Preprints
https://doi.org/10.5194/egusphere-2024-849
https://doi.org/10.5194/egusphere-2024-849
03 Apr 2024
 | 03 Apr 2024
Status: this preprint is open for discussion.

Reconstructing ice phenology of lake with complex surface cover: A case study of Lake Ulansu during 1941–2023

Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li

Abstract. Lake ice phenology plays a critical role in determining the hydrological and biogeochemical dynamics of the catchment and regional climate. Lakes with complex shorelines and abundant aquatic vegetation are challenging for lake ice phenology retrieval using remote sensing data, primarily due to mixed pixels containing plants, land and ice. To tackle this challenge, a new double-threshold moving t test (DMTT) algorithm, utilizing multisource satellite-derived brightness temperature data at a 3.125-km resolution and long-term weather data, was introduced to capture Lake Ulansu’s ice phenology from 1979 to 2023. Compared to the previous moving t test algorithm, the new DMTT algorithm employs air temperature time series to assist in determining abrupt change points and uses two distinct thresholds to calculate the freeze-up start (FUS) and break-up end (BUE) dates. This method improved the detection of ice information effectively for the mixed pixels. Furthermore, we extended Lake Ulansu's ice phenology detection backward to 1941 using a random forest (RF) model. The reconstructed ice phenology from 1941 to 2023 indicated that Lake Ulansu had average FUS and BUE dates of November 15 ± 5 and March 25 ± 6, respectively, with an average ice cover duration of 130 ± 8 days. Air temperature was the primary impact factor, accounting for 56.5 % and 67.3 % of the variations in the FUS and BUE dates, respectively. We reconstructed, for the first time, the longest ice phenology over a large shallow lake with complex surface cover. We argue DMTT can effectively be applied to retrieve lake ice phenology for this type of lake that have not been fully explored worldwide.

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Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li

Status: open (until 01 Aug 2024)

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Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li
Puzhen Huo, Peng Lu, Bin Cheng, Miao Yu, Qingkai Wang, Xuewei Li, and Zhijun Li

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Short summary
We developed a new method to retrieve lake ice phenology for the lake with a complex surface cover. The method is particularly useful for mixed-pixel satellite data. We implement this method on Lake Ulansu, a lake characterized by complex shorelines and rich aquatic plants in Northwest China. In connection with a random forest model, we reconstructed the longest lake ice phenology in China.