grant No. 42071385"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Natural Science Foundation","award":["Grant No. ZR2019MD041"],"award-info":[{"award-number":["Grant No. ZR2019MD041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8\/OLI and Sentinel-2\/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.<\/jats:p>","DOI":"10.3390\/rs13163240","type":"journal-article","created":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T22:51:27Z","timestamp":1629067887000},"page":"3240","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China"],"prefix":"10.3390","volume":"13","author":[{"given":"Guangzong","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1822-5980","authenticated-orcid":false,"given":"Mengquan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment Engineering, Ludong University, Yantai 264025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Marine Geosciences, Ocean University of China, Qingdao 266000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufang","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifeng","family":"Niu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guochang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lapointe, B.E., Burkholder, J.M., and van Alstyne, K.L. (2018). Harmful macroalgal blooms in a changing world: Causes, impacts, and management. Harmful Algal Blooms: A Compendium Desk Reference, John Wiley.","DOI":"10.1002\/9781118994672.ch15"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2016.04.019","article-title":"Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations","volume":"183","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1016\/j.rse.2010.04.011","article-title":"Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data","volume":"114","author":"Hu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1029\/2008EO330002","article-title":"Origin and offshore extent of floating algae in Olympic sailing area","volume":"89","author":"Hu","year":"2008","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1016\/j.marpolbul.2009.01.013","article-title":"World\u2019s largest macroalgal bloom caused by expansion of seaweed aquaculture in China","volume":"58","author":"Liu","year":"2009","journal-title":"Mar. Pollut. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"12297","DOI":"10.3390\/rs70912297","article-title":"World\u2019s largest macroalgal blooms altered phytoplankton biomass in summer in the Yellow Sea: Satellite observations","volume":"7","author":"Xing","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3354\/meps100097","article-title":"Competition with macroalgae and benthic cyanobacterial mats limits phytoplankton abundance in experimental microcosms","volume":"100","author":"Zedler","year":"1993","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.hal.2011.12.007","article-title":"Effects of the decomposing green macroalga Ulva (Enteromorpha) prolifera on the growth of four red-tide species","volume":"16","author":"Wang","year":"2012","journal-title":"Harmful Algae"},{"key":"ref_9","first-page":"24","article-title":"Influence of Enteromorpha prolifera (Chlorophyta) on the phytoplankton community structure","volume":"37","author":"Zhang","year":"2013","journal-title":"Mar. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.marpolbul.2010.05.015","article-title":"Recurrence of the world\u2019s largest green-tide in 2009 in Yellow Sea, China: Porphyra yezoensis aquaculture rafts confirmed as nursery for macroalgal blooms","volume":"60","author":"Liu","year":"2010","journal-title":"Mar. Pollut. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ecss.2013.05.021","article-title":"The world\u2019s largest macroalgal bloom in the Yellow Sea, China: Formation and implications","volume":"129","author":"Liu","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wu, M., Zhou, M., and Zhao, L. (2020). The seasonal dissipation of Ulva prolifera and its effects on environmental factors: Based on remote sensing images and field monitoring data. Geocarto Int., 1\u201319.","DOI":"10.1080\/10106049.2020.1745301"},{"key":"ref_13","first-page":"825","article-title":"Ulva prolifera green-tide outbreaks and their environmental impact in the Yellow Sea, China","volume":"6","author":"Zhang","year":"2019","journal-title":"Neurosurgery"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2017.01.037","article-title":"Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea","volume":"192","author":"Hu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.marpolbul.2018.08.035","article-title":"A study of the environmental factors influencing the growth phases of Ulva prolifera in the southern Yellow Sea, China","volume":"135","author":"Jin","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.marpolbul.2011.03.040","article-title":"Inter-and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007\u20132009, their origin and relationship to the expansion of coastal seaweed aquaculture in China","volume":"62","author":"Keesing","year":"2011","journal-title":"Mar. Pollut. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1002\/lno.10083","article-title":"Who made the world\u2019s largest green tide in China?