| Autor: |
Qingyu Gu, Jiahui Xu, Jingwen Ni, Xiaobao Peng, Haixi Zhou, Linxin Dong, Bailang Yu, Jianping Wu, Zhaojun Zheng, Yan Huang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 133, Iss , Pp 104102- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1569-8432 |
| DOI: |
10.1016/j.jag.2024.104102 |
| Popis: |
Snow depth (SD) is essential for studying climate change and hydrological cycle on the Tibetan Plateau (TP). Despite the effectiveness of passive microwave remote sensing for large-scale SD measurement, its low spatial resolution and scanning gaps limit its application, particularly in the TP region where the terrain is complex and snow distribution exhibits obvious heterogeneity. This study developed Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling models for the TP using ensemble learning methods and AMSR2 brightness temperature data from October 1, 2012, to April 30, 2021. We employed five ensemble methods—AdaBoost, GBDT, XGBoost, LightGBM, and Random Forest—with LightGBM achieving the highest accuracy (RMSE=2.66 cm). Recursive feature elimination (RFE) was applied to the LightGBM model, optimizing factor selection and maintaining high accuracy. The models excelled in estimating shallow snow areas (SD |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect