{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T06:29:09Z","timestamp":1773901749849,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Xinjiang Uyghur Autonomous Region Science and Technology Innovation Base Construction Plan Project","award":["2020D04039"],"award-info":[{"award-number":["2020D04039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lake ice phenology (LIP), hiding information about lake energy and material exchange, serves as an important indicator of climate change. Utilizing an efficient technique to swiftly extract lake ice information is crucial in the field of lake ice research. The Bayesian ensemble change detection (BECD) algorithm stands out as a powerful tool, requiring no threshold compared to other algorithms and, instead, utilizing the probability of abrupt changes to detect positions. This method is predominantly employed by automatically extracting change points from time series data, showcasing its efficiency and accuracy, especially in revealing phenological and seasonal characteristics. This paper focuses on Bosten Lake (BL) and employs PMRS data in conjunction with the Bayesian change detection algorithm. It introduces an automated method for extracting LIP information based on the Bayesian change detection algorithm. In this study, the BECD algorithm was employed to extract lake ice phenology information from passive microwave remote sensing data on Bosten Lake. The reliability of the passive microwave remote sensing data was further investigated through cross-validation with MOD10A1 data. Additionally, the Mann\u2013Kendall non-parametric test was applied to analyze the trends in lake ice phenology changes in Bosten Lake. Spatial variations were examined using MOD09GQ data. The results indicate: (1) The Bayesian change detection algorithm (BCDA), in conjunction with PMRS data, offers a high level of accuracy and reliability in extracting the lake ice freezing and thawing processes. It accurately captures the phenological parameters of BL\u2019s ice. (2) The average start date of lake ice freezing is in mid-December, lasting for about three months, and the start date of ice thawing is usually in mid-March. The freezing duration (FD) of lake ice is relatively short, shortening each year, while the thawing speed is faster. The stability of the lake ice complete ice cover duration is poor, averaging 84 days. (3) The dynamic evolution of BL ice is rapid and regionally distinct, with the lake center, southwest, and southeast regions being the earliest areas for ice formation and thawing, while the northwest coastal and Huang Shui Gou areas experience later ice formation. (4) Since 1978, BL\u2019s ice has exhibited noticeable trends: the onset of freezing, the commencement of thawing, complete thawing, and full freezing have progressively advanced in regard to dates. The periods of full ice coverage, ice presence, thawing, and freezing have all shown a tendency toward shorter durations. This study introduces an innovative method for LIP extraction, opening up new prospects for the study of lake ecosystem and strategy formulation, which is worthy of further exploration and application in other lakes and regions.<\/jats:p>","DOI":"10.3390\/s23249852","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9852","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Monitoring of Lake Ice Phenology Changes in Bosten Lake Based on Bayesian Change Detection Algorithm and Passive Microwave Remote Sensing (PMRS) Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Yimuran","family":"Kuluwan","sequence":"first","affiliation":[{"name":"Laboratory of Basin Information Integration and Ecological Security, College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusufujiang","family":"Rusuli","sequence":"additional","affiliation":[{"name":"Laboratory of Basin Information Integration and Ecological Security, College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China"},{"name":"Key Laboratory of Arid Lake Environment and Resources, Urumqi 830054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mireguli","family":"Ainiwaer","sequence":"additional","affiliation":[{"name":"Laboratory of Basin Information Integration and Ecological Security, College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"ref_1","unstructured":"Zhao, X., Huang, M., Tian, P., and Bo, L. (2022). Release of IPCC: Summary for the Public. Chin. Meteorol. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.4319\/lo.2009.54.6_part_2.2283","article-title":"Lakes as sentinels of climate change","volume":"54","author":"Adrian","year":"2009","journal-title":"Limnol. Oceanogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s10584-019-02623-2","article-title":"MODIS-observed variations of lake ice phenology in Xinjiang, China","volume":"158","author":"Cai","year":"2020","journal-title":"Clim. Chang."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.rse.2008.12.007","article-title":"Variability in ice phenology on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada, from Sea Winds\/QuikSCAT: 2000\u20132006","volume":"113","author":"Howell","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/s41558-018-0393-5","article-title":"Widespread loss of lake ice around the Northern Hemisphere in a warming world","volume":"9","author":"Sharma","year":"2019","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","unstructured":"Secretariat I 2023 (2023, July 28). Climate Change 2014\u2014Impacts, Adaptation and Vulnerability (Part A: Global and Sectoral Aspects). Available online: https:\/\/www.ipcc.ch\/site\/assets\/uploads\/2018\/02\/WGIIAR5-FrontMatterA_FINAL.pdf."},{"key":"ref_7","first-page":"701","article-title":"Temporal and Spatial Variation of Lake Ice Phenology and Its Influencing Factors in the Tibetan Plateau","volume":"43","author":"Wang","year":"2017","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"W04501","DOI":"10.1029\/2009WR007803","article-title":"Parsimonious modeling of hydrologic responses in engineered watersheds: Structural heterogeneity versus functional homogeneity","volume":"46","author":"Basu","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_9","first-page":"201","article-title":"Monitoring ice phenology variations in Khanka Lake based on passive remote sensing data from 1979 to 2019","volume":"26","author":"Ke","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.scitotenv.2017.07.027","article-title":"Monitoring Ice Variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data","volume":"607\u2013608","author":"Cai","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"193","DOI":"10.11834\/jrs.20221288","article-title":"Lake ice phenology of the Nam Co at Tibetan Plateau: Remote sensing and modelling","volume":"26","author":"Wu","year":"2022","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_12","first-page":"1438","article-title":"Remote Sensing Based Monitoring Method for Lake Ice Phenology on the Qinghai-Tibet Plateau","volume":"30","author":"Qiu","year":"2018","journal-title":"J. Lakes"},{"key":"ref_13","unstructured":"Ruan, Y. (2017). Passive Microwave Remote Sensing of High-Altitude Asian Lake Ice Freeze-Thaw Monitoring. [Master\u2019s Thesis, Jiangxi University of Science and Technology]."},{"key":"ref_14","first-page":"37","article-title":"Changes in Ice Phenology Characteristics of sayram Lake from 2000 to 2019","volume":"45","author":"Qin","year":"2022","journal-title":"Arid. Zone Geogr."},{"key":"ref_15","first-page":"1440","article-title":"Temporal and Spatial Variations in Lake Ice Phenology of Large Lakes in Xinjiang from 2000 to 2019","volume":"45","author":"Aiken","year":"2022","journal-title":"Arid. Zone Geogr."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Healey, S.P., Yang, Z., Stehman, S.V., Brewer, C.K., Brooks, E.B., Gorelick, N., Huang, C., Hughes, M.J., and Kennedy, R.E. (2017). How Similar Are Forest Disturbance Maps Derived from Different Landsat Time Series Algorithms?. Forests, 8.","DOI":"10.3390\/f8040098"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111181","DOI":"10.1016\/j.rse.2019.04.034","article-title":"Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm","volume":"232","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cai, Y., Liu, S., and Lin, H. (2020). Monitoring Vegetation Dynamics in the Dong ting Lake Wetland from 2000 to 2019 Using the BEAST Algorithm Based on Dense Landsat Time Series. Appl. Sci., 10.","DOI":"10.3390\/app10124209"},{"key":"ref_19","first-page":"1622","article-title":"Spatial and Temporal Changes and Trends in Regional Vegetation and Water Bodies in the Bosten Lake Area Based on a Multi-Endmember Unmixing Model","volume":"46","author":"Yaxiaer","year":"2023","journal-title":"Arid. Zone Geogr."},{"key":"ref_20","first-page":"8","article-title":"Analysis of Temporal and Spatial Changes in Snow Cover Area in the Tianshan Mountains of China Based on MODIS Data","volume":"41","author":"He","year":"2018","journal-title":"Arid. Zone Geogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.rse.2014.09.010","article-title":"Detecting Changes in Vegetation Trends Using Time Series Segmentation","volume":"156","author":"Jamali","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"185","article-title":"Tests for rank correlation coefficients","volume":"49","author":"Pearson","year":"1962","journal-title":"Biometrika"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neunet.2015.07.004","article-title":"Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks","volume":"72","author":"Walter","year":"2015","journal-title":"Neural Netw."},{"key":"ref_24","first-page":"86","article-title":"Rank Correlation Methods","volume":"25","author":"Kendall","year":"1990","journal-title":"Br. J. Psychol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric test against trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrical"},{"key":"ref_26","first-page":"296","article-title":"Detection of Changes in Qinghai Lake Ice Phenology Using Passive Microwave Remote Sensing","volume":"43","author":"Wang","year":"2021","journal-title":"J. Glaciol. Geocryol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2006.11.010","article-title":"Observations of Lake Baikal Ice from Satellite Altimetry and Radiometry","volume":"108","author":"Kouraev","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"1241","article-title":"Lake ice change at the Nam Co Lake on the Tibetan Plateau during 2000-2013 and influencing factors","volume":"34","author":"Gou","year":"2015","journal-title":"Prog. Geogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the Archive: How Free Data Has Enabled the Science and Monitoring Promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., and Davis, R.A. (1998). Introduction to Time Series and Forecasting, Springer.","DOI":"10.2307\/2533882"},{"key":"ref_32","unstructured":"Soyer, R. (2022). Bayesian Methods for Nonlinear Classification and Regression, Wiley."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hamilton, J.D. (1994). Time Series Analysis, Princeton University Press.","DOI":"10.1515\/9780691218632"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1017\/S0266466600012822","article-title":"Forecasting, Structural Time Series Models and the Kalman Filter, Andrew C. Harvey Cambridge University Press, 1939 Fore Casting, Structural Time Series Models and The Kalman Filter Adrew C. Harvey Cambridge University Press, 1989","volume":"8","author":"Diebold","year":"1992","journal-title":"Econom. Theory"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1073\/pnas.1014425108","article-title":"Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006","volume":"108","author":"Wang","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.ecocom.2005.04.010","article-title":"Nonlinear dynamics in ecosystem response to climatic change: Case studies and policy implications","volume":"2","author":"Burkett","year":"2005","journal-title":"Ecol. Complex."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/BF02985802","article-title":"The elements of statistical learning: Data mining, inference and prediction","volume":"27","author":"Franklin","year":"2005","journal-title":"Math. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.isprsjprs.2021.04.008","article-title":"Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine","volume":"176","author":"Hu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9852\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:39:41Z","timestamp":1760132381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":38,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249852"],"URL":"https:\/\/doi.org\/10.3390\/s23249852","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,15]]}}}