\u2014an integrated study on the initiation and early development of the green tide in Yellow Sea","volume":"60","author":"Wang","year":"2015","journal-title":"Limnol. Oceanogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2016.02.065","article-title":"Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique","volume":"178","author":"Xing","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.marenvres.2016.06.004","article-title":"A review of the green tides in the Yellow Sea, China","volume":"119","author":"Liu","year":"2016","journal-title":"Mar. Environ. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"479","DOI":"10.5194\/isprs-archives-XLII-4-W19-479-2019","article-title":"Detection of algal bloom in the coastal waters of boracay, philippines using Normalized Difference Vegetation Index (NDVI) and Floating Algae Index (FAI)","volume":"XLII-4\/W19","author":"Visitacion","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1029\/2012JC008292","article-title":"Quantification of floating macroalgae blooms using the scaled algae index","volume":"118","author":"Garcia","year":"2013","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1029\/2009JC005513","article-title":"Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008","volume":"114","author":"Shi","year":"2009","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1016\/j.rse.2009.05.012","article-title":"A novel ocean color index to detect floating algae in the global oceans","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.hal.2016.07.004","article-title":"Long-term trend of Ulva prolifera blooms in the western Yellow Sea","volume":"58","author":"Qi","year":"2016","journal-title":"Harmful Algae"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1364\/OE.27.004528","article-title":"A simple and effective method for monitoring floating green macroalgae blooms: A case study in the Yellow Sea","volume":"27","author":"Zhang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5513","DOI":"10.1080\/01431161.2012.663112","article-title":"Satellite monitoring of massive green macroalgae bloom (GMB): Imaging ability comparison of multi-source data and drifting velocity estimation","volume":"33","author":"Cui","year":"2012","journal-title":"Int. J. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"26810","DOI":"10.1364\/OE.26.026810","article-title":"Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: Case study of Yellow Sea using GOCI images","volume":"26","author":"Qiu","year":"2018","journal-title":"Opt. Express"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1007\/s11707-015-0528-1","article-title":"Multi-sensor monitoring of Ulva prolifera blooms in the Yellow Sea using different methods","volume":"10","author":"Xu","year":"2016","journal-title":"Front. Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mora-Soto, A., Palacios, M., Macaya, E.C., G\u00f3mez, I., Huovinen, P., P\u00e9rez-Matus, A., Young, M., Golding, N., Toro, M., and Yaqub, M. (2020). A high-resolution global map of Giant kelp (Macrocystis pyrifera) forests and intertidal green algae (Ulvophyceae) with Sentinel-2 imagery. Remote Sens., 12.","DOI":"10.3390\/rs12040694"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.scitotenv.2018.05.378","article-title":"Spatio-temporal patterns of Ulva prolifera blooms and the corresponding influence on chlorophyll-a concentration in the Southern Yellow Sea, China","volume":"640","author":"Sun","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, C., Sun, Q., Xing, Q., Wang, S., Tang, D., Zhu, D., and Xing, X. (2019). Variability in phytoplankton biomass and effects of sea surface temperature based on satellite data from the Yellow Sea, China. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0220058"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.marpolbul.2015.04.034","article-title":"Influence of nutrients pollution on the growth and organic matter output of Ulva prolifera in the southern Yellow Sea, China","volume":"95","author":"Zhou","year":"2015","journal-title":"Mar. Pollut. Bull."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, M., Zhang, R., Ren, Y., and Wen, X. (2019). Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sens., 11.","DOI":"10.3390\/rs11212479"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s12601-012-0034-2","article-title":"Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using geostationary ocean color imager (GOCI) data","volume":"47","author":"Son","year":"2012","journal-title":"Ocean. Sci. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1080\/01431161.2017.1286056","article-title":"High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea, China","volume":"38","author":"Xiao","year":"2017","journal-title":"Int. J. Remote. Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4402","DOI":"10.1080\/01431161.2018.1457228","article-title":"Hourly variation of green tide in the Yellow Sea during summer 2015 and 2016 using Geostationary Ocean Color Imager data","volume":"39","author":"Yang","year":"2018","journal-title":"Int. J. Remote. Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.rse.2004.08.007","article-title":"Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL","volume":"93","author":"Hu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote. Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(01)00328-5","article-title":"Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors","volume":"81","author":"Trishchenko","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2015.05.022","article-title":"Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae","volume":"167","author":"Hu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1029\/2009JC005511","article-title":"Moderate resolution imaging spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China","volume":"115","author":"Hu","year":"2010","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jia, T., Zhang, X., and Dong, R. (2019). Long-term spatial and temporal monitoring of cyanobacteria blooms using MODIS on google earth engine: A case study in Taihu Lake. Remote Sens., 11.","DOI":"10.3390\/rs11192269"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jia, M., Wang, Z., Wang, C., Mao, D., and Zhang, Y. (2019). A new vegetation index to detect periodically submerged Mangrove forest using single-tide sentinel-2 imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172043"},{"key":"ref_45","first-page":"102302","article-title":"Using Landsat 8 OLI data to differentiate Sargassum and Ulva prolifera blooms in the South Yellow Sea","volume":"98","author":"Sun","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1016\/j.patrec.2011.01.021","article-title":"Characteristic analysis of Otsu threshold and its applications","volume":"32","author":"Xu","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4726","DOI":"10.1109\/JSTARS.2014.2309707","article-title":"River delineation from remotely sensed imagery using a multi-scale classification approach","volume":"7","author":"Yang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112285","DOI":"10.1016\/j.rse.2021.112285","article-title":"Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine","volume":"255","author":"Jia","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Singh, G., Reynolds, C., Byrne, M., and Rosman, B. (2020). A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents. Remote Sens., 12.","DOI":"10.3390\/rs12244021"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Donchyts, G., Schellekens, J., Winsemius, H., Eisemann, E., and Van de Giesen, N. (2016). A 30 m resolution surface water mask including estimation of positional and thematic differences using landsat 8, srtm and openstreetmap: A case study in the Murray-Darling Basin, Australia. Remote Sens., 8.","DOI":"10.3390\/rs8050386"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"102001","DOI":"10.1016\/j.hal.2021.102001","article-title":"To what extent can Ulva and Sargassum be detected and separated in satellite imagery?","volume":"103","author":"Qi","year":"2021","journal-title":"Harmful Algae"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.marpolbul.2017.09.060","article-title":"Effect of the large-scale green tide on the species succession of green macroalgal micro-propagules in the coastal waters of Qingdao, China","volume":"126","author":"Miao","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_56","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.marpolbul.2018.05.035","article-title":"Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign","volume":"133","author":"Xing","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"35137","DOI":"10.1007\/s11356-020-09730-z","article-title":"Analysis of the interannual variation characteristics of the northernmost drift position of the green tide in the Yellow Sea","volume":"27","author":"Li","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"318","DOI":"10.2112\/SI102-038.1","article-title":"Automatic extraction of green tide using dual polarization Chinese GF-3 SAR images","volume":"102","author":"Yu","year":"2020","journal-title":"J. Coast. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1175\/1520-0426(1995)012<1215:TINCMF>2.0.CO;2","article-title":"The image navigation cloud mask for the Multiangle Imaging SpectroRadiometer (MISR)","volume":"12","author":"Girolamo","year":"1995","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.rse.2012.01.022","article-title":"Identification of \u201cever-cropped\u201d land (1984\u20132010) using Landsat annual maximum NDVI image composites: Southwestern Kansas case study","volume":"121","author":"Maxwell","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s11769-020-1129-9","article-title":"Influence of Sea Surface Temperature on Outbreak of Ulva prolifera in the Southern Yellow Sea, China","volume":"30","author":"Zhang","year":"2020","journal-title":"Chin. Geogr. Sci."},{"key":"ref_64","first-page":"1","article-title":"Current situation of prevention and mitigation of the Yellow Sea green tide and proposing control measurements in the early stage","volume":"42","author":"Wang","year":"2020","journal-title":"Haiyang Xuebao"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111279","DOI":"10.1016\/j.rse.2019.111279","article-title":"Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms","volume":"231","author":"Xing","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3240\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:46:32Z","timestamp":1760165192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,15]]},"references-count":65,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163240"],"URL":"https:\/\/doi.org\/10.3390\/rs13163240","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,15]]}}}