Kuluwan Yimuran, Rusuli Yusufujiang, Ainiwaer Mireguli
Laboratory of Basin Information Integration and Ecological Security, College of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China.
Key Laboratory of Arid Lake Environment and Resources, Urumqi 830054, China.
Sensors (Basel). 2023 Dec 15;23(24):9852. doi: 10.3390/s23249852.
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-Kendall 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's 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's 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.
湖泊冰物候（LIP）蕴含着湖泊能量和物质交换的信息，是气候变化的重要指标。利用高效技术快速提取湖泊冰信息在湖泊冰研究领域至关重要。贝叶斯集成变化检测（BECD）算法是一种强大的工具，与其他算法相比无需阈值，而是利用突变概率来检测位置。该方法主要通过从时间序列数据中自动提取变化点来应用，展现出其效率和准确性，尤其在揭示物候和季节特征方面。本文聚焦于博斯腾湖（BL），将被动微波遥感（PMRS）数据与贝叶斯变化检测算法结合使用。介绍了一种基于贝叶斯变化检测算法提取LIP信息的自动化方法。在本研究中，采用BECD算法从博斯腾湖的被动微波遥感数据中提取湖泊冰物候信息。通过与MOD10A1数据进行交叉验证，进一步研究了被动微波遥感数据的可靠性。此外，应用曼-肯德尔非参数检验分析博斯腾湖湖泊冰物候变化趋势。利用MOD09GQ数据研究空间变化情况。结果表明：（1）贝叶斯变化检测算法（BCDA）与PMRS数据相结合，在提取湖泊冰冻结和解冻过程方面具有较高的准确性和可靠性。它能准确捕捉博斯腾湖冰的物候参数。（2）湖泊冰冻结的平均开始日期在12月中旬，持续约三个月，解冻开始日期通常在3月中旬。湖泊冰的冻结持续时间（FD）相对较短，且逐年缩短，而解冻速度较快。湖泊冰完全覆盖持续时间的稳定性较差，平均为84天。（3）博斯腾湖冰的动态演变迅速且具有区域差异，湖中心、西南部和东南部地区是最早形成冰和解冻的区域，而西北沿岸和黄水沟地区冰形成较晚。（4）自1978年以来，博斯腾湖的冰呈现出明显趋势：冻结开始、解冻开始、完全解冻和完全冻结的日期逐渐提前。全冰覆盖期、有冰期、解冻期和冻结期均呈现持续时间缩短的趋势。本研究介绍了一种创新的LIP提取方法，为湖泊生态系统研究和策略制定开辟了新前景，值得在其他湖泊和地区进一步探索和应用。
